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Sample records for arthralgia predicts development

  1. Genomic risk prediction of aromatase inhibitor-related arthralgia in patients with breast cancer using a novel machine-learning algorithm.

    Science.gov (United States)

    Reinbolt, Raquel E; Sonis, Stephen; Timmers, Cynthia D; Fernández-Martínez, Juan Luis; Cernea, Ana; de Andrés-Galiana, Enrique J; Hashemi, Sepehr; Miller, Karin; Pilarski, Robert; Lustberg, Maryam B

    2018-01-01

    Many breast cancer (BC) patients treated with aromatase inhibitors (AIs) develop aromatase inhibitor-related arthralgia (AIA). Candidate gene studies to identify AIA risk are limited in scope. We evaluated the potential of a novel analytic algorithm (NAA) to predict AIA using germline single nucleotide polymorphisms (SNP) data obtained before treatment initiation. Systematic chart review of 700 AI-treated patients with stage I-III BC identified asymptomatic patients (n = 39) and those with clinically significant AIA resulting in AI termination or therapy switch (n = 123). Germline DNA was obtained and SNP genotyping performed using the Affymetrix UK BioBank Axiom Array to yield 695,277 SNPs. SNP clusters that most closely defined AIA risk were discovered using an NAA that sequentially combined statistical filtering and a machine-learning algorithm. NCBI PhenGenI and Ensemble databases defined gene attribution of the most discriminating SNPs. Phenotype, pathway, and ontologic analyses assessed functional and mechanistic validity. Demographics were similar in cases and controls. A cluster of 70 SNPs, correlating to 57 genes, was identified. This SNP group predicted AIA occurrence with a maximum accuracy of 75.93%. Strong associations with arthralgia, breast cancer, and estrogen phenotypes were seen in 19/57 genes (33%) and were functionally consistent. Using a NAA, we identified a 70 SNP cluster that predicted AIA risk with fair accuracy. Phenotype, functional, and pathway analysis of attributed genes was consistent with clinical phenotypes. This study is the first to link a specific SNP/gene cluster to AIA risk independent of candidate gene bias. © 2017 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

  2. EULAR definition of arthralgia suspicious for progression to rheumatoid arthritis.

    Science.gov (United States)

    van Steenbergen, Hanna W; Aletaha, Daniel; Beaart-van de Voorde, Liesbeth J J; Brouwer, Elisabeth; Codreanu, Catalin; Combe, Bernard; Fonseca, João E; Hetland, Merete L; Humby, Frances; Kvien, Tore K; Niedermann, Karin; Nuño, Laura; Oliver, Sue; Rantapää-Dahlqvist, Solbritt; Raza, Karim; van Schaardenburg, Dirkjan; Schett, Georg; De Smet, Liesbeth; Szücs, Gabriella; Vencovský, Jirí; Wiland, Piotr; de Wit, Maarten; Landewé, Robert L; van der Helm-van Mil, Annette H M

    2017-03-01

    During the transition to rheumatoid arthritis (RA) many patients pass through a phase characterised by the presence of symptoms without clinically apparent synovitis. These symptoms are not well-characterised. This taskforce aimed to define the clinical characteristics of patients with arthralgia who are considered at risk for RA by experts based on their clinical experience. The taskforce consisted of 18 rheumatologists, 1 methodologist, 2 patients, 3 health professionals and 1 research fellow. The process had three phases. In phase I, a list of parameters considered characteristic for clinically suspect arthralgia (CSA) was derived; the most important parameters were selected by a three-phased Delphi approach. In phase II, the experts evaluated 50 existing patients on paper, classified them as CSA/no-CSA and indicated their level of confidence. A provisional set of parameters was derived. This was studied for validation in phase III, where all rheumatologists collected patients with and without CSA from their outpatient clinics. The comprehensive list consisted of 55 parameters, of which 16 were considered most important. A multivariable model based on the data from phase II identified seven relevant parameters: symptom duration year, symptoms of metacarpophalangeal (MCP) joints, morning stiffness duration ≥60 min, most severe symptoms in early morning, first-degree relative with RA, difficulty with making a fist and positive squeeze test of MCP joints. In phase III, the combination of these parameters was accurate in identifying patients with arthralgia who were considered at risk of developing RA (area under the receiver operating characteristic curve 0.92, 95% CI 0.87 to 0.96). Test characteristics for different cut-off points were determined. A set of clinical characteristics for patients with arthralgia who are at risk of progression to RA was established. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted

  3. Patellofemoral Arthralgia, Overuse Syndromes of the Knee, and Chondromalacia Patella

    OpenAIRE

    Welsh, R. Peter

    1985-01-01

    Patellofemoral arthralgia is a very common syndrome affecting athletes. Most often, examination fails to define true pathology. Conservative treatment, an active exercise program, and sports may be undertaken without harm to the knee. The patellofemoral arthralgia syndrome must be differentiated from true chondromalacia patella, where there is actual degeneration of the patella's articular cartilage, and from other sources of internal derangement such as meniscal disease or osteochondral lesi...

  4. Pattern of arthralgia in an urban community in Southwestern Nigeria

    African Journals Online (AJOL)

    Results: A total of 90 cases were screened, with a male to female ratio of 1:1.5 and age range of 50-59 years. .... travelling, 9 (10.0%) thought the pain was caused .... Table 2: Knowledge, perception, symptoms, and location of arthralgia.

  5. Analysis of serum immune markers in seropositive and seronegative rheumatoid arthritis and in high-risk seropositive arthralgia patients

    NARCIS (Netherlands)

    Chalan, Paulina; Bijzet, Johan; van den Berg, Anke; Kluiver, Joost; Kroesen, Bart-Jan; Boots, Annemieke M. H.; Brouwer, Elisabeth

    2016-01-01

    Presence of autoantibodies precedes development of seropositive rheumatoid arthritis (SP RA) and seropositive arthralgia patients (SAP) are at risk of developing RA. The aims of the study are to identify additional serum immune markers discriminating between SP and seronegative (SN) RA, and markers

  6. Post-chemotherapy arthralgia and arthritis in lung cancer

    Directory of Open Access Journals (Sweden)

    Aref H Amiri

    2012-01-01

    Full Text Available Objective: Evaluate the characteristics of arthritis, arthralgia and musculoskeletal pain after chemotherapy in patients with lung cancer. Materials and Methods: In this study, we evaluate the characteristics of 17 patients with joint symptoms following receiving chemotherapy for lung cancer. Demographic information of patients including sex, age, time of rheumatologic findings after starting of chemotherapy, time of improvement after starting of medication, and relevant laboratory findings for each patient. Results: A total of seventeen patients (six women with mean age 41.2 ± 5.2 years and 11 men with mean age 42.5 ± 8.2 that received standard chemotherapy for lung cancer according to stage of disease. Joint symptoms usually began about seven months after the first session of chemotherapy. Patients had an average of two tender joints and 1 hr of morning stiffness. Four patients were positive for anti-nuclear antibody, and none of patient was positive for rheumatoid factor. Non-steroidal anti-inflammatory drugs, disease modifying anti-rheumatic drugs (DMARD, corticosteroids, and venlafaxine were prescribed. Four patients did not show an improvement. Follow-up was available for all patients. 11 patients showed favorable responses, characterized by a significant decrease (more than 50% in morning stiffness, pain, and tender joint counts after a mean of three months′ treatment. Two patients had complete resolution of symptoms and did not required further medications for arthritis, arthralgia or musculoskeletal pain. Conclusion: Chemotherapy-related arthropathy in lung cancer is not uncommon. Early treatment with NSAID, DMARD, and corticosteroids is effective in the majority of patients.

  7. Cost effectiveness of arthrocentesis as initial treatment for temporomandibular joint arthralgia: A randomized controlled trial

    NARCIS (Netherlands)

    Vos, L.M.; Stant, A.D.; Quik, E.H.; Huddleston Slater, J.J.R.; Stegenga, B.

    2013-01-01

    Objective: To determine the cost effectiveness of arthrocentesis as initial treatment compared to care as usual (CAU) for temporomandibular joint (TMJ) arthralgia. Materials and methods: 80 patients were randomly allocated to arthrocentesis as initial treatment (n = 40) or CAU (n = 40).

  8. Bicalutamide-Associated Acute Liver Injury and Migratory Arthralgia: A Rare but Clinically Important Adverse Effect

    Directory of Open Access Journals (Sweden)

    Helga M. Gretarsdottir

    2018-06-01

    Full Text Available We describe a case of acute liver injury and migratory arthralgia in a patient receiving bicalutamide treatment for prostate cancer. A 67-year-old male with metastatic prostate cancer presented with a 6-day history of migratory arthralgia. He had been undergoing treatment with bicalutamide for 4 months; 3 weeks prior to symptom appearance the bicalutamide dose had been increased. He had no other symptoms. Liver tests and inflammatory markers were markedly elevated. Serology for hepatitis viruses A, B, and C, CMV, and EBV and autoimmune causes were all negative, and an ultrasound of the upper abdomen was normal. There was no history of blood transfusion, intravenous drug abuse, or alcohol abuse. Due to the suspicion of a drug-induced symptomatology, bicalutamide was discontinued and the patient started on 30 mg prednisolone daily. Three weeks later he was symptom free and after 6 weeks his liver tests were almost normal. The Roussel Uclaf Causality Assessment Method (RUCAM suggested a high probability of liver injury. Bicalutamide has very rarely been reported as a causative agent for liver injury and to our knowledge never for migratory polyarthralgia. The migratory polyarthralgia was attributed to bicalutamide due to the absence of other etiological factors and the disappearance of symptoms after discontinuation of the drug. To our knowledge, this is the first published case report of migratory arthralgia and concomitant liver injury attributed to bicalutamide.

  9. Prediction of Subsidence Depression Development

    Czech Academy of Sciences Publication Activity Database

    Doležalová, Hana; Kajzar, Vlastimil

    2017-01-01

    Roč. 6, č. 4 (2017), s. 208-214 E-ISSN 2391-9361. [Cross-border Exchange of Experience in Production Engineering Using Principles of Mathematics. Rybnik, 07.06.2017-09.06.2017] Institutional support: RVO:68145535 Keywords : undermining * prediction * regression analysis Subject RIV: DH - Mining, incl. Coal Mining OBOR OECD: Mining and mineral processing

  10. Predictive models of moth development

    Science.gov (United States)

    Degree-day models link ambient temperature to insect life-stages, making such models valuable tools in integrated pest management. These models increase management efficacy by predicting pest phenology. In Wisconsin, the top insect pest of cranberry production is the cranberry fruitworm, Acrobasis v...

  11. [The radiological findings of caisson-induced bone infarcts. The relationship between acute arthralgia and bone infarcts (author's transl)].

    Science.gov (United States)

    Horváth, V F

    1978-07-01

    The radiological features, such as calcification in long bones due to infarcts, resulting from Caisson disease are described by the author on the basis of an extensive experience. The similar localisation of acute "arthralgia" and bone infarcts make it appear probable that the infarcts play a primary role in the production of "osteo-articular" pain. The author stresses the advisability of examining the adjacent portions of the tibia and femur at the initial pre-employment examination, since bone infarcts can be caused by a variety of conditions other than work in Caissons.

  12. Managing arthralgia in a postmenopausal woman taking an aromatase inhibitor for hormonesensitive early breast cancer: a case study

    International Nuclear Information System (INIS)

    Bryce, Jane; Bauer, Martina; Hadji, Peyman

    2012-01-01

    In order to reduce the risk of recurrence, adjuvant treatment with an aromatase inhibitor (AI) is recommended for postmenopausal women following surgery for hormone receptor-positive breast cancer. AIs are associated with improved disease-free survival compared with tamoxifen. The adverse events associated with AIs resemble those of menopause, such as bone density loss and musculoskeletal symptoms. We examine the case of a postmenopausal woman who was prescribed anastrozole, a nonsteroidal AI, as adjuvant therapy following surgery for estrogen and progesterone receptor-positive (ER and PgR+) breast cancer. A 58-year-old postmenopausal woman diagnosed with ER and PgR+ breast cancer was prescribed anastrozole as adjuvant therapy following a right-inferior quadrantectomy. After experiencing joint pain and stiffness, she was prescribed paracetamol and a topical nonsteroidal anti-inflammatory drug. She was also counseled on nonpharmacological interventions. However, she continued to experience symptoms, and reported that she was not taking anastrozole regularly. The case study patient ultimately found relief by switching to letrozole, another aromatase inhibitor. This approach is supported by recent studies examining the benefits of switching strategies between aromatase inhibitors in order to relieve symptoms of arthralgia/myalgia. Both adherence and strategies for managing aromatase inhibitor-associated arthralgia are key to deriving maximal clinical benefit from AI therapy. Switching from one aromatase inhibitor to another may provide a viable option in managing adverse events and enhancing adherence to medication

  13. Piroxicam and laser phototherapy in the treatment of TMJ arthralgia: a double-blind randomised controlled trial.

    Science.gov (United States)

    de Carli, M L; Guerra, M B; Nunes, T B; di Matteo, R C; de Luca, C E P; Aranha, A C C; Bolzan, M C; Witzel, A L

    2013-03-01

    This study aimed to evaluate the efficacy of piroxicam associated with low-level laser therapy compared with single therapies in 32 patients presenting temporomandibular joint arthralgia in a random and double-blind research design. The sample, divided into laser + piroxicam, laser + placebo piroxicam and placebo laser + piroxicam groups, was submitted to the treatment with infrared laser (830 nm, 100 mW, 28 s, 100 J cm(-2) ) at 10 temporomandibular joint and muscle points on each side during four sessions concomitant to take one capsule a day of piroxicam 20 mg during 10 days. The treatment was evaluated throughout four sessions and 30 days follow-up through visual analogue scale (VAS), maximum mouth opening and joint and muscle (temporal and masseter) pain on palpation. The results showed that all the study groups had a significant improvement in the VAS scores (P Piroxicam was effective in the reduction of joint and muscle pain on palpation (P piroxicam was not more effective than single therapies in the treatment of temporomandibular joint arthralgia. The use of piroxicam was more effective in the following 30 days. © 2012 Blackwell Publishing Ltd.

  14. Development of a Climate Prediction Market

    Science.gov (United States)

    Roulston, M. S.

    2017-12-01

    Winton, a global investment firm, is planning to establish a prediction market for climate. This prediction market will allow participants to place bets on global climate up to several decades in the future. Winton is pursuing this endeavour as part of its philanthropy that funds scientific research and the communication of scientific ideas. The Winton Climate Prediction Market will be based in the U.K. It will be structured as an online gambling site subject to the regulation of the Gambling Commission. Unlike existing betting sites, the Climate Prediction Market will be subsidized: a central market maker will inject money into the market. This is in contrast to traditional bookmakers or betting exchanges who set odds in their favour or charge commissions to make a profit. The philosophy of a subsidized prediction market is that the party seeking information should fund the market, rather than the participants who provide the information. The initial market will allow bets to be placed on the atmospheric concentration of carbon dioxide and the global mean temperature anomaly. It will thus produce implied forecasts of carbon dioxide concentration as well as global temperatures. If the initial market is successful, additional markets could be added which target other climate variables, such as regional temperatures or sea-level rise. These markets could be sponsored by organizations that are interested in predictions of the specific climate variables. An online platform for the Climate Prediction Market has been developed and has been tested internally at Winton.

  15. Analysis of four serum biomarkers in rheumatoid arthritis: association with extra articular manifestations in patients and arthralgia in relatives

    Directory of Open Access Journals (Sweden)

    Flávia R. Nass

    Full Text Available ABSTRACT Objectives: To evaluate the frequency of four serum biomarkers in RA patients and their relatives and identify possible associations with clinical findings of the disease. Methods: This was a transversal analytical study. Anti-cyclic citrullinated peptide (anti-CCP, anti-mutated citrullinated vimentin (anti-MCV and IgA-rheumatoid factor (RF were determined by ELISA and IgM-RF by latex agglutination in 210 RA patients, 198 relatives and 92 healthy controls from Southern Brazil. Clinical and demographic data were obtained through charts review and questionnaires. Results: A higher positivity for all antibodies was observed in RA patients when compared to relatives and controls (p < 0.0001. IgA-RF was more frequent in relatives compared to controls (14.6% vs. 5.4%, p = 0.03, OR = 2.98; 95% CI = 1.11-7.98 whereas anti-CCP was the most common biomarker among RA patients (75.6%. Concomitant positivity for the four biomarkers was more common in patients (46.2%, p < 0.0001. Relatives and controls were mostly positive for just one biomarker (20.2%, p < 0.0001 and 15.2%, p = 0.016, respectively. No association was observed between the number of positive biomarkers and age of disease onset, functional class or tobacco exposure. In seronegative patients predominate absence of extra articular manifestations (EAMs (p = 0.01; OR = 3.25; 95% CI = 1.16-10.66. Arthralgia was present in positive relatives, regardless the type of biomarker. Conclusions: A higher number of biomarkers was present in RA patients with EAMs. Positivity of biomarkers was related to arthralgia in relatives. These findings reinforce the link between distinct biomarkers and the pathophysiologic mechanisms of AR.

  16. Development of a Thermal Equilibrium Prediction Algorithm

    International Nuclear Information System (INIS)

    Aviles-Ramos, Cuauhtemoc

    2002-01-01

    A thermal equilibrium prediction algorithm is developed and tested using a heat conduction model and data sets from calorimetric measurements. The physical model used in this study is the exact solution of a system of two partial differential equations that govern the heat conduction in the calorimeter. A multi-parameter estimation technique is developed and implemented to estimate the effective volumetric heat generation and thermal diffusivity in the calorimeter measurement chamber, and the effective thermal diffusivity of the heat flux sensor. These effective properties and the exact solution are used to predict the heat flux sensor voltage readings at thermal equilibrium. Thermal equilibrium predictions are carried out considering only 20% of the total measurement time required for thermal equilibrium. A comparison of the predicted and experimental thermal equilibrium voltages shows that the average percentage error from 330 data sets is only 0.1%. The data sets used in this study come from calorimeters of different sizes that use different kinds of heat flux sensors. Furthermore, different nuclear material matrices were assayed in the process of generating these data sets. This study shows that the integration of this algorithm into the calorimeter data acquisition software will result in an 80% reduction of measurement time. This reduction results in a significant cutback in operational costs for the calorimetric assay of nuclear materials. (authors)

  17. Becoming Predictably Adaptable in Software Development

    Directory of Open Access Journals (Sweden)

    Michael Vakoc

    2017-10-01

    Full Text Available It’s difficult to state exact timelines in software development and it is even more difficult to say when features that users want will be delivered. We propose changes to current software development methodologies that enable companies to be predictably adaptable and deliver both on time and what customer asked for. We do so through research of current literature, interviews and personal experience working at an international company that builds products for millions of customers and is facing exactly the challenges described above.

  18. Prediction of Chemical Function: Model Development and ...

    Science.gov (United States)

    The United States Environmental Protection Agency’s Exposure Forecaster (ExpoCast) project is developing both statistical and mechanism-based computational models for predicting exposures to thousands of chemicals, including those in consumer products. The high-throughput (HT) screening-level exposures developed under ExpoCast can be combined with HT screening (HTS) bioactivity data for the risk-based prioritization of chemicals for further evaluation. The functional role (e.g. solvent, plasticizer, fragrance) that a chemical performs can drive both the types of products in which it is found and the concentration in which it is present and therefore impacting exposure potential. However, critical chemical use information (including functional role) is lacking for the majority of commercial chemicals for which exposure estimates are needed. A suite of machine-learning based models for classifying chemicals in terms of their likely functional roles in products based on structure were developed. This effort required collection, curation, and harmonization of publically-available data sources of chemical functional use information from government and industry bodies. Physicochemical and structure descriptor data were generated for chemicals with function data. Machine-learning classifier models for function were then built in a cross-validated manner from the descriptor/function data using the method of random forests. The models were applied to: 1) predict chemi

  19. Collaborative development of predictive toxicology applications.

    Science.gov (United States)

    Hardy, Barry; Douglas, Nicki; Helma, Christoph; Rautenberg, Micha; Jeliazkova, Nina; Jeliazkov, Vedrin; Nikolova, Ivelina; Benigni, Romualdo; Tcheremenskaia, Olga; Kramer, Stefan; Girschick, Tobias; Buchwald, Fabian; Wicker, Joerg; Karwath, Andreas; Gütlein, Martin; Maunz, Andreas; Sarimveis, Haralambos; Melagraki, Georgia; Afantitis, Antreas; Sopasakis, Pantelis; Gallagher, David; Poroikov, Vladimir; Filimonov, Dmitry; Zakharov, Alexey; Lagunin, Alexey; Gloriozova, Tatyana; Novikov, Sergey; Skvortsova, Natalia; Druzhilovsky, Dmitry; Chawla, Sunil; Ghosh, Indira; Ray, Surajit; Patel, Hitesh; Escher, Sylvia

    2010-08-31

    OpenTox provides an interoperable, standards-based Framework for the support of predictive toxicology data management, algorithms, modelling, validation and reporting. It is relevant to satisfying the chemical safety assessment requirements of the REACH legislation as it supports access to experimental data, (Quantitative) Structure-Activity Relationship models, and toxicological information through an integrating platform that adheres to regulatory requirements and OECD validation principles. Initial research defined the essential components of the Framework including the approach to data access, schema and management, use of controlled vocabularies and ontologies, architecture, web service and communications protocols, and selection and integration of algorithms for predictive modelling. OpenTox provides end-user oriented tools to non-computational specialists, risk assessors, and toxicological experts in addition to Application Programming Interfaces (APIs) for developers of new applications. OpenTox actively supports public standards for data representation, interfaces, vocabularies and ontologies, Open Source approaches to core platform components, and community-based collaboration approaches, so as to progress system interoperability goals.The OpenTox Framework includes APIs and services for compounds, datasets, features, algorithms, models, ontologies, tasks, validation, and reporting which may be combined into multiple applications satisfying a variety of different user needs. OpenTox applications are based on a set of distributed, interoperable OpenTox API-compliant REST web services. The OpenTox approach to ontology allows for efficient mapping of complementary data coming from different datasets into a unifying structure having a shared terminology and representation.Two initial OpenTox applications are presented as an illustration of the potential impact of OpenTox for high-quality and consistent structure-activity relationship modelling of REACH

  20. Collaborative development of predictive toxicology applications

    Directory of Open Access Journals (Sweden)

    Hardy Barry

    2010-08-01

    Full Text Available Abstract OpenTox provides an interoperable, standards-based Framework for the support of predictive toxicology data management, algorithms, modelling, validation and reporting. It is relevant to satisfying the chemical safety assessment requirements of the REACH legislation as it supports access to experimental data, (Quantitative Structure-Activity Relationship models, and toxicological information through an integrating platform that adheres to regulatory requirements and OECD validation principles. Initial research defined the essential components of the Framework including the approach to data access, schema and management, use of controlled vocabularies and ontologies, architecture, web service and communications protocols, and selection and integration of algorithms for predictive modelling. OpenTox provides end-user oriented tools to non-computational specialists, risk assessors, and toxicological experts in addition to Application Programming Interfaces (APIs for developers of new applications. OpenTox actively supports public standards for data representation, interfaces, vocabularies and ontologies, Open Source approaches to core platform components, and community-based collaboration approaches, so as to progress system interoperability goals. The OpenTox Framework includes APIs and services for compounds, datasets, features, algorithms, models, ontologies, tasks, validation, and reporting which may be combined into multiple applications satisfying a variety of different user needs. OpenTox applications are based on a set of distributed, interoperable OpenTox API-compliant REST web services. The OpenTox approach to ontology allows for efficient mapping of complementary data coming from different datasets into a unifying structure having a shared terminology and representation. Two initial OpenTox applications are presented as an illustration of the potential impact of OpenTox for high-quality and consistent structure

  1. Nordic Walking as an Exercise Intervention to Reduce Pain in Women With Aromatase Inhibitor-Associated Arthralgia: A Feasibility Study.

    Science.gov (United States)

    Fields, Jo; Richardson, Alison; Hopkinson, Jane; Fenlon, Deborah

    2016-10-01

    Women taking aromatase inhibitors as treatment for breast cancer commonly experience joint pain and stiffness (aromatase inhibitor-associated arthralgia [AIAA]), which can cause problems with adherence. There is evidence that exercise might be helpful, and Nordic walking could reduce joint pain compared to normal walking. To determine the feasibility of a trial of Nordic walking as an exercise intervention for women with AIAA. A feasibility study was carried out in a sample of women with AIAA using a randomized control design. Women were randomized to exercise (six-week supervised group Nordic walking training once per week with an increasing independent element, followed by six weeks 4 × 30 minutes/week independent Nordic walking); or enhanced usual care. Data were collected on recruitment, retention, exercise adherence, safety, and acceptability. The Brief Pain Inventory, GP Physical Activity Questionnaire, and biopsychosocial measures were completed at baseline, six and 12 weeks. Forty of 159 eligible women were recruited and attrition was 10%. There was no increased lymphedema and no long-term or serious injury. Adherence was >90% for weekly supervised group Nordic walking, and during independent Nordic walking, >80% women managed one to two Nordic walking sessions per week. From baseline to study end point, overall activity levels increased and pain reduced in both the intervention and control groups. Our findings indicate that women with AIAA are prepared to take up Nordic walking, complete a six-week supervised course and maintain increased activity levels over a 12-week period with no adverse effects. Copyright © 2016 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

  2. Clinical Predictive Modeling Development and Deployment through FHIR Web Services.

    Science.gov (United States)

    Khalilia, Mohammed; Choi, Myung; Henderson, Amelia; Iyengar, Sneha; Braunstein, Mark; Sun, Jimeng

    2015-01-01

    Clinical predictive modeling involves two challenging tasks: model development and model deployment. In this paper we demonstrate a software architecture for developing and deploying clinical predictive models using web services via the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard. The services enable model development using electronic health records (EHRs) stored in OMOP CDM databases and model deployment for scoring individual patients through FHIR resources. The MIMIC2 ICU dataset and a synthetic outpatient dataset were transformed into OMOP CDM databases for predictive model development. The resulting predictive models are deployed as FHIR resources, which receive requests of patient information, perform prediction against the deployed predictive model and respond with prediction scores. To assess the practicality of this approach we evaluated the response and prediction time of the FHIR modeling web services. We found the system to be reasonably fast with one second total response time per patient prediction.

  3. Development of anomalous detection using movie prediction

    International Nuclear Information System (INIS)

    Sakakibara, Yoji; Demachi, Kazuyuki; Kawai, Masaki; Chhatluli, Ritu; Kamiaka, Kazuma

    2012-01-01

    In this research, the new method to predict the near-future of the movie images captured by video camera based on the combination of the Principle Component Analysis (PCA) and the Singular Spectral Analysis (SSA). In the normal condition of machines, the real-time captured movie is supposed to correspond to the predicted one. If the error between the both becomes significantly large, it may suggest some anomalous motion of the machines. So the movie prediction method has a possibility of the sensitive anomalous detection system. (author)

  4. Developments in Property Predictions for Weld Metal

    National Research Council Canada - National Science Library

    Olson, D

    2003-01-01

    With the introduction of higher strength low-carbon steels, which have properties that are based on strengthening mechanisms other than the austenitic decomposition, new predictive expressions are required...

  5. The role of domain analysis in prediction instrument development

    NARCIS (Netherlands)

    van der Spoel, Sjoerd; Amrit, Chintan Amrit; van Hillegersberg, Jos

    2016-01-01

    In order to develop prediction instruments that have sufficient predictive power, it is essential to understand the specific domain the prediction instrument is developed for. This domain analysis is especially important for domains where human behavior, politics, or other soft factors play a role.

  6. Development of genomic prediction in sorghum

    NARCIS (Netherlands)

    Hunt, Colleen H.; Eeuwijk, van Fred A.; Mace, Emma S.; Hayes, Ben J.; Jordan, David R.

    2018-01-01

    Genomic selection can increase the rate of genetic gain in plant breeding programs by shortening the breeding cycle. Gain can also be increased through higher selection intensities, as the size of the population available for selection can be increased by predicting performance of nonphenotyped, but

  7. Sensitization predicts asthma development among wheezing toddlers in secondary healthcare

    NARCIS (Netherlands)

    Boersma, Nienke A.; Meijneke, Ruud W.H.; Kelder, Johannes C.; van der Ent, Cornelis K.; Balemans, Walter A.F.

    2017-01-01

    Introduction: Some wheezing toddlers develop asthma later in childhood. Sensitization is known to predict asthma in birth cohorts. However, its predictive value in secondary healthcare is uncertain. Aim: This study examines the predictive value of sensitization to inhalant allergens among wheezing

  8. Development of an attrition risk prediction tool.

    Science.gov (United States)

    Fowler, John; Norrie, Peter

    To review lecturers' and students' perceptions of the factors that may lead to attrition from pre-registration nursing and midwifery programmes and to identify ways to reduce the impact of such factors on the student's experience. Comparable attrition rates for nursing and midwifery students across various universities are difficult to monitor accurately; however, estimates that there is approximately a 25% national attrition rate are not uncommon. The financial and human implications of this are significant and worthy of investigation. A study was carried out in one medium-sized UK school of nursing and midwifery, aimed at identifying perceived factors associated with attrition and retention. Thirty-five lecturers were interviewed individually; 605 students completed a questionnaire, and of these, 10 were individually interviewed. Attrition data kept by the student service department were reviewed. Data were collected over an 18-month period in 2007-2008. Regression analysis of the student data identified eight significant predictors. Four of these were 'positive' factors in that they aided student retention and four were 'negative' in that they were associated with students' thoughts of resigning. Student attrition and retention is multifactorial, and, as such, needs to be managed holistically. One aspect of this management could be an attrition risk prediction tool.

  9. Early executive function predicts reasoning development.

    Science.gov (United States)

    Richland, Lindsey E; Burchinal, Margaret R

    2013-01-01

    Analogical reasoning is a core cognitive skill that distinguishes humans from all other species and contributes to general fluid intelligence, creativity, and adaptive learning capacities. Yet its origins are not well understood. In the study reported here, we analyzed large-scale longitudinal data from the Study of Early Child Care and Youth Development to test predictors of growth in analogical-reasoning skill from third grade to adolescence. Our results suggest an integrative resolution to the theoretical debate regarding contributory factors arising from smaller-scale, cross-sectional experiments on analogy development. Children with greater executive-function skills (both composite and inhibitory control) and vocabulary knowledge in early elementary school displayed higher scores on a verbal analogies task at age 15 years, even after adjusting for key covariates. We posit that knowledge is a prerequisite to analogy performance, but strong executive-functioning resources during early childhood are related to long-term gains in fundamental reasoning skills.

  10. Current Status and Prediction on Development of PE Market

    Institute of Scientific and Technical Information of China (English)

    Yu Jiao

    2003-01-01

    This article comprehensively analyzes the status of market demand/supply and import/export volumes of PE in the world and in China, and predicts the future development trends in the fields of PE production and consumption.

  11. Prediction of Chemical Function: Model Development and Application

    Science.gov (United States)

    The United States Environmental Protection Agency’s Exposure Forecaster (ExpoCast) project is developing both statistical and mechanism-based computational models for predicting exposures to thousands of chemicals, including those in consumer products. The high-throughput (...

  12. NOAA's National Air Quality Prediction and Development of Aerosol and Atmospheric Composition Prediction Components for NGGPS

    Science.gov (United States)

    Stajner, I.; McQueen, J.; Lee, P.; Stein, A. F.; Wilczak, J. M.; Upadhayay, S.; daSilva, A.; Lu, C. H.; Grell, G. A.; Pierce, R. B.

    2017-12-01

    NOAA's operational air quality predictions of ozone, fine particulate matter (PM2.5) and wildfire smoke over the United States and airborne dust over the contiguous 48 states are distributed at http://airquality.weather.gov. The National Air Quality Forecast Capability (NAQFC) providing these predictions was updated in June 2017. Ozone and PM2.5 predictions are now produced using the system linking the Community Multiscale Air Quality model (CMAQ) version 5.0.2 with meteorological inputs from the North American Mesoscale Forecast System (NAM) version 4. Predictions of PM2.5 include intermittent dust emissions and wildfire emissions from an updated version of BlueSky system. For the latter, the CMAQ system is initialized by rerunning it over the previous 24 hours to include wildfire emissions at the time when they were observed from the satellites. Post processing to reduce the bias in PM2.5 prediction was updated using the Kalman filter analog (KFAN) technique. Dust related aerosol species at the CMAQ domain lateral boundaries now come from the NEMS Global Aerosol Component (NGAC) v2 predictions. Further development of NAQFC includes testing of CMAQ predictions to 72 hours, Canadian fire emissions data from Environment and Climate Change Canada (ECCC) and the KFAN technique to reduce bias in ozone predictions. NOAA is developing the Next Generation Global Predictions System (NGGPS) with an aerosol and gaseous atmospheric composition component to improve and integrate aerosol and ozone predictions and evaluate their impacts on physics, data assimilation and weather prediction. Efforts are underway to improve cloud microphysics, investigate aerosol effects and include representations of atmospheric composition of varying complexity into NGGPS: from the operational ozone parameterization, GOCART aerosols, with simplified ozone chemistry, to CMAQ chemistry with aerosol modules. We will present progress on community building, planning and development of NGGPS.

  13. Development of a regional ensemble prediction method for probabilistic weather prediction

    International Nuclear Information System (INIS)

    Nohara, Daisuke; Tamura, Hidetoshi; Hirakuchi, Hiromaru

    2015-01-01

    A regional ensemble prediction method has been developed to provide probabilistic weather prediction using a numerical weather prediction model. To obtain consistent perturbations with the synoptic weather pattern, both of initial and lateral boundary perturbations were given by differences between control and ensemble member of the Japan Meteorological Agency (JMA)'s operational one-week ensemble forecast. The method provides a multiple ensemble member with a horizontal resolution of 15 km for 48-hour based on a downscaling of the JMA's operational global forecast accompanied with the perturbations. The ensemble prediction was examined in the case of heavy snow fall event in Kanto area on January 14, 2013. The results showed that the predictions represent different features of high-resolution spatiotemporal distribution of precipitation affected by intensity and location of extra-tropical cyclone in each ensemble member. Although the ensemble prediction has model bias of mean values and variances in some variables such as wind speed and solar radiation, the ensemble prediction has a potential to append a probabilistic information to a deterministic prediction. (author)

  14. Development of motion image prediction method using principal component analysis

    International Nuclear Information System (INIS)

    Chhatkuli, Ritu Bhusal; Demachi, Kazuyuki; Kawai, Masaki; Sakakibara, Hiroshi; Kamiaka, Kazuma

    2012-01-01

    Respiratory motion can induce the limit in the accuracy of area irradiated during lung cancer radiation therapy. Many methods have been introduced to minimize the impact of healthy tissue irradiation due to the lung tumor motion. The purpose of this research is to develop an algorithm for the improvement of image guided radiation therapy by the prediction of motion images. We predict the motion images by using principal component analysis (PCA) and multi-channel singular spectral analysis (MSSA) method. The images/movies were successfully predicted and verified using the developed algorithm. With the proposed prediction method it is possible to forecast the tumor images over the next breathing period. The implementation of this method in real time is believed to be significant for higher level of tumor tracking including the detection of sudden abdominal changes during radiation therapy. (author)

  15. The development of U. S. soil erosion prediction and modeling

    Directory of Open Access Journals (Sweden)

    John M. Laflen

    2013-09-01

    Full Text Available Soil erosion prediction technology began over 70 years ago when Austin Zingg published a relationship between soil erosion (by water and land slope and length, followed shortly by a relationship by Dwight Smith that expanded this equation to include conservation practices. But, it was nearly 20 years before this work's expansion resulted in the Universal Soil Loss Equation (USLE, perhaps the foremost achievement in soil erosion prediction in the last century. The USLE has increased in application and complexity, and its usefulness and limitations have led to the development of additional technologies and new science in soil erosion research and prediction. Main among these new technologies is the Water Erosion Prediction Project (WEPP model, which has helped to overcome many of the shortcomings of the USLE, and increased the scale over which erosion by water can be predicted. Areas of application of erosion prediction include almost all land types: urban, rural, cropland, forests, rangeland, and construction sites. Specialty applications of WEPP include prediction of radioactive material movement with soils at a superfund cleanup site, and near real-time daily estimation of soil erosion for the entire state of Iowa.

  16. Measurement and prediction of sensitization development in austenitic stainless steels

    International Nuclear Information System (INIS)

    Bruemmer, S.M.; Charlot, L.A.; Atteridge, D.G.

    1985-10-01

    The effects of thermal and thermomechanical treatments on sensitization development in Type 304 and 316 stainless steels have been measured and compared to model predictions. Sensitization development resulting from isothermal, continuous cooling and pipe welding treatments has been evaluated. An empirically-modified, theoretically-based model is shown to accurately predict material degree of sensitization (DOS) as expressed by the electrochemical potentiokinetic reactivation (EPR) test after both simple and complex treatments. Material DOS is also examined using analytical electron microscopy to document grain boundary chromium depletion and is compared to EPR test results. 9 refs., 13 figs

  17. Individualized prediction of perineural invasion in colorectal cancer: development and validation of a radiomics prediction model.

    Science.gov (United States)

    Huang, Yanqi; He, Lan; Dong, Di; Yang, Caiyun; Liang, Cuishan; Chen, Xin; Ma, Zelan; Huang, Xiaomei; Yao, Su; Liang, Changhong; Tian, Jie; Liu, Zaiyi

    2018-02-01

    To develop and validate a radiomics prediction model for individualized prediction of perineural invasion (PNI) in colorectal cancer (CRC). After computed tomography (CT) radiomics features extraction, a radiomics signature was constructed in derivation cohort (346 CRC patients). A prediction model was developed to integrate the radiomics signature and clinical candidate predictors [age, sex, tumor location, and carcinoembryonic antigen (CEA) level]. Apparent prediction performance was assessed. After internal validation, independent temporal validation (separate from the cohort used to build the model) was then conducted in 217 CRC patients. The final model was converted to an easy-to-use nomogram. The developed radiomics nomogram that integrated the radiomics signature and CEA level showed good calibration and discrimination performance [Harrell's concordance index (c-index): 0.817; 95% confidence interval (95% CI): 0.811-0.823]. Application of the nomogram in validation cohort gave a comparable calibration and discrimination (c-index: 0.803; 95% CI: 0.794-0.812). Integrating the radiomics signature and CEA level into a radiomics prediction model enables easy and effective risk assessment of PNI in CRC. This stratification of patients according to their PNI status may provide a basis for individualized auxiliary treatment.

  18. Time dependent patient no-show predictive modelling development.

    Science.gov (United States)

    Huang, Yu-Li; Hanauer, David A

    2016-05-09

    Purpose - The purpose of this paper is to develop evident-based predictive no-show models considering patients' each past appointment status, a time-dependent component, as an independent predictor to improve predictability. Design/methodology/approach - A ten-year retrospective data set was extracted from a pediatric clinic. It consisted of 7,291 distinct patients who had at least two visits along with their appointment characteristics, patient demographics, and insurance information. Logistic regression was adopted to develop no-show models using two-thirds of the data for training and the remaining data for validation. The no-show threshold was then determined based on minimizing the misclassification of show/no-show assignments. There were a total of 26 predictive model developed based on the number of available past appointments. Simulation was employed to test the effective of each model on costs of patient wait time, physician idle time, and overtime. Findings - The results demonstrated the misclassification rate and the area under the curve of the receiver operating characteristic gradually improved as more appointment history was included until around the 20th predictive model. The overbooking method with no-show predictive models suggested incorporating up to the 16th model and outperformed other overbooking methods by as much as 9.4 per cent in the cost per patient while allowing two additional patients in a clinic day. Research limitations/implications - The challenge now is to actually implement the no-show predictive model systematically to further demonstrate its robustness and simplicity in various scheduling systems. Originality/value - This paper provides examples of how to build the no-show predictive models with time-dependent components to improve the overbooking policy. Accurately identifying scheduled patients' show/no-show status allows clinics to proactively schedule patients to reduce the negative impact of patient no-shows.

  19. Sensitization predicts asthma development among wheezing toddlers in secondary healthcare.

    Science.gov (United States)

    Boersma, Nienke A; Meijneke, Ruud W H; Kelder, Johannes C; van der Ent, Cornelis K; Balemans, Walter A F

    2017-06-01

    Some wheezing toddlers develop asthma later in childhood. Sensitization is known to predict asthma in birth cohorts. However, its predictive value in secondary healthcare is uncertain. This study examines the predictive value of sensitization to inhalant allergens among wheezing toddlers in secondary healthcare for the development of asthma at school age (≥6 years). Preschool children (1-3 years) who presented with wheezing in secondary healthcare were screened on asthma at school age with the International Study of Asthma and Allergies in Childhood questionnaire. The positive and negative predictive value (PPV and NPV) of specific IgE to inhalant allergens (cut-off concentration 0.35 kU/L) and several non-invasive variables from a child's history (such as hospitalization, eczema, and parental atopy) were calculated. The additional predictive value of sensitization when combined with non-invasive predictors was examined in multivariate analysis and by ROC curves. Of 116 included children, 63% developed asthma at school age. Sensitization to inhalant allergens was a strong asthma predictor. The odds ratio (OR), PPV and NPV were 7.4%, 86%, and 55%, respectively. Eczema (OR 3.4) and hospital admission (OR 2.6) were significant non-invasive determinants. Adding sensitization to these non-invasive predictors in multivariate analysis resulted in a significantly better asthma prediction. The area under the ROC curve increased from 0.70 with only non-invasive predictors to 0.79 after adding sensitization. Sensitization to inhalant allergens is a strong predictor of school age asthma in secondary healthcare and has added predictive value when combined with non-invasive determinants. Pediatr Pulmonol. 2017;52:729-736. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  20. Research and development studies for predicting the thermal fatigue

    International Nuclear Information System (INIS)

    Moulin, D.; Garnier, J.; Fissolo, A.; Lejeail, Y.; Stephan, J.M.; Moinereau, D.; Masson, J.

    2001-01-01

    This paper presents some studies in development or realized in the EDF and CEA laboratories, concerning the thermal fatigue damage in nuclear reactor components. The first part presents the basic principles and the methods of lifetime prediction. The second part gives some examples on sodium loop, water loop, welded junctions resistance to thermal fatigue and tests on fatigue specimen. (A.L.B.)

  1. Development of an Integrated Moisture Index for predicting species composition

    Science.gov (United States)

    Louis R. Iverson; Charles T. Scott; Martin E. Dale; Anantha Prasad

    1996-01-01

    A geographic information system (GIS) approach was used to develop an Integrated Moisture Index (IMI), which was used to predict species composition for Ohio forests. Several landscape features (a slope-aspect shading index, cumulative flow of water downslope, curvature of the landscape, and the water-holding capacity of the soil) were derived from elevation and soils...

  2. The time since last menstrual period is important as a clinical predictor for non-steroidal aromatase inhibitor-related arthralgia.

    Science.gov (United States)

    Kanematsu, Miyuki; Morimoto, Masami; Honda, Junko; Nagao, Taeko; Nakagawa, Misako; Takahashi, Masako; Tangoku, Akira; Sasa, Mitsunori

    2011-10-10

    The clinical predictors of aromatase inhibitor-related arthralgia (AIA), a drug-related adverse reaction of aromatase inhibitors (AIs), remain unclear. AIA was prospectively surveyed every 4 months in 328 postmenopausal breast cancer patients administered a non-steroidal AI (anastrozole). Various clinicopathological parameters were recorded and analyzed (chi-square test, Fisher's exact test and logistic regression analysis). The mean observation period was 39.9 months. AIA manifested in 114 patients (34.8%), with peaks of onset at 4 (33.7%) and 8 months (11.4%) after starting AI administration. Some cases manifested even after 13 months. AIA tended to occur in younger patients (incidences of 46.3%, 37.4% and 28.0% for ages of 65 years, respectively (p = 0.063)) and decreased significantly with the age at menarche (53.3%, 35.3% and 15.4% for 15 years, respectively (p = 0.036)). The incidences were 45.1%, 46.3 and 25.1% for the time since the last menstrual period (LMP) 10 years, being significantly lower at > 10 years (p time since LMP > 10-year group versus the time since LMP became shorter ( 10 years since LMP. When the time since LMP was short, the onset of AIA was significantly earlier after starting AI administration.

  3. First experience with single-source dual-energy computed tomography in six patients with acute arthralgia: a feasibility experiment using joint aspiration as a reference

    Energy Technology Data Exchange (ETDEWEB)

    Diekhoff, Torsten; Kiefer, Tobias; Hamm, Bernd; Hermann, Kay-Geert A. [Charite - Universitaetsmedizin Berlin Campus Mitte, Humboldt-Universitaet zu Berlin, Freie Universitaet Berlin, Department of Radiology, Berlin (Germany); Ziegeler, Katharina; Feist, Eugen [Charite - Universitaetsmedizin Berlin Campus Mitte, Humboldt-Universitaet zu Berlin, Freie Universitaet Berlin, Department of Rheumatology and Clinical Immunology, Berlin (Germany); Mews, Juergen [Toshiba Medical Systems Europe, BV, Zoetermeer (Netherlands)

    2015-11-15

    Dual-energy computed tomography (DECT) is an emerging imaging technique for examining patients with suspected gout. Single-source dual-energy CT (S-DECT) is a new way of obtaining DECT information on conventional CT scanners rather than using special dual-source CT systems. We tested the feasibility of S-DECT (320-row CT; Aquilion ONE, Toshiba Medical Systems, Otawara, Japan) in 6 patients (5 men, 1 woman; mean age 61.3, range 48 to 69 years) with acute arthralgia and suspected gout, and compared the S-DECT findings with the results of joint aspiration. Three patients had a diagnosis of gouty arthritis with negatively birefringent crystals in synovial fluid, in addition to gouty tophi in S-DECT. Three patients had no detectable crystals by polarization microscopy and no tophi on DECT. Their final diagnoses were rheumatoid arthritis, activated osteoarthritis, and septic arthritis in one case each. This initial experience suggests that S-DECT might be a valuable alternative to dual-source CT. Hence, more patients may benefit from its additional diagnostic abilities in the future. (orig.)

  4. First experience with single-source dual-energy computed tomography in six patients with acute arthralgia: a feasibility experiment using joint aspiration as a reference

    International Nuclear Information System (INIS)

    Diekhoff, Torsten; Kiefer, Tobias; Hamm, Bernd; Hermann, Kay-Geert A.; Ziegeler, Katharina; Feist, Eugen; Mews, Juergen

    2015-01-01

    Dual-energy computed tomography (DECT) is an emerging imaging technique for examining patients with suspected gout. Single-source dual-energy CT (S-DECT) is a new way of obtaining DECT information on conventional CT scanners rather than using special dual-source CT systems. We tested the feasibility of S-DECT (320-row CT; Aquilion ONE, Toshiba Medical Systems, Otawara, Japan) in 6 patients (5 men, 1 woman; mean age 61.3, range 48 to 69 years) with acute arthralgia and suspected gout, and compared the S-DECT findings with the results of joint aspiration. Three patients had a diagnosis of gouty arthritis with negatively birefringent crystals in synovial fluid, in addition to gouty tophi in S-DECT. Three patients had no detectable crystals by polarization microscopy and no tophi on DECT. Their final diagnoses were rheumatoid arthritis, activated osteoarthritis, and septic arthritis in one case each. This initial experience suggests that S-DECT might be a valuable alternative to dual-source CT. Hence, more patients may benefit from its additional diagnostic abilities in the future. (orig.)

  5. From patients with arthralgia, pre-RA and recently diagnosed RA: What is the current status of understanding RA pathogenesis?

    NARCIS (Netherlands)

    M. Molendijk (Marlieke); J.M.W. Hazes (Mieke); E.W. Lubberts (Erik)

    2018-01-01

    textabstractIt is believed that therapy for rheumatoid arthritis (RA) is the most effective and beneficial within a short time frame around RA diagnosis. This insight has caused a shift from research in patients with established RA to patients at risk of developing RA and recently diagnosed

  6. Altered Natural Killer Cell Subsets in Seropositive Arthralgia and Early Rheumatoid Arthritis Are Associated with Autoantibody Status

    NARCIS (Netherlands)

    Chalan, Paulina; Bijzet, Johan; Kroesen, Bart-Jan; Boots, Annemieke M. H.; Brouwer, Elisabeth

    Objective. The role of natural killer (NK) cells in the immunopathogenesis of rheumatoid arthritis (RA) is unclear. Therefore, numerical and functional alterations of CD56(dim) and CD56(bright) NK cells in the early stages of RA development were studied. Methods. Whole blood samples from newly

  7. Development and design of photovoltaic power prediction system

    Science.gov (United States)

    Wang, Zhijia; Zhou, Hai; Cheng, Xu

    2018-02-01

    In order to reduce the impact of power grid safety caused by volatility and randomness of the energy produced in photovoltaic power plants, this paper puts forward a construction scheme on photovoltaic power generation prediction system, introducing the technical requirements, system configuration and function of each module, and discussing the main technical features of the platform software development. The scheme has been applied in many PV power plants in the northwest of China. It shows that the system can produce reasonable prediction results, providing a right guidance for dispatching and efficient running for PV power plant.

  8. Development of pipe wall thinning prediction software 'FALSET'

    International Nuclear Information System (INIS)

    Yoneda, Kimitoshi; Morita, Ryo; Inada, Fumio; Fujiwara, Kazutoshi

    2012-01-01

    Pipe wall thinning in power plants has been managed for maintaining plant integrity and safety with great importance. The target thinning phenomena are Flow Accelerated Corrosion (FAC) and Liquid Droplet Impingement Erosion (LDI). At present, the management is based on thinning rate and residual lifetime evaluation using pipe wall thickness measurement results. For the future, more safety and improvement in the management is required, and in this sense, prediction method of wall thinning is willing to be introduced. Therefore, prediction model of FAC and LDI have been constructed in CRIEPI, and to utilize these models to actual plant piping management easily, prediction software 'FALSET' is developed. FALSET has equipped with essential function for pipe wall thinning management in power plants, as follows; (1) Information and condition input of plant piping system and its component, (2) Wall thinning rate evaluation with CRIEPI's FAC/LDI prediction model, (3) Loading of wall thickness measurement data files and graphics of data trend, (4) Residual lifetime evaluation considering both measured and predicted thinning rate, (5) Statistical process and graphics of thinning rate and residual lifetime for multi-piping systems. With further verification and improvement of each function, there will be a perspective for this FALSET to be utilized as a management tool in power plants. (author)

  9. Development of predictive weather scenarios for early prediction of rice yield in South Korea

    Science.gov (United States)

    Shin, Y.; Cho, J.; Jung, I.

    2017-12-01

    International grain prices are becoming unstable due to frequent occurrence of abnormal weather phenomena caused by climate change. Early prediction of grain yield using weather forecast data is important for stabilization of international grain prices. The APEC Climate Center (APCC) is providing seasonal forecast data based on monthly climate prediction models for global seasonal forecasting services. The 3-month and 6-month seasonal forecast data using the multi-model ensemble (MME) technique are provided in their own website, ADSS (APCC Data Service System, http://adss.apcc21.org/). The spatial resolution of seasonal forecast data for each individual model is 2.5°×2.5°(about 250km) and the time scale is created as monthly. In this study, we developed customized weather forecast scenarios that are combined seasonal forecast data and observational data apply to early rice yield prediction model. Statistical downscale method was applied to produce meteorological input data of crop model because field scale crop model (ORYZA2000) requires daily weather data. In order to determine whether the forecasting data is suitable for the crop model, we produced spatio-temporal downscaled weather scenarios and evaluated the predictability by comparison with observed weather data at 57 ASOS stations in South Korea. The customized weather forecast scenarios can be applied to various application fields not only early rice yield prediction. Acknowledgement This work was carried out with the support of "Cooperative Research Program for Agriculture Science and Technology Development (Project No: PJ012855022017)" Rural Development Administration, Republic of Korea.

  10. Development of Next Generation Multiphase Pipe Flow Prediction Tools

    Energy Technology Data Exchange (ETDEWEB)

    Cem Sarica; Holden Zhang

    2006-05-31

    The developments of oil and gas fields in deep waters (5000 ft and more) will become more common in the future. It is inevitable that production systems will operate under multiphase flow conditions (simultaneous flow of gas, oil and water possibly along with sand, hydrates, and waxes). Multiphase flow prediction tools are essential for every phase of hydrocarbon recovery from design to operation. Recovery from deep-waters poses special challenges and requires accurate multiphase flow predictive tools for several applications, including the design and diagnostics of the production systems, separation of phases in horizontal wells, and multiphase separation (topside, seabed or bottom-hole). It is crucial for any multiphase separation technique, either at topside, seabed or bottom-hole, to know inlet conditions such as flow rates, flow patterns, and volume fractions of gas, oil and water coming into the separation devices. Therefore, the development of a new generation of multiphase flow predictive tools is needed. The overall objective of the proposed study is to develop a unified model for gas-oil-water three-phase flow in wells, flow lines, and pipelines to predict flow characteristics such as flow patterns, phase distributions, and pressure gradient encountered during petroleum production at different flow conditions (pipe diameter and inclination, fluid properties and flow rates). In the current multiphase modeling approach, flow pattern and flow behavior (pressure gradient and phase fractions) prediction modeling are separated. Thus, different models based on different physics are employed, causing inaccuracies and discontinuities. Moreover, oil and water are treated as a pseudo single phase, ignoring the distinct characteristics of both oil and water, and often resulting in inaccurate design that leads to operational problems. In this study, a new model is being developed through a theoretical and experimental study employing a revolutionary approach. The

  11. Development of system on predicting uranium concentration from pregnant solution

    International Nuclear Information System (INIS)

    Yi Weiping

    2004-01-01

    Uranium concentration from pregnant solution is primary index of process for in-situ leaching of uranium, and the suitable method with which to predicate this index and effective means to solve it with were continuously studied hard. SPUC-system on predicting uranium concentration based on GM model of gray system theory is developed, and the mathematical model, constitution, function and theory foundation of this system are introduced. (authors)

  12. Development of degradation prediction technology for rotating machines

    International Nuclear Information System (INIS)

    Osaki, Kenji; Watanabe, Yukio; Kitajima, Yasumi; Hattori, Hitoshi; Uhara, Yoshihiko; Miyoshi, Toshiaki; O'shima, Eiji

    1999-01-01

    In order to rationalize facility maintenance management, it is desirable to develop degradation prediction technologies that reduce the workload for example of replacing worn bearings of rotating machines. For this purpose, we are developing a system that performs degradation prediction respecting casing deformation, curvature and crack of shaft, bearing sleeve corrosion, and bearing wear for primary loop recirculation (PLR) pumps, which are important equipment in BWR plants, and for sea water pumps whose bearings are replaced frequently. By means of a physical model of degradation that takes into account various degradation phenomena, this system performs life estimation, taking into consideration the distribution of the vibration characteristic caused by the differences in assembling processes and the distribution of the degradation characteristic. The design of the degradation prediction system was examined and a part of the analysis method was developed. The bearing characteristic analysis with consideration to surface roughness was performed and the vibration response analysis evaluated the effects of factors influencing vibration characteristics of pump, such as bearing misalignment, on bearing forces. The component test of an upper bearing of 1/2 scale PLR pump motor was carried out, and the bearing wear characteristic was clarified. The research was carried out by the Japan Power Engineering and Inspection Corporation (JAPEIC) which was entrusted by the Ministry of International Trade and Industry (MITI). (author)

  13. Predicting the future development of depression or PTSD after injury.

    Science.gov (United States)

    Richmond, Therese S; Ruzek, Josef; Ackerson, Theimann; Wiebe, Douglas J; Winston, Flaura; Kassam-Adams, Nancy

    2011-01-01

    The objective was to develop a predictive screener that when given soon after injury will accurately differentiate those who will later develop depression or posttraumatic stress disorder (PTSD) from those who will not. This study used a prospective, longitudinal cohort design. Subjects were randomly selected from all injured patients in the emergency department; the majority was assessed within 1 week postinjury with a short predictive screener, followed with in-person interviews after 3 and 6 months to determine the emergence of depression or PTSD within 6 months after injury. A total of 192 completed a risk factor survey at baseline; 165 were assessed over 6 months. Twenty-six subjects [15.8%, 95% confidence interval (CI) 10.2-21.3] were diagnosed with depression, four (2.4%, 95% CI 0.7-5.9) with PTSD and one with both. The final eight-item predictive screener was derived; optimal cutoff scores were ≥2 (of 4) depression risk items and ≥3 (of 5) PTSD risk items. The final screener demonstrated excellent sensitivity and moderate specificity both for clinically significant symptoms and for the diagnoses of depression and PTSD. A simple screener that can help identify those patients at highest risk for future development of PTSD and depression postinjury allows the judicious allocation of costly mental health resources. Copyright © 2011 Elsevier Inc. All rights reserved.

  14. Development of Next Generation Multiphase Pipe Flow Prediction Tools

    Energy Technology Data Exchange (ETDEWEB)

    Tulsa Fluid Flow

    2008-08-31

    The developments of fields in deep waters (5000 ft and more) is a common occurrence. It is inevitable that production systems will operate under multiphase flow conditions (simultaneous flow of gas-oil-and water possibly along with sand, hydrates, and waxes). Multiphase flow prediction tools are essential for every phase of the hydrocarbon recovery from design to operation. The recovery from deep-waters poses special challenges and requires accurate multiphase flow predictive tools for several applications including the design and diagnostics of the production systems, separation of phases in horizontal wells, and multiphase separation (topside, seabed or bottom-hole). It is very crucial to any multiphase separation technique that is employed either at topside, seabed or bottom-hole to know inlet conditions such as the flow rates, flow patterns, and volume fractions of gas, oil and water coming into the separation devices. The overall objective was to develop a unified model for gas-oil-water three-phase flow in wells, flow lines, and pipelines to predict the flow characteristics such as flow patterns, phase distributions, and pressure gradient encountered during petroleum production at different flow conditions (pipe diameter and inclination, fluid properties and flow rates). The project was conducted in two periods. In Period 1 (four years), gas-oil-water flow in pipes were investigated to understand the fundamental physical mechanisms describing the interaction between the gas-oil-water phases under flowing conditions, and a unified model was developed utilizing a novel modeling approach. A gas-oil-water pipe flow database including field and laboratory data was formed in Period 2 (one year). The database was utilized in model performance demonstration. Period 1 primarily consisted of the development of a unified model and software to predict the gas-oil-water flow, and experimental studies of the gas-oil-water project, including flow behavior description and

  15. The time since last menstrual period is important as a clinical predictor for non-steroidal aromatase inhibitor-related arthralgia

    International Nuclear Information System (INIS)

    Kanematsu, Miyuki; Morimoto, Masami; Honda, Junko; Nagao, Taeko; Nakagawa, Misako; Takahashi, Masako; Tangoku, Akira; Sasa, Mitsunori

    2011-01-01

    The clinical predictors of aromatase inhibitor-related arthralgia (AIA), a drug-related adverse reaction of aromatase inhibitors (AIs), remain unclear. AIA was prospectively surveyed every 4 months in 328 postmenopausal breast cancer patients administered a non-steroidal AI (anastrozole). Various clinicopathological parameters were recorded and analyzed (chi-square test, Fisher's exact test and logistic regression analysis). The mean observation period was 39.9 months. AIA manifested in 114 patients (34.8%), with peaks of onset at 4 (33.7%) and 8 months (11.4%) after starting AI administration. Some cases manifested even after 13 months. AIA tended to occur in younger patients (incidences of 46.3%, 37.4% and 28.0% for ages of < 55, 55-65 and > 65 years, respectively (p = 0.063)) and decreased significantly with the age at menarche (53.3%, 35.3% and 15.4% for < 12, 12-15 and > 15 years, respectively (p = 0.036)). The incidences were 45.1%, 46.3 and 25.1% for the time since the last menstrual period (LMP) < 5 years, 5-10 years and > 10 years, being significantly lower at > 10 years (p < 0.001). In logistic regression analysis, the AIA incidence was significantly lower in the time since LMP > 10-year group versus the < 5-year group (odds ratio 0.44, p = 0.002), but the age at menarche showed no association. AIA manifested significantly earlier (≤ 6 months) as the time since LMP became shorter (< 5 years). AIA tends to manifest early after starting AI, but some cases show delayed onset. The incidence was significantly lower in patients with a duration of > 10 years since LMP. When the time since LMP was short, the onset of AIA was significantly earlier after starting AI administration

  16. Development of a predictive system for SLM product quality

    Science.gov (United States)

    Park, H. S.; Tran, N. H.; Nguyen, D. S.

    2017-08-01

    Recently, layer by layer manufacturing or additive manufacturing (AM) has been used in many application fields. Selective laser melting (SLM) is the most attractive method for building layer by layer from metallic powders. However, applications of AM in general and SLM in particular to industry have some barriers due to the quality of the manufactured parts which are affected by the high residual stresses and large deformation. SLM process is characterized by high heat source and fast solidification which lead to large thermal stress. The aim of this research is to develop a system for predicting the printed part quality during SLM process by simulation in consideration of the temperature distribution on the workpiece. For carrying out the system, model for predicting the temperature distribution was established. From this model, influences of process parameters to temperature distribution were analysed. The thermal model in consideration of relationship among printing parameters with temperature distribution is used for optimizing printing process parameters. Then, these results are used for calculating residual stress and predicting the workpiece deformation. The functionality of the proposed predictive system is proven through a case study on aluminium material manufactured on a MetalSys150 - SLM machine.

  17. Development of a Mobile Application for Building Energy Prediction Using Performance Prediction Model

    Directory of Open Access Journals (Sweden)

    Yu-Ri Kim

    2016-03-01

    Full Text Available Recently, the Korean government has enforced disclosure of building energy performance, so that such information can help owners and prospective buyers to make suitable investment plans. Such a building energy performance policy of the government makes it mandatory for the building owners to obtain engineering audits and thereby evaluate the energy performance levels of their buildings. However, to calculate energy performance levels (i.e., asset rating methodology, a qualified expert needs to have access to at least the full project documentation and/or conduct an on-site inspection of the buildings. Energy performance certification costs a lot of time and money. Moreover, the database of certified buildings is still actually quite small. A need, therefore, is increasing for a simplified and user-friendly energy performance prediction tool for non-specialists. Also, a database which allows building owners and users to compare best practices is required. In this regard, the current study developed a simplified performance prediction model through experimental design, energy simulations and ANOVA (analysis of variance. Furthermore, using the new prediction model, a related mobile application was also developed.

  18. Early Antenatal Prediction of Gestational Diabetes in Obese Women: Development of Prediction Tools for Targeted Intervention.

    Directory of Open Access Journals (Sweden)

    Sara L White

    Full Text Available All obese women are categorised as being of equally high risk of gestational diabetes (GDM whereas the majority do not develop the disorder. Lifestyle and pharmacological interventions in unselected obese pregnant women have been unsuccessful in preventing GDM. Our aim was to develop a prediction tool for early identification of obese women at high risk of GDM to facilitate targeted interventions in those most likely to benefit. Clinical and anthropometric data and non-fasting blood samples were obtained at 15+0-18+6 weeks' gestation in 1303 obese pregnant women from UPBEAT, a randomised controlled trial of a behavioural intervention. Twenty one candidate biomarkers associated with insulin resistance, and a targeted nuclear magnetic resonance (NMR metabolome were measured. Prediction models were constructed using stepwise logistic regression. Twenty six percent of women (n = 337 developed GDM (International Association of Diabetes and Pregnancy Study Groups criteria. A model based on clinical and anthropometric variables (age, previous GDM, family history of type 2 diabetes, systolic blood pressure, sum of skinfold thicknesses, waist:height and neck:thigh ratios provided an area under the curve of 0.71 (95%CI 0.68-0.74. This increased to 0.77 (95%CI 0.73-0.80 with addition of candidate biomarkers (random glucose, haemoglobin A1c (HbA1c, fructosamine, adiponectin, sex hormone binding globulin, triglycerides, but was not improved by addition of NMR metabolites (0.77; 95%CI 0.74-0.81. Clinically translatable models for GDM prediction including readily measurable variables e.g. mid-arm circumference, age, systolic blood pressure, HbA1c and adiponectin are described. Using a ≥35% risk threshold, all models identified a group of high risk obese women of whom approximately 50% (positive predictive value later developed GDM, with a negative predictive value of 80%. Tools for early pregnancy identification of obese women at risk of GDM are described

  19. Developing a comprehensive training curriculum for integrated predictive maintenance

    Science.gov (United States)

    Wurzbach, Richard N.

    2002-03-01

    On-line equipment condition monitoring is a critical component of the world-class production and safety histories of many successful nuclear plant operators. From addressing availability and operability concerns of nuclear safety-related equipment to increasing profitability through support system reliability and reduced maintenance costs, Predictive Maintenance programs have increasingly become a vital contribution to the maintenance and operation decisions of nuclear facilities. In recent years, significant advancements have been made in the quality and portability of many of the instruments being used, and software improvements have been made as well. However, the single most influential component of the success of these programs is the impact of a trained and experienced team of personnel putting this technology to work. Changes in the nature of the power generation industry brought on by competition, mergers, and acquisitions, has taken the historically stable personnel environment of power generation and created a very dynamic situation. As a result, many facilities have seen a significant turnover in personnel in key positions, including predictive maintenance personnel. It has become the challenge for many nuclear operators to maintain the consistent contribution of quality data and information from predictive maintenance that has become important in the overall equipment decision process. These challenges can be met through the implementation of quality training to predictive maintenance personnel and regular updating and re-certification of key technology holders. The use of data management tools and services aid in the sharing of information across sites within an operating company, and with experts who can contribute value-added data management and analysis. The overall effectiveness of predictive maintenance programs can be improved through the incorporation of newly developed comprehensive technology training courses. These courses address the use of

  20. Developing Predictive Maintenance Expertise to Improve Plant Equipment Reliability

    International Nuclear Information System (INIS)

    Wurzbach, Richard N.

    2002-01-01

    On-line equipment condition monitoring is a critical component of the world-class production and safety histories of many successful nuclear plant operators. From addressing availability and operability concerns of nuclear safety-related equipment to increasing profitability through support system reliability and reduced maintenance costs, Predictive Maintenance programs have increasingly become a vital contribution to the maintenance and operation decisions of nuclear facilities. In recent years, significant advancements have been made in the quality and portability of many of the instruments being used, and software improvements have been made as well. However, the single most influential component of the success of these programs is the impact of a trained and experienced team of personnel putting this technology to work. Changes in the nature of the power generation industry brought on by competition, mergers, and acquisitions, has taken the historically stable personnel environment of power generation and created a very dynamic situation. As a result, many facilities have seen a significant turnover in personnel in key positions, including predictive maintenance personnel. It has become the challenge for many nuclear operators to maintain the consistent contribution of quality data and information from predictive maintenance that has become important in the overall equipment decision process. These challenges can be met through the implementation of quality training to predictive maintenance personnel and regular updating and re-certification of key technology holders. The use of data management tools and services aid in the sharing of information across sites within an operating company, and with experts who can contribute value-added data management and analysis. The overall effectiveness of predictive maintenance programs can be improved through the incorporation of newly developed comprehensive technology training courses. These courses address the use of

  1. Cost prediction following traumatic brain injury: model development and validation.

    Science.gov (United States)

    Spitz, Gershon; McKenzie, Dean; Attwood, David; Ponsford, Jennie L

    2016-02-01

    The ability to predict costs following a traumatic brain injury (TBI) would assist in planning treatment and support services by healthcare providers, insurers and other agencies. The objective of the current study was to develop predictive models of hospital, medical, paramedical, and long-term care (LTC) costs for the first 10 years following a TBI. The sample comprised 798 participants with TBI, the majority of whom were male and aged between 15 and 34 at time of injury. Costing information was obtained for hospital, medical, paramedical, and LTC costs up to 10 years postinjury. Demographic and injury-severity variables were collected at the time of admission to the rehabilitation hospital. Duration of PTA was the most important single predictor for each cost type. The final models predicted 44% of hospital costs, 26% of medical costs, 23% of paramedical costs, and 34% of LTC costs. Greater costs were incurred, depending on cost type, for individuals with longer PTA duration, obtaining a limb or chest injury, a lower GCS score, older age at injury, not being married or defacto prior to injury, living in metropolitan areas, and those reporting premorbid excessive or problem alcohol use. This study has provided a comprehensive analysis of factors predicting various types of costs following TBI, with the combination of injury-related and demographic variables predicting 23-44% of costs. PTA duration was the strongest predictor across all cost categories. These factors may be used for the planning and case management of individuals following TBI. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

  2. Development of Antimicrobial Peptide Prediction Tool for Aquaculture Industries.

    Science.gov (United States)

    Gautam, Aditi; Sharma, Asuda; Jaiswal, Sarika; Fatma, Samar; Arora, Vasu; Iquebal, M A; Nandi, S; Sundaray, J K; Jayasankar, P; Rai, Anil; Kumar, Dinesh

    2016-09-01

    Microbial diseases in fish, plant, animal and human are rising constantly; thus, discovery of their antidote is imperative. The use of antibiotic in aquaculture further compounds the problem by development of resistance and consequent consumer health risk by bio-magnification. Antimicrobial peptides (AMPs) have been highly promising as natural alternative to chemical antibiotics. Though AMPs are molecules of innate immune defense of all advance eukaryotic organisms, fish being heavily dependent on their innate immune defense has been a good source of AMPs with much wider applicability. Machine learning-based prediction method using wet laboratory-validated fish AMP can accelerate the AMP discovery using available fish genomic and proteomic data. Earlier AMP prediction servers are based on multi-phyla/species data, and we report here the world's first AMP prediction server in fishes. It is freely accessible at http://webapp.cabgrid.res.in/fishamp/ . A total of 151 AMPs related to fish collected from various databases and published literature were taken for this study. For model development and prediction, N-terminus residues, C-terminus residues and full sequences were considered. Best models were with kernels polynomial-2, linear and radial basis function with accuracy of 97, 99 and 97 %, respectively. We found that performance of support vector machine-based models is superior to artificial neural network. This in silico approach can drastically reduce the time and cost of AMP discovery. This accelerated discovery of lead AMP molecules having potential wider applications in diverse area like fish and human health as substitute of antibiotics, immunomodulator, antitumor, vaccine adjuvant and inactivator, and also for packaged food can be of much importance for industries.

  3. Development of a Predictive Model for Induction Success of Labour

    Directory of Open Access Journals (Sweden)

    Cristina Pruenza

    2018-03-01

    Full Text Available Induction of the labour process is an extraordinarily common procedure used in some pregnancies. Obstetricians face the need to end a pregnancy, for medical reasons usually (maternal or fetal requirements or less frequently, social (elective inductions for convenience. The success of induction procedure is conditioned by a multitude of maternal and fetal variables that appear before or during pregnancy or birth process, with a low predictive value. The failure of the induction process involves performing a caesarean section. This project arises from the clinical need to resolve a situation of uncertainty that occurs frequently in our clinical practice. Since the weight of clinical variables is not adequately weighted, we consider very interesting to know a priori the possibility of success of induction to dismiss those inductions with high probability of failure, avoiding unnecessary procedures or postponing end if possible. We developed a predictive model of induced labour success as a support tool in clinical decision making. Improve the predictability of a successful induction is one of the current challenges of Obstetrics because of its negative impact. The identification of those patients with high chances of failure, will allow us to offer them better care improving their health outcomes (adverse perinatal outcomes for mother and newborn, costs (medication, hospitalization, qualified staff and patient perceived quality. Therefore a Clinical Decision Support System was developed to give support to the Obstetricians. In this article, we had proposed a robust method to explore and model a source of clinical information with the purpose of obtaining all possible knowledge. Generally, in classification models are difficult to know the contribution that each attribute provides to the model. We had worked in this direction to offer transparency to models that may be considered as black boxes. The positive results obtained from both the

  4. Survey of gastrointestinal reactions to foods in adults in relation to atopy, presence of mucus in the stools, swelling of joints and arthralgia in patients with gastrointestinal reactions to foods.

    Science.gov (United States)

    Bengtsson, U; Hanson, L A; Ahlstedt, S

    1996-12-01

    Food intolerance in adults is mostly associated with vague symptoms and not clearly related to atopy and food allergy. A combination of different pathogenetic mechanisms may be responsible for the symptoms. The aim of this study was to describe patients with a history of food-related gastrointestinal symptoms in relation to the presence of mucus in the stools, joint swelling and arthralgia and to determine whether or not there is an association between the presence of these parameters, atopic disease and the presence of immune complexes in serum. Fifty-eight patients consecutively referred to our clinic with food-related gastrointestinal symptoms were investigated. Thirty-five patients (60%) had mucus in their stools, 24 patients (41%) complained about joint swelling and 41 patients (71%) had arthralgia. There were no correlations between these parameters and atopy according to Phadiatope test or skin prick test (SPT). No correlations were found between the occurrence of mucus in the stools, arthralgia and joint swelling. There were significantly higher levels of circulating immune complexes in patients with a history of arthralgia compared with patients with no such history (P mucus in the stools. However, there were significant positive correlations between food-related gastrointestinal symptoms in the following instances: chocolate-induced gastrointestinal symptoms and mucus in the stools (P = 0.006), vegetable-induced gastrointestinal symptoms and mucus in the stools (P = 0.002) and meat-induced gastrointestinal symptoms and mucus in the stools (P = 0.003). In a group of individuals without food-related symptoms investigated separately, a very low frequency of mucus in the stools, joint swelling and arthralgia was seen (none, two and three individuals of the 20 subjects, respectively). Of 41 patients with immediate onset of gastrointestinal symptoms, 20 were atopic according to Phadiatope and SPT. Of 11 patients with late onset of symptoms 10 were negative in

  5. Potential Predictability of ZPD of Children’s Cognitive Development

    Directory of Open Access Journals (Sweden)

    Parviz Birjandi

    2011-05-01

    Full Text Available Obtaining information on whether the child has the potential for growth is not an easy task. Research shows that using different matrix like Raven or different batteries in a static way cannot
    be indicative of children further development. This study attempts to probe the potential predictability of children’s performance during Dynamic Assessment of their Future development.
    41 children between ages 3 to 6 years old participated in this study. The data in pretest, ZPD, and posttest were converted into Rasch Measure. The results of different analysis indicate that relying on children’s actual performance cannot be an indicative factor of their development in the future.

  6. Predictive models reduce talent development costs in female gymnastics.

    Science.gov (United States)

    Pion, Johan; Hohmann, Andreas; Liu, Tianbiao; Lenoir, Matthieu; Segers, Veerle

    2017-04-01

    This retrospective study focuses on the comparison of different predictive models based on the results of a talent identification test battery for female gymnasts. We studied to what extent these models have the potential to optimise selection procedures, and at the same time reduce talent development costs in female artistic gymnastics. The dropout rate of 243 female elite gymnasts was investigated, 5 years past talent selection, using linear (discriminant analysis) and non-linear predictive models (Kohonen feature maps and multilayer perceptron). The coaches classified 51.9% of the participants correct. Discriminant analysis improved the correct classification to 71.6% while the non-linear technique of Kohonen feature maps reached 73.7% correctness. Application of the multilayer perceptron even classified 79.8% of the gymnasts correctly. The combination of different predictive models for talent selection can avoid deselection of high-potential female gymnasts. The selection procedure based upon the different statistical analyses results in decrease of 33.3% of cost because the pool of selected athletes can be reduced to 92 instead of 138 gymnasts (as selected by the coaches). Reduction of the costs allows the limited resources to be fully invested in the high-potential athletes.

  7. Developing and Validating a Predictive Model for Stroke Progression

    Directory of Open Access Journals (Sweden)

    L.E. Craig

    2011-12-01

    Full Text Available Background: Progression is believed to be a common and important complication in acute stroke, and has been associated with increased mortality and morbidity. Reliable identification of predictors of early neurological deterioration could potentially benefit routine clinical care. The aim of this study was to identify predictors of early stroke progression using two independent patient cohorts. Methods: Two patient cohorts were used for this study – the first cohort formed the training data set, which included consecutive patients admitted to an urban teaching hospital between 2000 and 2002, and the second cohort formed the test data set, which included patients admitted to the same hospital between 2003 and 2004. A standard definition of stroke progression was used. The first cohort (n = 863 was used to develop the model. Variables that were statistically significant (p 0.1 in turn. The second cohort (n = 216 was used to test the performance of the model. The performance of the predictive model was assessed in terms of both calibration and discrimination. Multiple imputation methods were used for dealing with the missing values. Results: Variables shown to be significant predictors of stroke progression were conscious level, history of coronary heart disease, presence of hyperosmolarity, CT lesion, living alone on admission, Oxfordshire Community Stroke Project classification, presence of pyrexia and smoking status. The model appears to have reasonable discriminative properties [the median receiver-operating characteristic curve value was 0.72 (range 0.72–0.73] and to fit well with the observed data, which is indicated by the high goodness-of-fit p value [the median p value from the Hosmer-Lemeshow test was 0.90 (range 0.50–0.92]. Conclusion: The predictive model developed in this study contains variables that can be easily collected in practice therefore increasing its usability in clinical practice. Using this analysis approach, the

  8. Developing and validating a predictive model for stroke progression.

    Science.gov (United States)

    Craig, L E; Wu, O; Gilmour, H; Barber, M; Langhorne, P

    2011-01-01

    Progression is believed to be a common and important complication in acute stroke, and has been associated with increased mortality and morbidity. Reliable identification of predictors of early neurological deterioration could potentially benefit routine clinical care. The aim of this study was to identify predictors of early stroke progression using two independent patient cohorts. Two patient cohorts were used for this study - the first cohort formed the training data set, which included consecutive patients admitted to an urban teaching hospital between 2000 and 2002, and the second cohort formed the test data set, which included patients admitted to the same hospital between 2003 and 2004. A standard definition of stroke progression was used. The first cohort (n = 863) was used to develop the model. Variables that were statistically significant (p p > 0.1) in turn. The second cohort (n = 216) was used to test the performance of the model. The performance of the predictive model was assessed in terms of both calibration and discrimination. Multiple imputation methods were used for dealing with the missing values. Variables shown to be significant predictors of stroke progression were conscious level, history of coronary heart disease, presence of hyperosmolarity, CT lesion, living alone on admission, Oxfordshire Community Stroke Project classification, presence of pyrexia and smoking status. The model appears to have reasonable discriminative properties [the median receiver-operating characteristic curve value was 0.72 (range 0.72-0.73)] and to fit well with the observed data, which is indicated by the high goodness-of-fit p value [the median p value from the Hosmer-Lemeshow test was 0.90 (range 0.50-0.92)]. The predictive model developed in this study contains variables that can be easily collected in practice therefore increasing its usability in clinical practice. Using this analysis approach, the discrimination and calibration of the predictive model appear

  9. Developing and Validating a Predictive Model for Stroke Progression

    Science.gov (United States)

    Craig, L.E.; Wu, O.; Gilmour, H.; Barber, M.; Langhorne, P.

    2011-01-01

    Background Progression is believed to be a common and important complication in acute stroke, and has been associated with increased mortality and morbidity. Reliable identification of predictors of early neurological deterioration could potentially benefit routine clinical care. The aim of this study was to identify predictors of early stroke progression using two independent patient cohorts. Methods Two patient cohorts were used for this study – the first cohort formed the training data set, which included consecutive patients admitted to an urban teaching hospital between 2000 and 2002, and the second cohort formed the test data set, which included patients admitted to the same hospital between 2003 and 2004. A standard definition of stroke progression was used. The first cohort (n = 863) was used to develop the model. Variables that were statistically significant (p 0.1) in turn. The second cohort (n = 216) was used to test the performance of the model. The performance of the predictive model was assessed in terms of both calibration and discrimination. Multiple imputation methods were used for dealing with the missing values. Results Variables shown to be significant predictors of stroke progression were conscious level, history of coronary heart disease, presence of hyperosmolarity, CT lesion, living alone on admission, Oxfordshire Community Stroke Project classification, presence of pyrexia and smoking status. The model appears to have reasonable discriminative properties [the median receiver-operating characteristic curve value was 0.72 (range 0.72–0.73)] and to fit well with the observed data, which is indicated by the high goodness-of-fit p value [the median p value from the Hosmer-Lemeshow test was 0.90 (range 0.50–0.92)]. Conclusion The predictive model developed in this study contains variables that can be easily collected in practice therefore increasing its usability in clinical practice. Using this analysis approach, the discrimination and

  10. Development of a neural net paradigm that predicts simulator sickness

    Energy Technology Data Exchange (ETDEWEB)

    Allgood, G.O.

    1993-03-01

    A disease exists that affects pilots and aircrew members who use Navy Operational Flight Training Systems. This malady, commonly referred to as simulator sickness and whose symptomatology closely aligns with that of motion sickness, can compromise the use of these systems because of a reduced utilization factor, negative transfer of training, and reduction in combat readiness. A report is submitted that develops an artificial neural network (ANN) and behavioral model that predicts the onset and level of simulator sickness in the pilots and aircrews who sue these systems. It is proposed that the paradigm could be implemented in real time as a biofeedback monitor to reduce the risk to users of these systems. The model captures the neurophysiological impact of use (human-machine interaction) by developing a structure that maps the associative and nonassociative behavioral patterns (learned expectations) and vestibular (otolith and semicircular canals of the inner ear) and tactile interaction, derived from system acceleration profiles, onto an abstract space that predicts simulator sickness for a given training flight.

  11. Predicting risk behaviors: development and validation of a diagnostic scale.

    Science.gov (United States)

    Witte, K; Cameron, K A; McKeon, J K; Berkowitz, J M

    1996-01-01

    The goal of this study was to develop and validate the Risk Behavior Diagnosis (RBD) Scale for use by health care providers and practitioners interested in promoting healthy behaviors. Theoretically guided by the Extended Parallel Process Model (EPPM; a fear appeal theory), the RBD scale was designed to work in conjunction with an easy-to-use formula to determine which types of health risk messages would be most appropriate for a given individual or audience. Because some health risk messages promote behavior change and others backfire, this type of scale offers guidance to practitioners on how to develop the best persuasive message possible to motivate healthy behaviors. The results of the study demonstrate the RBD scale to have a high degree of content, construct, and predictive validity. Specific examples and practical suggestions are offered to facilitate use of the scale for health practitioners.

  12. Observant, Nonaggressive Temperament Predicts Theory of Mind Development

    Science.gov (United States)

    Wellman, Henry M.; Lane, Jonathan D.; LaBounty, Jennifer; Olson, Sheryl L.

    2010-01-01

    Temperament dimensions influence children’s approach to and participation in social interactive experiences which reflect and impact children’s social understandings. Therefore, temperament differences might substantially impact theory of mind development in early childhood. Using longitudinal data, we report that certain early temperament characteristics (at age 3) – lack of aggressiveness, a shy-withdrawn stance to social interaction, and social-perceptual sensitivity – predict children’s more advanced theory-of-mind understanding two years later. The findings contribute to our understanding of how theory of mind develops in the formative preschool period; they may also inform debates as to the evolutionary origins of theory of mind. PMID:21499499

  13. Development of a fourth generation predictive capability maturity model.

    Energy Technology Data Exchange (ETDEWEB)

    Hills, Richard Guy; Witkowski, Walter R.; Urbina, Angel; Rider, William J.; Trucano, Timothy Guy

    2013-09-01

    The Predictive Capability Maturity Model (PCMM) is an expert elicitation tool designed to characterize and communicate completeness of the approaches used for computational model definition, verification, validation, and uncertainty quantification associated for an intended application. The primary application of this tool at Sandia National Laboratories (SNL) has been for physics-based computational simulations in support of nuclear weapons applications. The two main goals of a PCMM evaluation are 1) the communication of computational simulation capability, accurately and transparently, and 2) the development of input for effective planning. As a result of the increasing importance of computational simulation to SNLs mission, the PCMM has evolved through multiple generations with the goal to provide more clarity, rigor, and completeness in its application. This report describes the approach used to develop the fourth generation of the PCMM.

  14. Development and external validation of a risk-prediction model to predict 5-year overall survival in advanced larynx cancer

    NARCIS (Netherlands)

    Petersen, Japke F.; Stuiver, Martijn M.; Timmermans, Adriana J.; Chen, Amy; Zhang, Hongzhen; O'Neill, James P.; Deady, Sandra; Vander Poorten, Vincent; Meulemans, Jeroen; Wennerberg, Johan; Skroder, Carl; Day, Andrew T.; Koch, Wayne; van den Brekel, Michiel W. M.

    2017-01-01

    TNM-classification inadequately estimates patient-specific overall survival (OS). We aimed to improve this by developing a risk-prediction model for patients with advanced larynx cancer. Cohort study. We developed a risk prediction model to estimate the 5-year OS rate based on a cohort of 3,442

  15. Comorbidity predicts poor prognosis in nasopharyngeal carcinoma: Development and validation of a predictive score model

    International Nuclear Information System (INIS)

    Guo, Rui; Chen, Xiao-Zhong; Chen, Lei; Jiang, Feng; Tang, Ling-Long; Mao, Yan-Ping; Zhou, Guan-Qun; Li, Wen-Fei; Liu, Li-Zhi; Tian, Li; Lin, Ai-Hua; Ma, Jun

    2015-01-01

    Background and purpose: The impact of comorbidity on prognosis in nasopharyngeal carcinoma (NPC) is poorly characterized. Material and methods: Using the Adult Comorbidity Evaluation-27 (ACE-27) system, we assessed the prognostic value of comorbidity and developed, validated and confirmed a predictive score model in a training set (n = 658), internal validation set (n = 658) and independent set (n = 652) using area under the receiver operating curve analysis. Results: Comorbidity was present in 40.4% of 1968 patients (mild, 30.1%; moderate, 9.1%; severe, 1.2%). Compared to an ACE-27 score ⩽1, patients with an ACE-27 score >1 in the training set had shorter overall survival (OS) and disease-free survival (DFS) (both P < 0.001), similar results were obtained in the other sets (P < 0.05). In multivariate analysis, ACE-27 score was a significant independent prognostic factor for OS and DFS. The combined risk score model including ACE-27 had superior prognostic value to TNM stage alone in the internal validation set (0.70 vs. 0.66; P = 0.02), independent set (0.73 vs. 0.67; P = 0.002) and all patients (0.71 vs. 0.67; P < 0.001). Conclusions: Comorbidity significantly affects prognosis, especially in stages II and III, and should be incorporated into the TNM staging system for NPC. Assessment of comorbidity may improve outcome prediction and help tailor individualized treatment

  16. Development of nondestructive method for prediction of crack instability

    International Nuclear Information System (INIS)

    Schroeder, J.L.; Eylon, D.; Shell, E.B.; Matikas, T.E.

    2000-01-01

    A method to characterize the deformation zone at a crack tip and predict upcoming fracture under load using white light interference microscopy was developed and studied. Cracks were initiated in notched Ti-6Al-4V specimens through fatigue loading. Following crack initiation, specimens were subjected to static loading during in-situ observation of the deformation area ahead of the crack. Nondestructive in-situ observations were performed using white light interference microscopy. Profilometer measurements quantified the area, volume, and shape of the deformation ahead of the crack front. Results showed an exponential relationship between the area and volume of deformation and the stress intensity factor of the cracked alloy. These findings also indicate that it is possible to determine a critical rate of change in deformation versus the stress intensity factor that can predict oncoming catastrophic failure. In addition, crack front deformation zones were measured as a function of time under sustained load, and crack tip deformation zone enlargement over time was observed

  17. Scoring system development for prediction of extravesical bladder cancer

    Directory of Open Access Journals (Sweden)

    Prelević Rade

    2014-01-01

    Full Text Available Background/Aim. Staging of bladder cancer is crucial for optimal management of the disease. However, clinical staging is not perfectly accurate. The aim of this study was to derive a simple scoring system in prediction of pathological advanced muscle-invasive bladder cancer (MIBC. Methods. Logistic regression and bootstrap methods were used to create an integer score for estimating the risk in prediction of pathological advanced MIBC using precystectomy clinicopathological data: demographic, initial transurethral resection (TUR [grade, stage, multiplicity of tumors, lymphovascular invasion (LVI], hydronephrosis, abdominal and pelvic CT radiography (size of the tumor, tumor base width, and pathological stage after radical cystectomy (RC. Advanced MIBC in surgical specimen was defined as pT3-4 tumor. Receiving operating characteristic (ROC curve quantified the area under curve (AUC as predictive accuracy. Clinical usefulness was assessed by using decision curve analysis. Results. This single-center retrospective study included 233 adult patients with BC undergoing RC at the Military Medical Academy, Belgrade. Organ confined disease was observed in 101 (43.3% patients, and 132 (56.7% had advanced MIBC. In multivariable analysis, 3 risk factors most strongly associated with advanced MIBC: grade of initial TUR [odds ratio (OR = 4.7], LVI (OR = 2, and hydronephrosis (OR = 3.9. The resultant total possible score ranged from 0 to 15, with the cut-off value of > 8 points, the AUC was 0.795, showing good discriminatory ability. The model showed excellent calibration. Decision curve analysis showed a net benefit across all threshold probabilities and clinical usefulness of the model. Conclusion. We developed a unique scoring system which could assist in predicting advanced MIBC in patients before RC. The scoring system showed good performance characteristics and introducing of such a tool into daily clinical decision-making may lead to more appropriate

  18. Development of residual stress prediction model in pipe weldment

    Energy Technology Data Exchange (ETDEWEB)

    Eom, Yun Yong; Lim, Se Young; Choi, Kang Hyeuk; Cho, Young Sam; Lim, Jae Hyuk [Korea Advanced Institute of Science and Technology, Taejon (Korea, Republic of)

    2002-03-15

    When Leak Before Break(LBB) concepts is applied to high energy piping of nuclear power plants, residual weld stresses is a important variable. The main purpose of his research is to develop the numerical model which can predict residual weld stresses. Firstly, basic theories were described which need to numerical analysis of welding parts. Before the analysis of pipe, welding of a flat plate was analyzed and compared. Appling the data of used pipes, thermal/mechanical analysis were accomplished and computed temperature gradient and residual stress distribution. For thermal analysis, proper heat flux was regarded as the heat source and convection/radiation heat transfer were considered at surfaces. The residual stresses were counted from the computed temperature gradient and they were compared and verified with a result of another research.

  19. A computational model predicting disruption of blood vessel development.

    Directory of Open Access Journals (Sweden)

    Nicole Kleinstreuer

    2013-04-01

    Full Text Available Vascular development is a complex process regulated by dynamic biological networks that vary in topology and state across different tissues and developmental stages. Signals regulating de novo blood vessel formation (vasculogenesis and remodeling (angiogenesis come from a variety of biological pathways linked to endothelial cell (EC behavior, extracellular matrix (ECM remodeling and the local generation of chemokines and growth factors. Simulating these interactions at a systems level requires sufficient biological detail about the relevant molecular pathways and associated cellular behaviors, and tractable computational models that offset mathematical and biological complexity. Here, we describe a novel multicellular agent-based model of vasculogenesis using the CompuCell3D (http://www.compucell3d.org/ modeling environment supplemented with semi-automatic knowledgebase creation. The model incorporates vascular endothelial growth factor signals, pro- and anti-angiogenic inflammatory chemokine signals, and the plasminogen activating system of enzymes and proteases linked to ECM interactions, to simulate nascent EC organization, growth and remodeling. The model was shown to recapitulate stereotypical capillary plexus formation and structural emergence of non-coded cellular behaviors, such as a heterologous bridging phenomenon linking endothelial tip cells together during formation of polygonal endothelial cords. Molecular targets in the computational model were mapped to signatures of vascular disruption derived from in vitro chemical profiling using the EPA's ToxCast high-throughput screening (HTS dataset. Simulating the HTS data with the cell-agent based model of vascular development predicted adverse effects of a reference anti-angiogenic thalidomide analog, 5HPP-33, on in vitro angiogenesis with respect to both concentration-response and morphological consequences. These findings support the utility of cell agent-based models for simulating a

  20. Regional hippocampal volumes and development predict learning and memory.

    Science.gov (United States)

    Tamnes, Christian K; Walhovd, Kristine B; Engvig, Andreas; Grydeland, Håkon; Krogsrud, Stine K; Østby, Ylva; Holland, Dominic; Dale, Anders M; Fjell, Anders M

    2014-01-01

    The hippocampus is an anatomically and functionally heterogeneous structure, but longitudinal studies of its regional development are scarce and it is not known whether protracted maturation of the hippocampus in adolescence is related to memory development. First, we investigated hippocampal subfield development using 170 longitudinally acquired brain magnetic resonance imaging scans from 85 participants aged 8-21 years. Hippocampal subfield volumes were estimated by the use of automated segmentation of 7 subfields, including the cornu ammonis (CA) sectors and the dentate gyrus (DG), while longitudinal subfield volumetric change was quantified using a nonlinear registration procedure. Second, associations between subfield volumes and change and verbal learning/memory across multiple retention intervals (5 min, 30 min and 1 week) were tested. It was hypothesized that short and intermediate memory would be more closely related to CA2-3/CA4-DG and extended, remote memory to CA1. Change rates were significantly different across hippocampal subfields, but nearly all subfields showed significant volume decreases over time throughout adolescence. Several subfield volumes were larger in the right hemisphere and in males, while for change rates there were no hemisphere or sex differences. Partly in support of the hypotheses, greater volume of CA1 and CA2-3 was related to recall and retention after an extended delay, while longitudinal reduction of CA2-3 and CA4-DG was related to learning. This suggests continued regional development of the hippocampus across adolescence and that volume and volume change in specific subfields differentially predict verbal learning and memory over different retention intervals, but future high-resolution studies are called for. © 2014 S. Karger AG, Basel.

  1. In-operation inspection technology development 'development of degradation prediction technology for rotating machinery'

    International Nuclear Information System (INIS)

    Osaki, K.; Watanabe, Y.; Uhara, Y.; Hattori, H.; O'shima, E.; Matsumoto, K.

    2001-01-01

    In order to rationalize facility maintenance management and improve reliabilities of rotating machines, it is desirable to develop the technology for estimating bearing wear and predicting bearing wear growth. Therefore, we developed a bearing wear analysis method for evaluating bearing wear growth in the mixed lubrication, and developed a degradation prediction system which estimates the bearing wear and predicts bearing wear growth from external parameters, such as shaft vibration. In bearing wear analysis, the influence of bearing surface roughness and elastic deformation are considered. This analysis model was validated by the bearing wear test. The developed system can predict degradation respecting bearing wear, casing deformation, shaft curvature and bearing sleeve corrosion, using some physical models of degradation that take into account various degradation phenomena. Furthermore, this system can estimate bearing life, taking into consideration the distribution of the vibration characteristic caused by the differences in assembling processes and the distribution of the degradation characteristic. This system was validated by the degradation simulation test. (authors)

  2. Developing an Ensemble Prediction System based on COSMO-DE

    Science.gov (United States)

    Theis, S.; Gebhardt, C.; Buchhold, M.; Ben Bouallègue, Z.; Ohl, R.; Paulat, M.; Peralta, C.

    2010-09-01

    The numerical weather prediction model COSMO-DE is a configuration of the COSMO model with a horizontal grid size of 2.8 km. It has been running operationally at DWD since 2007, it covers the area of Germany and produces forecasts with a lead time of 0-21 hours. The model COSMO-DE is convection-permitting, which means that it does without a parametrisation of deep convection and simulates deep convection explicitly. One aim is an improved forecast of convective heavy rain events. Convection-permitting models are in operational use at several weather services, but currently not in ensemble mode. It is expected that an ensemble system could reveal the advantages of a convection-permitting model even better. The probabilistic approach is necessary, because the explicit simulation of convective processes for more than a few hours cannot be viewed as a deterministic forecast anymore. This is due to the chaotic behaviour and short life cycle of the processes which are simulated explicitly now. In the framework of the project COSMO-DE-EPS, DWD is developing and implementing an ensemble prediction system (EPS) for the model COSMO-DE. The project COSMO-DE-EPS comprises the generation of ensemble members, as well as the verification and visualization of the ensemble forecasts and also statistical postprocessing. A pre-operational mode of the EPS with 20 ensemble members is foreseen to start in 2010. Operational use is envisaged to start in 2012, after an upgrade to 40 members and inclusion of statistical postprocessing. The presentation introduces the project COSMO-DE-EPS and describes the design of the ensemble as it is planned for the pre-operational mode. In particular, the currently implemented method for the generation of ensemble members will be explained and discussed. The method includes variations of initial conditions, lateral boundary conditions, and model physics. At present, pragmatic methods are applied which resemble the basic ideas of a multi-model approach

  3. Stargardt disease: towards developing a model to predict phenotype.

    Science.gov (United States)

    Heathfield, Laura; Lacerda, Miguel; Nossek, Christel; Roberts, Lisa; Ramesar, Rajkumar S

    2013-10-01

    Stargardt disease is an ABCA4-associated retinopathy, which generally follows an autosomal recessive inheritance pattern and is a frequent cause of macular degeneration in childhood. ABCA4 displays significant allelic heterogeneity whereby different mutations can cause retinal diseases with varying severity and age of onset. A genotype-phenotype model has been proposed linking ABCA4 mutations, purported ABCA4 functional protein activity and severity of disease, as measured by degree of visual loss and the age of onset. It has, however, been difficult to verify this model statistically in observational studies, as the number of individuals sharing any particular mutation combination is typically low. Seven founder mutations have been identified in a large number of Caucasian Afrikaner patients in South Africa, making it possible to test the genotype-phenotype model. A generalised linear model was developed to predict and assess the relative pathogenic contribution of the seven mutations to the age of onset of Stargardt disease. It is shown that the pathogenicity of an individual mutation can differ significantly depending on the genetic context in which it occurs. The results reported here may be used to identify suitable candidates for inclusion in clinical trials, as well as guide the genetic counselling of affected individuals and families.

  4. Further developments in performance prediction techniques of adiabatic diesel engines

    Energy Technology Data Exchange (ETDEWEB)

    Rasihhan, Y

    1990-01-01

    The engine cycle simulation program 'SPICE', developed at Bath University, has been used extensively for insulated diesel engine research. The present study introduces more comprehensive engine heat transfer models thus enabling us to study the insulated engine heat transfer and performance characteristics in more detail. The new version of 'SPICE' separates the gas to wall heat transfer into two parts, convective and radiative. For this purpose, a detailed radiative heat transfer model which considers both the flame (gas and soot) and wall to wall radiative heat transfer is written. The previous engine resistance model is refined and replaced by a more detailed resistance model which considers piston-liner conduction heat transfer and 2-D heat flow in the liner. The wall surface temperature swing is also included in the engine heat transfer calculations which is quite significant in low conductivity ceramic insulated engines. A 1-D finite difference model is written for the transient heat transfer region of the wall and linked to the engine resistance model. This new version of 'SPICE' is used to predict the insulated engine heat transfer and performance for the experimental Petter PH1W engine for various insulation levels and schemes. An answer to the controversy of increase in engine heat loss with insulation is looked for. The effect of wall deposits on engine heat transfer and its significance for the insulated engine is highlighted. (Author).

  5. Recent developments in skin mimic systems to predict transdermal permeation.

    Science.gov (United States)

    Waters, Laura J

    2015-01-01

    In recent years there has been a drive to create experimental techniques that can facilitate the accurate and precise prediction of transdermal permeation without the use of in vivo studies. This review considers why permeation data is essential, provides a brief summary as to how skin acts as a natural barrier to permeation and discusses why in vivo studies are undesirable. This is followed by an in-depth discussion on the extensive range of alternative methods that have been developed in recent years. All of the major 'skin mimic systems' are considered including: in vitro models using synthetic membranes, mathematical models including quantitative structure-permeability relationships (QSPRs), human skin equivalents and chromatographic based methods. All of these model based systems are ideally trying to achieve the same end-point, namely a reliable in vitro-in vivo correlation, i.e. matching non-in vivo obtained data with that from human clinical trials. It is only by achieving this aim, that any new method of obtaining permeation data can be acknowledged as a potential replacement for animal studies, for the determination of transdermal permeation. In this review, the relevance and potential applicability of the various models systems will also be discussed.

  6. NOAA's National Air Quality Predictions and Development of Aerosol and Atmospheric Composition Prediction Components for the Next Generation Global Prediction System

    Science.gov (United States)

    Stajner, I.; Hou, Y. T.; McQueen, J.; Lee, P.; Stein, A. F.; Tong, D.; Pan, L.; Huang, J.; Huang, H. C.; Upadhayay, S.

    2016-12-01

    NOAA provides operational air quality predictions using the National Air Quality Forecast Capability (NAQFC): ozone and wildfire smoke for the United States and airborne dust for the contiguous 48 states at http://airquality.weather.gov. NOAA's predictions of fine particulate matter (PM2.5) became publicly available in February 2016. Ozone and PM2.5 predictions are produced using a system that operationally links the Community Multiscale Air Quality (CMAQ) model with meteorological inputs from the North American mesoscale forecast Model (NAM). Smoke and dust predictions are provided using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Current NAQFC focus is on updating CMAQ to version 5.0.2, improving PM2.5 predictions, and updating emissions estimates, especially for NOx using recently observed trends. Wildfire smoke emissions from a newer version of the USFS BlueSky system are being included in a new configuration of the NAQFC NAM-CMAQ system, which is re-run for the previous 24 hours when the wildfires were observed from satellites, to better represent wildfire emissions prior to initiating predictions for the next 48 hours. In addition, NOAA is developing the Next Generation Global Prediction System (NGGPS) to represent the earth system for extended weather prediction. NGGPS will include a representation of atmospheric dynamics, physics, aerosols and atmospheric composition as well as coupling with ocean, wave, ice and land components. NGGPS is being developed with a broad community involvement, including community developed components and academic research to develop and test potential improvements for potentially inclusion in NGGPS. Several investigators at NOAA's research laboratories and in academia are working to improve the aerosol and gaseous chemistry representation for NGGPS, to develop and evaluate the representation of atmospheric composition, and to establish and improve the coupling with radiation and microphysics

  7. In-Operation Inspection Technology development. Development of the degradation prediction technique

    International Nuclear Information System (INIS)

    Nakamuta, Yasushi; Miyoshi, Toshiaki; O'shima, Eiji

    1999-01-01

    As In-Operation Inspection Technology (IOI) , we selected primary loop recirculation (PLR) pump, sea water pump, small diameter pipe branch in the steam generator (SG) room and motor driven valve for the typical component of the nuclear power plant, and we are developing the technology which can forecast the residual life of parts in the plan until FY2000. With respect to PLR pump and sea water pump, technical procedure for predicting the propagation of bearing wear, under the combined effect of several degradation conditions of each pump during the plant operation are under development. With respect to pipe branch, we are developing the non-contact laser sensors, and we are constructing the system which forecasts high cycle fatigue in the root of pipe branch by monitoring the vibration of pipe branch. With respect to motor driven valve, technical procedure for predicting the thermal degradation of gaskets and gland packing, technical procedure for predicting the stem nut wear and wear of hunging portion of valve disc, and technical procedure for detecting the degradation of driving parts, without disassembling the motor driven valve, are under development. (author)

  8. Development of Interpretable Predictive Models for BPH and Prostate Cancer.

    Science.gov (United States)

    Bermejo, Pablo; Vivo, Alicia; Tárraga, Pedro J; Rodríguez-Montes, J A

    2015-01-01

    Traditional methods for deciding whether to recommend a patient for a prostate biopsy are based on cut-off levels of stand-alone markers such as prostate-specific antigen (PSA) or any of its derivatives. However, in the last decade we have seen the increasing use of predictive models that combine, in a non-linear manner, several predictives that are better able to predict prostate cancer (PC), but these fail to help the clinician to distinguish between PC and benign prostate hyperplasia (BPH) patients. We construct two new models that are capable of predicting both PC and BPH. An observational study was performed on 150 patients with PSA ≥3 ng/mL and age >50 years. We built a decision tree and a logistic regression model, validated with the leave-one-out methodology, in order to predict PC or BPH, or reject both. Statistical dependence with PC and BPH was found for prostate volume (P-value BPH prediction. PSA and volume together help to build predictive models that accurately distinguish among PC, BPH, and patients without any of these pathologies. Our decision tree and logistic regression models outperform the AUC obtained in the compared studies. Using these models as decision support, the number of unnecessary biopsies might be significantly reduced.

  9. Kenya develops tool to predict malaria | IDRC - International ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    2010-10-13

    Oct 13, 2010 ... In collaboration with scientists from the Kenya Meteorological Department and the International Centre ... a scientific model that uses weather predictions, information about the reproductive mechanisms of ... Related articles ...

  10. Architectural Development and Performance Analysis of a Primary Data Cache with Read Miss Address Prediction Capability

    National Research Council Canada - National Science Library

    Christensen, Kathryn

    1998-01-01

    .... The Predictive Read Cache (PRC) further improves the overall memory hierarchy performance by tracking the data read miss patterns of memory accesses, developing a prediction for the next access and prefetching the data into the faster cache memory...

  11. Development of estrogen receptor beta binding prediction model using large sets of chemicals.

    Science.gov (United States)

    Sakkiah, Sugunadevi; Selvaraj, Chandrabose; Gong, Ping; Zhang, Chaoyang; Tong, Weida; Hong, Huixiao

    2017-11-03

    We developed an ER β binding prediction model to facilitate identification of chemicals specifically bind ER β or ER α together with our previously developed ER α binding model. Decision Forest was used to train ER β binding prediction model based on a large set of compounds obtained from EADB. Model performance was estimated through 1000 iterations of 5-fold cross validations. Prediction confidence was analyzed using predictions from the cross validations. Informative chemical features for ER β binding were identified through analysis of the frequency data of chemical descriptors used in the models in the 5-fold cross validations. 1000 permutations were conducted to assess the chance correlation. The average accuracy of 5-fold cross validations was 93.14% with a standard deviation of 0.64%. Prediction confidence analysis indicated that the higher the prediction confidence the more accurate the predictions. Permutation testing results revealed that the prediction model is unlikely generated by chance. Eighteen informative descriptors were identified to be important to ER β binding prediction. Application of the prediction model to the data from ToxCast project yielded very high sensitivity of 90-92%. Our results demonstrated ER β binding of chemicals could be accurately predicted using the developed model. Coupling with our previously developed ER α prediction model, this model could be expected to facilitate drug development through identification of chemicals that specifically bind ER β or ER α .

  12. Catalytic cracking models developed for predictive control purposes

    Directory of Open Access Journals (Sweden)

    Dag Ljungqvist

    1993-04-01

    Full Text Available The paper deals with state-space modeling issues in the context of model-predictive control, with application to catalytic cracking. Emphasis is placed on model establishment, verification and online adjustment. Both the Fluid Catalytic Cracking (FCC and the Residual Catalytic Cracking (RCC units are discussed. Catalytic cracking units involve complex interactive processes which are difficult to operate and control in an economically optimal way. The strong nonlinearities of the FCC process mean that the control calculation should be based on a nonlinear model with the relevant constraints included. However, the model can be simple compared to the complexity of the catalytic cracking plant. Model validity is ensured by a robust online model adjustment strategy. Model-predictive control schemes based on linear convolution models have been successfully applied to the supervisory dynamic control of catalytic cracking units, and the control can be further improved by the SSPC scheme.

  13. The organization of professional predictions on the development of automation for stope equipment

    Energy Technology Data Exchange (ETDEWEB)

    Kanygin, U.M.; Markashov, V.E.; Pashchevskii, U.G.

    1980-01-01

    The problems of organizing and conducting experimental predictions on the development of automation for stope equipment are examined. Professional evaluations are developed, and the order for processing the results is given, together with a calculation program for use with the ES-1020 computer. Several results from predictive studies of the development of automation for use with stope equipment are given.

  14. Development and validation of a risk model for prediction of hazardous alcohol consumption in general practice attendees: the predictAL study.

    Science.gov (United States)

    King, Michael; Marston, Louise; Švab, Igor; Maaroos, Heidi-Ingrid; Geerlings, Mirjam I; Xavier, Miguel; Benjamin, Vicente; Torres-Gonzalez, Francisco; Bellon-Saameno, Juan Angel; Rotar, Danica; Aluoja, Anu; Saldivia, Sandra; Correa, Bernardo; Nazareth, Irwin

    2011-01-01

    Little is known about the risk of progression to hazardous alcohol use in people currently drinking at safe limits. We aimed to develop a prediction model (predictAL) for the development of hazardous drinking in safe drinkers. A prospective cohort study of adult general practice attendees in six European countries and Chile followed up over 6 months. We recruited 10,045 attendees between April 2003 to February 2005. 6193 European and 2462 Chilean attendees recorded AUDIT scores below 8 in men and 5 in women at recruitment and were used in modelling risk. 38 risk factors were measured to construct a risk model for the development of hazardous drinking using stepwise logistic regression. The model was corrected for over fitting and tested in an external population. The main outcome was hazardous drinking defined by an AUDIT score ≥8 in men and ≥5 in women. 69.0% of attendees were recruited, of whom 89.5% participated again after six months. The risk factors in the final predictAL model were sex, age, country, baseline AUDIT score, panic syndrome and lifetime alcohol problem. The predictAL model's average c-index across all six European countries was 0.839 (95% CI 0.805, 0.873). The Hedge's g effect size for the difference in log odds of predicted probability between safe drinkers in Europe who subsequently developed hazardous alcohol use and those who did not was 1.38 (95% CI 1.25, 1.51). External validation of the algorithm in Chilean safe drinkers resulted in a c-index of 0.781 (95% CI 0.717, 0.846) and Hedge's g of 0.68 (95% CI 0.57, 0.78). The predictAL risk model for development of hazardous consumption in safe drinkers compares favourably with risk algorithms for disorders in other medical settings and can be a useful first step in prevention of alcohol misuse.

  15. Development and validation of a risk model for prediction of hazardous alcohol consumption in general practice attendees: the predictAL study.

    Directory of Open Access Journals (Sweden)

    Michael King

    Full Text Available Little is known about the risk of progression to hazardous alcohol use in people currently drinking at safe limits. We aimed to develop a prediction model (predictAL for the development of hazardous drinking in safe drinkers.A prospective cohort study of adult general practice attendees in six European countries and Chile followed up over 6 months. We recruited 10,045 attendees between April 2003 to February 2005. 6193 European and 2462 Chilean attendees recorded AUDIT scores below 8 in men and 5 in women at recruitment and were used in modelling risk. 38 risk factors were measured to construct a risk model for the development of hazardous drinking using stepwise logistic regression. The model was corrected for over fitting and tested in an external population. The main outcome was hazardous drinking defined by an AUDIT score ≥8 in men and ≥5 in women.69.0% of attendees were recruited, of whom 89.5% participated again after six months. The risk factors in the final predictAL model were sex, age, country, baseline AUDIT score, panic syndrome and lifetime alcohol problem. The predictAL model's average c-index across all six European countries was 0.839 (95% CI 0.805, 0.873. The Hedge's g effect size for the difference in log odds of predicted probability between safe drinkers in Europe who subsequently developed hazardous alcohol use and those who did not was 1.38 (95% CI 1.25, 1.51. External validation of the algorithm in Chilean safe drinkers resulted in a c-index of 0.781 (95% CI 0.717, 0.846 and Hedge's g of 0.68 (95% CI 0.57, 0.78.The predictAL risk model for development of hazardous consumption in safe drinkers compares favourably with risk algorithms for disorders in other medical settings and can be a useful first step in prevention of alcohol misuse.

  16. Developing robust arsenic awareness prediction models using machine learning algorithms.

    Science.gov (United States)

    Singh, Sushant K; Taylor, Robert W; Rahman, Mohammad Mahmudur; Pradhan, Biswajeet

    2018-04-01

    Arsenic awareness plays a vital role in ensuring the sustainability of arsenic mitigation technologies. Thus far, however, few studies have dealt with the sustainability of such technologies and its associated socioeconomic dimensions. As a result, arsenic awareness prediction has not yet been fully conceptualized. Accordingly, this study evaluated arsenic awareness among arsenic-affected communities in rural India, using a structured questionnaire to record socioeconomic, demographic, and other sociobehavioral factors with an eye to assessing their association with and influence on arsenic awareness. First a logistic regression model was applied and its results compared with those produced by six state-of-the-art machine-learning algorithms (Support Vector Machine [SVM], Kernel-SVM, Decision Tree [DT], k-Nearest Neighbor [k-NN], Naïve Bayes [NB], and Random Forests [RF]) as measured by their accuracy at predicting arsenic awareness. Most (63%) of the surveyed population was found to be arsenic-aware. Significant arsenic awareness predictors were divided into three types: (1) socioeconomic factors: caste, education level, and occupation; (2) water and sanitation behavior factors: number of family members involved in water collection, distance traveled and time spent for water collection, places for defecation, and materials used for handwashing after defecation; and (3) social capital and trust factors: presence of anganwadi and people's trust in other community members, NGOs, and private agencies. Moreover, individuals' having higher social network positively contributed to arsenic awareness in the communities. Results indicated that both the SVM and the RF algorithms outperformed at overall prediction of arsenic awareness-a nonlinear classification problem. Lower-caste, less educated, and unemployed members of the population were found to be the most vulnerable, requiring immediate arsenic mitigation. To this end, local social institutions and NGOs could play a

  17. Predicting the 6-month risk of severe hypoglycemia among adults with diabetes: Development and external validation of a prediction model.

    Science.gov (United States)

    Schroeder, Emily B; Xu, Stan; Goodrich, Glenn K; Nichols, Gregory A; O'Connor, Patrick J; Steiner, John F

    2017-07-01

    To develop and externally validate a prediction model for the 6-month risk of a severe hypoglycemic event among individuals with pharmacologically treated diabetes. The development cohort consisted of 31,674 Kaiser Permanente Colorado members with pharmacologically treated diabetes (2007-2015). The validation cohorts consisted of 38,764 Kaiser Permanente Northwest members and 12,035 HealthPartners members. Variables were chosen that would be available in electronic health records. We developed 16-variable and 6-variable models, using a Cox counting model process that allows for the inclusion of multiple 6-month observation periods per person. Across the three cohorts, there were 850,992 6-month observation periods, and 10,448 periods with at least one severe hypoglycemic event. The six-variable model contained age, diabetes type, HgbA1c, eGFR, history of a hypoglycemic event in the prior year, and insulin use. Both prediction models performed well, with good calibration and c-statistics of 0.84 and 0.81 for the 16-variable and 6-variable models, respectively. In the external validation cohorts, the c-statistics were 0.80-0.84. We developed and validated two prediction models for predicting the 6-month risk of hypoglycemia. The 16-variable model had slightly better performance than the 6-variable model, but in some practice settings, use of the simpler model may be preferred. Copyright © 2017 Elsevier Inc. All rights reserved.

  18. Can preventable adverse events be predicted among hospitalized older patients? The development and validation of a predictive model.

    NARCIS (Netherlands)

    Steeg, L. van de; Langelaan, M.; Wagner, C.

    2014-01-01

    Objective: To develop and validate a predictive model for preventable adverse events (AEs) in hospitalized older patients, using clinically important risk factors that are readily available on admission. Design: Data from two retrospective patient record review studies on AEs were used. Risk factors

  19. Scaling predictive modeling in drug development with cloud computing.

    Science.gov (United States)

    Moghadam, Behrooz Torabi; Alvarsson, Jonathan; Holm, Marcus; Eklund, Martin; Carlsson, Lars; Spjuth, Ola

    2015-01-26

    Growing data sets with increased time for analysis is hampering predictive modeling in drug discovery. Model building can be carried out on high-performance computer clusters, but these can be expensive to purchase and maintain. We have evaluated ligand-based modeling on cloud computing resources where computations are parallelized and run on the Amazon Elastic Cloud. We trained models on open data sets of varying sizes for the end points logP and Ames mutagenicity and compare with model building parallelized on a traditional high-performance computing cluster. We show that while high-performance computing results in faster model building, the use of cloud computing resources is feasible for large data sets and scales well within cloud instances. An additional advantage of cloud computing is that the costs of predictive models can be easily quantified, and a choice can be made between speed and economy. The easy access to computational resources with no up-front investments makes cloud computing an attractive alternative for scientists, especially for those without access to a supercomputer, and our study shows that it enables cost-efficient modeling of large data sets on demand within reasonable time.

  20. Development of Indoor Air Pollution Concentration Prediction by Geospatial Analysis

    Directory of Open Access Journals (Sweden)

    Adyati Pradini Yudison

    2015-07-01

    Full Text Available People living near busy roads are potentially exposed to traffic-induced air pollutants. The pollutants may intrude into the indoor environment, causing health risks to the occupants. Prediction of pollutant exposure therefore is of great importance for impact assessment and policy making related to environmentally sustainable transport. This study involved the selection of spatial interpolation methods that can be used for prediction of indoor air quality based on outdoor pollutant mapping without indoor measurement data. The research was undertaken in the densely populated area of Karees, Bandung, Indonesia. The air pollutant NO2 was monitored in this area as a preliminary study. Nitrogen dioxide concentrations were measured by passive diffusion tube. Outdoor NO2 concentrations were measured at 94 locations, consisting of 30 roadside and 64 outdoor locations. Residential indoor NO2 concentrations were measured at 64 locations. To obtain a spatially continuous air quality map, the spatial interpolation methods of inverse distance weighting (IDW and Kriging were applied. Selection of interpolation method was done based on the smallest root mean square error (RMSE and standard deviation (SD. The most appropriate interpolation method for outdoor NO2 concentration mapping was Kriging with an SD value of 5.45 µg/m3 and an RMSE value of 5.45 µg/m3, while for indoor NO2 concentration mapping the IDW was best fitted with an RMSE value of 5.92 µg/m3 and an SD value of 5.92 µg/m3.

  1. White Matter Volume Predicts Language Development in Congenital Heart Disease.

    Science.gov (United States)

    Rollins, Caitlin K; Asaro, Lisa A; Akhondi-Asl, Alireza; Kussman, Barry D; Rivkin, Michael J; Bellinger, David C; Warfield, Simon K; Wypij, David; Newburger, Jane W; Soul, Janet S

    2017-02-01

    To determine whether brain volume is reduced at 1 year of age and whether these volumes are associated with neurodevelopment in biventricular congenital heart disease (CHD) repaired in infancy. Infants with biventricular CHD (n = 48) underwent brain magnetic resonance imaging (MRI) and neurodevelopmental testing with the Bayley Scales of Infant Development-II and the MacArthur-Bates Communicative Development Inventories at 1 year of age. A multitemplate based probabilistic segmentation algorithm was applied to volumetric MRI data. We compared volumes with those of 13 healthy control infants of comparable ages. In the group with CHD, we measured Spearman correlations between neurodevelopmental outcomes and the residuals from linear regression of the volumes on corrected chronological age at MRI and sex. Compared with controls, infants with CHD had reductions of 54 mL in total brain (P = .009), 40 mL in cerebral white matter (P Development-II scores but did correlate positively with MacArthur-Bates Communicative Development Inventory language development. Infants with biventricular CHD show total brain volume reductions at 1 year of age, driven by differences in cerebral white matter. White matter volume correlates with language development, but not broader developmental indices. These findings suggest that abnormalities in white matter development detected months after corrective heart surgery may contribute to language impairment. ClinicalTrials.gov: NCT00006183. Copyright © 2016 Elsevier Inc. All rights reserved.

  2. Prediction of the development of reading comprehension: a longitudinal study

    NARCIS (Netherlands)

    Verhoeven, L.T.W.; Leeuwe, J.F.J. van

    2008-01-01

    Specific effects of word decoding, vocabulary and listening comprehension abilities on the development of reading comprehension were longitudinally examined for a representative sample of 2143 Dutch children throughout the elementary school period. An attempt was made to test two theoretical

  3. A Computational Model Predicting Disruption of Blood Vessel Development

    Science.gov (United States)

    Vascular development is a complex process regulated by dynamic biological networks that vary in topology and state across different tissues and developmental stages. Signals regulating de novo blood vessel formation (vasculogenesis) and remodeling (angiogenesis) come from a varie...

  4. Understanding Predicting and Supporting Leader Self-Development

    National Research Council Canada - National Science Library

    Boyce, Lisa A; Wisecarver, Michelle M; Zaccaro, Stephen J

    2005-01-01

    .... The model depicts a person with a mastery, work, and career-growth orientation as more motivated to perform leader self-development and more skilled at performing instructional and self-regulatory...

  5. Approximator: Predicting Interruptibility in Software Development with Commodity Computers

    DEFF Research Database (Denmark)

    Tell, Paolo; Jalaliniya, Shahram; Andersen, Kristian S. M.

    2015-01-01

    Assessing the presence and availability of a remote colleague is key in coordination in global software development but is not easily done using existing computer-mediated channels. Previous research has shown that automated estimation of interruptibility is feasible and can achieve a precision....... These early but promising results represent a starting point for designing tools with support for interruptibility capable of improving distributed awareness and cooperation to be used in global software development....

  6. Predicting the development of stress urinary incontinence 3 years after hysterectomy

    NARCIS (Netherlands)

    Lakeman, M.M.E.; van der Vaart, C.H.; van der Steeg, J.W.; Roovers, J.P.W.R.

    2011-01-01

    We aimed to develop a prediction rule to predict the individual risk to develop stress urinary incontinence (SUI) after hysterectomy. Prospective observational study with 3-year follow-up among women who underwent abdominal or vaginal hysterectomy for benign conditions, excluding vaginal prolapse,

  7. Predicting the development of stress urinary incontinence 3 years after hysterectomy

    NARCIS (Netherlands)

    Lakeman, Marielle M. E.; Van Der Vaart, C. Huub; Van Der Steeg, Jan Willem; Roovers, Jan-Paul W. R.

    Introduction and hypothesis We aimed to develop a prediction rule to predict the individual risk to develop stress urinary incontinence (SUI) after hysterectomy. Methods Prospective observational study with 3-year follow-up among women who underwent abdominal or vaginal hysterectomy for benign

  8. Thermal hydraulic test for reactor safety system - Critical heat flux experiment and development of prediction models

    Energy Technology Data Exchange (ETDEWEB)

    Chang, Soon Heung; Baek, Won Pil; Yang, Soo Hyung; No, Chang Hyun [Korea Advanced Institute of Science and Technology, Taejon (Korea)

    2000-04-01

    To acquire CHF data through the experiments and develop prediction models, research was conducted. Final objectives of research are as follows: 1) Production of tube CHF data for low and middle pressure and mass flux and Flow Boiling Visualization. 2) Modification and suggestion of tube CHF prediction models. 3) Development of fuel bundle CHF prediction methodology base on tube CHF prediction models. The major results of research are as follows: 1) Production of the CHF data for low and middle pressure and mass flux. - Acquisition of CHF data (764) for low and middle pressure and flow conditions - Analysis of CHF trends based on the CHF data - Assessment of existing CHF prediction methods with the CHF data 2) Modification and suggestion of tube CHF prediction models. - Development of a unified CHF model applicable for a wide parametric range - Development of a threshold length correlation - Improvement of CHF look-up table using the threshold length correlation 3) Development of fuel bundle CHF prediction methodology base on tube CHF prediction models. - Development of bundle CHF prediction methodology using correction factor. 11 refs., 134 figs., 25 tabs. (Author)

  9. Developing a Predictive for Unscheduled Maintenance Requirements on United States Air Force Installations

    National Research Council Canada - National Science Library

    Kovich, Matthew D; Norton, J. D

    2008-01-01

    .... This paper develops one such method by using linear regression and time series analysis to develop a predictive model to forecast future year man-hour and funding requirements for unscheduled maintenance...

  10. Developing a Predictive Model for Unscheduled Maintenance Requirements on United States Air Force Installations

    National Research Council Canada - National Science Library

    Kovich, Matthew D; Norton, J. D

    2008-01-01

    .... This paper develops one such method by using linear regression and time series analysis to develop a predictive model to forecast future year man-hour and funding requirements for unscheduled maintenance...

  11. Developments in outburst prediction by microseismic monitoring from the surface

    Energy Technology Data Exchange (ETDEWEB)

    Davies, A W; Styles, P; Jones, V K

    1987-01-01

    Violent outbursts of coal and firedamp affect production operations in most of the coal producing countries of the world, often leading to heavy loss of life. Significant changes in the pattern of Welsh outbursts from 1978 onwards are described with a far larger proportion occurring on longwall faces than was previously the case and with a much higher incidence of spontaneous outbursts, which carry a greater risk than those which are deliberately induced. The elaborate defences in use appeared inadequate to deal with the changing circumstances as methane based alarms only operated after the outburst phenomenon had initiated. An earlier warning of an incipient outburst was required and evidence suggested that seismic monitoring might provide this early warning. A surface located seismometer was installed giving radio transmitted signals to a tape recorder in the colliery control room. This provided promising historical records and led to five surface seismometer stations being commissioned feeding signals, suitably treated, to a micro-processor located in the mine control room. The programming of the micro-processor was arranged to give a real time alarm at pre-set levels of seismic activity in defined areas of the mine. Experience with the new predictive tool is described, as well as the use made of the new facility by management, including changed methods of outburst stress relief.

  12. Developing descriptors to predict mechanical properties of nanotubes.

    Science.gov (United States)

    Borders, Tammie L; Fonseca, Alexandre F; Zhang, Hengji; Cho, Kyeongjae; Rusinko, Andrew

    2013-04-22

    Descriptors and quantitative structure property relationships (QSPR) were investigated for mechanical property prediction of carbon nanotubes (CNTs). 78 molecular dynamics (MD) simulations were carried out, and 20 descriptors were calculated to build quantitative structure property relationships (QSPRs) for Young's modulus and Poisson's ratio in two separate analyses: vacancy only and vacancy plus methyl functionalization. In the first analysis, C(N2)/C(T) (number of non-sp2 hybridized carbons per the total carbons) and chiral angle were identified as critical descriptors for both Young's modulus and Poisson's ratio. Further analysis and literature findings indicate the effect of chiral angle is negligible at larger CNT radii for both properties. Raman spectroscopy can be used to measure C(N2)/C(T), providing a direct link between experimental and computational results. Poisson's ratio approaches two different limiting values as CNT radii increases: 0.23-0.25 for chiral and armchair CNTs and 0.10 for zigzag CNTs (surface defects <3%). In the second analysis, the critical descriptors were C(N2)/C(T), chiral angle, and M(N)/C(T) (number of methyl groups per total carbons). These results imply new types of defects can be represented as a new descriptor in QSPR models. Finally, results are qualified and quantified against experimental data.

  13. Development of mathematical models for predicting the iron ...

    African Journals Online (AJOL)

    Facing the increase of surface water samples contaminated by ETMs, usually from the geochemical background, the emergence of new human diseases is worrying. To solve this problem, we have developed several models based on different learning algorithms qualified by high performance, using different transfer ...

  14. Observant, Nonaggressive Temperament Predicts Theory-of-Mind Development

    Science.gov (United States)

    Wellman, Henry M.; Lane, Jonathan D.; LaBounty, Jennifer; Olson, Sheryl L.

    2011-01-01

    Temperament dimensions influence children's approach to and participation in social interactive experiences which reflect and impact children's social understandings. Therefore, temperament differences might substantially impact theory-of-mind development in early childhood. Using longitudinal data, we report that certain early temperament…

  15. Mind the gap : predicting cardiovascular risk during drug development

    NARCIS (Netherlands)

    Chain, Anne S. Y.

    2012-01-01

    Cardiovascular safety issues, specifically drug-induced QT/QTc-interval prolongation, remain a major cause of drug attrition during clinical development and is one of the main causes for post-market drug withdrawals accounting for 15-34% of all drug discontinuation. Given the potentially fatal

  16. Can an earthquake prediction and warning system be developed?

    Science.gov (United States)

    N.N, Ambraseys

    1990-01-01

    Natural disasters affect countries large and small, rich, or poor, whatever their political persuasion. The toll exacted by natural calamities each year drains the human and economic resources of every nation and stands as one of the formidable barriers to national, regional, and world development.

  17. Development of the predictive maintenance system prototype for the rod control system

    International Nuclear Information System (INIS)

    Lim, H. S.; Hong, H. P.; Koo, J. M.; Kim, Y. B.; Han, H. W.

    2003-01-01

    The demand for safety and reliability of Nuclear Power Plants (NPPs) has been constantly increasing and economical operation is also an important issue. Developing and adopting predictive maintenance technology for the major systems or equipment is considered as a way to achieve these goals. This paper describes the development of a predictive maintenance system prototype for the Rod Control System, which adopts an advanced methodology. Bayesian Belief Networks (BBN) has been adopted for the real time fault diagnosis and prediction of the system. Through a simulation test, it was confirmed that the prototype monitors and secures sound operability of rod drive mechanism and its control system, and also provides the predictive maintenance information

  18. Development of predictive control strategies for building climate control

    OpenAIRE

    NAGPAL, HIMANSHU

    2018-01-01

    APPROVED The rapid growth in energy usage and CO2 emissions has become a critical issue for the whole world. It is noteworthy that buildings are a major contributor to global primary energy consumption. Among building services, use of energy in heating-ventilation-air-conditioning (HVAC) system is particularly significant (about 50\\% of the total building energy consumption). Therefore, the development and implementation of effective control strategies to optimize the operation of HVAC sys...

  19. Improving Predictive Modeling in Pediatric Drug Development: Pharmacokinetics, Pharmacodynamics, and Mechanistic Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Slikker, William; Young, John F.; Corley, Rick A.; Dorman, David C.; Conolly, Rory B.; Knudsen, Thomas; Erstad, Brian L.; Luecke, Richard H.; Faustman, Elaine M.; Timchalk, Chuck; Mattison, Donald R.

    2005-07-26

    A workshop was conducted on November 18?19, 2004, to address the issue of improving predictive models for drug delivery to developing humans. Although considerable progress has been made for adult humans, large gaps remain for predicting pharmacokinetic/pharmacodynamic (PK/PD) outcome in children because most adult models have not been tested during development. The goals of the meeting included a description of when, during development, infants/children become adultlike in handling drugs. The issue of incorporating the most recent advances into the predictive models was also addressed: both the use of imaging approaches and genomic information were considered. Disease state, as exemplified by obesity, was addressed as a modifier of drug pharmacokinetics and pharmacodynamics during development. Issues addressed in this workshop should be considered in the development of new predictive and mechanistic models of drug kinetics and dynamics in the developing human.

  20. Experimentally aided development of a turbine heat transfer prediction method

    International Nuclear Information System (INIS)

    Forest, A.E.; White, A.J.; Lai, C.C.; Guo, S.M.; Oldfield, M.L.G.; Lock, G.D.

    2004-01-01

    In the design of cooled turbomachinery blading a central role is played by the computer methods used to optimise the aerodynamic and thermal performance of the turbine aerofoils. Estimates of the heat load on the turbine blading should be as accurate as possible, in order that adequate life may be obtained with the minimum cooling air requirement. Computer methods are required which are able to model transonic flows, which are a mixture of high temperature combustion gases and relatively cool air injected through holes in the aerofoil surface. These holes may be of complex geometry, devised after empirical studies of the optimum shape and the most cost effective manufacturing technology. The method used here is a further development of the heat transfer design code (HTDC), originally written by Rolls-Royce plc under subcontract to Rolls-Royce Inc for the United States Air Force. The physical principles of the modelling employed in the code are explained without extensive mathematical details. The paper describes the calibration of the code in conjunction with a series of experimental measurements on a scale model of a high-pressure nozzle guide vane at non-dimensionally correct engine conditions. The results are encouraging, although indicating that some further work is required in modelling highly accelerated pressure surface flow

  1. Drought prediction and sustainable development of the ecological environment.

    Science.gov (United States)

    Xu, X H; Lv, Z Q; Zhou, X Y; Jiang, N

    2017-12-01

    In the 1990s ecological early warning research began with the aim of elucidating the effect of drought in dry regions of the world. Drought has been a prevalent natural disaster, ravaging the Yun'nan province of China for over 5 years since 2009. Due to the extensive range, depth and devastating losses, the drought has reached a once-in-a-century severity. Yun'nan province suffered particularly badly from the drought, which took its toll on both the ecological environment and the sustainable economic development of the province. We chose to study Pu'er city in Yun'nun province for this research, and analysed the drought traits of Pu'er city utilizing geographic information technology. We applied the Mann-Kendall test for trend, linear tendency estimation and percentage of precipitation anomalies, as well as using combinations of monthly data searches of meteorological reports from 1980-2010. The results showed that except for a small rise in spring precipitation, the overall rainfall of Pu'er city showed a decreasing trend. The results of this study can provide an adequate and reliable theoretical basis and technological methods for use in government decision making, and promote research into early warning ecology.

  2. Development and external validation of a risk-prediction model to predict 5-year overall survival in advanced larynx cancer.

    Science.gov (United States)

    Petersen, Japke F; Stuiver, Martijn M; Timmermans, Adriana J; Chen, Amy; Zhang, Hongzhen; O'Neill, James P; Deady, Sandra; Vander Poorten, Vincent; Meulemans, Jeroen; Wennerberg, Johan; Skroder, Carl; Day, Andrew T; Koch, Wayne; van den Brekel, Michiel W M

    2018-05-01

    TNM-classification inadequately estimates patient-specific overall survival (OS). We aimed to improve this by developing a risk-prediction model for patients with advanced larynx cancer. Cohort study. We developed a risk prediction model to estimate the 5-year OS rate based on a cohort of 3,442 patients with T3T4N0N+M0 larynx cancer. The model was internally validated using bootstrapping samples and externally validated on patient data from five external centers (n = 770). The main outcome was performance of the model as tested by discrimination, calibration, and the ability to distinguish risk groups based on tertiles from the derivation dataset. The model performance was compared to a model based on T and N classification only. We included age, gender, T and N classification, and subsite as prognostic variables in the standard model. After external validation, the standard model had a significantly better fit than a model based on T and N classification alone (C statistic, 0.59 vs. 0.55, P statistic to 0.68. A risk prediction model for patients with advanced larynx cancer, consisting of readily available clinical variables, gives more accurate estimations of the estimated 5-year survival rate when compared to a model based on T and N classification alone. 2c. Laryngoscope, 128:1140-1145, 2018. © 2017 The American Laryngological, Rhinological and Otological Society, Inc.

  3. Development of a noise prediction model based on advanced fuzzy approaches in typical industrial workrooms.

    Science.gov (United States)

    Aliabadi, Mohsen; Golmohammadi, Rostam; Khotanlou, Hassan; Mansoorizadeh, Muharram; Salarpour, Amir

    2014-01-01

    Noise prediction is considered to be the best method for evaluating cost-preventative noise controls in industrial workrooms. One of the most important issues is the development of accurate models for analysis of the complex relationships among acoustic features affecting noise level in workrooms. In this study, advanced fuzzy approaches were employed to develop relatively accurate models for predicting noise in noisy industrial workrooms. The data were collected from 60 industrial embroidery workrooms in the Khorasan Province, East of Iran. The main acoustic and embroidery process features that influence the noise were used to develop prediction models using MATLAB software. Multiple regression technique was also employed and its results were compared with those of fuzzy approaches. Prediction errors of all prediction models based on fuzzy approaches were within the acceptable level (lower than one dB). However, Neuro-fuzzy model (RMSE=0.53dB and R2=0.88) could slightly improve the accuracy of noise prediction compared with generate fuzzy model. Moreover, fuzzy approaches provided more accurate predictions than did regression technique. The developed models based on fuzzy approaches as useful prediction tools give professionals the opportunity to have an optimum decision about the effectiveness of acoustic treatment scenarios in embroidery workrooms.

  4. Predictive Coding Accelerates Word Recognition and Learning in the Early Stages of Language Development

    Science.gov (United States)

    Ylinen, Sari; Bosseler, Alexis; Junttila, Katja; Huotilainen, Minna

    2017-01-01

    The ability to predict future events in the environment and learn from them is a fundamental component of adaptive behavior across species. Here we propose that inferring predictions facilitates speech processing and word learning in the early stages of language development. Twelve- and 24-month olds' electrophysiological brain responses to heard…

  5. A Wavelet Kernel-Based Primal Twin Support Vector Machine for Economic Development Prediction

    Directory of Open Access Journals (Sweden)

    Fang Su

    2013-01-01

    Full Text Available Economic development forecasting allows planners to choose the right strategies for the future. This study is to propose economic development prediction method based on the wavelet kernel-based primal twin support vector machine algorithm. As gross domestic product (GDP is an important indicator to measure economic development, economic development prediction means GDP prediction in this study. The wavelet kernel-based primal twin support vector machine algorithm can solve two smaller sized quadratic programming problems instead of solving a large one as in the traditional support vector machine algorithm. Economic development data of Anhui province from 1992 to 2009 are used to study the prediction performance of the wavelet kernel-based primal twin support vector machine algorithm. The comparison of mean error of economic development prediction between wavelet kernel-based primal twin support vector machine and traditional support vector machine models trained by the training samples with the 3–5 dimensional input vectors, respectively, is given in this paper. The testing results show that the economic development prediction accuracy of the wavelet kernel-based primal twin support vector machine model is better than that of traditional support vector machine.

  6. Development and validation of outcome prediction models for aneurysmal subarachnoid haemorrhage : The SAHIT multinational cohort study

    NARCIS (Netherlands)

    Jaja, Blessing N R; Saposnik, Gustavo; Lingsma, Hester F.; Macdonald, Erin; Thorpe, Kevin E.; Mamdani, Muhammed; Steyerberg, Ewout W.; Molyneux, Andrew; Manoel, Airton Leonardo De Oliveira; Schatlo, Bawarjan; Hanggi, Daniel; Hasan, David M.; Wong, George K C; Etminan, Nima; Fukuda, Hitoshi; Torner, James C.; Schaller, Karl L.; Suarez, Jose I.; Stienen, Martin N.; Vergouwen, Mervyn D.I.; Rinkel, Gabriel J.E.; Spears, Julian; Cusimano, Michael D.; Todd, Michael; Le Roux, Peter; Kirkpatrick, Peter J.; Pickard, John; Van Den Bergh, Walter M.; Murray, Gordon D; Johnston, S. Claiborne; Yamagata, Sen; Mayer, Stephan A.; Schweizer, Tom A.; Macdonald, R. Loch

    2018-01-01

    Objective To develop and validate a set of practical prediction tools that reliably estimate the outcome of subarachnoid haemorrhage from ruptured intracranial aneurysms (SAH). Design Cohort study with logistic regression analysis to combine predictors and treatment modality. Setting Subarachnoid

  7. Development of an aerodyanmic theory capable of predicting surface loads on slender wings with vortex flow

    Science.gov (United States)

    Gloss, B. B.; Johnson, F. T.

    1976-01-01

    The Boeing Commercial Airplane Company developed an inviscid three-dimensional lifting surface method that shows promise in being able to accurately predict loads, subsonic and supersonic, on wings with leading-edge separation and reattachment.

  8. Predictive Eco-Cruise Control (ECC) system : model development, modeling and potential benefits.

    Science.gov (United States)

    2013-02-01

    The research develops a reference model of a predictive eco-cruise control (ECC) system that intelligently modulates vehicle speed within a pre-set speed range to minimize vehicle fuel consumption levels using roadway topographic information. The stu...

  9. Integration of Fast Predictive Model and SLM Process Development Chamber, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — This STTR project seeks to develop a fast predictive model for selective laser melting (SLM) processes and then integrate that model with an SLM chamber that allows...

  10. Development of an integrated method for long-term water quality prediction using seasonal climate forecast

    Directory of Open Access Journals (Sweden)

    J. Cho

    2016-10-01

    Full Text Available The APEC Climate Center (APCC produces climate prediction information utilizing a multi-climate model ensemble (MME technique. In this study, four different downscaling methods, in accordance with the degree of utilizing the seasonal climate prediction information, were developed in order to improve predictability and to refine the spatial scale. These methods include: (1 the Simple Bias Correction (SBC method, which directly uses APCC's dynamic prediction data with a 3 to 6 month lead time; (2 the Moving Window Regression (MWR method, which indirectly utilizes dynamic prediction data; (3 the Climate Index Regression (CIR method, which predominantly uses observation-based climate indices; and (4 the Integrated Time Regression (ITR method, which uses predictors selected from both CIR and MWR. Then, a sampling-based temporal downscaling was conducted using the Mahalanobis distance method in order to create daily weather inputs to the Soil and Water Assessment Tool (SWAT model. Long-term predictability of water quality within the Wecheon watershed of the Nakdong River Basin was evaluated. According to the Korean Ministry of Environment's Provisions of Water Quality Prediction and Response Measures, modeling-based predictability was evaluated by using 3-month lead prediction data issued in February, May, August, and November as model input of SWAT. Finally, an integrated approach, which takes into account various climate information and downscaling methods for water quality prediction, was presented. This integrated approach can be used to prevent potential problems caused by extreme climate in advance.

  11. Development of a prototype system for prediction of the group error at the maintenance work

    International Nuclear Information System (INIS)

    Yoshino, Kenji; Hirotsu, Yuuko

    2001-01-01

    This paper described on development and performance evaluation of a prototype system for prediction of the group error at the maintenance work. The results so far are as follows. (1) When a user inputs the existence and the grade of the feature factor of the maintenance work as a prediction object, an organization and an organization factor and a group PSF put into the system. The maintenance group error to target can be predicted through the prediction model which consists of a class of seven stages. (2) This system by utilizing the information on a prediction result database, it can be use not only for prediction of a maintenance group but for various safe Activity, such as KYT(Kiken Yochi Training) and TBM(Tool Box Meeting). (3) This system predicts a cooperation error at highest rate, and subsequently. Predicts the detection error at a high rate. and to the decision-making. Error, the transfer error and the state cognitive error, and state error, it has the characteristics predicted at almost same rate. (4) if it has full knowledge even if the feature, such as the enforcement conditions of maintenance work, and organization, even if the user has neither the knowledge about a human factor, users experience, anyone of this system is slight about the extent, generating of a maintenance group error made difficult from the former logically and systematically, it can predict with business time for about 15 minutes. (author)

  12. Development of crankshaft dynamic stress prediction; Jitsudoji crankshaft oryoku yosoku shuho no kaihatsu

    Energy Technology Data Exchange (ETDEWEB)

    Takahashi, S; Iwamoto, A; Miyazawa, H; Sato, K; Ozaki, H [Honda R and D Co. Ltd., Tokyo (Japan)

    1997-10-01

    In this paper, the development of the simulation model which predicts the stress of the crankshaft under running condition precisely is described. This simulation model considers about the nonlinearity of the oil film stiffness in the main bearing, the dynamic characteristic of the crankshaft system including resonance and the cylinder block stiffness. By the development of this stress analysis simulation, the stress m each part of the crankshaft during durability testing could be precisely predicted. 1 ref., 10 figs.

  13. Development of equipment reliability process using predictive technologies at Hamaoka Nuclear Power Station

    International Nuclear Information System (INIS)

    Taniguchi, Yuji; Sakuragi, Futoshi; Hamada, Seiichi

    2014-01-01

    Development of equipment reliability(ER) process, specifically for predictive maintenance (PdM) technologies integrated condition based maintenance (CBM) process, at Hamaoka Nuclear Power Station is introduced in this paper. Integration of predictive maintenance technologies such as vibration, oil analysis and thermo monitoring is more than important to establish strong maintenance strategies and to direct a specific technical development. In addition, a practical example of CBM is also presented to support the advantage of the idea. (author)

  14. New encouraging developments in contact prediction: Assessment of the CASP11 results

    KAUST Repository

    Monastyrskyy, Bohdan

    2015-10-01

    © 2015 Wiley Periodicals, Inc. This article provides a report on the state-of-the-art in the prediction of intra-molecular residue-residue contacts in proteins based on the assessment of the predictions submitted to the CASP11 experiment. The assessment emphasis is placed on the accuracy in predicting long-range contacts. Twenty-nine groups participated in contact prediction in CASP11. At least eight of them used the recently developed evolutionary coupling techniques, with the top group (CONSIP2) reaching precision of 27% on target proteins that could not be modeled by homology. This result indicates a breakthrough in the development of methods based on the correlated mutation approach. Successful prediction of contacts was shown to be practically helpful in modeling three-dimensional structures; in particular target T0806 was modeled exceedingly well with accuracy not yet seen for ab initio targets of this size (>250 residues).

  15. Development of laboratory acceleration test method for service life prediction of concrete structures

    International Nuclear Information System (INIS)

    Cho, M. S.; Song, Y. C.; Bang, K. S.; Lee, J. S.; Kim, D. K.

    1999-01-01

    Service life prediction of nuclear power plants depends on the application of history of structures, field inspection and test, the development of laboratory acceleration tests, their analysis method and predictive model. In this study, laboratory acceleration test method for service life prediction of concrete structures and application of experimental test results are introduced. This study is concerned with environmental condition of concrete structures and is to develop the acceleration test method for durability factors of concrete structures e.g. carbonation, sulfate attack, freeze-thaw cycles and shrinkage-expansion etc

  16. In-Hospital Risk Prediction for Post-stroke Depression. Development and Validation of the Post-stroke Depression Prediction Scale

    NARCIS (Netherlands)

    Thóra Hafsteinsdóttir; Roelof G.A. Ettema; Diederick Grobbee; Prof. Dr. Marieke J. Schuurmans; Janneke van Man-van Ginkel; Eline Lindeman

    2013-01-01

    Background and Purpose—The timely detection of post-stroke depression is complicated by a decreasing length of hospital stay. Therefore, the Post-stroke Depression Prediction Scale was developed and validated. The Post-stroke Depression Prediction Scale is a clinical prediction model for the early

  17. Development of Wind Farm AEP Prediction Program Considering Directional Wake Effect

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Kyoungboo; Cho, Kyungho; Huh, Jongchul [Jeju Nat’l Univ., Jeju (Korea, Republic of)

    2017-07-15

    For accurate AEP prediction in a wind farm, it is necessary to effectively calculate the wind speed reduction and the power loss due to the wake effect in each wind direction. In this study, a computer program for AEP prediction considering directional wake effect was developed. The results of the developed program were compared with the actual AEP of the wind farm and the calculation result of existing commercial software to confirm the accuracy of prediction. The applied equations are identical with those of commercial software based on existing theories, but there is a difference in the calculation process of the detection of the wake effect area in each wind direction. As a result, the developed program predicted to be less than 1% of difference to the actual capacity factor and showed more than 2% of better results compared with the existing commercial software.

  18. Predictive coding accelerates word recognition and learning in the early stages of language development.

    Science.gov (United States)

    Ylinen, Sari; Bosseler, Alexis; Junttila, Katja; Huotilainen, Minna

    2017-11-01

    The ability to predict future events in the environment and learn from them is a fundamental component of adaptive behavior across species. Here we propose that inferring predictions facilitates speech processing and word learning in the early stages of language development. Twelve- and 24-month olds' electrophysiological brain responses to heard syllables are faster and more robust when the preceding word context predicts the ending of a familiar word. For unfamiliar, novel word forms, however, word-expectancy violation generates a prediction error response, the strength of which significantly correlates with children's vocabulary scores at 12 months. These results suggest that predictive coding may accelerate word recognition and support early learning of novel words, including not only the learning of heard word forms but also their mapping to meanings. Prediction error may mediate learning via attention, since infants' attention allocation to the entire learning situation in natural environments could account for the link between prediction error and the understanding of word meanings. On the whole, the present results on predictive coding support the view that principles of brain function reported across domains in humans and non-human animals apply to language and its development in the infant brain. A video abstract of this article can be viewed at: http://hy.fi/unitube/video/e1cbb495-41d8-462e-8660-0864a1abd02c. [Correction added on 27 January 2017, after first online publication: The video abstract link was added.]. © 2016 John Wiley & Sons Ltd.

  19. Developing models for the prediction of hospital healthcare waste generation rate.

    Science.gov (United States)

    Tesfahun, Esubalew; Kumie, Abera; Beyene, Abebe

    2016-01-01

    An increase in the number of health institutions, along with frequent use of disposable medical products, has contributed to the increase of healthcare waste generation rate. For proper handling of healthcare waste, it is crucial to predict the amount of waste generation beforehand. Predictive models can help to optimise healthcare waste management systems, set guidelines and evaluate the prevailing strategies for healthcare waste handling and disposal. However, there is no mathematical model developed for Ethiopian hospitals to predict healthcare waste generation rate. Therefore, the objective of this research was to develop models for the prediction of a healthcare waste generation rate. A longitudinal study design was used to generate long-term data on solid healthcare waste composition, generation rate and develop predictive models. The results revealed that the healthcare waste generation rate has a strong linear correlation with the number of inpatients (R(2) = 0.965), and a weak one with the number of outpatients (R(2) = 0.424). Statistical analysis was carried out to develop models for the prediction of the quantity of waste generated at each hospital (public, teaching and private). In these models, the number of inpatients and outpatients were revealed to be significant factors on the quantity of waste generated. The influence of the number of inpatients and outpatients treated varies at different hospitals. Therefore, different models were developed based on the types of hospitals. © The Author(s) 2015.

  20. Development of Prediction Model and Experimental Validation in Predicting the Curcumin Content of Turmeric (Curcuma longa L.).

    Science.gov (United States)

    Akbar, Abdul; Kuanar, Ananya; Joshi, Raj K; Sandeep, I S; Mohanty, Sujata; Naik, Pradeep K; Mishra, Antaryami; Nayak, Sanghamitra

    2016-01-01

    The drug yielding potential of turmeric ( Curcuma longa L.) is largely due to the presence of phyto-constituent 'curcumin.' Curcumin has been found to possess a myriad of therapeutic activities ranging from anti-inflammatory to neuroprotective. Lack of requisite high curcumin containing genotypes and variation in the curcumin content of turmeric at different agro climatic regions are the major stumbling blocks in commercial production of turmeric. Curcumin content of turmeric is greatly influenced by environmental factors. Hence, a prediction model based on artificial neural network (ANN) was developed to map genome environment interaction basing on curcumin content, soli and climatic factors from different agroclimatic regions for prediction of maximum curcumin content at various sites to facilitate the selection of suitable region for commercial cultivation of turmeric. The ANN model was developed and tested using a data set of 119 generated by collecting samples from 8 different agroclimatic regions of Odisha. The curcumin content from these samples was measured that varied from 7.2% to 0.4%. The ANN model was trained with 11 parameters of soil and climatic factors as input and curcumin content as output. The results showed that feed-forward ANN model with 8 nodes (MLFN-8) was the most suitable one with R 2 value of 0.91. Sensitivity analysis revealed that minimum relative humidity, altitude, soil nitrogen content and soil pH had greater effect on curcumin content. This ANN model has shown proven efficiency for predicting and optimizing the curcumin content at a specific site.

  1. Development of prediction model and experimental validation in predicting the curcumin content of turmeric (Curcuma longa L.

    Directory of Open Access Journals (Sweden)

    Abdul Akbar

    2016-10-01

    Full Text Available The drug yielding potential of turmeric (Curcuma longa L. is largely due to the presence of phyto-constituent ‘curcumin’. Curcumin has been found to possess a myriad of therapeutic activities ranging from anti-inflammatory to neuroprotective. Lack of requisite high curcumin containing genotypes and variation in the curcumin content of turmeric at different agro climatic regions are the major stumbling blocks in commercial production of turmeric. Curcumin content of turmeric is greatly influenced by environmental factors. Hence, a prediction model based on artificial neural network (ANN was developed to map genome environment interaction basing on curcumin content, soli and climatic factors from different agroclimatic regions for prediction of maximum curcumin content at various sites to facilitate the selection of suitable region for commercial cultivation of turmeric. The ANN model was developed and tested using a data set of 119 generated by collecting samples from 8 different agroclimatic regions of Odisha. The curcumin content from these samples was measured that varied from 7.2% to 0.4%. The ANN model was trained with 11 parameters of soil and climatic factors as input and curcumin content as output. The results showed that feed-forward ANN model with 8 nodes (MLFN-8 was the most suitable one with R2 value of 0.91. Sensitivity analysis revealed that minimum relative humidity, altitude, soil nitrogen content and soil pH had greater effect on curcumin content. This ANN model has shown proven efficiency for predicting and optimizing the curcumin content at a specific site.

  2. Structural maturation and brain activity predict future working memory capacity during childhood development.

    Science.gov (United States)

    Ullman, Henrik; Almeida, Rita; Klingberg, Torkel

    2014-01-29

    Human working memory capacity develops during childhood and is a strong predictor of future academic performance, in particular, achievements in mathematics and reading. Predicting working memory development is important for the early identification of children at risk for poor cognitive and academic development. Here we show that structural and functional magnetic resonance imaging data explain variance in children's working memory capacity 2 years later, which was unique variance in addition to that predicted using cognitive tests. While current working memory capacity correlated with frontoparietal cortical activity, the future capacity could be inferred from structure and activity in basal ganglia and thalamus. This gives a novel insight into the neural mechanisms of childhood development and supports the idea that neuroimaging can have a unique role in predicting children's cognitive development.

  3. Development of incident progress prediction technologies for nuclear emergency preparedness. Current status and future subjects

    International Nuclear Information System (INIS)

    Yoshida, Yoshitaka; Yamamoto, Yasunori; Kusunoki, Takayoshi; Kawasaki, Ikuo; Yanagi, Chihiro; Kinoshita, Ikuo; Iwasaki, Yoshito

    2014-01-01

    Nuclear licensees are required to maintain a prediction system during normal condition for using a nuclear emergency by the Basic Plan for Disaster Prevention of government. With prediction of the incident progress, if the present condition of nuclear power plant is understood appropriately and it grows more serious with keeping the present situation, it is in predicting what kind of situation will be occurred in the near future, choosing the effective countermeasures against the coming threat, and understanding the time available of intervention time. Following the accident on September 30 1999 in the nuclear fuel fabrication facility in Tokai Village of Ibaraki Prefecture, the Institute of Nuclear Safety System started development of incident progress prediction technologies for nuclear emergency preparedness. We have performed technical applications and made improvements in nuclear emergency exercises and verified the developed systems using the observed values of the Fukushima Daiichi Nuclear Power Plant accident. As a result, our developed Incident Progress Prediction System was applied to nuclear emergency exercises and we accumulated knowledge and experience by which we improved the system to make predictions more rapidly and more precisely, including for example, the development of a prediction method for leak size of reactor coolant. On the other hand, if a rapidly progressing incident occurs, since end users need simple and quick predictions about the public's protection and evacuation areas, we developed the Radioactive Materials Release, Radiation Dose and Radiological Protection Area Prediction System which changed solving an inverse problem into a forward problem solution. In view of the water-level-decline incident of the spent fuel storage facility at the Fukushima Daiichi Nuclear Power Plant, the spent fuel storage facility water level and the water temperature evaluation tool were improved. Such incident progress prediction technologies were

  4. Development and Validation of a Prediction Model to Estimate Individual Risk of Pancreatic Cancer.

    Science.gov (United States)

    Yu, Ami; Woo, Sang Myung; Joo, Jungnam; Yang, Hye-Ryung; Lee, Woo Jin; Park, Sang-Jae; Nam, Byung-Ho

    2016-01-01

    There is no reliable screening tool to identify people with high risk of developing pancreatic cancer even though pancreatic cancer represents the fifth-leading cause of cancer-related death in Korea. The goal of this study was to develop an individualized risk prediction model that can be used to screen for asymptomatic pancreatic cancer in Korean men and women. Gender-specific risk prediction models for pancreatic cancer were developed using the Cox proportional hazards model based on an 8-year follow-up of a cohort study of 1,289,933 men and 557,701 women in Korea who had biennial examinations in 1996-1997. The performance of the models was evaluated with respect to their discrimination and calibration ability based on the C-statistic and Hosmer-Lemeshow type χ2 statistic. A total of 1,634 (0.13%) men and 561 (0.10%) women were newly diagnosed with pancreatic cancer. Age, height, BMI, fasting glucose, urine glucose, smoking, and age at smoking initiation were included in the risk prediction model for men. Height, BMI, fasting glucose, urine glucose, smoking, and drinking habit were included in the risk prediction model for women. Smoking was the most significant risk factor for developing pancreatic cancer in both men and women. The risk prediction model exhibited good discrimination and calibration ability, and in external validation it had excellent prediction ability. Gender-specific risk prediction models for pancreatic cancer were developed and validated for the first time. The prediction models will be a useful tool for detecting high-risk individuals who may benefit from increased surveillance for pancreatic cancer.

  5. Language and reading development in the brain today: neuromarkers and the case for prediction.

    Science.gov (United States)

    Buchweitz, Augusto

    2016-01-01

    The goal of this article is to provide an account of language development in the brain using the new information about brain function gleaned from cognitive neuroscience. This account goes beyond describing the association between language and specific brain areas to advocate the possibility of predicting language outcomes using brain-imaging data. The goal is to address the current evidence about language development in the brain and prediction of language outcomes. Recent studies will be discussed in the light of the evidence generated for predicting language outcomes and using new methods of analysis of brain data. The present account of brain behavior will address: (1) the development of a hardwired brain circuit for spoken language; (2) the neural adaptation that follows reading instruction and fosters the "grafting" of visual processing areas of the brain onto the hardwired circuit of spoken language; and (3) the prediction of language development and the possibility of translational neuroscience. Brain imaging has allowed for the identification of neural indices (neuromarkers) that reflect typical and atypical language development; the possibility of predicting risk for language disorders has emerged. A mandate to develop a bridge between neuroscience and health and cognition-related outcomes may pave the way for translational neuroscience. Copyright © 2016 Sociedade Brasileira de Pediatria. Published by Elsevier Editora Ltda. All rights reserved.

  6. Thermodynamic and Kinematic Flow Characteristics of Some Developing and Non-Developing Disturbances in Predict

    Science.gov (United States)

    2014-12-01

    PROCESSING FROM THE DROPSONDES The dropsonde data used in this thesis is in an EOL format, which is an ascii text format containing a header and...Depression investigation of cloud-systems in the Tropics (PREDICT) 2010 Quality Controlled Dropsonde Data Set’ under the ‘ EOL file format’ section found at

  7. Development of a wind farm noise propagation prediction model - project progress to date

    International Nuclear Information System (INIS)

    Robinson, P.; Bullmore, A.; Bass, J.; Sloth, E.

    1998-01-01

    This paper describes a twelve month measurement campaign which is part of a European project (CEC Project JOR3-CT95-0051) with the aim to substantially reduce the uncertainties involved in predicting environmentally radiated noise levels from wind farms (1). This will be achieved by comparing noise levels measure at varying distances from single and multiple sources over differing complexities of terrain with those predicted using a number of currently adopted sound propagation models. Specific objectives within the project are to: establish the important parameters controlling the propagation of wind farm noise to the far field; develop a planning tool for predicting wind farm noise emission levels under practically encountered conditions; place confidence limits on the upper and lower bounds of the noise levels predicted, thus enabling developers to quantify the risk whether noise emission from wind farms will cause nuisance to nearby residents. (Author)

  8. In-operation inspection technology development-4 ''development of degradation prediction technology for motor-operated valves''

    International Nuclear Information System (INIS)

    Kikuo, Takeshima; Yuichi, Higashikawa; Masahiro, Koike; Kenji, Matsumoto; Eiji, O'shima

    2001-01-01

    A method for degradation predicting technology has been proposed for motor operated valves in nuclear power plants which is based on the concept of condition monitoring for maintenance. This method (degradation prediction technology) eliminates the unnecessary overhaul of valves and realizes high reliability and economy. The degradation mechanism was clarified by long time heating experiments of gasket and gland packing and the wear test for them and stem nut to research valve parts degradation by stress (pressure, temperature, etc) during plant operation. Effective electric power measurements for motor operated valves were confirmed to be useful discovering valve part failures. The motor operated valve degradation prediction system was developed on the basis of the experiment results and mechanism. The system is able to predict the degradation of valve parts (gasket/gland packing, stem, stem nut, etc) utilizing plant data (pressure, temperature, etc) and effective power of the motor. The life of valve parts can be estimated from the experimental results. (authors)

  9. The value of contrast-enhanced CT scan in prediction of development of contusional hemorrhage

    International Nuclear Information System (INIS)

    Yokoyama, Kazuhiro; Kyoi, Kikuo; Sakaki, Toshisuke; Kinugawa, Kazuhiko; Morimoto, Tetsuya

    1983-01-01

    It is often experienced that even if there are no significant findings on the initial plain CT scan in the patient with cerebral contusion, the patient has thereafter a serious clinical course and requires emergency operation for so-called contusional hemorrhage. In order to predict of the development of contusional hemorrhage we performed contrast-enhanced CT scan at the time of patient's arrival within 12 hours after injury, if there was cerebral contusion on the initial plain CT scan, and repeated plain CT scan 24 hours after the contrast-enhanced CT scan. If enhancement was demonstrated on the contrastenhanced CT scan, we predicted the development of contusional hemorrhage and if not demonstrated, we predicted no more development of contusional hemorrhage and then we studied the correlation between the prediction and the plain CT 24 hours after the contrast-enhanced CT scan. The results were as follows: 1) The prediction was correct in 13 cases out of 16 cases in which the development of contusional hemorrhage was observed. In 18 cases where no development of contusional hemorrhage was observed, the prediction was correct without exception. 2) Most of the cases in which enhancement was demonstrated were ones examined not before 3 hours after injury. 3) The extent of enhancement shown on contrastenhanced CT scan was well consistent with that of contusional hemorrhage on the plain CT scan 24 hours after the contrast-enhanced CT scan. From these results, the contrast-enhanced CT scan in acute stage of head injury was considered to by very useful in prediction of the development of contusional hemorrhage. (author)

  10. Method for predicting future developments of traffic noise in urban areas in Europe

    NARCIS (Netherlands)

    Salomons, E.; Hout, D. van den; Janssen, S.; Kugler, U.; MacA, V.

    2010-01-01

    Traffic noise in urban areas in Europe is a major environmental stressor. In this study we present a method for predicting how environmental noise can be expected to develop in the future. In the project HEIMTSA scenarios were developed for all relevant environmental stressors to health, for all

  11. Randomized, Multicenter, Placebo-Controlled Clinical Trial of Duloxetine Versus Placebo for Aromatase Inhibitor-Associated Arthralgias in Early-Stage Breast Cancer: SWOG S1202.

    Science.gov (United States)

    Henry, N Lynn; Unger, Joseph M; Schott, Anne F; Fehrenbacher, Louis; Flynn, Patrick J; Prow, Debra M; Sharer, Carl W; Burton, Gary V; Kuzma, Charles S; Moseley, Anna; Lew, Danika L; Fisch, Michael J; Moinpour, Carol M; Hershman, Dawn L; Wade, James L

    2018-02-01

    Purpose Adherence to aromatase inhibitor (AI) therapy for early-stage breast cancer is limited by AI-associated musculoskeletal symptoms (AIMSS). Duloxetine is US Food and Drug Administration approved for treatment of multiple chronic pain disorders. We hypothesized that treatment of AIMSS with duloxetine would improve average joint pain compared with placebo. Methods This randomized, double-blind, phase III trial included AI-treated postmenopausal women with early-stage breast cancer and who had average joint pain score of ≥ 4 out of 10 that developed or worsened since AI therapy initiation. Patients were randomly assigned 1:1 to duloxetine or placebo for 13 weeks. The primary end point was average joint pain through 12 weeks, examined using multivariable linear mixed models, adjusted for stratification factors (baseline pain score of 4 to 6 v 7 to 10 and prior taxane use). Clinically significant change in average pain was defined as a ≥ 2-point decrease from baseline. Results Of 299 enrolled patients, 127 patients treated with duloxetine and 128 who received placebo were evaluable for the primary analysis. By 12 weeks, the average joint pain score was 0.82 points lower for patients who received duloxetine compared with those who received placebo (95% CI, -1.24 to -0.40; P = .0002). Similar patterns were observed for worst joint pain, joint stiffness, pain interference, and functioning. Rates of adverse events of any grade were higher in the duloxetine-treated group (78% v 50%); rates of grade 3 adverse events were similar. Conclusion Results of treatment with duloxetine for AIMSS were superior to those of placebo among women with early-stage breast cancer, although it resulted in more frequent low-grade toxicities.

  12. Developing and implementing the use of predictive models for estimating water quality at Great Lakes beaches

    Science.gov (United States)

    Francy, Donna S.; Brady, Amie M.G.; Carvin, Rebecca B.; Corsi, Steven R.; Fuller, Lori M.; Harrison, John H.; Hayhurst, Brett A.; Lant, Jeremiah; Nevers, Meredith B.; Terrio, Paul J.; Zimmerman, Tammy M.

    2013-01-01

    Predictive models have been used at beaches to improve the timeliness and accuracy of recreational water-quality assessments over the most common current approach to water-quality monitoring, which relies on culturing fecal-indicator bacteria such as Escherichia coli (E. coli.). Beach-specific predictive models use environmental and water-quality variables that are easily and quickly measured as surrogates to estimate concentrations of fecal-indicator bacteria or to provide the probability that a State recreational water-quality standard will be exceeded. When predictive models are used for beach closure or advisory decisions, they are referred to as “nowcasts.” During the recreational seasons of 2010-12, the U.S. Geological Survey (USGS), in cooperation with 23 local and State agencies, worked to improve existing nowcasts at 4 beaches, validate predictive models at another 38 beaches, and collect data for predictive-model development at 7 beaches throughout the Great Lakes. This report summarizes efforts to collect data and develop predictive models by multiple agencies and to compile existing information on the beaches and beach-monitoring programs into one comprehensive report. Local agencies measured E. coli concentrations and variables expected to affect E. coli concentrations such as wave height, turbidity, water temperature, and numbers of birds at the time of sampling. In addition to these field measurements, equipment was installed by the USGS or local agencies at or near several beaches to collect water-quality and metrological measurements in near real time, including nearshore buoys, weather stations, and tributary staff gages and monitors. The USGS worked with local agencies to retrieve data from existing sources either manually or by use of tools designed specifically to compile and process data for predictive-model development. Predictive models were developed by use of linear regression and (or) partial least squares techniques for 42 beaches

  13. Some uses of predictive probability of success in clinical drug development

    Directory of Open Access Journals (Sweden)

    Mauro Gasparini

    2013-03-01

    Full Text Available Predictive probability of success is a (subjective Bayesian evaluation of the prob- ability of a future successful event in a given state of information. In the context of pharmaceutical clinical drug development, successful events relate to the accrual of positive evidence on the therapy which is being developed, like demonstration of su- perior efficacy or ascertainment of safety. Positive evidence will usually be obtained via standard frequentist tools, according to the regulations imposed in the world of pharmaceutical development.Within a single trial, predictive probability of success can be identified with expected power, i.e. the evaluation of the success probability of the trial. Success means, for example, obtaining a significant result of a standard superiority test.Across trials, predictive probability of success can be the probability of a successful completion of an entire part of clinical development, for example a successful phase III development in the presence of phase II data.Calculations of predictive probability of success in the presence of normal data with known variance will be illustrated, both for within-trial and across-trial predictions.

  14. Risk Prediction of New Adjacent Vertebral Fractures After PVP for Patients with Vertebral Compression Fractures: Development of a Prediction Model

    International Nuclear Information System (INIS)

    Zhong, Bin-Yan; He, Shi-Cheng; Zhu, Hai-Dong; Wu, Chun-Gen; Fang, Wen; Chen, Li; Guo, Jin-He; Deng, Gang; Zhu, Guang-Yu; Teng, Gao-Jun

    2017-01-01

    PurposeWe aim to determine the predictors of new adjacent vertebral fractures (AVCFs) after percutaneous vertebroplasty (PVP) in patients with osteoporotic vertebral compression fractures (OVCFs) and to construct a risk prediction score to estimate a 2-year new AVCF risk-by-risk factor condition.Materials and MethodsPatients with OVCFs who underwent their first PVP between December 2006 and December 2013 at Hospital A (training cohort) and Hospital B (validation cohort) were included in this study. In training cohort, we assessed the independent risk predictors and developed the probability of new adjacent OVCFs (PNAV) score system using the Cox proportional hazard regression analysis. The accuracy of this system was then validated in both training and validation cohorts by concordance (c) statistic.Results421 patients (training cohort: n = 256; validation cohort: n = 165) were included in this study. In training cohort, new AVCFs after the first PVP treatment occurred in 33 (12.9%) patients. The independent risk factors were intradiscal cement leakage and preexisting old vertebral compression fracture(s). The estimated 2-year absolute risk of new AVCFs ranged from less than 4% in patients with neither independent risk factors to more than 45% in individuals with both factors.ConclusionsThe PNAV score is an objective and easy approach to predict the risk of new AVCFs.

  15. Risk Prediction of New Adjacent Vertebral Fractures After PVP for Patients with Vertebral Compression Fractures: Development of a Prediction Model

    Energy Technology Data Exchange (ETDEWEB)

    Zhong, Bin-Yan; He, Shi-Cheng; Zhu, Hai-Dong [Southeast University, Department of Radiology, Medical School, Zhongda Hospital (China); Wu, Chun-Gen [Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Department of Diagnostic and Interventional Radiology (China); Fang, Wen; Chen, Li; Guo, Jin-He; Deng, Gang; Zhu, Guang-Yu; Teng, Gao-Jun, E-mail: gjteng@vip.sina.com [Southeast University, Department of Radiology, Medical School, Zhongda Hospital (China)

    2017-02-15

    PurposeWe aim to determine the predictors of new adjacent vertebral fractures (AVCFs) after percutaneous vertebroplasty (PVP) in patients with osteoporotic vertebral compression fractures (OVCFs) and to construct a risk prediction score to estimate a 2-year new AVCF risk-by-risk factor condition.Materials and MethodsPatients with OVCFs who underwent their first PVP between December 2006 and December 2013 at Hospital A (training cohort) and Hospital B (validation cohort) were included in this study. In training cohort, we assessed the independent risk predictors and developed the probability of new adjacent OVCFs (PNAV) score system using the Cox proportional hazard regression analysis. The accuracy of this system was then validated in both training and validation cohorts by concordance (c) statistic.Results421 patients (training cohort: n = 256; validation cohort: n = 165) were included in this study. In training cohort, new AVCFs after the first PVP treatment occurred in 33 (12.9%) patients. The independent risk factors were intradiscal cement leakage and preexisting old vertebral compression fracture(s). The estimated 2-year absolute risk of new AVCFs ranged from less than 4% in patients with neither independent risk factors to more than 45% in individuals with both factors.ConclusionsThe PNAV score is an objective and easy approach to predict the risk of new AVCFs.

  16. Prediction of infarction development after endovascular stroke therapy with dual-energy computed tomography.

    Science.gov (United States)

    Djurdjevic, Tanja; Rehwald, Rafael; Knoflach, Michael; Matosevic, Benjamin; Kiechl, Stefan; Gizewski, Elke Ruth; Glodny, Bernhard; Grams, Astrid Ellen

    2017-03-01

    After intraarterial recanalisation (IAR), the haemorrhage and the blood-brain barrier (BBB) disruption can be distinguished using dual-energy computed tomography (DECT). The aim of the present study was to investigate whether future infarction development can be predicted from DECT. DECT scans of 20 patients showing 45 BBB disrupted areas after IAR were assessed and compared with follow-up examinations. Receiver operator characteristic (ROC) analyses using densities from the iodine map (IM) and virtual non-contrast (VNC) were performed. Future infarction areas are denser than future non-infarction areas on IM series (23.44 ± 24.86 vs. 5.77 ± 2.77; p VNC series (29.71 ± 3.33 vs. 35.33 ± 3.50; p 17.13 HU; p VNC series allowed prediction of infarction volume. Future infarction development after IAR can be reliably predicted with the IM series. The prediction of haemorrhages and of infarction size is less reliable. • The IM series (DECT) can predict future infarction development after IAR. • Later haemorrhages can be predicted using the IM and the BW series. • The volume of definable hypodense areas in VNC correlates with infarction volume.

  17. Development of Building Thermal Load and Discomfort Degree Hour Prediction Models Using Data Mining Approaches

    Directory of Open Access Journals (Sweden)

    Yaolin Lin

    2018-06-01

    Full Text Available Thermal load and indoor comfort level are two important building performance indicators, rapid predictions of which can help significantly reduce the computation time during design optimization. In this paper, a three-step approach is used to develop and evaluate prediction models. Firstly, the Latin Hypercube Sampling Method (LHSM is used to generate a representative 19-dimensional design database and DesignBuilder is then used to obtain the thermal load and discomfort degree hours through simulation. Secondly, samples from the database are used to develop and validate seven prediction models, using data mining approaches including multilinear regression (MLR, chi-square automatic interaction detector (CHAID, exhaustive CHAID (ECHAID, back-propagation neural network (BPNN, radial basis function network (RBFN, classification and regression trees (CART, and support vector machines (SVM. It is found that the MLR and BPNN models outperform the others in the prediction of thermal load with average absolute error of less than 1.19%, and the BPNN model is the best at predicting discomfort degree hour with 0.62% average absolute error. Finally, two hybrid models—MLR (MLR + BPNN and MLR-BPNN—are developed. The MLR-BPNN models are found to be the best prediction models, with average absolute error of 0.82% in thermal load and 0.59% in discomfort degree hour.

  18. An Analysis of a Developing and Non-Developing Disturbance During the Predict Experiment

    Science.gov (United States)

    2015-09-25

    cyclogenesis model and accompanying scientific hypotheses were established ob- servationally in the Atlantic and eastern Pacific sectors by DMW09. In...the Tropics (PREDICT) experiment was conducted during the 2010 Atlantic hurricane season (Montgomery et al. 2012). This experiment was designed to...primarily by dropsonde delivered by a Gulfstream V (GV). The aircraft was op- erated from St. Croix, U.S. Virgin Islands, and had an operational range

  19. Logistic regression modelling: procedures and pitfalls in developing and interpreting prediction models

    Directory of Open Access Journals (Sweden)

    Nataša Šarlija

    2017-01-01

    Full Text Available This study sheds light on the most common issues related to applying logistic regression in prediction models for company growth. The purpose of the paper is 1 to provide a detailed demonstration of the steps in developing a growth prediction model based on logistic regression analysis, 2 to discuss common pitfalls and methodological errors in developing a model, and 3 to provide solutions and possible ways of overcoming these issues. Special attention is devoted to the question of satisfying logistic regression assumptions, selecting and defining dependent and independent variables, using classification tables and ROC curves, for reporting model strength, interpreting odds ratios as effect measures and evaluating performance of the prediction model. Development of a logistic regression model in this paper focuses on a prediction model of company growth. The analysis is based on predominantly financial data from a sample of 1471 small and medium-sized Croatian companies active between 2009 and 2014. The financial data is presented in the form of financial ratios divided into nine main groups depicting following areas of business: liquidity, leverage, activity, profitability, research and development, investing and export. The growth prediction model indicates aspects of a business critical for achieving high growth. In that respect, the contribution of this paper is twofold. First, methodological, in terms of pointing out pitfalls and potential solutions in logistic regression modelling, and secondly, theoretical, in terms of identifying factors responsible for high growth of small and medium-sized companies.

  20. In-operation inspection technology development 'development of degradation prediction technology for rotating machinery'

    Energy Technology Data Exchange (ETDEWEB)

    Osaki, K.; Watanabe, Y.; Uhara, Y.; Hattori, H. [Toshiba Ceramics Co. Ltd., Ykohama (Japan); O' shima, E. [Tokyo Institute of Technology (Japan); Matsumoto, K. [Japan Power Engineering and Inspection Corporation, Chiba (Japan)

    2001-07-01

    In order to rationalize facility maintenance management and improve reliabilities of rotating machines, it is desirable to develop the technology for estimating bearing wear and predicting bearing wear growth. Therefore, we developed a bearing wear analysis method for evaluating bearing wear growth in the mixed lubrication, and developed a degradation prediction system which estimates the bearing wear and predicts bearing wear growth from external parameters, such as shaft vibration. In bearing wear analysis, the influence of bearing surface roughness and elastic deformation are considered. This analysis model was validated by the bearing wear test. The developed system can predict degradation respecting bearing wear, casing deformation, shaft curvature and bearing sleeve corrosion, using some physical models of degradation that take into account various degradation phenomena. Furthermore, this system can estimate bearing life, taking into consideration the distribution of the vibration characteristic caused by the differences in assembling processes and the distribution of the degradation characteristic. This system was validated by the degradation simulation test. (authors)

  1. Development of 1RM Prediction Equations for Bench Press in Moderately Trained Men.

    Science.gov (United States)

    Macht, Jordan W; Abel, Mark G; Mullineaux, David R; Yates, James W

    2016-10-01

    Macht, JW, Abel, MG, Mullineaux, DR, and Yates, JW. Development of 1RM prediction equations for bench press in moderately trained men. J Strength Cond Res 30(10): 2901-2906, 2016-There are a variety of established 1 repetition maximum (1RM) prediction equations, however, very few prediction equations use anthropometric characteristics exclusively or in part, to estimate 1RM strength. Therefore, the purpose of this study was to develop an original 1RM prediction equation for bench press using anthropometric and performance characteristics in moderately trained male subjects. Sixty male subjects (21.2 ± 2.4 years) completed a 1RM bench press and were randomly assigned a load to complete as many repetitions as possible. In addition, body composition, upper-body anthropometric characteristics, and handgrip strength were assessed. Regression analysis was used to develop a performance-based 1RM prediction equation: 1RM = 1.20 repetition weight + 2.19 repetitions to fatigue - 0.56 biacromial width (cm) + 9.6 (R = 0.99, standard error of estimate [SEE] = 3.5 kg). Regression analysis to develop a nonperformance-based 1RM prediction equation yielded: 1RM (kg) = 0.997 cross-sectional area (CSA) (cm) + 0.401 chest circumference (cm) - 0.385%fat - 0.185 arm length (cm) + 36.7 (R = 0.81, SEE = 13.0 kg). The performance prediction equations developed in this study had high validity coefficients, minimal mean bias, and small limits of agreement. The anthropometric equations had moderately high validity coefficient but larger limits of agreement. The practical applications of this study indicate that the inclusion of anthropometric characteristics and performance variables produce a valid prediction equation for 1RM strength. In addition, the CSA of the arm uses a simple nonperformance method of estimating the lifter's 1RM. This information may be used to predict the starting load for a lifter performing a 1RM prediction protocol or a 1RM testing protocol.

  2. Development of a prediction model of severe reaction in boiled egg challenges.

    Science.gov (United States)

    Sugiura, Shiro; Matsui, Teruaki; Nakagawa, Tomoko; Sasaki, Kemal; Nakata, Joon; Kando, Naoyuki; Ito, Komei

    2016-07-01

    We have proposed a new scoring system (Anaphylaxis SCoring Aichi: ASCA) for a quantitative evaluation of the anaphylactic reaction that is observed in an oral food challenge (OFC). Furthermore, the TS/Pro (Total Score of ASCA/cumulative protein dose) can be a marker to represent the overall severity of a food allergy. We aimed to develop a prediction model for a severe allergic reaction that is provoked in a boiled egg white challenge. We used two separate datasets to develop and validate the prediction model, respectively. The development dataset included 198 OFCs, that tested positive. The validation dataset prospectively included 140 consecutive OFCs, irrespective of the result. A 'severe reaction' was defined as a TS/Pro higher than 31 (the median score of the development dataset). A multivariate logistic regression analysis was performed to identify the factors associated with a severe reaction and develop the prediction model. The following four factors were independently associated with a severe reaction: ovomucoid specific IgE class (OM-sIgE: 0-6), aged 5 years or over, a complete avoidance of egg, and a total IgE prediction model. The model showed good discrimination in a receiver operating characteristic analysis; area under the curve (AUC) = 0.84 in development dataset, AUC = 0.85 in validation dataset. The prediction model significantly improved the AUC in both datasets compared to OM-sIgE alone. This simple scoring prediction model was useful for avoiding risky OFC. Copyright © 2016 Japanese Society of Allergology. Production and hosting by Elsevier B.V. All rights reserved.

  3. A prediction algorithm for first onset of major depression in the general population: development and validation.

    Science.gov (United States)

    Wang, JianLi; Sareen, Jitender; Patten, Scott; Bolton, James; Schmitz, Norbert; Birney, Arden

    2014-05-01

    Prediction algorithms are useful for making clinical decisions and for population health planning. However, such prediction algorithms for first onset of major depression do not exist. The objective of this study was to develop and validate a prediction algorithm for first onset of major depression in the general population. Longitudinal study design with approximate 3-year follow-up. The study was based on data from a nationally representative sample of the US general population. A total of 28 059 individuals who participated in Waves 1 and 2 of the US National Epidemiologic Survey on Alcohol and Related Conditions and who had not had major depression at Wave 1 were included. The prediction algorithm was developed using logistic regression modelling in 21 813 participants from three census regions. The algorithm was validated in participants from the 4th census region (n=6246). Major depression occurred since Wave 1 of the National Epidemiologic Survey on Alcohol and Related Conditions, assessed by the Alcohol Use Disorder and Associated Disabilities Interview Schedule-diagnostic and statistical manual for mental disorders IV. A prediction algorithm containing 17 unique risk factors was developed. The algorithm had good discriminative power (C statistics=0.7538, 95% CI 0.7378 to 0.7699) and excellent calibration (F-adjusted test=1.00, p=0.448) with the weighted data. In the validation sample, the algorithm had a C statistic of 0.7259 and excellent calibration (Hosmer-Lemeshow χ(2)=3.41, p=0.906). The developed prediction algorithm has good discrimination and calibration capacity. It can be used by clinicians, mental health policy-makers and service planners and the general public to predict future risk of having major depression. The application of the algorithm may lead to increased personalisation of treatment, better clinical decisions and more optimal mental health service planning.

  4. Methods of developing core collections based on the predicted genotypic value of rice ( Oryza sativa L.).

    Science.gov (United States)

    Li, C T; Shi, C H; Wu, J G; Xu, H M; Zhang, H Z; Ren, Y L

    2004-04-01

    The selection of an appropriate sampling strategy and a clustering method is important in the construction of core collections based on predicted genotypic values in order to retain the greatest degree of genetic diversity of the initial collection. In this study, methods of developing rice core collections were evaluated based on the predicted genotypic values for 992 rice varieties with 13 quantitative traits. The genotypic values of the traits were predicted by the adjusted unbiased prediction (AUP) method. Based on the predicted genotypic values, Mahalanobis distances were calculated and employed to measure the genetic similarities among the rice varieties. Six hierarchical clustering methods, including the single linkage, median linkage, centroid, unweighted pair-group average, weighted pair-group average and flexible-beta methods, were combined with random, preferred and deviation sampling to develop 18 core collections of rice germplasm. The results show that the deviation sampling strategy in combination with the unweighted pair-group average method of hierarchical clustering retains the greatest degree of genetic diversities of the initial collection. The core collections sampled using predicted genotypic values had more genetic diversity than those based on phenotypic values.

  5. Prediction of infarction development after endovascular stroke therapy with dual-energy computed tomography

    International Nuclear Information System (INIS)

    Djurdjevic, Tanja; Gizewski, Elke Ruth; Grams, Astrid Ellen; Rehwald, Rafael; Glodny, Bernhard; Knoflach, Michael; Matosevic, Benjamin; Kiechl, Stefan

    2017-01-01

    After intraarterial recanalisation (IAR), the haemorrhage and the blood-brain barrier (BBB) disruption can be distinguished using dual-energy computed tomography (DECT). The aim of the present study was to investigate whether future infarction development can be predicted from DECT. DECT scans of 20 patients showing 45 BBB disrupted areas after IAR were assessed and compared with follow-up examinations. Receiver operator characteristic (ROC) analyses using densities from the iodine map (IM) and virtual non-contrast (VNC) were performed. Future infarction areas are denser than future non-infarction areas on IM series (23.44 ± 24.86 vs. 5.77 ± 2.77; p < 0.0001) and more hypodense on VNC series (29.71 ± 3.33 vs. 35.33 ± 3.50; p < 0.0001). ROC analyses for the IM series showed an area under the curve (AUC) of 0.99 (cut-off: <9.97 HU; p < 0.05; sensitivity 91.18 %; specificity 100.00 %; accuracy 0.93) for the prediction of future infarctions. The AUC for the prediction of haemorrhagic infarctions was 0.78 (cut-off >17.13 HU; p < 0.05; sensitivity 90.00 %; specificity 62.86 %; accuracy 0.69). The VNC series allowed prediction of infarction volume. Future infarction development after IAR can be reliably predicted with the IM series. The prediction of haemorrhages and of infarction size is less reliable. (orig.)

  6. Prediction of infarction development after endovascular stroke therapy with dual-energy computed tomography

    Energy Technology Data Exchange (ETDEWEB)

    Djurdjevic, Tanja; Gizewski, Elke Ruth; Grams, Astrid Ellen [Medical University of Innsbruck, Department of Neuroradiology, Innsbruck (Austria); Rehwald, Rafael; Glodny, Bernhard [Medical University of Innsbruck, Department of Radiology, Innsbruck (Austria); Knoflach, Michael; Matosevic, Benjamin; Kiechl, Stefan [Medical University of Innsbruck, Department of Neurology, Innsbruck (Austria)

    2017-03-15

    After intraarterial recanalisation (IAR), the haemorrhage and the blood-brain barrier (BBB) disruption can be distinguished using dual-energy computed tomography (DECT). The aim of the present study was to investigate whether future infarction development can be predicted from DECT. DECT scans of 20 patients showing 45 BBB disrupted areas after IAR were assessed and compared with follow-up examinations. Receiver operator characteristic (ROC) analyses using densities from the iodine map (IM) and virtual non-contrast (VNC) were performed. Future infarction areas are denser than future non-infarction areas on IM series (23.44 ± 24.86 vs. 5.77 ± 2.77; p < 0.0001) and more hypodense on VNC series (29.71 ± 3.33 vs. 35.33 ± 3.50; p < 0.0001). ROC analyses for the IM series showed an area under the curve (AUC) of 0.99 (cut-off: <9.97 HU; p < 0.05; sensitivity 91.18 %; specificity 100.00 %; accuracy 0.93) for the prediction of future infarctions. The AUC for the prediction of haemorrhagic infarctions was 0.78 (cut-off >17.13 HU; p < 0.05; sensitivity 90.00 %; specificity 62.86 %; accuracy 0.69). The VNC series allowed prediction of infarction volume. Future infarction development after IAR can be reliably predicted with the IM series. The prediction of haemorrhages and of infarction size is less reliable. (orig.)

  7. Measurement and prediction of thermochemical history effects on sensitization development in austenitic stainless steels

    International Nuclear Information System (INIS)

    Bruemmer, S.M.; Charlot, L.A.

    1985-11-01

    The effects of thermal and thermomechanical treatments on sensitization development in Type 304 and 316 stainless steels have been measured and compared to model predictions. Sensitization development resulting from isothermal, continuous cooling and pipe welding treatments has been evaluated. An empirically modified, theoretically based model is shown to accurately predict material degree of sensitization (DOS) as expressed by the electrochemical potentiokinetic reactivation (EPR) test after both simple and complex treatments. Material DOS is also examined using analytical electron microscopy to document grain boundary chromium depletion and is compared to EPR test results

  8. A machine learning approach for predicting the relationship between energy resources and economic development

    Science.gov (United States)

    Cogoljević, Dušan; Alizamir, Meysam; Piljan, Ivan; Piljan, Tatjana; Prljić, Katarina; Zimonjić, Stefan

    2018-04-01

    The linkage between energy resources and economic development is a topic of great interest. Research in this area is also motivated by contemporary concerns about global climate change, carbon emissions fluctuating crude oil prices, and the security of energy supply. The purpose of this research is to develop and apply the machine learning approach to predict gross domestic product (GDP) based on the mix of energy resources. Our results indicate that GDP predictive accuracy can be improved slightly by applying a machine learning approach.

  9. Developing prediction equations and a mobile phone application to identify infants at risk of obesity.

    Science.gov (United States)

    Santorelli, Gillian; Petherick, Emily S; Wright, John; Wilson, Brad; Samiei, Haider; Cameron, Noël; Johnson, William

    2013-01-01

    Advancements in knowledge of obesity aetiology and mobile phone technology have created the opportunity to develop an electronic tool to predict an infant's risk of childhood obesity. The study aims were to develop and validate equations for the prediction of childhood obesity and integrate them into a mobile phone application (App). Anthropometry and childhood obesity risk data were obtained for 1868 UK-born White or South Asian infants in the Born in Bradford cohort. Logistic regression was used to develop prediction equations (at 6 ± 1.5, 9 ± 1.5 and 12 ± 1.5 months) for risk of childhood obesity (BMI at 2 years >91(st) centile and weight gain from 0-2 years >1 centile band) incorporating sex, birth weight, and weight gain as predictors. The discrimination accuracy of the equations was assessed by the area under the curve (AUC); internal validity by comparing area under the curve to those obtained in bootstrapped samples; and external validity by applying the equations to an external sample. An App was built to incorporate six final equations (two at each age, one of which included maternal BMI). The equations had good discrimination (AUCs 86-91%), with the addition of maternal BMI marginally improving prediction. The AUCs in the bootstrapped and external validation samples were similar to those obtained in the development sample. The App is user-friendly, requires a minimum amount of information, and provides a risk assessment of low, medium, or high accompanied by advice and website links to government recommendations. Prediction equations for risk of childhood obesity have been developed and incorporated into a novel App, thereby providing proof of concept that childhood obesity prediction research can be integrated with advancements in technology.

  10. A systematic review of predictive models for asthma development in children.

    Science.gov (United States)

    Luo, Gang; Nkoy, Flory L; Stone, Bryan L; Schmick, Darell; Johnson, Michael D

    2015-11-28

    Asthma is the most common pediatric chronic disease affecting 9.6 % of American children. Delay in asthma diagnosis is prevalent, resulting in suboptimal asthma management. To help avoid delay in asthma diagnosis and advance asthma prevention research, researchers have proposed various models to predict asthma development in children. This paper reviews these models. A systematic review was conducted through searching in PubMed, EMBASE, CINAHL, Scopus, the Cochrane Library, the ACM Digital Library, IEEE Xplore, and OpenGrey up to June 3, 2015. The literature on predictive models for asthma development in children was retrieved, with search results limited to human subjects and children (birth to 18 years). Two independent reviewers screened the literature, performed data extraction, and assessed article quality. The literature search returned 13,101 references in total. After manual review, 32 of these references were determined to be relevant and are discussed in the paper. We identify several limitations of existing predictive models for asthma development in children, and provide preliminary thoughts on how to address these limitations. Existing predictive models for asthma development in children have inadequate accuracy. Efforts to improve these models' performance are needed, but are limited by a lack of a gold standard for asthma development in children.

  11. Potentiality Prediction of Electric Power Replacement Based on Power Market Development Strategy

    Science.gov (United States)

    Miao, Bo; Yang, Shuo; Liu, Qiang; Lin, Jingyi; Zhao, Le; Liu, Chang; Li, Bin

    2017-05-01

    The application of electric power replacement plays an important role in promoting the development of energy conservation and emission reduction in our country. To exploit the potentiality of regional electric power replacement, the regional GDP (gross domestic product) and energy consumption are taken as potentiality evaluation indicators. The principal component factors are extracted with PCA (principal component analysis), and the integral potentiality analysis is made to the potentiality of electric power replacement in the national various regions; a region is taken as a research object, and the potentiality of electric power replacement is defined and quantified. The analytical model for the potentiality of multi-scenario electric power replacement is developed, and prediction is made to the energy consumption with the grey prediction model. The relevant theoretical research is utilized to realize prediction analysis on the potentiality amount of multi-scenario electric power replacement.

  12. Can personality traits predict the future development of heart disease in hospitalized psychiatric veterans?

    Science.gov (United States)

    Williams, Wright; Kunik, Mark E; Springer, Justin; Graham, David P

    2013-11-01

    To examine which personality traits are associated with the new onset of chronic coronary heart disease (CHD) in psychiatric inpatients within 16 years after their initial evaluation. We theorized that personality measures of depression, anxiety, hostility, social isolation, and substance abuse would predict CHD development in psychiatric inpatients. We used a longitudinal database of psychological test data from 349 Veterans first admitted to a psychiatric unit between October 1, 1983, and September 30, 1987. Veterans Affairs and national databases were assessed to determine the development of new-onset chronic CHD over the intervening 16-year period. New-onset CHD developed in 154 of the 349 (44.1%) subjects. Thirty-one psychometric variables from five personality tests significantly predicted the development of CHD. We performed a factor analysis of these variables because they overlapped and four factors emerged, with positive adaptive functioning the only significant factor (OR=0.798, p=0.038). These results support previous research linking personality traits to the development of CHD, extending this association to a population of psychiatric inpatients. Compilation of these personality measures showed that 31 overlapping psychometric variables predicted those Veterans who developed a diagnosis of heart disease within 16 years after their initial psychiatric hospitalization. Our results suggest that personality variables measuring positive adaptive functioning are associated with a reduced risk of developing chronic CHD.

  13. Work characteristics predict the development of multi-site musculoskeletal pain

    NARCIS (Netherlands)

    Oakman, J.; Wind, A. de; Heuvel, S.G. van den; Beek, A.J. van der

    2017-01-01

    Purpose. Musculoskeletal pain in more than one body region is common and a barrier to sustaining employment. We aimed to examine whether work characteristics predict the development of multi-site pain (MSP), and to determine differences in work-related predictors between age groups. Methods. This

  14. Development and evaluation of multi-agent models predicting Twitter trends in multiple domains

    NARCIS (Netherlands)

    Attema, T.; Maanen, P.P. van; Meeuwissen, E.

    2015-01-01

    This paper concerns multi-agent models predicting Twitter trends. We use a step-wise approach to develop a novel agent-based model with the following properties: (1) it uses individual behavior parameters for a set of Twitter users and (2) it uses a retweet graph to model the underlying social

  15. Development of dynamic compartment models for prediction of radionuclide behaviors in rice paddy fields

    International Nuclear Information System (INIS)

    Takahashi, Tomoyuki; Tomita, Ken'ichi; Yamamoto, Kazuhide; Uchida, Shigeo

    2007-01-01

    We are developing dynamic compartment models for prediction of behaviors of some important radionuclides in rice paddy fields for safety assessment of nuclear facilities. For a verification of these models, we report calculations for several different deposition patterns of radionuclides. (author)

  16. Development and validation of a digital work simulation to predict workplace deviance

    NARCIS (Netherlands)

    Dubbelt, L.; Oostrom, J.K.; drs. Hiemstra, A.M.F.; Modderman, J.P.L.

    2015-01-01

    ”This paper describes a new and innovative measure that is developed to predict workplace deviance through the measurement of Machiavellianism and Compliant Behavior. Two field studies were conducted to study the validity of the digital work simulation. In Study 1, (N = 113) support was found for

  17. Development of advanced stability theory suction prediction techniques for laminar flow control. [on swept wings

    Science.gov (United States)

    Srokowski, A. J.

    1978-01-01

    The problem of obtaining accurate estimates of suction requirements on swept laminar flow control wings was discussed. A fast accurate computer code developed to predict suction requirements by integrating disturbance amplification rates was described. Assumptions and approximations used in the present computer code are examined in light of flow conditions on the swept wing which may limit their validity.

  18. Towards a consensus on datasets and evaluation metrics for developing B-cell epitope prediction tools

    DEFF Research Database (Denmark)

    Greenbaum, Jason A.; Andersen, Pernille; Blythe, Martin

    2007-01-01

    and immunology communities. Improving the accuracy of B-cell epitope prediction methods depends on a community consensus on the data and metrics utilized to develop and evaluate such tools. A workshop, sponsored by the National Institute of Allergy and Infectious Disease (NIAID), was recently held in Washington...

  19. Development of a flood-induced health risk prediction model for Africa

    Science.gov (United States)

    Lee, D.; Block, P. J.

    2017-12-01

    Globally, many floods occur in developing or tropical regions where the impact on public health is substantial, including death and injury, drinking water, endemic disease, and so on. Although these flood impacts on public health have been investigated, integrated management of floods and flood-induced health risks is technically and institutionally limited. Specifically, while the use of climatic and hydrologic forecasts for disaster management has been highlighted, analogous predictions for forecasting the magnitude and impact of health risks are lacking, as is the infrastructure for health early warning systems, particularly in developing countries. In this study, we develop flood-induced health risk prediction model for African regions using season-ahead flood predictions with climate drivers and a variety of physical and socio-economic information, such as local hazard, exposure, resilience, and health vulnerability indicators. Skillful prediction of flood and flood-induced health risks can contribute to practical pre- and post-disaster responses in both local- and global-scales, and may eventually be integrated into multi-hazard early warning systems for informed advanced planning and management. This is especially attractive for areas with limited observations and/or little capacity to develop flood-induced health risk warning systems.

  20. A Parsimonious Instrument for Predicting Students' Intent to Pursue a Sales Career: Scale Development and Validation

    Science.gov (United States)

    Peltier, James W.; Cummins, Shannon; Pomirleanu, Nadia; Cross, James; Simon, Rob

    2014-01-01

    Students' desire and intention to pursue a career in sales continue to lag behind industry demand for sales professionals. This article develops and validates a reliable and parsimonious scale for measuring and predicting student intention to pursue a selling career. The instrument advances previous scales in three ways. The instrument is…

  1. Limitations of polyethylene glycol-induced precipitation as predictive tool for protein solubility during formulation development.

    Science.gov (United States)

    Hofmann, Melanie; Winzer, Matthias; Weber, Christian; Gieseler, Henning

    2018-05-01

    Polyethylene glycol (PEG)-induced protein precipitation is often used to extrapolate apparent protein solubility at specific formulation compositions. The procedure was used for several fields of application such as protein crystal growth but also protein formulation development. Nevertheless, most studies focused on applicability in protein crystal growth. In contrast, this study focuses on applicability of PEG-induced precipitation during high-concentration protein formulation development. In this study, solubility of three different model proteins was investigated over a broad range of pH. Solubility values predicted by PEG-induced precipitation were compared to real solubility behaviour determined by either turbidity or content measurements. Predicted solubility by PEG-induced precipitation was confirmed for an Fc fusion protein and a monoclonal antibody. In contrast, PEG-induced precipitation failed to predict solubility of a single-domain antibody construct. Applicability of PEG-induced precipitation as indicator of protein solubility during formulation development was found to be not valid for one of three model molecules. Under certain conditions, PEG-induced protein precipitation is not valid for prediction of real protein solubility behaviour. The procedure should be used carefully as tool for formulation development, and the results obtained should be validated by additional investigations. © 2017 Royal Pharmaceutical Society.

  2. Development of VIS/NIR spectroscopic system for real-time prediction of fresh pork quality

    Science.gov (United States)

    Zhang, Haiyun; Peng, Yankun; Zhao, Songwei; Sasao, Akira

    2013-05-01

    Quality attributes of fresh meat will influence nutritional value and consumers' purchasing power. The aim of the research was to develop a prototype for real-time detection of quality in meat. It consisted of hardware system and software system. A VIS/NIR spectrograph in the range of 350 to 1100 nm was used to collect the spectral data. In order to acquire more potential information of the sample, optical fiber multiplexer was used. A conveyable and cylindrical device was designed and fabricated to hold optical fibers from multiplexer. High power halogen tungsten lamp was collected as the light source. The spectral data were obtained with the exposure time of 2.17ms from the surface of the sample by press down the trigger switch on the self-developed system. The system could automatically acquire, process, display and save the data. Moreover the quality could be predicted on-line. A total of 55 fresh pork samples were used to develop prediction model for real time detection. The spectral data were pretreated with standard normalized variant (SNV) and partial least squares regression (PLSR) was used to develop prediction model. The correlation coefficient and root mean square error of the validation set for water content and pH were 0.810, 0.653, and 0.803, 0.098 respectively. The research shows that the real-time non-destructive detection system based on VIS/NIR spectroscopy can be efficient to predict the quality of fresh meat.

  3. Development of equations for predicting Puerto Rican subtropical dry forest biomass and volume

    Science.gov (United States)

    Thomas J. Brandeis; Matthew Delaney; Bernard R. Parresol; Larry Royer

    2006-01-01

    Carbon accounting, forest health monitoring and sustainable management of the subtropical dry forests of Puerto Rico and other Caribbean Islands require an accurate assessment of forest aboveground biomass (AGB) and stem volume. One means of improving assessment accuracy is the development of predictive equations derived from locally collected data. Forest inventory...

  4. Development of Antisocial Personality Disorder in Detained Youths: The Predictive Value of Mental Disorders

    Science.gov (United States)

    Washburn, Jason J.; Romero, Erin Gregory; Welty, Leah J.; Abram, Karen M.; Teplin, Linda A.; McClelland, Gary M.; Paskar, Leah D.

    2007-01-01

    Antisocial personality disorder (APD) is a serious public and mental health concern. Understanding how well conduct disorder (CD) and other mental disorders predict the development of APD among youths involved in the juvenile justice system is critical for prevention. The authors used a stratified random sample of 1,112 detained youths to examine…

  5. Development and validation of multivariable models to predict mortality and hospitalization in patients with heart failure

    NARCIS (Netherlands)

    Voors, Adriaan A.; Ouwerkerk, Wouter; Zannad, Faiez; van Veldhuisen, Dirk J.; Samani, Nilesh J.; Ponikowski, Piotr; Ng, Leong L.; Metra, Marco; ter Maaten, Jozine M.; Lang, Chim C.; Hillege, Hans L.; van der Harst, Pim; Filippatos, Gerasimos; Dickstein, Kenneth; Cleland, John G.; Anker, Stefan D.; Zwinderman, Aeilko H.

    Introduction From a prospective multicentre multicountry clinical trial, we developed and validated risk models to predict prospective all-cause mortality and hospitalizations because of heart failure (HF) in patients with HF. Methods and results BIOSTAT-CHF is a research programme designed to

  6. Development and validation of multivariable models to predict mortality and hospitalization in patients with heart failure

    NARCIS (Netherlands)

    Voors, Adriaan A.; Ouwerkerk, Wouter; Zannad, Faiez; van Veldhuisen, Dirk J.; Samani, Nilesh J.; Ponikowski, Piotr; Ng, Leong L.; Metra, Marco; ter Maaten, Jozine M.; Lang, Chim C.; Hillege, Hans L.; van der Harst, Pim; Filippatos, Gerasimos; Dickstein, Kenneth; Cleland, John G.; Anker, Stefan D.; Zwinderman, Aeilko H.

    2017-01-01

    Introduction From a prospective multicentre multicountry clinical trial, we developed and validated risk models to predict prospective all-cause mortality and hospitalizations because of heart failure (HF) in patients with HF. Methods and results BIOSTAT-CHF is a research programme designed to

  7. Angiogenic Factors in Cord Blood of Preterm Infants Predicts Subsequently Developing Bronchopulmonary Dysplasia

    Directory of Open Access Journals (Sweden)

    Wen-Chien Yang

    2015-12-01

    Conclusion: Cord blood level of PlGF, rather than VEGF or sFlt-1, was significantly increased in the BPD group. Consistent with our previous report, cord blood level of PlGF may be considered as a biomarker to predict subsequently developing BPD in preterm infants.

  8. Development of a prognostic model for predicting spontaneous singleton preterm birth.

    Science.gov (United States)

    Schaaf, Jelle M; Ravelli, Anita C J; Mol, Ben Willem J; Abu-Hanna, Ameen

    2012-10-01

    To develop and validate a prognostic model for prediction of spontaneous preterm birth. Prospective cohort study using data of the nationwide perinatal registry in The Netherlands. We studied 1,524,058 singleton pregnancies between 1999 and 2007. We developed a multiple logistic regression model to estimate the risk of spontaneous preterm birth based on maternal and pregnancy characteristics. We used bootstrapping techniques to internally validate our model. Discrimination (AUC), accuracy (Brier score) and calibration (calibration graphs and Hosmer-Lemeshow C-statistic) were used to assess the model's predictive performance. Our primary outcome measure was spontaneous preterm birth at model included 13 variables for predicting preterm birth. The predicted probabilities ranged from 0.01 to 0.71 (IQR 0.02-0.04). The model had an area under the receiver operator characteristic curve (AUC) of 0.63 (95% CI 0.63-0.63), the Brier score was 0.04 (95% CI 0.04-0.04) and the Hosmer Lemeshow C-statistic was significant (pvalues of predicted probability. The positive predictive value was 26% (95% CI 20-33%) for the 0.4 probability cut-off point. The model's discrimination was fair and it had modest calibration. Previous preterm birth, drug abuse and vaginal bleeding in the first half of pregnancy were the most important predictors for spontaneous preterm birth. Although not applicable in clinical practice yet, this model is a next step towards early prediction of spontaneous preterm birth that enables caregivers to start preventive therapy in women at higher risk. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

  9. MEDEX 2015: Heart Rate Variability Predicts Development of Acute Mountain Sickness.

    Science.gov (United States)

    Sutherland, Angus; Freer, Joseph; Evans, Laura; Dolci, Alberto; Crotti, Matteo; Macdonald, Jamie Hugo

    2017-09-01

    Sutherland, Angus, Joseph Freer, Laura Evans, Alberto Dolci, Matteo Crotti, and Jamie Hugo Macdonald. MEDEX 2015: Heart rate variability predicts development of acute mountain sickness. High Alt Med Biol. 18: 199-208, 2017. Acute mountain sickness (AMS) develops when the body fails to acclimatize to atmospheric changes at altitude. Preascent prediction of susceptibility to AMS would be a useful tool to prevent subsequent harm. Changes to peripheral oxygen saturation (SpO 2 ) on hypoxic exposure have previously been shown to be of poor predictive value. Heart rate variability (HRV) has shown promise in the early prediction of AMS, but its use pre-expedition has not previously been investigated. We aimed to determine whether pre- and intraexpedition HRV assessment could predict susceptibility to AMS at high altitude with better diagnostic accuracy than SpO 2 . Forty-four healthy volunteers undertook an expedition in the Nepali Himalaya to >5000 m. SpO 2 and HRV parameters were recorded at rest in normoxia and in a normobaric hypoxic chamber before the expedition. On the expedition HRV parameters and SpO 2 were collected again at 3841 m. A daily Lake Louise Score was obtained to assess AMS symptomology. Low frequency/high frequency (LF/HF) ratio in normoxia (cutpoint ≤2.28 a.u.) and LF following 15 minutes of exposure to normobaric hypoxia had moderate (area under the curve ≥0.8) diagnostic accuracy. LF/HF ratio in normoxia had the highest sensitivity (85%) and specificity (88%) for predicting AMS on subsequent ascent to altitude. In contrast, pre-expedition SpO 2 measurements had poor (area under the curve <0.7) diagnostic accuracy and inferior sensitivity and specificity. Pre-ascent measurement of HRV in normoxia was found to be of better diagnostic accuracy for AMS prediction than all measures of HRV in hypoxia, and better than peripheral oxygen saturation monitoring.

  10. Development and Validation of a Multidisciplinary Tool for Accurate and Efficient Rotorcraft Noise Prediction (MUTE)

    Science.gov (United States)

    Liu, Yi; Anusonti-Inthra, Phuriwat; Diskin, Boris

    2011-01-01

    A physics-based, systematically coupled, multidisciplinary prediction tool (MUTE) for rotorcraft noise was developed and validated with a wide range of flight configurations and conditions. MUTE is an aggregation of multidisciplinary computational tools that accurately and efficiently model the physics of the source of rotorcraft noise, and predict the noise at far-field observer locations. It uses systematic coupling approaches among multiple disciplines including Computational Fluid Dynamics (CFD), Computational Structural Dynamics (CSD), and high fidelity acoustics. Within MUTE, advanced high-order CFD tools are used around the rotor blade to predict the transonic flow (shock wave) effects, which generate the high-speed impulsive noise. Predictions of the blade-vortex interaction noise in low speed flight are also improved by using the Particle Vortex Transport Method (PVTM), which preserves the wake flow details required for blade/wake and fuselage/wake interactions. The accuracy of the source noise prediction is further improved by utilizing a coupling approach between CFD and CSD, so that the effects of key structural dynamics, elastic blade deformations, and trim solutions are correctly represented in the analysis. The blade loading information and/or the flow field parameters around the rotor blade predicted by the CFD/CSD coupling approach are used to predict the acoustic signatures at far-field observer locations with a high-fidelity noise propagation code (WOPWOP3). The predicted results from the MUTE tool for rotor blade aerodynamic loading and far-field acoustic signatures are compared and validated with a variation of experimental data sets, such as UH60-A data, DNW test data and HART II test data.

  11. Development and validation of a predictive model for excessive postpartum blood loss: A retrospective, cohort study.

    Science.gov (United States)

    Rubio-Álvarez, Ana; Molina-Alarcón, Milagros; Arias-Arias, Ángel; Hernández-Martínez, Antonio

    2018-03-01

    postpartum haemorrhage is one of the leading causes of maternal morbidity and mortality worldwide. Despite the use of uterotonics agents as preventive measure, it remains a challenge to identify those women who are at increased risk of postpartum bleeding. to develop and to validate a predictive model to assess the risk of excessive bleeding in women with vaginal birth. retrospective cohorts study. "Mancha-Centro Hospital" (Spain). the elaboration of the predictive model was based on a derivation cohort consisting of 2336 women between 2009 and 2011. For validation purposes, a prospective cohort of 953 women between 2013 and 2014 were employed. Women with antenatal fetal demise, multiple pregnancies and gestations under 35 weeks were excluded METHODS: we used a multivariate analysis with binary logistic regression, Ridge Regression and areas under the Receiver Operating Characteristic curves to determine the predictive ability of the proposed model. there was 197 (8.43%) women with excessive bleeding in the derivation cohort and 63 (6.61%) women in the validation cohort. Predictive factors in the final model were: maternal age, primiparity, duration of the first and second stages of labour, neonatal birth weight and antepartum haemoglobin levels. Accordingly, the predictive ability of this model in the derivation cohort was 0.90 (95% CI: 0.85-0.93), while it remained 0.83 (95% CI: 0.74-0.92) in the validation cohort. this predictive model is proved to have an excellent predictive ability in the derivation cohort, and its validation in a latter population equally shows a good ability for prediction. This model can be employed to identify women with a higher risk of postpartum haemorrhage. Copyright © 2017 Elsevier Ltd. All rights reserved.

  12. A Conceptual Development of Quench Prediction App build on LSTM and ELQA framework

    OpenAIRE

    Mertik, Matej; Wielgosz, Maciej; Skoczeń, Andrzej

    2016-01-01

    This article presents a development of web application for quench prediction in \\gls{te-mpe-ee} at CERN. The authors describe an ELectrical Quality Assurance (ELQA) framework, a platform which was designed for rapid development of web integrated data analysis applications for different analysis needed during the hardware commissioning of the Large Hadron Collider (LHC). In second part the article describes a research carried out with the data collected from Quench Detection System by means of...

  13. Development of Demonstrably Predictive Models for Emissions from Alternative Fuels Based Aircraft Engines

    Science.gov (United States)

    2017-05-01

    Engineering Chemistry Fundamentals, Vol. 5, No. 3, 1966, pp. 356–363. [14] Burns, R. A., Development of scalar and velocity imaging diagnostics...in an Aero- Engine Model Combustor at Elevated Pressure Using URANS and Finite- Rate Chemistry ,” 50th AIAA/ASME/SAE/ASEE Joint Propulsion Conference...FINAL REPORT Development of Demonstrably Predictive Models for Emissions from Alternative Fuels Based Aircraft Engines SERDP Project WP-2151

  14. Developing a risk prediction model for the functional outcome after hip arthroscopy.

    Science.gov (United States)

    Stephan, Patrick; Röling, Maarten A; Mathijssen, Nina M C; Hannink, Gerjon; Bloem, Rolf M

    2018-04-19

    Hip arthroscopic treatment is not equally beneficial for every patient undergoing this procedure. Therefore, the purpose of this study was to develop a clinical prediction model for functional outcome after surgery based on preoperative factors. Prospective data was collected on a cohort of 205 patients having undergone hip arthroscopy between 2011 and 2015. Demographic and clinical variables and patient reported outcome (PRO) scores were collected, and considered as potential predictors. Successful outcome was defined as either a Hip Outcome Score (HOS)-ADL score of over 80% or improvement of 23%, defined by the minimal clinical important difference, 1 year after surgery. The prediction model was developed using backward logistic regression. Regression coefficients were converted into an easy to use prediction rule. The analysis included 203 patients, of which 74% had a successful outcome. Female gender (OR: 0.37 (95% CI 0.17-0.83); p = 0.02), pincer impingement (OR: 0.47 (95% CI 0.21-1.09); p = 0.08), labral tear (OR: 0.46 (95% CI 0.20-1.06); p = 0.07), HOS-ADL score (IQR OR: 2.01 (95% CI 0.99-4.08); p = 0.05), WHOQOL physical (IQR OR: 0.43 (95% CI 0.22-0.87); p = 0.02) and WHOQOL psychological (IQR OR: 2.40 (95% CI 1.38-4.18); p = prediction model of successful functional outcome 1 year after hip arthroscopy. The model's discriminating accuracy turned out to be fair, as 71% (95% CI: 64-80%) of the patients were classified correctly. The developed prediction model can predict the functional outcome of patients that are considered for a hip arthroscopic intervention, containing six easy accessible preoperative risk factors. The model can be further improved trough external validation and/or adding additional potential predictors.

  15. Developing risk prediction models for kidney injury and assessing incremental value for novel biomarkers.

    Science.gov (United States)

    Kerr, Kathleen F; Meisner, Allison; Thiessen-Philbrook, Heather; Coca, Steven G; Parikh, Chirag R

    2014-08-07

    The field of nephrology is actively involved in developing biomarkers and improving models for predicting patients' risks of AKI and CKD and their outcomes. However, some important aspects of evaluating biomarkers and risk models are not widely appreciated, and statistical methods are still evolving. This review describes some of the most important statistical concepts for this area of research and identifies common pitfalls. Particular attention is paid to metrics proposed within the last 5 years for quantifying the incremental predictive value of a new biomarker. Copyright © 2014 by the American Society of Nephrology.

  16. Predictive information processing is a fundamental learning mechanism present in early development: evidence from infants.

    Science.gov (United States)

    Trainor, Laurel J

    2012-02-01

    Evidence is presented that predictive coding is fundamental to brain function and present in early infancy. Indeed, mismatch responses to unexpected auditory stimuli are among the earliest robust cortical event-related potential responses, and have been measured in young infants in response to many types of deviation, including in pitch, timing, and melodic pattern. Furthermore, mismatch responses change quickly with specific experience, suggesting that predictive coding reflects a powerful, early-developing learning mechanism. Copyright © 2011 Elsevier B.V. All rights reserved.

  17. Reliable B cell epitope predictions: impacts of method development and improved benchmarking

    DEFF Research Database (Denmark)

    Kringelum, Jens Vindahl; Lundegaard, Claus; Lund, Ole

    2012-01-01

    biomedical applications such as; rational vaccine design, development of disease diagnostics and immunotherapeutics. However, experimental mapping of epitopes is resource intensive making in silico methods an appealing complementary approach. To date, the reported performance of methods for in silico mapping...... evaluation data set improved from 0.712 to 0.727. Our results thus demonstrate that given proper benchmark definitions, B-cell epitope prediction methods achieve highly significant predictive performances suggesting these tools to be a powerful asset in rational epitope discovery. The updated version...

  18. Development of Simple Drying Model for Performance Prediction of Solar Dryer: Theoretical Analysis

    DEFF Research Database (Denmark)

    Singh, Shobhana; Kumar, Subodh

    2012-01-01

    An analytical moisture diffusion model which considers the influence of external resistance to mass transfer is developed to predict thermal performance of dryer system. The moisture diffusion coefficient, Deff that is necessary to evaluate the prediction model has been determined in terms...... of experimental drying parameters. A laboratory model of mixed-mode solar dryer system is tested with cylindrical potato samples of thickness 5 and 18 mm under simulated indoor conditions. The potato samples were dried at a constant absorbed thermal energy of 750 W/m2 and air mass flow rate of 0.011 kg...

  19. Development of ANN Model for Wind Speed Prediction as a Support for Early Warning System

    Directory of Open Access Journals (Sweden)

    Ivan Marović

    2017-01-01

    Full Text Available The impact of natural disasters increases every year with more casualties and damage to property and the environment. Therefore, it is important to prevent consequences by implementation of the early warning system (EWS in order to announce the possibility of the harmful phenomena occurrence. In this paper, focus is placed on the implementation of the EWS on the micro location in order to announce possible harmful phenomena occurrence caused by wind. In order to predict such phenomena (wind speed, an artificial neural network (ANN prediction model is developed. The model is developed on the basis of the input data obtained by local meteorological station on the University of Rijeka campus area in the Republic of Croatia. The prediction model is validated and evaluated by visual and common calculation approaches, after which it was found that it is possible to perform very good wind speed prediction for time steps Δt=1 h, Δt=3 h, and Δt=8 h. The developed model is implemented in the EWS as a decision support for improvement of the existing “procedure plan in a case of the emergency caused by stormy wind or hurricane, snow and occurrence of the ice on the University of Rijeka campus.”

  20. Developing a stochastic traffic volume prediction model for public-private partnership projects

    Science.gov (United States)

    Phong, Nguyen Thanh; Likhitruangsilp, Veerasak; Onishi, Masamitsu

    2017-11-01

    Transportation projects require an enormous amount of capital investment resulting from their tremendous size, complexity, and risk. Due to the limitation of public finances, the private sector is invited to participate in transportation project development. The private sector can entirely or partially invest in transportation projects in the form of Public-Private Partnership (PPP) scheme, which has been an attractive option for several developing countries, including Vietnam. There are many factors affecting the success of PPP projects. The accurate prediction of traffic volume is considered one of the key success factors of PPP transportation projects. However, only few research works investigated how to predict traffic volume over a long period of time. Moreover, conventional traffic volume forecasting methods are usually based on deterministic models which predict a single value of traffic volume but do not consider risk and uncertainty. This knowledge gap makes it difficult for concessionaires to estimate PPP transportation project revenues accurately. The objective of this paper is to develop a probabilistic traffic volume prediction model. First, traffic volumes were estimated following the Geometric Brownian Motion (GBM) process. Monte Carlo technique is then applied to simulate different scenarios. The results show that this stochastic approach can systematically analyze variations in the traffic volume and yield more reliable estimates for PPP projects.

  1. Predicting Nitrogen Transport From Individual Sewage Disposal Systems for a Proposed Development in Adams County, Colorado

    Science.gov (United States)

    Heatwole, K. K.; McCray, J.; Lowe, K.

    2005-12-01

    Individual sewage disposal systems (ISDS) have demonstrated the capability to be an effective method of treatment for domestic wastewater. They also are advantageous from a water resources standpoint because there is little water leaving the local hydrologic system. However, if unfavorable settings exist, ISDS can have a detrimental effect on local water-quality. This presentation will focus on assessing the potential impacts of a large housing development to area water quality. The residential development plans to utilize ISDS to accommodate all domestic wastewater generated within the development. The area of interest is located just west of Brighton, Colorado, on the northwestern margin of the Denver Basin. Efforts of this research will focus on impacts of ISDS to local groundwater and surface water systems. The Arapahoe Aquifer, which exists at relatively shallow depths in the area of proposed development, is suspected to be vulnerable to contamination from ISDS. Additionally, the local water quality of the Arapahoe Aquifer was not well known at the start of the study. As a result, nitrate was selected as a fo-cus water quality parameter because it is easily produced through nitrification of septic tank effluent and because of the previous agricultural practices that could be another potential source of nitrate. Several different predictive tools were used to attempt to predict the potential impacts of ISDS to water quality in the Arapahoe Aquifer. The objectives of these tools were to 1) assess the vulnerability of the Arapahoe Aquifer to ni-trate contamination, 2) predict the nitrate load to the aquifer, and 3) determine the sensitivity of different parameter inputs and the overall prediction uncertainty. These predictive tools began with very simple mass-loading calcula-tions and progressed to more complex, vadose-zone numerical contaminant transport modeling.

  2. Predictive factors for the development of diabetes in women with previous gestational diabetes mellitus

    DEFF Research Database (Denmark)

    Damm, P.; Kühl, C.; Bertelsen, Aksel

    1992-01-01

    OBJECTIVES: The purpose of this study was to determine the incidence of diabetes in women with previous dietary-treated gestational diabetes mellitus and to identify predictive factors for development of diabetes. STUDY DESIGN: Two to 11 years post partum, glucose tolerance was investigated in 241...... women with previous dietary-treated gestational diabetes mellitus and 57 women without previous gestational diabetes mellitus (control group). RESULTS: Diabetes developed in 42 (17.4%) women with previous gestational diabetes mellitus (3.7% insulin-dependent diabetes mellitus and 13.7% non...... of previous patients with gestational diabetes mellitus in whom plasma insulin was measured during an oral glucose tolerance test in late pregnancy a low insulin response at diagnosis was found to be an independent predictive factor for diabetes development. CONCLUSIONS: Women with previous dietary...

  3. Prospects for development of unified global flood observation and prediction systems (Invited)

    Science.gov (United States)

    Lettenmaier, D. P.

    2013-12-01

    Floods are among the most damaging of natural hazards, with global flood losses in 2011 alone estimated to have exceeded $100B. Historically, flood economic damages have been highest in the developed world (due in part to encroachment on historical flood plains), but loss of life, and human impacts have been greatest in the developing world. However, as the 2011 Thailand floods show, industrializing countries, many of which do not have well developed flood protection systems, are increasingly vulnerable to economic damages as they become more industrialized. At present, unified global flood observation and prediction systems are in their infancy; notwithstanding that global weather forecasting is a mature field. The summary for this session identifies two evolving capabilities that hold promise for development of more sophisticated global flood forecast systems: global hydrologic models and satellite remote sensing (primarily of precipitation, but also of flood inundation). To this I would add the increasing sophistication and accuracy of global precipitation analysis (and forecast) fields from numerical weather prediction models. In this brief overview, I will review progress in all three areas, and especially the evolution of hydrologic data assimilation which integrates modeling and data sources. I will also comment on inter-governmental and inter-agency cooperation, and related issues that have impeded progress in the development and utilization of global flood observation and prediction systems.

  4. Development of computer code for determining prediction parameters of radionuclide migration in soil layer

    International Nuclear Information System (INIS)

    Ogawa, Hiromichi; Ohnuki, Toshihiko

    1986-07-01

    A computer code (MIGSTEM-FIT) has been developed to determine the prediction parameters, retardation factor, water flow velocity, dispersion coefficient, etc., of radionuclide migration in soil layer from the concentration distribution of radionuclide in soil layer or in effluent. In this code, the solution of the predicting equation for radionuclide migration is compared with the concentration distribution measured, and the most adequate values of parameter can be determined by the flexible tolerance method. The validity of finite differential method, which was one of the method to solve the predicting equation, was confirmed by comparison with the analytical solution, and also the validity of fitting method was confirmed by the fitting of the concentration distribution calculated from known parameters. From the examination about the error, it was found that the error of the parameter obtained by using this code was smaller than that of the concentration distribution measured. (author)

  5. Development and validation of a predictive technology for creep closure of underground rooms in salt

    International Nuclear Information System (INIS)

    Munson, D.E.; DeVries, K.L.

    1991-07-01

    Because of the concern for public health and safety, when compared to normal engineering practice, radioactive waste repositories have quite unusual requirements governing performance assessment. In part, performance assessment requires prediction of time-dependent or creep response of the repository hundreds to thousands of years into the future. In salt, one specific need is to predict, with confidence, the time at which the repository rooms creep closed sufficiently to encapsulate the waste and seal the repository. Thus, a major task of the Waste Isolation Pilot Plant (WIPP) Program is to develop and validate this predictive technology to calculate creep of repository rooms in the bedded salt deposits of Southeastern New Mexico. 19 refs., 15 figs., 2 tabs

  6. Development of wavelet-ANN models to predict water quality parameters in Hilo Bay, Pacific Ocean.

    Science.gov (United States)

    Alizadeh, Mohamad Javad; Kavianpour, Mohamad Reza

    2015-09-15

    The main objective of this study is to apply artificial neural network (ANN) and wavelet-neural network (WNN) models for predicting a variety of ocean water quality parameters. In this regard, several water quality parameters in Hilo Bay, Pacific Ocean, are taken under consideration. Different combinations of water quality parameters are applied as input variables to predict daily values of salinity, temperature and DO as well as hourly values of DO. The results demonstrate that the WNN models are superior to the ANN models. Also, the hourly models developed for DO prediction outperform the daily models of DO. For the daily models, the most accurate model has R equal to 0.96, while for the hourly model it reaches up to 0.98. Overall, the results show the ability of the model to monitor the ocean parameters, in condition with missing data, or when regular measurement and monitoring are impossible. Copyright © 2015 Elsevier Ltd. All rights reserved.

  7. Development of a Diagnostic Prediction Model for Conductive Conditions in Neonates Using Wideband Acoustic Immittance.

    Science.gov (United States)

    Myers, Joshua; Kei, Joseph; Aithal, Sreedevi; Aithal, Venkatesh; Driscoll, Carlie; Khan, Asaduzzaman; Manuel, Alehandrea; Joseph, Anjali; Malicka, Alicja N

    2018-03-03

    Wideband acoustic immittance (WAI) is an emerging test of middle-ear function with potential applications for neonates in screening and diagnostic settings. Previous large-scale diagnostic accuracy studies have assessed the performance of WAI against evoked otoacoustic emissions, but further research is needed using a more stringent reference standard. Research into suitable quantitative techniques to analyze the large volume of data produced by WAI is still in its infancy. Prediction models are an attractive method for analysis of multivariate data because they provide individualized probabilities that a subject has the condition. A clinically useful prediction model must accurately discriminate between normal and abnormal cases and be well calibrated (i.e., give accurate predictions). The present study aimed to develop a diagnostic prediction model for detecting conductive conditions in neonates using WAI. A stringent reference standard was created by combining results of high-frequency tympanometry and distortion product otoacoustic emissions. High-frequency tympanometry and distortion product otoacoustic emissions were performed on both ears of 629 healthy neonates to assess outer- and middle-ear function. Wideband absorbance and complex admittance (magnitude and phase) were measured at frequencies ranging from 226 to 8000 Hz in each neonate at ambient pressure using a click stimulus. Results from one ear of each neonate were used to develop the prediction model. WAI results were used as logistic regression predictors to model the probability that an ear had outer/middle-ear dysfunction. WAI variables were modeled both linearly and nonlinearly, to test whether allowing nonlinearity improved model fit and thus calibration. The best-fitting model was validated using the opposite ears and with bootstrap resampling. The best-fitting model used absorbance at 1000 and 2000 Hz, admittance magnitude at 1000 and 2000 Hz, and admittance phase at 1000 and 4000 Hz modeled

  8. Anatomical Cystocele Recurrence: Development and Internal Validation of a Prediction Model.

    Science.gov (United States)

    Vergeldt, Tineke F M; van Kuijk, Sander M J; Notten, Kim J B; Kluivers, Kirsten B; Weemhoff, Mirjam

    2016-02-01

    To develop a prediction model that estimates the risk of anatomical cystocele recurrence after surgery. The databases of two multicenter prospective cohort studies were combined, and we performed a retrospective secondary analysis of these data. Women undergoing an anterior colporrhaphy without mesh materials and without previous pelvic organ prolapse (POP) surgery filled in a questionnaire, underwent translabial three-dimensional ultrasonography, and underwent staging of POP preoperatively and postoperatively. We developed a prediction model using multivariable logistic regression and internally validated it using standard bootstrapping techniques. The performance of the prediction model was assessed by computing indices of overall performance, discriminative ability, calibration, and its clinical utility by computing test characteristics. Of 287 included women, 149 (51.9%) had anatomical cystocele recurrence. Factors included in the prediction model were assisted delivery, preoperative cystocele stage, number of compartments involved, major levator ani muscle defects, and levator hiatal area during Valsalva. Potential predictors that were excluded after backward elimination because of high P values were age, body mass index, number of vaginal deliveries, and family history of POP. The shrinkage factor resulting from the bootstrap procedure was 0.91. After correction for optimism, Nagelkerke's R and the Brier score were 0.15 and 0.22, respectively. This indicates satisfactory model fit. The area under the receiver operating characteristic curve of the prediction model was 71.6% (95% confidence interval 65.7-77.5). After correction for optimism, the area under the receiver operating characteristic curve was 69.7%. This prediction model, including history of assisted delivery, preoperative stage, number of compartments, levator defects, and levator hiatus, estimates the risk of anatomical cystocele recurrence.

  9. Profiles of verbal working memory growth predict speech and language development in children with cochlear implants.

    Science.gov (United States)

    Kronenberger, William G; Pisoni, David B; Harris, Michael S; Hoen, Helena M; Xu, Huiping; Miyamoto, Richard T

    2013-06-01

    Verbal short-term memory (STM) and working memory (WM) skills predict speech and language outcomes in children with cochlear implants (CIs) even after conventional demographic, device, and medical factors are taken into account. However, prior research has focused on single end point outcomes as opposed to the longitudinal process of development of verbal STM/WM and speech-language skills. In this study, the authors investigated relations between profiles of verbal STM/WM development and speech-language development over time. Profiles of verbal STM/WM development were identified through the use of group-based trajectory analysis of repeated digit span measures over at least a 2-year time period in a sample of 66 children (ages 6-16 years) with CIs. Subjects also completed repeated assessments of speech and language skills during the same time period. Clusters representing different patterns of development of verbal STM (digit span forward scores) were related to the growth rate of vocabulary and language comprehension skills over time. Clusters representing different patterns of development of verbal WM (digit span backward scores) were related to the growth rate of vocabulary and spoken word recognition skills over time. Different patterns of development of verbal STM/WM capacity predict the dynamic process of development of speech and language skills in this clinical population.

  10. Using NDVI and guided sampling to develop yield prediction maps of processing tomato crop

    Energy Technology Data Exchange (ETDEWEB)

    Fortes, A.; Henar Prieto, M. del; García-Martín, A.; Córdoba, A.; Martínez, L.; Campillo, C.

    2015-07-01

    The use of yield prediction maps is an important tool for the delineation of within-field management zones. Vegetation indices based on crop reflectance are of potential use in the attainment of this objective. There are different types of vegetation indices based on crop reflectance, the most commonly used of which is the NDVI (normalized difference vegetation index). NDVI values are reported to have good correlation with several vegetation parameters including the ability to predict yield. The field research was conducted in two commercial farms of processing tomato crop, Cantillana and Enviciados. An NDVI prediction map developed through ordinary kriging technique was used for guided sampling of processing tomato yield. Yield was studied and related with NDVI, and finally a prediction map of crop yield for the entire plot was generated using two geostatistical methodologies (ordinary and regression kriging). Finally, a comparison was made between the yield obtained at validation points and the yield values according to the prediction maps. The most precise yield maps were obtained with the regression kriging methodology with RRMSE values of 14% and 17% in Cantillana and Enviciados, respectively, using the NDVI as predictor. The coefficient of correlation between NDVI and yield was correlated in the point samples taken in the two locations, with values of 0.71 and 0.67 in Cantillana and Enviciados, respectively. The results suggest that the use of a massive sampling parameter such as NDVI is a good indicator of the distribution of within-field yield variation. (Author)

  11. Development of METAL-ACTIVE SITE and ZINCCLUSTER tool to predict active site pockets.

    Science.gov (United States)

    Ajitha, M; Sundar, K; Arul Mugilan, S; Arumugam, S

    2018-03-01

    The advent of whole genome sequencing leads to increasing number of proteins with known amino acid sequences. Despite many efforts, the number of proteins with resolved three dimensional structures is still low. One of the challenging tasks the structural biologists face is the prediction of the interaction of metal ion with any protein for which the structure is unknown. Based on the information available in Protein Data Bank, a site (METALACTIVE INTERACTION) has been generated which displays information for significant high preferential and low-preferential combination of endogenous ligands for 49 metal ions. User can also gain information about the residues present in the first and second coordination sphere as it plays a major role in maintaining the structure and function of metalloproteins in biological system. In this paper, a novel computational tool (ZINCCLUSTER) is developed, which can predict the zinc metal binding sites of proteins even if only the primary sequence is known. The purpose of this tool is to predict the active site cluster of an uncharacterized protein based on its primary sequence or a 3D structure. The tool can predict amino acids interacting with a metal or vice versa. This tool is based on the occurrence of significant triplets and it is tested to have higher prediction accuracy when compared to that of other available techniques. © 2017 Wiley Periodicals, Inc.

  12. Effects of ocean initial perturbation on developing phase of ENSO in a coupled seasonal prediction model

    Science.gov (United States)

    Lee, Hyun-Chul; Kumar, Arun; Wang, Wanqiu

    2018-03-01

    Coupled prediction systems for seasonal and inter-annual variability in the tropical Pacific are initialized from ocean analyses. In ocean initial states, small scale perturbations are inevitably smoothed or distorted by the observational limits and data assimilation procedures, which tends to induce potential ocean initial errors for the El Nino-Southern Oscillation (ENSO) prediction. Here, the evolution and effects of ocean initial errors from the small scale perturbation on the developing phase of ENSO are investigated by an ensemble of coupled model predictions. Results show that the ocean initial errors at the thermocline in the western tropical Pacific grow rapidly to project on the first mode of equatorial Kelvin wave and propagate to the east along the thermocline. In boreal spring when the surface buoyancy flux weakens in the eastern tropical Pacific, the subsurface errors influence sea surface temperature variability and would account for the seasonal dependence of prediction skill in the NINO3 region. It is concluded that the ENSO prediction in the eastern tropical Pacific after boreal spring can be improved by increasing the observational accuracy of subsurface ocean initial states in the western tropical Pacific.

  13. Development and validation of a predictive equation for lean body mass in children and adolescents.

    Science.gov (United States)

    Foster, Bethany J; Platt, Robert W; Zemel, Babette S

    2012-05-01

    Lean body mass (LBM) is not easy to measure directly in the field or clinical setting. Equations to predict LBM from simple anthropometric measures, which account for the differing contributions of fat and lean to body weight at different ages and levels of adiposity, would be useful to both human biologists and clinicians. To develop and validate equations to predict LBM in children and adolescents across the entire range of the adiposity spectrum. Dual energy X-ray absorptiometry was used to measure LBM in 836 healthy children (437 females) and linear regression was used to develop sex-specific equations to estimate LBM from height, weight, age, body mass index (BMI) for age z-score and population ancestry. Equations were validated using bootstrapping methods and in a local independent sample of 332 children and in national data collected by NHANES. The mean difference between measured and predicted LBM was - 0.12% (95% limits of agreement - 11.3% to 8.5%) for males and - 0.14% ( - 11.9% to 10.9%) for females. Equations performed equally well across the entire adiposity spectrum, as estimated by BMI z-score. Validation indicated no over-fitting. LBM was predicted within 5% of measured LBM in the validation sample. The equations estimate LBM accurately from simple anthropometric measures.

  14. Development of a Skin Burn Predictive Model adapted to Laser Irradiation

    Science.gov (United States)

    Sonneck-Museux, N.; Scheer, E.; Perez, L.; Agay, D.; Autrique, L.

    2016-12-01

    Laser technology is increasingly used, and it is crucial for both safety and medical reasons that the impact of laser irradiation on human skin can be accurately predicted. This study is mainly focused on laser-skin interactions and potential lesions (burns). A mathematical model dedicated to heat transfers in skin exposed to infrared laser radiations has been developed. The model is validated by studying heat transfers in human skin and simultaneously performing experimentations an animal model (pig). For all experimental tests, pig's skin surface temperature is recorded. Three laser wavelengths have been tested: 808 nm, 1940 nm and 10 600 nm. The first is a diode laser producing radiation absorbed deep within the skin. The second wavelength has a more superficial effect. For the third wavelength, skin is an opaque material. The validity of the developed models is verified by comparison with experimental results (in vivo tests) and the results of previous studies reported in the literature. The comparison shows that the models accurately predict the burn degree caused by laser radiation over a wide range of conditions. The results show that the important parameter for burn prediction is the extinction coefficient. For the 1940 nm wavelength especially, significant differences between modeling results and literature have been observed, mainly due to this coefficient's value. This new model can be used as a predictive tool in order to estimate the amount of injury induced by several types (couple power-time) of laser aggressions on the arm, the face and on the palm of the hand.

  15. Sensory hypersensitivity predicts enhanced attention capture by faces in the early development of ASD

    Directory of Open Access Journals (Sweden)

    E.J.H Jones

    2018-01-01

    Full Text Available Sensory sensitivity is prevalent among young children with ASD, but its relation to social communication impairment is unclear. Recently, increased sensory hypersensitivity has been linked to greater activity of the neural salience network (Green et al., 2016. Increased neural sensitivity to stimuli, especially social stimuli, could provide greater opportunity for social learning and improved outcomes. Consistent with this framework, in Experiment 1 we found that parent report of greater sensory hypersensitivity at 2 years in toddlers with ASD (N = 27 was predictive of increased neural responsiveness to social stimuli (larger amplitude event-related potential/ERP responses to faces at P1, P400 and Nc at 4 years, and this in turn was related to parent report of increased social approach at 4 years. In Experiment 2, parent report of increased perceptual sensitivity at 6 months in infants at low and high familial risk for ASD (N = 35 predicted larger ERP P1 amplitude to faces at 18 months. Increased sensory hypersensitivity in early development thus predicted greater attention capture by faces in later development, and this related to more optimal social behavioral development. Sensory hypersensitivity may index a child's ability to benefit from supportive environments during development. Early sensory symptoms may not always be developmentally problematic for individuals with ASD. Keywords: Autism, Sensory hypersensitivity, Social attention, Salience network, Infant, EEG

  16. Development of a disease risk prediction model for downy mildew (Peronospora sparsa) in boysenberry.

    Science.gov (United States)

    Kim, Kwang Soo; Beresford, Robert M; Walter, Monika

    2014-01-01

    Downy mildew caused by Peronospora sparsa has resulted in serious production losses in boysenberry (Rubus hybrid), blackberry (Rubus fruticosus), and rose (Rosa sp.) in New Zealand, Mexico, and the United States and the United Kingdom, respectively. Development of a model to predict downy mildew risk would facilitate development and implementation of a disease warning system for efficient fungicide spray application in the crops affected by this disease. Because detailed disease observation data were not available, a two-step approach was applied to develop an empirical risk prediction model for P. sparsa. To identify the weather patterns associated with a high incidence of downy mildew berry infections (dryberry disease) and derive parameters for the empirical model, classification and regression tree (CART) analysis was performed. Then, fuzzy sets were applied to develop a simple model to predict the disease risk based on the parameters derived from the CART analysis. High-risk seasons with a boysenberry downy mildew incidence >10% coincided with months when the number of hours per day with temperature of 15 to 20°C averaged >9.8 over the month and the number of days with rainfall in the month was >38.7%. The Fuzzy Peronospora Sparsa (FPS) model, developed using fuzzy sets, defined relationships among high-risk events, temperature, and rainfall conditions. In a validation study, the FPS model provided correct identification of both seasons with high downy mildew risk for boysenberry, blackberry, and rose and low risk in seasons when no disease was observed. As a result, the FPS model had a significant degree of agreement between predicted and observed risks of downy mildew for those crops (P = 0.002).

  17. Development and verification test of integral reactor major components - Development of MCP impeller design, performance prediction code and experimental verification

    Energy Technology Data Exchange (ETDEWEB)

    Chung, Myung Kyoon; Oh, Woo Hyoung; Song, Jae Wook [Korea Advanced Institute of Science and Technology, Taejon (Korea)

    1999-03-01

    The present study is aimed at developing a computational code for design and performance prediction of an axial-flow pump. The proposed performance prediction method is tested against a model axial-flow pump streamline curvature method. The preliminary design is made by using the ideal velocity triangles at inlet and exit and the three dimensional blade shape is calculated by employing the free vortex design method. Then the detailed blading design is carried out by using experimental database of double circular arc cambered hydrofoils. To computationally determine the design incidence, deviation, blade camber, solidity and stagger angle, a number of correlation equations are developed form the experimental database and a theorical formula for the lift coefficient is adopted. A total of 8 equations are solved iteratively using an under-relaxation factor. An experimental measurement is conducted under a non-cavitating condition to obtain the off-design performance curve and also a cavitation test is carried out by reducing the suction pressure. The experimental results are very satisfactorily compared with the predictions by the streamline curvature method. 28 refs., 26 figs., 11 tabs. (Author)

  18. The North American Multi-Model Ensemble (NMME): Phase-1 Seasonal to Interannual Prediction, Phase-2 Toward Developing Intra-Seasonal Prediction

    Science.gov (United States)

    Kirtman, Ben P.; Min, Dughong; Infanti, Johnna M.; Kinter, James L., III; Paolino, Daniel A.; Zhang, Qin; vandenDool, Huug; Saha, Suranjana; Mendez, Malaquias Pena; Becker, Emily; hide

    2013-01-01

    The recent US National Academies report "Assessment of Intraseasonal to Interannual Climate Prediction and Predictability" was unequivocal in recommending the need for the development of a North American Multi-Model Ensemble (NMME) operational predictive capability. Indeed, this effort is required to meet the specific tailored regional prediction and decision support needs of a large community of climate information users. The multi-model ensemble approach has proven extremely effective at quantifying prediction uncertainty due to uncertainty in model formulation, and has proven to produce better prediction quality (on average) then any single model ensemble. This multi-model approach is the basis for several international collaborative prediction research efforts, an operational European system and there are numerous examples of how this multi-model ensemble approach yields superior forecasts compared to any single model. Based on two NOAA Climate Test Bed (CTB) NMME workshops (February 18, and April 8, 2011) a collaborative and coordinated implementation strategy for a NMME prediction system has been developed and is currently delivering real-time seasonal-to-interannual predictions on the NOAA Climate Prediction Center (CPC) operational schedule. The hindcast and real-time prediction data is readily available (e.g., http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/) and in graphical format from CPC (http://origin.cpc.ncep.noaa.gov/products/people/wd51yf/NMME/index.html). Moreover, the NMME forecast are already currently being used as guidance for operational forecasters. This paper describes the new NMME effort, presents an overview of the multi-model forecast quality, and the complementary skill associated with individual models.

  19. New Guideline for the Reporting of Studies Developing, Validating, or Updating a Multivariable Clinical Prediction Model : The TRIPOD Statement

    NARCIS (Netherlands)

    Moons, Karel G. M.; Altman, Douglas G.; Reitsma, Johannes B.; Collins, Gary S.

    Prediction models are developed to aid health care providers in estimating the probability that a specific outcome or disease is present (diagnostic prediction models) or will occur in the future (prognostic prediction models), to inform their decision making. Prognostic models here also include

  20. Using multicriteria decision analysis during drug development to predict reimbursement decisions.

    Science.gov (United States)

    Williams, Paul; Mauskopf, Josephine; Lebiecki, Jake; Kilburg, Anne

    2014-01-01

    Pharmaceutical companies design clinical development programs to generate the data that they believe will support reimbursement for the experimental compound. The objective of the study was to present a process for using multicriteria decision analysis (MCDA) by a pharmaceutical company to estimate the probability of a positive recommendation for reimbursement for a new drug given drug and environmental attributes. The MCDA process included 1) selection of decisions makers who were representative of those making reimbursement decisions in a specific country; 2) two pre-workshop questionnaires to identify the most important attributes and their relative importance for a positive recommendation for a new drug; 3) a 1-day workshop during which participants undertook three tasks: i) they agreed on a final list of decision attributes and their importance weights, ii) they developed level descriptions for these attributes and mapped each attribute level to a value function, and iii) they developed profiles for hypothetical products 'just likely to be reimbursed'; and 4) use of the data from the workshop to develop a prediction algorithm based on a logistic regression analysis. The MCDA process is illustrated using case studies for three countries, the United Kingdom, Germany, and Spain. The extent to which the prediction algorithms for each country captured the decision processes for the workshop participants in our case studies was tested using a post-meeting questionnaire that asked the participants to make recommendations for a set of hypothetical products. The data collected in the case study workshops resulted in a prediction algorithm: 1) for the United Kingdom, the probability of a positive recommendation for different ranges of cost-effectiveness ratios; 2) for Spain, the probability of a positive recommendation at the national and regional levels; and 3) for Germany, the probability of a determination of clinical benefit. The results from the post

  1. Serial-order short-term memory predicts vocabulary development: evidence from a longitudinal study.

    Science.gov (United States)

    Leclercq, Anne-Lise; Majerus, Steve

    2010-03-01

    Serial-order short-term memory (STM), as opposed to item STM, has been shown to be very consistently associated with lexical learning abilities in cross-sectional study designs. This study investigated longitudinal predictions between serial-order STM and vocabulary development. Tasks maximizing the temporary retention of either serial-order or item information were administered to kindergarten children aged 4 and 5. At age 4, age 5, and from age 4 to age 5, serial-order STM capacities, but not item STM capacities, were specifically associated with vocabulary development. Moreover, the increase of serial-order STM capacity from age 4 to age 5 predicted the increase of vocabulary knowledge over the same time period. These results support a theoretical position that assumes an important role for serial-order STM capacities in vocabulary acquisition.

  2. Development and validation of the 3-D CFD model for CANDU-6 moderator temperature predictions

    International Nuclear Information System (INIS)

    Yoon, Churl; Rhee, Bo Wook; Min, Byung Joo

    2003-03-01

    A computational fluid dynamics model for predicting the moderator circulation inside the CANada Deuterium Uranium (CANDU) reactor vessel has been developed to estimate the local subcooling of the moderator in the vicinity of the Calandria tubes. The buoyancy effect induced by internal heating is accounted for by Boussinesq approximation. The standard κ-ε turbulence model associated with logarithmic wall treatment is applied to predict the turbulent jet flows from the inlet nozzles. The matrix of the Calandria tubes in the core region is simplified to porous media, in which an-isotropic hydraulic impedance is modeled using an empirical correlation of the frictional pressure loss. The governing equations are solved by CFX-4.4, a commercial CFD code developed by AEA technology. The CFD model has been successfully verified and validated against experimental data obtained in the Stern Laboratories Inc. (SLI) in Hamilton, Ontario

  3. Contribution to Accident Prediction Models Development for Rural Two-Lane Roads in Serbia

    Directory of Open Access Journals (Sweden)

    Draženko Glavić

    2016-08-01

    Full Text Available Over the last three decades numerous research efforts have been conducted worldwide to determine the relationship between traffic accidents and traffic and road characteristics. So far, the mentioned studies have not been carried out in Serbia and in the region. This paper represents one of the first attempts to develop accident prediction models in Serbia. The paper provides a comprehensive literature review, describes procedures for collection and analysis of the traffic accident data, as well as the methodology used to develop the accident prediction models. The paper presents models obtained by both univariate and multivariate regression analyses. The obtained results are compared to the results of other studies and comparisons are discussed. Finally, the paper presents conclusions and important points for future research. The results of this research can find theoretical as well as practical application.

  4. Development and Analysis of Group Contribution Plus Models for Property Prediction of Organic Chemical Systems

    DEFF Research Database (Denmark)

    Mustaffa, Azizul Azri

    for the GIPs are then used in the UNIFAC model to calculate activity coefficients. This approach can increase the application range of any “host” UNIFAC model by providing a reliable predictive model towards fast and efficient product development. This PhD project is focused on the analysis and further......Prediction of properties is important in chemical process-product design. Reliable property models are needed for increasingly complex and wider range of chemicals. Group-contribution methods provide useful tool but there is a need to validate them and improve their accuracy when complex chemicals...... are present in the mixtures. In accordance with that, a combined group-contribution and atom connectivity approach that is able to extend the application range of property models has been developed for mixture properties. This so-called Group-ContributionPlus (GCPlus) approach is a hybrid model which combines...

  5. Development and validation of a dynamic outcome prediction model for paracetamol-induced acute liver failure

    DEFF Research Database (Denmark)

    Bernal, William; Wang, Yanzhong; Maggs, James

    2016-01-01

    : The models developed here show very good discrimination and calibration, confirmed in independent datasets, and suggest that many patients undergoing transplantation based on existing criteria might have survived with medical management alone. The role and indications for emergency liver transplantation......BACKGROUND: Early, accurate prediction of survival is central to management of patients with paracetamol-induced acute liver failure to identify those needing emergency liver transplantation. Current prognostic tools are confounded by recent improvements in outcome independent of emergency liver...... transplantation, and constrained by static binary outcome prediction. We aimed to develop a simple prognostic tool to reflect current outcomes and generate a dynamic updated estimation of risk of death. METHODS: Patients with paracetamol-induced acute liver failure managed at intensive care units in the UK...

  6. Challenges for the development of a biotic ligand model predicting copper toxicity in estuaries and seas

    OpenAIRE

    de Polo, A; Scrimshaw, MD

    2012-01-01

    This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2011 SETAC. An effort is ongoing to develop a biotic ligand model (BLM) that predicts copper (Cu) toxicity in estuarine and marine environments. At present, the BLM accounts for the effects of water chemistry on Cu speciation, but it does not consider the influence of water chemistry on the physiology of the organisms. We discuss how chemistry affects Cu toxicity not only by ...

  7. Development of a Breast Cancer Risk Prediction Model for Women in Nigeria.

    Science.gov (United States)

    Wang, Shengfeng; Ogundiran, Temidayo O; Ademola, Adeyinka; Oluwasola, Olayiwola A; Adeoye, Adewunmi O; Sofoluwe, Adenike; Morhason-Bello, Imran; Odedina, Stella O; Agwai, Imaria; Adebamowo, Clement; Obajimi, Millicent; Ojengbede, Oladosu; Olopade, Olufunmilayo I; Huo, Dezheng

    2018-04-20

    Risk prediction models have been widely used to identify women at higher risk of breast cancer. We aim to develop a model for absolute breast cancer risk prediction for Nigerian women. A total of 1,811 breast cancer cases and 2,225 controls from the Nigerian Breast Cancer Study (NBCS, 1998~2015) were included. Subjects were randomly divided into the training and validation sets. Incorporating local incidence rates, multivariable logistic regressions were used to develop the model. The NBCS model included age, age at menarche, parity, duration of breast feeding, family history of breast cancer, height, body mass index, benign breast diseases and alcohol consumption. The model developed in the training set performed well in the validation set. The discriminating accuracy of the NBCS model (area under ROC curve [AUC]=0.703, 95% confidence interval [CI]: 0.687-0.719) was better than the Black Women's Health Study (BWHS) model (AUC=0.605, 95% CI: 0.586-0.624), Gail model for White population (AUC=0.551, 95% CI: 0.531-0.571), and Gail model for Black population (AUC=0.545, 95% CI: 0.525-0.565). Compared to the BWHS, two Gail models, the net reclassification improvement of the NBCS model were 8.26%, 13.45% and 14.19%, respectively. We have developed a breast cancer risk prediction model specific to women in Nigeria, which provides a promising and indispensable tool to identify women in need of breast cancer early detection in SSA populations. Our model is the first breast cancer risk prediction model in Africa. It can be used to identify women at high-risk for breast cancer screening. Copyright ©2018, American Association for Cancer Research.

  8. Discovery of serum biomarkers predicting development of a subsequent depressive episode in social anxiety disorder.

    Science.gov (United States)

    Gottschalk, M G; Cooper, J D; Chan, M K; Bot, M; Penninx, B W J H; Bahn, S

    2015-08-01

    Although social anxiety disorder (SAD) is strongly associated with the subsequent development of a depressive disorder (major depressive disorder or dysthymia), no underlying biological risk factors are known. We aimed to identify biomarkers which predict depressive episodes in SAD patients over a 2-year follow-up period. One hundred sixty-five multiplexed immunoassay analytes were investigated in blood serum of 143 SAD patients without co-morbid depressive disorders, recruited within the Netherlands Study of Depression and Anxiety (NESDA). Predictive performance of identified biomarkers, clinical variables and self-report inventories was assessed using receiver operating characteristics curves (ROC) and represented by the area under the ROC curve (AUC). Stepwise logistic regression resulted in the selection of four serum analytes (AXL receptor tyrosine kinase, vascular cell adhesion molecule 1, vitronectin, collagen IV) and four additional variables (Inventory of Depressive Symptomatology, Beck Anxiety Inventory somatic subscale, depressive disorder lifetime diagnosis, BMI) as optimal set of patient parameters. When combined, an AUC of 0.86 was achieved for the identification of SAD individuals who later developed a depressive disorder. Throughout our analyses, biomarkers yielded superior discriminative performance compared to clinical variables and self-report inventories alone. We report the discovery of a serum marker panel with good predictive performance to identify SAD individuals prone to develop subsequent depressive episodes in a naturalistic cohort design. Furthermore, we emphasise the importance to combine biological markers, clinical variables and self-report inventories for disease course predictions in psychiatry. Following replication in independent cohorts, validated biomarkers could help to identify SAD patients at risk of developing a depressive disorder, thus facilitating early intervention. Copyright © 2015 Elsevier Inc. All rights reserved.

  9. Development and validation of a prediction model for loss of physical function in elderly hemodialysis patients.

    Science.gov (United States)

    Fukuma, Shingo; Shimizu, Sayaka; Shintani, Ayumi; Kamitani, Tsukasa; Akizawa, Tadao; Fukuhara, Shunichi

    2017-09-05

    Among aging hemodialysis patients, loss of physical function has become a major issue. We developed and validated a model of predicting loss of physical function among elderly hemodialysis patients. We conducted a cohort study involving maintenance hemodialysis patients  ≥65 years of age from the Dialysis Outcomes and Practice Pattern Study in Japan. The derivation cohort included 593 early phase (1996-2004) patients and the temporal validation cohort included 447 late-phase (2005-12) patients. The main outcome was the incidence of loss of physical function, defined as the 12-item Short Form Health Survey physical function score decreasing to 0 within a year. Using backward stepwise logistic regression by Akaike's Information Criteria, six predictors (age, gender, dementia, mental health, moderate activity and ascending stairs) were selected for the final model. Points were assigned based on the regression coefficients and the total score was calculated by summing the points for each predictor. In total, 65 (11.0%) and 53 (11.9%) hemodialysis patients lost their physical function within 1 year in the derivation and validation cohorts, respectively. This model has good predictive performance quantified by both discrimination and calibration. The proportion of the loss of physical function increased sequentially through low-, middle-, and high-score categories based on the model (2.5%, 11.7% and 22.3% in the validation cohort, respectively). The loss of physical function was strongly associated with 1-year mortality [adjusted odds ratio 2.48 (95% confidence interval 1.26-4.91)]. We developed and validated a risk prediction model with good predictive performance for loss of physical function in elderly hemodialysis patients. Our simple prediction model may help physicians and patients make more informed decisions for healthy longevity. © The Author 2017. Published by Oxford University Press on behalf of ERA-EDTA.

  10. Development of Models to Predict the Redox State of Nuclear Waste Containment Glass

    Energy Technology Data Exchange (ETDEWEB)

    Pinet, O.; Guirat, R.; Advocat, T. [Commissariat a l' Energie Atomique (CEA), Departement de Traitement et de Conditionnement des Dechets, Marcoule, BP 71171, 30207 Bagnols-sur-Ceze Cedex (France); Phalippou, J. [Universite de Montpellier II, Laboratoire des Colloides, Verres et Nanomateriaux, 34095 Montpellier Cedex 5 (France)

    2008-07-01

    Vitrification is one of the recommended immobilization routes for nuclear waste, and is currently implemented at industrial scale in several countries, notably for high-level waste. To optimize nuclear waste vitrification, research is conducted to specify suitable glass formulations and develop more effective processes. This research is based not only on experiments at laboratory or technological scale, but also on computer models. Vitrified nuclear waste often contains several multi-valent species whose oxidation state can impact the properties of the melt and of the final glass; these include iron, cerium, ruthenium, manganese, chromium and nickel. Cea is therefore also developing models to predict the final glass redox state. Given the raw materials and production conditions, the model predicts the oxygen fugacity at equilibrium in the melt. It can also estimate the ratios between the oxidation states of the multi-valent species contained in the molten glass. The oxidizing or reductive nature of the atmosphere above the glass melt is also taken into account. Unlike the models used in the conventional glass industry based on empirical methods with a limited range of application, the models proposed are based on the thermodynamic properties of the redox species contained in the waste vitrification feed stream. The thermodynamic data on which the model is based concern the relationship between the glass redox state and the oxygen fugacity in the molten glass. The model predictions were compared with oxygen fugacity measurements for some fifty glasses. The experiments carried out at laboratory and industrial scale with a cold crucible melter. The oxygen fugacity of the glass samples was measured by electrochemical methods and compared with the predicted value. The differences between the predicted and measured oxygen fugacity values were generally less than 0.5 Log unit. (authors)

  11. Patients in palliative care-Development of a predictive model for anxiety using routine data.

    Science.gov (United States)

    Hofmann, Sonja; Hess, Stephanie; Klein, Carsten; Lindena, Gabriele; Radbruch, Lukas; Ostgathe, Christoph

    2017-01-01

    Anxiety is one of the most common psychological symptoms in patients in a palliative care situation. This study aims to develop a predictive model for anxiety using data from the standard documentation routine. Data sets of palliative care patients collected by the German quality management benchmarking system called Hospice and Palliative Care Evaluation (HOPE) from 2007 to 2011 were randomly divided into a training set containing two-thirds of the data and a test set with the remaining one-third. We dichotomized anxiety levels, proxy rated by medical staff using the validated HOPE Symptom and Problem Checklist, into two groups with no or mild anxiety versus moderate or severe anxiety. Using the training set, a multivariable logistic regression model was developed by backward stepwise selection. Predictive accuracy was evaluated by the area under the receiver operating characteristic curve (AUC) based on the test set. An analysis of 9924 data sets suggests a predictive model for anxiety in patients receiving palliative care which contains gender, age, ECOG, living situation, pain, nausea, dyspnea, loss of appetite, tiredness, need for assistance with activities of daily living, problems with organization of care, medication with sedatives/anxiolytics, antidepressants, antihypertensive drugs, laxatives, and antibiotics. It results in a fair predictive value (AUC = 0.72). Routinely collected data providing individual-, disease- and therapy-related information contain valuable information that is useful for the prediction of anxiety risks in patients receiving palliative care. These findings could thus be advantageous for providing appropriate support for patients in palliative care settings and should receive special attention in future research.

  12. Work characteristics predict the development of multi-site musculoskeletal pain.

    Science.gov (United States)

    Oakman, Jodi; de Wind, Astrid; van den Heuvel, Swenne G; van der Beek, Allard J

    2017-10-01

    Musculoskeletal pain in more than one body region is common and a barrier to sustaining employment. We aimed to examine whether work characteristics predict the development of multi-site pain (MSP), and to determine differences in work-related predictors between age groups. This study is based on 5136 employees from the Study on Transitions in Employment, Ability and Motivation (STREAM) who reported no MSP at baseline. Measures included physical, emotional, mental, and psychological job demands, social support and autonomy. Predictors of MSP were studied by logistic regression analyses. Univariate and multivariate analyses with age stratification (45-49, 50-54, 55-59, and 60-64 years) were done to explore differences between age groups. All work characteristics with the exception of autonomy were predictive of the development of MSP, with odds ratios varying from 1.21 (95% CI 1.04-1.40) for mental job demands to 1.63 (95% CI 1.43-1.86) for physical job demands. No clear pattern of age-related differences in the predictors of MSP emerged, with the exception of social support, which was predictive of MSP developing in all age groups except for the age group 60-64 years. Adverse physical and psychosocial work characteristics are associated with MSP. Organisations need to comprehensively assess work environments to ensure that all relevant workplace hazards, physical and psychosocial, are identified and then controlled for across all age groups.

  13. Prediction system of hydroponic plant growth and development using algorithm Fuzzy Mamdani method

    Science.gov (United States)

    Sudana, I. Made; Purnawirawan, Okta; Arief, Ulfa Mediaty

    2017-03-01

    Hydroponics is a method of farming without soil. One of the Hydroponic plants is Watercress (Nasturtium Officinale). The development and growth process of hydroponic Watercress was influenced by levels of nutrients, acidity and temperature. The independent variables can be used as input variable system to predict the value level of plants growth and development. The prediction system is using Fuzzy Algorithm Mamdani method. This system was built to implement the function of Fuzzy Inference System (Fuzzy Inference System/FIS) as a part of the Fuzzy Logic Toolbox (FLT) by using MATLAB R2007b. FIS is a computing system that works on the principle of fuzzy reasoning which is similar to humans' reasoning. Basically FIS consists of four units which are fuzzification unit, fuzzy logic reasoning unit, base knowledge unit and defuzzification unit. In addition to know the effect of independent variables on the plants growth and development that can be visualized with the function diagram of FIS output surface that is shaped three-dimensional, and statistical tests based on the data from the prediction system using multiple linear regression method, which includes multiple linear regression analysis, T test, F test, the coefficient of determination and donations predictor that are calculated using SPSS (Statistical Product and Service Solutions) software applications.

  14. Machine Learning Algorithms Outperform Conventional Regression Models in Predicting Development of Hepatocellular Carcinoma

    Science.gov (United States)

    Singal, Amit G.; Mukherjee, Ashin; Elmunzer, B. Joseph; Higgins, Peter DR; Lok, Anna S.; Zhu, Ji; Marrero, Jorge A; Waljee, Akbar K

    2015-01-01

    Background Predictive models for hepatocellular carcinoma (HCC) have been limited by modest accuracy and lack of validation. Machine learning algorithms offer a novel methodology, which may improve HCC risk prognostication among patients with cirrhosis. Our study's aim was to develop and compare predictive models for HCC development among cirrhotic patients, using conventional regression analysis and machine learning algorithms. Methods We enrolled 442 patients with Child A or B cirrhosis at the University of Michigan between January 2004 and September 2006 (UM cohort) and prospectively followed them until HCC development, liver transplantation, death, or study termination. Regression analysis and machine learning algorithms were used to construct predictive models for HCC development, which were tested on an independent validation cohort from the Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) Trial. Both models were also compared to the previously published HALT-C model. Discrimination was assessed using receiver operating characteristic curve analysis and diagnostic accuracy was assessed with net reclassification improvement and integrated discrimination improvement statistics. Results After a median follow-up of 3.5 years, 41 patients developed HCC. The UM regression model had a c-statistic of 0.61 (95%CI 0.56-0.67), whereas the machine learning algorithm had a c-statistic of 0.64 (95%CI 0.60–0.69) in the validation cohort. The machine learning algorithm had significantly better diagnostic accuracy as assessed by net reclassification improvement (pmachine learning algorithm (p=0.047). Conclusion Machine learning algorithms improve the accuracy of risk stratifying patients with cirrhosis and can be used to accurately identify patients at high-risk for developing HCC. PMID:24169273

  15. Development and optimization of SPECT gated blood pool cluster analysis for the prediction of CRT outcome

    Energy Technology Data Exchange (ETDEWEB)

    Lalonde, Michel, E-mail: mlalonde15@rogers.com; Wassenaar, Richard [Department of Physics, Carleton University, Ottawa, Ontario K1S 5B6 (Canada); Wells, R. Glenn; Birnie, David; Ruddy, Terrence D. [Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario K1Y 4W7 (Canada)

    2014-07-15

    Purpose: Phase analysis of single photon emission computed tomography (SPECT) radionuclide angiography (RNA) has been investigated for its potential to predict the outcome of cardiac resynchronization therapy (CRT). However, phase analysis may be limited in its potential at predicting CRT outcome as valuable information may be lost by assuming that time-activity curves (TAC) follow a simple sinusoidal shape. A new method, cluster analysis, is proposed which directly evaluates the TACs and may lead to a better understanding of dyssynchrony patterns and CRT outcome. Cluster analysis algorithms were developed and optimized to maximize their ability to predict CRT response. Methods: About 49 patients (N = 27 ischemic etiology) received a SPECT RNA scan as well as positron emission tomography (PET) perfusion and viability scans prior to undergoing CRT. A semiautomated algorithm sampled the left ventricle wall to produce 568 TACs from SPECT RNA data. The TACs were then subjected to two different cluster analysis techniques, K-means, and normal average, where several input metrics were also varied to determine the optimal settings for the prediction of CRT outcome. Each TAC was assigned to a cluster group based on the comparison criteria and global and segmental cluster size and scores were used as measures of dyssynchrony and used to predict response to CRT. A repeated random twofold cross-validation technique was used to train and validate the cluster algorithm. Receiver operating characteristic (ROC) analysis was used to calculate the area under the curve (AUC) and compare results to those obtained for SPECT RNA phase analysis and PET scar size analysis methods. Results: Using the normal average cluster analysis approach, the septal wall produced statistically significant results for predicting CRT results in the ischemic population (ROC AUC = 0.73;p < 0.05 vs. equal chance ROC AUC = 0.50) with an optimal operating point of 71% sensitivity and 60% specificity. Cluster

  16. Development and optimization of SPECT gated blood pool cluster analysis for the prediction of CRT outcome

    International Nuclear Information System (INIS)

    Lalonde, Michel; Wassenaar, Richard; Wells, R. Glenn; Birnie, David; Ruddy, Terrence D.

    2014-01-01

    Purpose: Phase analysis of single photon emission computed tomography (SPECT) radionuclide angiography (RNA) has been investigated for its potential to predict the outcome of cardiac resynchronization therapy (CRT). However, phase analysis may be limited in its potential at predicting CRT outcome as valuable information may be lost by assuming that time-activity curves (TAC) follow a simple sinusoidal shape. A new method, cluster analysis, is proposed which directly evaluates the TACs and may lead to a better understanding of dyssynchrony patterns and CRT outcome. Cluster analysis algorithms were developed and optimized to maximize their ability to predict CRT response. Methods: About 49 patients (N = 27 ischemic etiology) received a SPECT RNA scan as well as positron emission tomography (PET) perfusion and viability scans prior to undergoing CRT. A semiautomated algorithm sampled the left ventricle wall to produce 568 TACs from SPECT RNA data. The TACs were then subjected to two different cluster analysis techniques, K-means, and normal average, where several input metrics were also varied to determine the optimal settings for the prediction of CRT outcome. Each TAC was assigned to a cluster group based on the comparison criteria and global and segmental cluster size and scores were used as measures of dyssynchrony and used to predict response to CRT. A repeated random twofold cross-validation technique was used to train and validate the cluster algorithm. Receiver operating characteristic (ROC) analysis was used to calculate the area under the curve (AUC) and compare results to those obtained for SPECT RNA phase analysis and PET scar size analysis methods. Results: Using the normal average cluster analysis approach, the septal wall produced statistically significant results for predicting CRT results in the ischemic population (ROC AUC = 0.73;p < 0.05 vs. equal chance ROC AUC = 0.50) with an optimal operating point of 71% sensitivity and 60% specificity. Cluster

  17. Playing off the curve - testing quantitative predictions of skill acquisition theories in development of chess performance.

    Science.gov (United States)

    Gaschler, Robert; Progscha, Johanna; Smallbone, Kieran; Ram, Nilam; Bilalić, Merim

    2014-01-01

    Learning curves have been proposed as an adequate description of learning processes, no matter whether the processes manifest within minutes or across years. Different mechanisms underlying skill acquisition can lead to differences in the shape of learning curves. In the current study, we analyze the tournament performance data of 1383 chess players who begin competing at young age and play tournaments for at least 10 years. We analyze the performance development with the goal to test the adequacy of learning curves, and the skill acquisition theories they are based on, for describing and predicting expertise acquisition. On the one hand, we show that the skill acquisition theories implying a negative exponential learning curve do a better job in both describing early performance gains and predicting later trajectories of chess performance than those theories implying a power function learning curve. On the other hand, the learning curves of a large proportion of players show systematic qualitative deviations from the predictions of either type of skill acquisition theory. While skill acquisition theories predict larger performance gains in early years and smaller gains in later years, a substantial number of players begin to show substantial improvements with a delay of several years (and no improvement in the first years), deviations not fully accounted for by quantity of practice. The current work adds to the debate on how learning processes on a small time scale combine to large-scale changes.

  18. Development of migration prediction system (MIGSTEM) for cationic species of radionuclides through soil layers

    International Nuclear Information System (INIS)

    Ohnuki, Toshihiko; Takebe, Shinichi; Yamamoto, Tadatoshi

    1989-01-01

    The migration prediction system (MIGSTEM) has been developed for estimating the migration of cationic species of radionuclides through soil layers systematically. The MIGSTEM consists of the migration experiments, the one-dimensional fitting code (inverse analysis code) for determining retardation factor and dispersivity (migration factors) and the three-dimensional differential code (prediction code) for estimating the migration of the radionuclides. The migration experiments are carried out for obtaining the concentration profiles of the radionuclides in unsaturated and saturated soil layers. Using the inverse analysis code, the migration factors are obtained at one time by fitting the concentration profiles calculated to those observed. The prediction code can give the contours of concentration and the one-dimensional concentration profiles at selected time, as well as the changing in the concentration at a selected position with time. The validity of the MIGSTEM was obtained by the benchmark test on the prediction and inverse analysis codes. The MIGSTEM was applied to estimate the migration of Sr 2+ through the sandy soil. (author)

  19. The Development of a Machine Learning Inpatient Acute Kidney Injury Prediction Model.

    Science.gov (United States)

    Koyner, Jay L; Carey, Kyle A; Edelson, Dana P; Churpek, Matthew M

    2018-03-28

    To develop an acute kidney injury risk prediction model using electronic health record data for longitudinal use in hospitalized patients. Observational cohort study. Tertiary, urban, academic medical center from November 2008 to January 2016. All adult inpatients without pre-existing renal failure at admission, defined as first serum creatinine greater than or equal to 3.0 mg/dL, International Classification of Diseases, 9th Edition, code for chronic kidney disease stage 4 or higher or having received renal replacement therapy within 48 hours of first serum creatinine measurement. None. Demographics, vital signs, diagnostics, and interventions were used in a Gradient Boosting Machine algorithm to predict serum creatinine-based Kidney Disease Improving Global Outcomes stage 2 acute kidney injury, with 60% of the data used for derivation and 40% for validation. Area under the receiver operator characteristic curve (AUC) was calculated in the validation cohort, and subgroup analyses were conducted across admission serum creatinine, acute kidney injury severity, and hospital location. Among the 121,158 included patients, 17,482 (14.4%) developed any Kidney Disease Improving Global Outcomes acute kidney injury, with 4,251 (3.5%) developing stage 2. The AUC (95% CI) was 0.90 (0.90-0.90) for predicting stage 2 acute kidney injury within 24 hours and 0.87 (0.87-0.87) within 48 hours. The AUC was 0.96 (0.96-0.96) for receipt of renal replacement therapy (n = 821) in the next 48 hours. Accuracy was similar across hospital settings (ICU, wards, and emergency department) and admitting serum creatinine groupings. At a probability threshold of greater than or equal to 0.022, the algorithm had a sensitivity of 84% and a specificity of 85% for stage 2 acute kidney injury and predicted the development of stage 2 a median of 41 hours (interquartile range, 12-141 hr) prior to the development of stage 2 acute kidney injury. Readily available electronic health record data can be used

  20. Developing A New Predictive Dispersion Equation Based on Tidal Average (TA) Condition in Alluvial Estuaries

    Science.gov (United States)

    Anak Gisen, Jacqueline Isabella; Nijzink, Remko C.; Savenije, Hubert H. G.

    2014-05-01

    Dispersion mathematical representation of tidal mixing between sea water and fresh water in The definition of dispersion somehow remains unclear as it is not directly measurable. The role of dispersion is only meaningful if it is related to the appropriate temporal and spatial scale of mixing, which are identified as the tidal period, tidal excursion (longitudinal), width of estuary (lateral) and mixing depth (vertical). Moreover, the mixing pattern determines the salt intrusion length in an estuary. If a physically based description of the dispersion is defined, this would allow the analytical solution of the salt intrusion problem. The objective of this study is to develop a predictive equation for estimating the dispersion coefficient at tidal average (TA) condition, which can be applied in the salt intrusion model to predict the salinity profile for any estuary during different events. Utilizing available data of 72 measurements in 27 estuaries (including 6 recently studied estuaries in Malaysia), regressions analysis has been performed with various combinations of dimensionless parameters . The predictive dispersion equations have been developed for two different locations, at the mouth D0TA and at the inflection point D1TA (where the convergence length changes). Regressions have been carried out with two separated datasets: 1) more reliable data for calibration; and 2) less reliable data for validation. The combination of dimensionless ratios that give the best performance is selected as the final outcome which indicates that the dispersion coefficient is depending on the tidal excursion, tidal range, tidal velocity amplitude, friction and the Richardson Number. A limitation of the newly developed equation is that the friction is generally unknown. In order to compensate this problem, further analysis has been performed adopting the hydraulic model of Cai et. al. (2012) to estimate the friction and depth. Keywords: dispersion, alluvial estuaries, mixing, salt

  1. Development of a risk prediction model for Barrett's esophagus in an Australian population.

    Science.gov (United States)

    Ireland, C J; Fielder, A L; Thompson, S K; Laws, T A; Watson, D I; Esterman, A

    2017-11-01

    Esophageal adenocarcinoma has poor 5-year survival rates. Increased survival might be achieved with earlier treatment, but requires earlier identification of the precursor, Barrett's esophagus. Population screening is not cost effective, this may be improved by targeted screening directed at individuals more likely to have Barrett's esophagus. To develop a risk prediction tool for Barrett's esophagus, this study compared individuals with Barrett's esophagus against population controls. Participants completed a questionnaire comprising 35 questions addressing medical history, symptom history, lifestyle factors, anthropomorphic measures, and demographic details. Statistical analysis addressed differences between cases and controls, and entailed initial variable selection, checking of model assumptions, and establishing calibration and discrimination. The area under the curve (AUC) was used to assess overall accuracy. One hundred and twenty individuals with Barrett's esophagus and 235 population controls completed the questionnaire. Significant differences were identified for age, gender, reflux history, family reflux history, history of hypertension, alcoholic drinks per week, and body mass index. These were used to develop a risk prediction model. The AUC was 0.82 (95% CI 0.78-0.87). Good calibration between predicted and observed risk was noted (Hosmer-Lemeshow test P = 0.67). At the point minimizing false positives and false negatives, the model achieved a sensitivity of 84.96% and a specificity of 66%. A well-calibrated risk prediction model with good discrimination has been developed to identify patients with Barrett's esophagus. The model needs to be externally validated before consideration for clinical practice. © The Authors 2017. Published by Oxford University Press on behalf of International Society for Diseases of the Esophagus. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  2. Limited Sampling Strategy for Accurate Prediction of Pharmacokinetics of Saroglitazar: A 3-point Linear Regression Model Development and Successful Prediction of Human Exposure.

    Science.gov (United States)

    Joshi, Shuchi N; Srinivas, Nuggehally R; Parmar, Deven V

    2018-03-01

    Our aim was to develop and validate the extrapolative performance of a regression model using a limited sampling strategy for accurate estimation of the area under the plasma concentration versus time curve for saroglitazar. Healthy subject pharmacokinetic data from a well-powered food-effect study (fasted vs fed treatments; n = 50) was used in this work. The first 25 subjects' serial plasma concentration data up to 72 hours and corresponding AUC 0-t (ie, 72 hours) from the fasting group comprised a training dataset to develop the limited sampling model. The internal datasets for prediction included the remaining 25 subjects from the fasting group and all 50 subjects from the fed condition of the same study. The external datasets included pharmacokinetic data for saroglitazar from previous single-dose clinical studies. Limited sampling models were composed of 1-, 2-, and 3-concentration-time points' correlation with AUC 0-t of saroglitazar. Only models with regression coefficients (R 2 ) >0.90 were screened for further evaluation. The best R 2 model was validated for its utility based on mean prediction error, mean absolute prediction error, and root mean square error. Both correlations between predicted and observed AUC 0-t of saroglitazar and verification of precision and bias using Bland-Altman plot were carried out. None of the evaluated 1- and 2-concentration-time points models achieved R 2 > 0.90. Among the various 3-concentration-time points models, only 4 equations passed the predefined criterion of R 2 > 0.90. Limited sampling models with time points 0.5, 2, and 8 hours (R 2 = 0.9323) and 0.75, 2, and 8 hours (R 2 = 0.9375) were validated. Mean prediction error, mean absolute prediction error, and root mean square error were prediction of saroglitazar. The same models, when applied to the AUC 0-t prediction of saroglitazar sulfoxide, showed mean prediction error, mean absolute prediction error, and root mean square error model predicts the exposure of

  3. Integration of research infrastructures and ecosystem models toward development of predictive ecology

    Science.gov (United States)

    Luo, Y.; Huang, Y.; Jiang, J.; MA, S.; Saruta, V.; Liang, G.; Hanson, P. J.; Ricciuto, D. M.; Milcu, A.; Roy, J.

    2017-12-01

    The past two decades have witnessed rapid development in sensor technology. Built upon the sensor development, large research infrastructure facilities, such as National Ecological Observatory Network (NEON) and FLUXNET, have been established. Through networking different kinds of sensors and other data collections at many locations all over the world, those facilities generate large volumes of ecological data every day. The big data from those facilities offer an unprecedented opportunity for advancing our understanding of ecological processes, educating teachers and students, supporting decision-making, and testing ecological theory. The big data from the major research infrastructure facilities also provides foundation for developing predictive ecology. Indeed, the capability to predict future changes in our living environment and natural resources is critical to decision making in a world where the past is no longer a clear guide to the future. We are living in a period marked by rapid climate change, profound alteration of biogeochemical cycles, unsustainable depletion of natural resources, and deterioration of air and water quality. Projecting changes in future ecosystem services to the society becomes essential not only for science but also for policy making. We will use this panel format to outline major opportunities and challenges in integrating research infrastructure and ecosystem models toward developing predictive ecology. Meanwhile, we will also show results from an interactive model-experiment System - Ecological Platform for Assimilating Data into models (EcoPAD) - that have been implemented at the Spruce and Peatland Responses Under Climatic and Environmental change (SPRUCE) experiment in Northern Minnesota and Montpellier Ecotron, France. EcoPAD is developed by integrating web technology, eco-informatics, data assimilation techniques, and ecosystem modeling. EcoPAD is designed to streamline data transfer seamlessly from research infrastructure

  4. Development and validation of multivariable predictive model for thromboembolic events in lymphoma patients.

    Science.gov (United States)

    Antic, Darko; Milic, Natasa; Nikolovski, Srdjan; Todorovic, Milena; Bila, Jelena; Djurdjevic, Predrag; Andjelic, Bosko; Djurasinovic, Vladislava; Sretenovic, Aleksandra; Vukovic, Vojin; Jelicic, Jelena; Hayman, Suzanne; Mihaljevic, Biljana

    2016-10-01

    Lymphoma patients are at increased risk of thromboembolic events but thromboprophylaxis in these patients is largely underused. We sought to develop and validate a simple model, based on individual clinical and laboratory patient characteristics that would designate lymphoma patients at risk for thromboembolic event. The study population included 1,820 lymphoma patients who were treated in the Lymphoma Departments at the Clinics of Hematology, Clinical Center of Serbia and Clinical Center Kragujevac. The model was developed using data from a derivation cohort (n = 1,236), and further assessed in the validation cohort (n = 584). Sixty-five patients (5.3%) in the derivation cohort and 34 (5.8%) patients in the validation cohort developed thromboembolic events. The variables independently associated with risk for thromboembolism were: previous venous and/or arterial events, mediastinal involvement, BMI>30 kg/m(2) , reduced mobility, extranodal localization, development of neutropenia and hemoglobin level 3). For patients classified at risk (intermediate and high-risk scores), the model produced negative predictive value of 98.5%, positive predictive value of 25.1%, sensitivity of 75.4%, and specificity of 87.5%. A high-risk score had positive predictive value of 65.2%. The diagnostic performance measures retained similar values in the validation cohort. Developed prognostic Thrombosis Lymphoma - ThroLy score is more specific for lymphoma patients than any other available score targeting thrombosis in cancer patients. Am. J. Hematol. 91:1014-1019, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  5. Predictive Factors Associated with Solar Energy Development in Laikipia District Central Kenya

    Directory of Open Access Journals (Sweden)

    Oscar Wambuguh

    2015-10-01

    Full Text Available The abundance of sunlight and the availability affordable solar technologies in many areas far from grid-based electricity has sparked the development of renewable energy technologies (RETs which tap solar radiation energy to provide electricity. A study on solar photovoltaics (SPVs use and utilization took place in the Wiyumiririe Location of Kenya. A purposive randomized convenience sample of 246 households was selected and landowner interviews conducted guided by a questionnaire, followed by field surveys and observations. Although solar energy contributed less than a quarter of total household energy needs, residents specifically associated it with specific developmental initiatives. Correlation and logistic regression model analyses showed that solar power development was closely associated (and thus can be predicted from five main independent variables. The findings of the study allowed the development of a probabilistic model general enough to be applicable elsewhere in the development of alternative energy resources particularly those based on solar input.

  6. Prediction of warmed-over flavour development in cooked chicken by colorimetric sensor array.

    Science.gov (United States)

    Kim, Su-Yeon; Li, Jinglei; Lim, Na-Ri; Kang, Bo-Sik; Park, Hyun-Jin

    2016-11-15

    The aim of this study was to develop a simple and rapid method based on colorimetric sensor array (CSA) for evaluation of warmed-over flavour (WOF) in cooked chicken. All samples were classified according to storage time by CSA coupled with principle component analysis (PCA) or hierarchical cluster analysis (HCA). The CSA data were used to establish prediction models with thiobarbituric acid reactive substances (TBARS), pentanal, hexanal, or heptanal associated with WOF by partial least square regression (PLSR). For the TBARS model, the coefficient of determination (rp(2)) was 0.9997 in the prediction range of 0.28-0.69mg/kg. In each of the models for pentanal, hexanal, and heptanal, all rp(2) were higher than 0.960 in the range of 0.58-2.10mg/kg, 5.50-11.69mg/kg, and 0.09-0.16mg/kg, respectively. These results demonstrate that the CSA was able to predict WOF development and to distinguish between each storage time. Copyright © 2016 Elsevier Ltd. All rights reserved.

  7. Development of a high-fidelity numerical model for hazard prediction in the urban environment

    International Nuclear Information System (INIS)

    Lien, F.S.; Yee, E.; Ji, H.; Keats, A.; Hsieh, K.J.

    2005-01-01

    The release of chemical, biological, radiological, or nuclear (CBRN) agents by terrorists or rogue states in a North American city (densely populated urban centre) and the subsequent exposure, deposition, and contamination are emerging threats in an uncertain world. The transport, dispersion, deposition, and fate of a CBRN agent released in an urban environment is an extremely complex problem that encompasses potentially multiple space and time scales. The availability of high-fidelity, time-dependent models for the prediction of a CBRN agent's movement and fate in a complex urban environment can provide the strongest technical and scientific foundation for support of Canada's more broadly based effort at advancing counter-terrorism planning and operational capabilities. The objective of this paper is to report the progress of developing and validating an integrated, state-of-the-art, high-fidelity multi-scale, multi-physics modeling system for the accurate and efficient prediction of urban flow and dispersion of CBRN materials. Development of this proposed multi-scale modeling system will provide the real-time modeling and simulation tool required to predict injuries, casualties, and contamination and to make relevant decisions (based on the strongest technical and scientific foundations) in order to minimize the consequences of a CBRN incident based on a pre-determined decision making framework. (author)

  8. Development of Artificial Neural Network Model for Diesel Fuel Properties Prediction using Vibrational Spectroscopy.

    Science.gov (United States)

    Bolanča, Tomislav; Marinović, Slavica; Ukić, Sime; Jukić, Ante; Rukavina, Vinko

    2012-06-01

    This paper describes development of artificial neural network models which can be used to correlate and predict diesel fuel properties from several FTIR-ATR absorbances and Raman intensities as input variables. Multilayer feed forward and radial basis function neural networks have been used to rapid and simultaneous prediction of cetane number, cetane index, density, viscosity, distillation temperatures at 10% (T10), 50% (T50) and 90% (T90) recovery, contents of total aromatics and polycyclic aromatic hydrocarbons of commercial diesel fuels. In this study two-phase training procedures for multilayer feed forward networks were applied. While first phase training algorithm was constantly the back propagation one, two second phase training algorithms were varied and compared, namely: conjugate gradient and quasi Newton. In case of radial basis function network, radial layer was trained using K-means radial assignment algorithm and three different radial spread algorithms: explicit, isotropic and K-nearest neighbour. The number of hidden layer neurons and experimental data points used for the training set have been optimized for both neural networks in order to insure good predictive ability by reducing unnecessary experimental work. This work shows that developed artificial neural network models can determine main properties of diesel fuels simultaneously based on a single and fast IR or Raman measurement.

  9. Quantitative structure-activity relationship (QSAR) for insecticides: development of predictive in vivo insecticide activity models.

    Science.gov (United States)

    Naik, P K; Singh, T; Singh, H

    2009-07-01

    Quantitative structure-activity relationship (QSAR) analyses were performed independently on data sets belonging to two groups of insecticides, namely the organophosphates and carbamates. Several types of descriptors including topological, spatial, thermodynamic, information content, lead likeness and E-state indices were used to derive quantitative relationships between insecticide activities and structural properties of chemicals. A systematic search approach based on missing value, zero value, simple correlation and multi-collinearity tests as well as the use of a genetic algorithm allowed the optimal selection of the descriptors used to generate the models. The QSAR models developed for both organophosphate and carbamate groups revealed good predictability with r(2) values of 0.949 and 0.838 as well as [image omitted] values of 0.890 and 0.765, respectively. In addition, a linear correlation was observed between the predicted and experimental LD(50) values for the test set data with r(2) of 0.871 and 0.788 for both the organophosphate and carbamate groups, indicating that the prediction accuracy of the QSAR models was acceptable. The models were also tested successfully from external validation criteria. QSAR models developed in this study should help further design of novel potent insecticides.

  10. Development of a predictive energy equation for maintenance hemodialysis patients: a pilot study.

    Science.gov (United States)

    Byham-Gray, Laura; Parrott, J Scott; Ho, Wai Yin; Sundell, Mary B; Ikizler, T Alp

    2014-01-01

    The study objectives were to explore the predictors of measured resting energy expenditure (mREE) among a sample of maintenance hemodialysis (MHD) patients, to generate a predictive energy equation (MHDE), and to compare such models to another commonly used predictive energy equation in nutritional care, the Mifflin-St. Jeor equation (MSJE). The study was a retrospective, cross-sectional cohort design conducted at the Vanderbilt University Medical Center. Study subjects were adult MHD patients (N = 67). Data collected from several clinical trials were analyzed using Pearson's correlation and multivariate linear regression procedures. Demographic, anthropometric, clinical, and laboratory data were examined as potential predictors of mREE. Limits of agreement between the MHDE and the MSJE were evaluated using Bland-Altman plots. The a priori α was set at P lean body mass [LBM]) of mREE included (R(2) = 0.489) FFM, ALB, age, and CRP. Two additional models (MHDE-CRP and MHDE-CR) with acceptable predictability (R(2) = 0.460 and R(2) = 0.451) were derived to improve the clinical utility of the developed energy equation (MHDE-LBM). Using Bland-Altman plots, the MHDE over- and underpredicted mREE less often than the MSJE. Predictive models (MHDE) including selective demographic, clinical, and anthropometric data explained less than 50% variance of mREE but had better precision in determining energy requirements for MHD patients when compared with MSJE. Further research is necessary to improve predictive models of mREE in the MHD population and to test its validity and clinical application. Copyright © 2014 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.

  11. Predicting Climate-sensitive Infectious Diseases: Development of a Federal Science Plan and the Path Forward

    Science.gov (United States)

    Trtanj, J.; Balbus, J. M.; Brown, C.; Shimamoto, M. M.

    2017-12-01

    The transmission and spread of infectious diseases, especially vector-borne diseases, water-borne diseases and zoonosis, are influenced by short and long-term climate factors, in conjunction with numerous other drivers. Public health interventions, including vaccination, vector control programs, and outreach campaigns could be made more effective if the geographic range and timing of increased disease risk could be more accurately targeted, and high risk areas and populations identified. While some progress has been made in predictive modeling for transmission of these diseases using climate and weather data as inputs, they often still start after the first case appears, the skill of those models remains limited, and their use by public health officials infrequent. And further, predictions with lead times of weeks, months or seasons are even rarer, yet the value of acting early holds the potential to save more lives, reduce cost and enhance both economic and national security. Information on high-risk populations and areas for infectious diseases is also potentially useful for the federal defense and intelligence communities as well. The US Global Change Research Program, through its Interagency Group on Climate Change and Human Health (CCHHG), has put together a science plan that pulls together federal scientists and programs working on predictive modeling of climate-sensitive diseases, and draws on academic and other partners. Through a series of webinars and an in-person workshop, the CCHHG has convened key federal and academic stakeholders to assess the current state of science and develop an integrated science plan to identify data and observation systems needs as well as a targeted research agenda for enhancing predictive modeling. This presentation will summarize the findings from this effort and engage AGU members on plans and next steps to improve predictive modeling for infectious diseases.

  12. Developing a clinical utility framework to evaluate prediction models in radiogenomics

    Science.gov (United States)

    Wu, Yirong; Liu, Jie; Munoz del Rio, Alejandro; Page, David C.; Alagoz, Oguzhan; Peissig, Peggy; Onitilo, Adedayo A.; Burnside, Elizabeth S.

    2015-03-01

    Combining imaging and genetic information to predict disease presence and behavior is being codified into an emerging discipline called "radiogenomics." Optimal evaluation methodologies for radiogenomics techniques have not been established. We aim to develop a clinical decision framework based on utility analysis to assess prediction models for breast cancer. Our data comes from a retrospective case-control study, collecting Gail model risk factors, genetic variants (single nucleotide polymorphisms-SNPs), and mammographic features in Breast Imaging Reporting and Data System (BI-RADS) lexicon. We first constructed three logistic regression models built on different sets of predictive features: (1) Gail, (2) Gail+SNP, and (3) Gail+SNP+BI-RADS. Then, we generated ROC curves for three models. After we assigned utility values for each category of findings (true negative, false positive, false negative and true positive), we pursued optimal operating points on ROC curves to achieve maximum expected utility (MEU) of breast cancer diagnosis. We used McNemar's test to compare the predictive performance of the three models. We found that SNPs and BI-RADS features augmented the baseline Gail model in terms of the area under ROC curve (AUC) and MEU. SNPs improved sensitivity of the Gail model (0.276 vs. 0.147) and reduced specificity (0.855 vs. 0.912). When additional mammographic features were added, sensitivity increased to 0.457 and specificity to 0.872. SNPs and mammographic features played a significant role in breast cancer risk estimation (p-value < 0.001). Our decision framework comprising utility analysis and McNemar's test provides a novel framework to evaluate prediction models in the realm of radiogenomics.

  13. Damage assessment of low-cycle fatigue by crack growth prediction. Development of growth prediction model and its application

    International Nuclear Information System (INIS)

    Kamaya, Masayuki; Kawakubo, Masahiro

    2012-01-01

    In this study, the fatigue damage was assumed to be equivalent to the crack initiation and its growth, and fatigue life was assessed by predicting the crack growth. First, a low-cycle fatigue test was conducted in air at room temperature under constant cyclic strain range of 1.2%. The crack initiation and change in crack size during the test were examined by replica investigation. It was found that a crack of 41.2 μm length was initiated almost at the beginning of the test. The identified crack growth rate was shown to correlate well with the strain intensity factor, whose physical meaning was discussed in this study. The fatigue life prediction model (equation) under constant strain range was derived by integrating the crack growth equation defined using the strain intensity factor, and the predicted fatigue lives were almost identical to those obtained by low-cycle fatigue tests. The change in crack depth predicted by the equation also agreed well with the experimental results. Based on the crack growth prediction model, it was shown that the crack size would be less than 0.1 mm even when the estimated fatigue damage exceeded the critical value of the design fatigue curve, in which a twenty-fold safety margin was used for the assessment. It was revealed that the effect of component size and surface roughness, which have been investigated empirically by fatigue tests, could be reasonably explained by considering the crack initiation and growth. Furthermore, the environmental effect on the fatigue life was shown to be brought about by the acceleration of crack growth. (author)

  14. Knowledge base to develop expert system prototype for predicting groundwater pollution from nitrogen fertilizer

    International Nuclear Information System (INIS)

    Ta-oun, M.; Daud, M.; Bardaie, M.Z.; Jusop, S.

    1999-01-01

    An expert system for prediction the impact of nitrogen fertilizer on groundwater pollution potential was established by using CLIPS (NASA's Jonson Space Centre). The knowledge base could be extracted from FAO reports, ministry of agriculture and rural development Malaysia report, established literature and domain expert for preparing an expert system skeleton. An expert system was used to correlate the availability of nitrogen fertilizer with the vulnerability of groundwater to pollution in Peninsula Malaysia and to identify potential groundwater quality problems. An n-fertilizer groundwater pollution potential index produced b using the vulnerability of groundwater to pollution yields a more accurate screening toll for identifying potential pollution problems than by considering vulnerability alone. An expert system can predict the groundwater pollution potential under several conditions of agricultural activities and existing environments. (authors)

  15. Predicting the structural development in Danish livestock and how it affects control strategies against FMD

    DEFF Research Database (Denmark)

    Christiansen, Lasse Engbo; Hisham Beshara Halasa, Tariq; Boklund, Anette

    2012-01-01

    farms were classified by production type and size each year. A total of 88 classes were used. For each species group (cattle, swine, and sheep and goat) a transition probability matrix (TPM) was estimated based on the ten year to year transitions. It was hypothesized that there might be regional......The purpose of this study was to assess if the optimal control strategy against foot-and-mouth disease (FMD) spread is invariant to structural development in Danish livestock until 2030. The DTU-DADS model as presented by Halasa et al. uses demographic information of all farms including...... significantly different TPMs. These TPMs were used in a Markov chain to predict the distribution of farms in year 2030. However, the predictions were unrealistic as far too many farms opened – since all closed farms were allowed to reopen. It was decided to make the closed state a terminal state and make...

  16. Thalamic functional connectivity predicts seizure laterality in individual TLE patients: Application of a biomarker development strategy

    Directory of Open Access Journals (Sweden)

    Daniel S. Barron

    2015-01-01

    No significant differences in functional connection strength in patient and control groups were observed with Mann-Whitney Tests (corrected for multiple comparisons. Notwithstanding the lack of group differences, individual patient difference scores (from control mean connection strength successfully predicted seizure onset zone as shown in ROC curves: discriminant analysis (two-dimensional predicted seizure onset zone with 85% sensitivity and 91% specificity; logistic regression (four-dimensional achieved 86% sensitivity and 100% specificity. The strongest markers in both analyses were left thalamo-hippocampal and right thalamo-entorhinal cortex functional connection strength. Thus, this study shows that thalamic functional connections are sensitive and specific markers of seizure onset laterality in individual temporal lobe epilepsy patients. This study also advances an overall strategy for the programmatic development of neuroimaging biomarkers in clinical and genetic populations: a disease model informed by coordinate-based meta-analysis was used to anatomically constrain individual patient analyses.

  17. Development of Shear Capacity Prediction Model for FRP-RC Beam without Web Reinforcement

    Directory of Open Access Journals (Sweden)

    Md. Arman Chowdhury

    2016-01-01

    Full Text Available Available codes and models generally use partially modified shear design equation, developed earlier for steel reinforced concrete, for predicting the shear capacity of FRP-RC members. Consequently, calculated shear capacity shows under- or overestimation. Furthermore, in most models some affecting parameters of shear strength are overlooked. In this study, a new and simplified shear capacity prediction model is proposed considering all the parameters. A large database containing 157 experimental results of FRP-RC beams without shear reinforcement is assembled from the published literature. A parametric study is then performed to verify the accuracy of the proposed model. Again, a comprehensive review of 9 codes and 12 available models is done, published back from 1997 to date for comparison with the proposed model. Hence, it is observed that the proposed equation shows overall optimized performance compared to all the codes and models within the range of used experimental dataset.

  18. THE DEVELOPMENT AND USE OF A MODEL TO PREDICT SUSTAINABILITY OF CHANGE IN HEALTH CARE SETTINGS.

    Science.gov (United States)

    Molfenter, Todd; Ford, James H; Bhattacharya, Abhik

    2011-01-01

    Innovations adopted through organizational change initiatives are often not sustained leading to diminished quality, productivity, and consumer satisfaction. Research explaining variance in the use of adopted innovations in health care settings is sparse, suggesting the need for a theoretical model to guide research and practice. In this article, we describe the development of a hybrid conjoint decision theoretic model designed to predict the sustainability of organizational change in health care settings. An initial test of the model's predictive validity using expert scored hypothetic profiles resulted in an r-squared value of .77. The test of this model offers a theoretical base for future research on the sustainability of change in health care settings.

  19. Toward integration of genomic selection with crop modelling: the development of an integrated approach to predicting rice heading dates.

    Science.gov (United States)

    Onogi, Akio; Watanabe, Maya; Mochizuki, Toshihiro; Hayashi, Takeshi; Nakagawa, Hiroshi; Hasegawa, Toshihiro; Iwata, Hiroyoshi

    2016-04-01

    It is suggested that accuracy in predicting plant phenotypes can be improved by integrating genomic prediction with crop modelling in a single hierarchical model. Accurate prediction of phenotypes is important for plant breeding and management. Although genomic prediction/selection aims to predict phenotypes on the basis of whole-genome marker information, it is often difficult to predict phenotypes of complex traits in diverse environments, because plant phenotypes are often influenced by genotype-environment interaction. A possible remedy is to integrate genomic prediction with crop/ecophysiological modelling, which enables us to predict plant phenotypes using environmental and management information. To this end, in the present study, we developed a novel method for integrating genomic prediction with phenological modelling of Asian rice (Oryza sativa, L.), allowing the heading date of untested genotypes in untested environments to be predicted. The method simultaneously infers the phenological model parameters and whole-genome marker effects on the parameters in a Bayesian framework. By cultivating backcross inbred lines of Koshihikari × Kasalath in nine environments, we evaluated the potential of the proposed method in comparison with conventional genomic prediction, phenological modelling, and two-step methods that applied genomic prediction to phenological model parameters inferred from Nelder-Mead or Markov chain Monte Carlo algorithms. In predicting heading dates of untested lines in untested environments, the proposed and two-step methods tended to provide more accurate predictions than the conventional genomic prediction methods, particularly in environments where phenotypes from environments similar to the target environment were unavailable for training genomic prediction. The proposed method showed greater accuracy in prediction than the two-step methods in all cross-validation schemes tested, suggesting the potential of the integrated approach in

  20. Further Development of Ko Displacement Theory for Deformed Shape Predictions of Nonuniform Aerospace Structures

    Science.gov (United States)

    Ko, William L.; Fleischer, Van Tran

    2009-01-01

    The Ko displacement theory previously formulated for deformed shape predictions of nonuniform beam structures is further developed mathematically. The further-developed displacement equations are expressed explicitly in terms of geometrical parameters of the beam and bending strains at equally spaced strain-sensing stations along the multiplexed fiber-optic sensor line installed on the bottom surface of the beam. The bending strain data can then be input into the displacement equations for calculations of local slopes, deflections, and cross-sectional twist angles for generating the overall deformed shapes of the nonuniform beam. The further-developed displacement theory can also be applied to the deformed shape predictions of nonuniform two-point supported beams, nonuniform panels, nonuniform aircraft wings and fuselages, and so forth. The high degree of accuracy of the further-developed displacement theory for nonuniform beams is validated by finite-element analysis of various nonuniform beam structures. Such structures include tapered tubular beams, depth-tapered unswept and swept wing boxes, width-tapered wing boxes, and double-tapered wing boxes, all under combined bending and torsional loads. The Ko displacement theory, combined with the fiber-optic strain-sensing system, provide a powerful tool for in-flight deformed shape monitoring of unmanned aerospace vehicles by ground-based pilots to maintain safe flights.

  1. Development of a risk index for the prediction of chronic post-surgical pain.

    Science.gov (United States)

    Althaus, A; Hinrichs-Rocker, A; Chapman, R; Arránz Becker, O; Lefering, R; Simanski, C; Weber, F; Moser, K-H; Joppich, R; Trojan, S; Gutzeit, N; Neugebauer, E

    2012-07-01

    The incidence of chronic post-surgical pain (CPSP) after various common operations is 10% to 50%. Identification of patients at risk of developing chronic pain, and the management and prevention of CPSP remains inadequate. The aim of this study was to develop an easily applicable risk index for the detection of high-risk patients that takes into account the multifactorial aetiology of CPSP. A comprehensive item pool was derived from a systematic literature search. Items that turned out significant in bivariate analyses were then analysed multivariately, using logistic regression analyses. The items that yielded significant predictors in the multivariate analyses were compiled into an index. The cut-off score for a high risk of developing CPSP with an optimal trade-off between sensitivity and specificity was identified. The data of 150 patients who underwent different types of surgery were included in the analyses. Six months after surgery, 43.3% of the patients reported CPSP. Five predictors multivariately contributed to the prediction of CPSP: capacity overload, preoperative pain in the operating field, other chronic preoperative pain, post-surgical acute pain and co-morbid stress symptoms. These results suggest that several easily assessable preoperative and perioperative patient characteristics can predict a patient's risk of developing CPSP. The risk index may help caregivers to tailor individual pain management and to assist high-risk patients with pain coping. © 2011 European Federation of International Association for the Study of Pain Chapters.

  2. Development of a modified equilibrium model for biomass pilot-scale fluidized bed gasifier performance predictions

    International Nuclear Information System (INIS)

    Rodriguez-Alejandro, David A.; Nam, Hyungseok; Maglinao, Amado L.; Capareda, Sergio C.; Aguilera-Alvarado, Alberto F.

    2016-01-01

    The objective of this work is to develop a thermodynamic model considering non-stoichiometric restrictions. The model validation was done from experimental works using a bench-scale fluidized bed gasifier with wood chips, dairy manure, and sorghum. The model was used for a further parametric study to predict the performance of a pilot-scale fluidized biomass gasifier. The Gibbs free energy minimization was applied to the modified equilibrium model considering a heat loss to the surroundings, carbon efficiency, and two non-equilibrium factors based on empirical correlations of ER and gasification temperature. The model was in a good agreement with RMS <4 for the produced gas. The parametric study ranges were 0.01 < ER < 0.99 and 500 °C < T < 900 °C to predict syngas concentrations and its LHV (lower heating value) for the optimization. Higher aromatics in tar were contained in WC gasification compared to manure gasification. A wood gasification tar simulation was produced to predict the amount of tars at specific conditions. The operating conditions for the highest quality syngas were reconciled experimentally with three biomass wastes using a fluidized bed gasifier. The thermodynamic model was used to predict the gasification performance at conditions beyond the actual operation. - Highlights: • Syngas from experimental gasification was used to create a non-equilibrium model. • Different types of biomass (HTS, DM, and WC) were used for gasification modelling. • Different tar compositions were identified with a simulation of tar yields. • The optimum operating conditions were found through the developed model.

  3. Development of a risk prediction model among professional hockey players with visible signs of concussion.

    Science.gov (United States)

    Bruce, Jared M; Echemendia, Ruben J; Meeuwisse, Willem; Hutchison, Michael G; Aubry, Mark; Comper, Paul

    2017-04-04

    Little research examines how to best identify concussed athletes. The purpose of the present study was to develop a preliminary risk decision model that uses visible signs (VS) and mechanisms of injury (MOI) to predict the likelihood of subsequent concussion diagnosis. Coders viewed and documented VS and associated MOI for all NHL games over the course of the 2013-2014 and 2014-2015 regular seasons. After coding was completed, player concussions were identified from the NHL injury surveillance system and it was determined whether players exhibiting VS were subsequently diagnosed with concussions by club medical staff as a result of the coded event. Among athletes exhibiting VS, suspected loss of consciousness, motor incoordination or balance problems, being in a fight, having an initial hit from another player's shoulder and having a secondary hit on the ice were all associated with increased risk of subsequent concussion diagnosis. In contrast, having an initial hit with a stick was associated with decreased risk of subsequent concussion diagnosis. A risk prediction model using a combination of the above VS and MOI was superior to approaches that relied on individual VS and associated MOI (sensitivity=81%, specificity=72%, positive predictive value=26%). Combined use of VS and MOI significantly improves a clinician's ability to identify players who need to be evaluated for possible concussion. A preliminary concussion prediction log has been developed from these data. Pending prospective validation, the use of these methods may improve early concussion detection and evaluation. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

  4. Development and validation of a nomogram predicting recurrence risk in women with symptomatic urinary tract infection.

    Science.gov (United States)

    Cai, Tommaso; Mazzoli, Sandra; Migno, Serena; Malossini, Gianni; Lanzafame, Paolo; Mereu, Liliana; Tateo, Saverio; Wagenlehner, Florian M E; Pickard, Robert S; Bartoletti, Riccardo

    2014-09-01

    To develop and externally validate a novel nomogram predicting recurrence risk probability at 12 months in women after an episode of urinary tract infection. The study included 768 women from Santa Maria Annunziata Hospital, Florence, Italy, affected by urinary tract infections from January 2005 to December 2009. Another 373 women with the same criteria enrolled at Santa Chiara Hospital, Trento, Italy, from January 2010 to June 2012 were used to externally validate and calibrate the nomogram. Univariate and multivariate Cox regression models tested the relationship between urinary tract infection recurrence risk, and patient clinical and laboratory characteristics. The nomogram was evaluated by calculating concordance probabilities, as well as testing calibration of predicted urinary tract infection recurrence with observed urinary tract infections. Nomogram variables included: number of partners, bowel function, type of pathogens isolated (Gram-positive/negative), hormonal status, number of previous urinary tract infection recurrences and previous treatment of asymptomatic bacteriuria. Of the original development data, 261 out of 768 women presented at least one episode of recurrence of urinary tract infection (33.9%). The nomogram had a concordance index of 0.85. The nomogram predictions were well calibrated. This model showed high discrimination accuracy and favorable calibration characteristics. In the validation group (373 women), the overall c-index was 0.83 (P = 0.003, 95% confidence interval 0.51-0.99), whereas the area under the receiver operating characteristic curve was 0.85 (95% confidence interval 0.79-0.91). The present nomogram accurately predicts the recurrence risk of urinary tract infection at 12 months, and can assist in identifying women at high risk of symptomatic recurrence that can be suitable candidates for a prophylactic strategy. © 2014 The Japanese Urological Association.

  5. A physical multifield model predicts the development of volume and structure in the human brain

    Science.gov (United States)

    Rooij, Rijk de; Kuhl, Ellen

    2018-03-01

    The prenatal development of the human brain is characterized by a rapid increase in brain volume and a development of a highly folded cortex. At the cellular level, these events are enabled by symmetric and asymmetric cell division in the ventricular regions of the brain followed by an outwards cell migration towards the peripheral regions. The role of mechanics during brain development has been suggested and acknowledged in past decades, but remains insufficiently understood. Here we propose a mechanistic model that couples cell division, cell migration, and brain volume growth to accurately model the developing brain between weeks 10 and 29 of gestation. Our model accurately predicts a 160-fold volume increase from 1.5 cm3 at week 10 to 235 cm3 at week 29 of gestation. In agreement with human brain development, the cortex begins to form around week 22 and accounts for about 30% of the total brain volume at week 29. Our results show that cell division and coupling between cell density and volume growth are essential to accurately model brain volume development, whereas cell migration and diffusion contribute mainly to the development of the cortex. We demonstrate that complex folding patterns, including sinusoidal folds and creases, emerge naturally as the cortex develops, even for low stiffness contrasts between the cortex and subcortex.

  6. Prediction of linear B-cell epitopes of hepatitis C virus for vaccine development

    Science.gov (United States)

    2015-01-01

    Background High genetic heterogeneity in the hepatitis C virus (HCV) is the major challenge of the development of an effective vaccine. Existing studies for developing HCV vaccines have mainly focused on T-cell immune response. However, identification of linear B-cell epitopes that can stimulate B-cell response is one of the major tasks of peptide-based vaccine development. Owing to the variability in B-cell epitope length, the prediction of B-cell epitopes is much more complex than that of T-cell epitopes. Furthermore, the motifs of linear B-cell epitopes in different pathogens are quite different (e. g. HCV and hepatitis B virus). To cope with this challenge, this work aims to propose an HCV-customized sequence-based prediction method to identify B-cell epitopes of HCV. Results This work establishes an experimentally verified dataset comprising the B-cell response of HCV dataset consisting of 774 linear B-cell epitopes and 774 non B-cell epitopes from the Immune Epitope Database. An interpretable rule mining system of B-cell epitopes (IRMS-BE) is proposed to select informative physicochemical properties (PCPs) and then extracts several if-then rule-based knowledge for identifying B-cell epitopes. A web server Bcell-HCV was implemented using an SVM with the 34 informative PCPs, which achieved a training accuracy of 79.7% and test accuracy of 70.7% better than the SVM-based methods for identifying B-cell epitopes of HCV and the two general-purpose methods. This work performs advanced analysis of the 34 informative properties, and the results indicate that the most effective property is the alpha-helix structure of epitopes, which influences the connection between host cells and the E2 proteins of HCV. Furthermore, 12 interpretable rules are acquired from top-five PCPs and achieve a sensitivity of 75.6% and specificity of 71.3%. Finally, a conserved promising vaccine candidate, PDREMVLYQE, is identified for inclusion in a vaccine against HCV. Conclusions This work

  7. Predicting the local impacts of energy development: a critical guide to forecasting methods and models

    Energy Technology Data Exchange (ETDEWEB)

    Sanderson, D.; O' Hare, M.

    1977-05-01

    Models forecasting second-order impacts from energy development vary in their methodology, output, assumptions, and quality. As a rough dichotomy, they either simulate community development over time or combine various submodels providing community snapshots at selected points in time. Using one or more methods - input/output models, gravity models, econometric models, cohort-survival models, or coefficient models - they estimate energy-development-stimulated employment, population, public and private service needs, and government revenues and expenditures at some future time (ranging from annual to average year predictions) and for different governmental jurisdictions (municipal, county, state, etc.). Underlying assumptions often conflict, reflecting their different sources - historical data, comparative data, surveys, and judgments about future conditions. Model quality, measured by special features, tests, exportability and usefulness to policy-makers, reveals careful and thorough work in some cases and hurried operations with insufficient in-depth analysis in others.

  8. Contribution to the development of a Local Predictive Approach of the boiling crisis

    International Nuclear Information System (INIS)

    Montout, M.

    2009-01-01

    EDF aims at developing a 'Local Predictive Approach' of the boiling crisis for PWR core configurations, i.e. an approach resulting in (empirical) critical heat flux predictors based on local parameters provided by NEPTUNE-CFD code (for boiling bubbly flows, only in a first stage). Within this general framework, this PhD work consisted in assess one modelling of NEPTUNE-CFD code selected to simulate boiling bubble flows, then improve it. The latter objective led us to focus on the mechanistic modelling of subcooled nucleate boiling in forced convection. After a literature review, we identified physical improvements to be accounted for, especially with respect to bubble sliding phenomenon along the heated wall. Subsequently, we developed a force balance model in order to provide needed closure laws related to bubble detachment diameter from the nucleation site and lift-off bubble diameter from the wall. A new boiling model including such developments was eventually proposed, and preliminary assessed. (author)

  9. Development of mathematical model to predict the mechanical properties of friction stir

    Directory of Open Access Journals (Sweden)

    R. Palanivel

    2011-01-01

    Full Text Available This paper presents a systematic approach to develop the mathematical model for predicting the ultimate tensile strength,yield strength, and percentage of elongation of AA6351 aluminum alloy which is widely used in automotive, aircraft anddefense Industries by incorporating (FSW friction stir welding process parameter such as tool rotational speed, weldingspeed, and axial force. FSW has been carried out based on three factors five level central composite rotatable design withfull replications technique. Response surface methodology (RSM is employed to develop the mathematical model. Analysisof variance (ANOVA Technique is used to check the adequacy of the developed mathematical model. The developedmathematical model can be used effectively at 95% confidence level. The effect of FSW process parameter on mechanicalproperties of AA6351 aluminum alloy has been analyzed in detail.

  10. Development and Application of Predictive Tools for MHD Stability Limits in Tokamaks

    Energy Technology Data Exchange (ETDEWEB)

    Brennan, Dylan [Princeton Univ., NJ (United States); Miller, G. P. [Univ. of Tulsa, Tulsa, AZ (United States)

    2016-10-03

    This is a project to develop and apply analytic and computational tools to answer physics questions relevant to the onset of non-ideal magnetohydrodynamic (MHD) instabilities in toroidal magnetic confinement plasmas. The focused goal of the research is to develop predictive tools for these instabilities, including an inner layer solution algorithm, a resistive wall with control coils, and energetic particle effects. The production phase compares studies of instabilities in such systems using analytic techniques, PEST- III and NIMROD. Two important physics puzzles are targeted as guiding thrusts for the analyses. The first is to form an accurate description of the physics determining whether the resistive wall mode or a tearing mode will appear first as β is increased at low rotation and low error fields in DIII-D. The second is to understand the physical mechanism behind recent NIMROD results indicating strong damping and stabilization from energetic particle effects on linear resistive modes. The work seeks to develop a highly relevant predictive tool for ITER, advance the theoretical description of this physics in general, and analyze these instabilities in experiments such as ASDEX Upgrade, DIII-D, JET, JT-60U and NTSX. The awardee on this grant is the University of Tulsa. The research efforts are supervised principally by Dr. Brennan. Support is included for two graduate students, and a strong collaboration with Dr. John M. Finn of LANL. The work includes several ongoing collaborations with General Atomics, PPPL, and the NIMROD team, among others.

  11. Development of the statistical ARIMA model: an application for predicting the upcoming of MJO index

    Science.gov (United States)

    Hermawan, Eddy; Nurani Ruchjana, Budi; Setiawan Abdullah, Atje; Gede Nyoman Mindra Jaya, I.; Berliana Sipayung, Sinta; Rustiana, Shailla

    2017-10-01

    This study is mainly concerned in development one of the most important equatorial atmospheric phenomena that we call as the Madden Julian Oscillation (MJO) which having strong impacts to the extreme rainfall anomalies over the Indonesian Maritime Continent (IMC). In this study, we focused to the big floods over Jakarta and surrounded area that suspecting caused by the impacts of MJO. We concentrated to develop the MJO index using the statistical model that we call as Box-Jenkis (ARIMA) ini 1996, 2002, and 2007, respectively. They are the RMM (Real Multivariate MJO) index as represented by RMM1 and RMM2, respectively. There are some steps to develop that model, starting from identification of data, estimated, determined model, before finally we applied that model for investigation some big floods that occurred at Jakarta in 1996, 2002, and 2007 respectively. We found the best of estimated model for the RMM1 and RMM2 prediction is ARIMA (2,1,2). Detailed steps how that model can be extracted and applying to predict the rainfall anomalies over Jakarta for 3 to 6 months later is discussed at this paper.

  12. Intracellular signaling entropy can be a biomarker for predicting the development of cervical intraepithelial neoplasia.

    Directory of Open Access Journals (Sweden)

    Masakazu Sato

    Full Text Available While the mortality rates for cervical cancer have been drastically reduced after the introduction of the Pap smear test, it still is one of the leading causes of death in women worldwide. Additionally, studies that appropriately evaluate the risk of developing cervical lesions are needed. Therefore, we investigated whether intracellular signaling entropy, which is measured with microarray data, could be useful for predicting the risks of developing cervical lesions. We used three datasets, GSE63514 (histology, GSE27678 (cytology and GSE75132 (cytology, a prospective study. From the data in GSE63514, the entropy rate was significantly increased with disease progression (normal < cervical intraepithelial neoplasia, CIN < cancer (Kruskal-Wallis test, p < 0.0001. From the data in GSE27678, similar results (normal < low-grade squamous intraepithelial lesions, LSILs < high-grade squamous intraepithelial lesions, HSILs ≤ cancer were obtained (Kruskal-Wallis test, p < 0.001. From the data in GSE75132, the entropy rate tended to be higher in the HPV-persistent groups than the HPV-negative group. The group that was destined to progress to CIN 3 or higher had a tendency to have a higher entropy rate than the HPV16-positive without progression group. In conclusion, signaling entropy was suggested to be different for different lesion statuses and could be a useful biomarker for predicting the development of cervical intraepithelial neoplasia.

  13. Development of scaling factor prediction method for radionuclide composition in low-level radioactive waste

    International Nuclear Information System (INIS)

    Park, Jin Beak

    1995-02-01

    Low-level radioactive waste management require the knowledge of the natures and quantities of radionuclides in the immobilized or packaged waste. U. S. NRC rules require programs that measure the concentrations of all relevant nuclides either directly or indirectly by relating difficult-to-measure radionuclides to other easy-to-measure radionuclides with application of scaling factors. Scaling factors previously developed through statistical approach can give only generic ones and have many difficult problem about sampling procedures. Generic scaling factors can not take into account for plant operation history. In this study, a method to predict plant-specific and operational history dependent scaling factors is developed. Realistic and detailed approach are taken to find scaling factors at reactor coolant. This approach begin with fission product release mechanisms and fundamental release properties of fuel-source nuclide such as fission product and transuranic nuclide. Scaling factors at various waste streams are derived from the predicted reactor coolant scaling factors with the aid of radionuclide retention and build up model. This model make use of radioactive material balance within the radioactive waste processing systems. Scaling factors at reactor coolant and waste streams which can include the effects of plant operation history have been developed according to input parameters of plant operation history

  14. Development and Application of Predictive Tools for MHD Stability Limits in Tokamaks

    International Nuclear Information System (INIS)

    Brennan, Dylan; Miller, G. P.

    2016-01-01

    This is a project to develop and apply analytic and computational tools to answer physics questions relevant to the onset of non-ideal magnetohydrodynamic (MHD) instabilities in toroidal magnetic confinement plasmas. The focused goal of the research is to develop predictive tools for these instabilities, including an inner layer solution algorithm, a resistive wall with control coils, and energetic particle effects. The production phase compares studies of instabilities in such systems using analytic techniques, PEST- III and NIMROD. Two important physics puzzles are targeted as guiding thrusts for the analyses. The first is to form an accurate description of the physics determining whether the resistive wall mode or a tearing mode will appear first as β is increased at low rotation and low error fields in DIII-D. The second is to understand the physical mechanism behind recent NIMROD results indicating strong damping and stabilization from energetic particle effects on linear resistive modes. The work seeks to develop a highly relevant predictive tool for ITER, advance the theoretical description of this physics in general, and analyze these instabilities in experiments such as ASDEX Upgrade, DIII-D, JET, JT-60U and NTSX. The awardee on this grant is the University of Tulsa. The research efforts are supervised principally by Dr. Brennan. Support is included for two graduate students, and a strong collaboration with Dr. John M. Finn of LANL. The work includes several ongoing collaborations with General Atomics, PPPL, and the NIMROD team, among others.

  15. Simulation and Prediction of Decarbonated Development in Tourist Attractions Associated with Low-carbon Economy

    Directory of Open Access Journals (Sweden)

    Yuyan Luo

    2014-04-01

    Full Text Available In the field of tourism, the development of tourist attractions is gradually playing a crucial role in tourism economy, regional economy and national economy. While tourism economy is stimulated by growing demand, tourist attractions have been facing the situation that ecological environment is becoming fragile and environmental protection is increasingly difficult in China. As low-carbon economy is highlighted more than ever before, how to develop green economy, how to apply theories and technologies, which are related to low-carbon economy, to push forward decarbonation, to protect the ecological environment, and to boost the development of tourism economy have become the core problems for the sustainable development of tourist attractions system. In addition, this system has drawn the attention of scholars and practitioners in recent years. On the basis of low-carbon economy, this paper tries to define the decarbonated development goals and the connotation of tourist attractions system. In addition, it also discusses system structure associated with system dynamics and system engineering, and constructs system simulation model. In the end, a case study is conducted, that is, to predict the development trend of Jiuzhai Valley by adopting the constructed system so as to extend the previous research on low-carbon tourism and to guide the decarbonated development in tourist attractions.

  16. Development of Predictive QSAR Models of 4-Thiazolidinones Antitrypanosomal Activity using Modern Machine Learning Algorithms.

    Science.gov (United States)

    Kryshchyshyn, Anna; Devinyak, Oleg; Kaminskyy, Danylo; Grellier, Philippe; Lesyk, Roman

    2017-11-14

    This paper presents novel QSAR models for the prediction of antitrypanosomal activity among thiazolidines and related heterocycles. The performance of four machine learning algorithms: Random Forest regression, Stochastic gradient boosting, Multivariate adaptive regression splines and Gaussian processes regression have been studied in order to reach better levels of predictivity. The results for Random Forest and Gaussian processes regression are comparable and outperform other studied methods. The preliminary descriptor selection with Boruta method improved the outcome of machine learning methods. The two novel QSAR-models developed with Random Forest and Gaussian processes regression algorithms have good predictive ability, which was proved by the external evaluation of the test set with corresponding Q 2 ext =0.812 and Q 2 ext =0.830. The obtained models can be used further for in silico screening of virtual libraries in the same chemical domain in order to find new antitrypanosomal agents. Thorough analysis of descriptors influence in the QSAR models and interpretation of their chemical meaning allows to highlight a number of structure-activity relationships. The presence of phenyl rings with electron-withdrawing atoms or groups in para-position, increased number of aromatic rings, high branching but short chains, high HOMO energy, and the introduction of 1-substituted 2-indolyl fragment into the molecular structure have been recognized as trypanocidal activity prerequisites. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  17. Propensity Customer the Proposition of Lawsuits: Development and test of Predictive Model for the Electricity Sector

    Directory of Open Access Journals (Sweden)

    Victor Manoel Cunha de Almeida

    2014-11-01

    Full Text Available The study aimed to propose and test a model to predict the propensity of lawsuits of Power sector utility customers. The effects of customer profile, motives of complaints, and the history of administrative actions, on the propensity to lawsuits, were investigated. The paradigm of disconfirmation of expectations was used as a theoretical framework for this study. We adopted a substantive approach to the development and testing of the predictive model. The technique of Classification Tree was chosen to operationalize the model. The method specified in this study for the creation of the decision tree was the CHAID (Chi Square Interaction Detector. Data analysis shows that the propensity to the proposition of a lawsuit does not solely depend on the nature of the problem faced by the client, but the profile and trajectory relationship with the utility provider. The results of this study offer empirical support to the theoretical paradigm of disconfirmation of expectations, more specifically, with regard to the Satisfaction Theory, Attribution Theory and the Theory of Justice and Equity. The main managerial contribution of the study lies in propose a predictive model that allows utility providers to assign each customer a probability to propose a lawsuit, which enables the proactive adoption of practices by the managers, aiming to better serve the public.

  18. Development of a predictive model to determine micropollutant removal using granular activated carbon

    Directory of Open Access Journals (Sweden)

    D. J. de Ridder

    2009-12-01

    Full Text Available The occurrence of organic micropollutants in drinking water and its sources has opened up a field of study related to monitoring concentration levels in water sources, evaluating their toxicity and estimating their removal in drinking water treatment processes. Because a large number of organic micropollutants is currently present (although in relatively low concentrations in drinking water sources, a method should be developed to select which micropollutants has to be evaluated with priority. In this paper, a screening model is presented that can predict solute removal by activated carbon, in ultrapure water and in natural water. Solute removal prediction is based on a combination of solute hydrophobicity (expressed as log D, the pH corrected log Kow, solute charge and the carbon dose. Solute molecular weight was also considered as model input parameter, but this solute property appeared to relate insufficiently to solute removal.

    Removal of negatively charged solutes by preloaded activated carbon was reduced while the removal of positively charged solutes was increased, compared with freshly regenerated activated carbon. Differences in charged solute removal by freshly regenerated activated carbon were small, indicating that charge interactions are an important mechanism in adsorption onto preloaded carbon. The predicted solute removal was within 20 removal-% deviation of experimentally measured values for most solutes.

  19. Development of patient specific cardiovascular models predicting dynamics in response to orthostatic stress challenges

    DEFF Research Database (Denmark)

    Ottesen, Johnny T.

    2013-01-01

    Physiological realistic models of the controlled cardiovascular system are constructed and validated against clinical data. Special attention is paid to the control of blood pressure, cerebral blood flow velocity, and heart rate during postural challenges, including sit-to-stand and head-up tilt....... This study describes development of patient specific models, and how sensitivity analysis and nonlinear optimization methods can be used to predict patient specific characteristics when analyzed using experimental data. Finally, we discuss how a given model can be used to understand physiological changes...

  20. Empirical Model Development for Predicting Shock Response on Composite Materials Subjected to Pyroshock Loading

    Science.gov (United States)

    Gentz, Steven J.; Ordway, David O; Parsons, David S.; Garrison, Craig M.; Rodgers, C. Steven; Collins, Brian W.

    2015-01-01

    The NASA Engineering and Safety Center (NESC) received a request to develop an analysis model based on both frequency response and wave propagation analyses for predicting shock response spectrum (SRS) on composite materials subjected to pyroshock loading. The model would account for near-field environment (approx. 9 inches from the source) dominated by direct wave propagation, mid-field environment (approx. 2 feet from the source) characterized by wave propagation and structural resonances, and far-field environment dominated by lower frequency bending waves in the structure. This report documents the outcome of the assessment.

  1. Perioperative Respiratory Adverse Events in Pediatric Ambulatory Anesthesia: Development and Validation of a Risk Prediction Tool.

    Science.gov (United States)

    Subramanyam, Rajeev; Yeramaneni, Samrat; Hossain, Mohamed Monir; Anneken, Amy M; Varughese, Anna M

    2016-05-01

    Perioperative respiratory adverse events (PRAEs) are the most common cause of serious adverse events in children receiving anesthesia. Our primary aim of this study was to develop and validate a risk prediction tool for the occurrence of PRAE from the onset of anesthesia induction until discharge from the postanesthesia care unit in children younger than 18 years undergoing elective ambulatory anesthesia for surgery and radiology. The incidence of PRAE was studied. We analyzed data from 19,059 patients from our department's quality improvement database. The predictor variables were age, sex, ASA physical status, morbid obesity, preexisting pulmonary disorder, preexisting neurologic disorder, and location of ambulatory anesthesia (surgery or radiology). Composite PRAE was defined as the presence of any 1 of the following events: intraoperative bronchospasm, intraoperative laryngospasm, postoperative apnea, postoperative laryngospasm, postoperative bronchospasm, or postoperative prolonged oxygen requirement. Development and validation of the risk prediction tool for PRAE were performed using a split sampling technique to split the database into 2 independent cohorts based on the year when the patient received ambulatory anesthesia for surgery and radiology using logistic regression. A risk score was developed based on the regression coefficients from the validation tool. The performance of the risk prediction tool was assessed by using tests of discrimination and calibration. The overall incidence of composite PRAE was 2.8%. The derivation cohort included 8904 patients, and the validation cohort included 10,155 patients. The risk of PRAE was 3.9% in the development cohort and 1.8% in the validation cohort. Age ≤ 3 years (versus >3 years), ASA physical status II or III (versus ASA physical status I), morbid obesity, preexisting pulmonary disorder, and surgery (versus radiology) significantly predicted the occurrence of PRAE in a multivariable logistic regression

  2. Development of a Multicomponent Prediction Model for Acute Esophagitis in Lung Cancer Patients Receiving Chemoradiotherapy

    International Nuclear Information System (INIS)

    De Ruyck, Kim; Sabbe, Nick; Oberije, Cary; Vandecasteele, Katrien; Thas, Olivier; De Ruysscher, Dirk; Lambin, Phillipe; Van Meerbeeck, Jan; De Neve, Wilfried; Thierens, Hubert

    2011-01-01

    Purpose: To construct a model for the prediction of acute esophagitis in lung cancer patients receiving chemoradiotherapy by combining clinical data, treatment parameters, and genotyping profile. Patients and Methods: Data were available for 273 lung cancer patients treated with curative chemoradiotherapy. Clinical data included gender, age, World Health Organization performance score, nicotine use, diabetes, chronic disease, tumor type, tumor stage, lymph node stage, tumor location, and medical center. Treatment parameters included chemotherapy, surgery, radiotherapy technique, tumor dose, mean fractionation size, mean and maximal esophageal dose, and overall treatment time. A total of 332 genetic polymorphisms were considered in 112 candidate genes. The predicting model was achieved by lasso logistic regression for predictor selection, followed by classic logistic regression for unbiased estimation of the coefficients. Performance of the model was expressed as the area under the curve of the receiver operating characteristic and as the false-negative rate in the optimal point on the receiver operating characteristic curve. Results: A total of 110 patients (40%) developed acute esophagitis Grade ≥2 (Common Terminology Criteria for Adverse Events v3.0). The final model contained chemotherapy treatment, lymph node stage, mean esophageal dose, gender, overall treatment time, radiotherapy technique, rs2302535 (EGFR), rs16930129 (ENG), rs1131877 (TRAF3), and rs2230528 (ITGB2). The area under the curve was 0.87, and the false-negative rate was 16%. Conclusion: Prediction of acute esophagitis can be improved by combining clinical, treatment, and genetic factors. A multicomponent prediction model for acute esophagitis with a sensitivity of 84% was constructed with two clinical parameters, four treatment parameters, and four genetic polymorphisms.

  3. Development of Castration Resistant Prostate Cancer can be Predicted by a DNA Hypermethylation Profile.

    Science.gov (United States)

    Angulo, Javier C; Andrés, Guillermo; Ashour, Nadia; Sánchez-Chapado, Manuel; López, Jose I; Ropero, Santiago

    2016-03-01

    Detection of DNA hypermethylation has emerged as a novel molecular biomarker for prostate cancer diagnosis and evaluation of prognosis. We sought to define whether a hypermethylation profile of patients with prostate cancer on androgen deprivation would predict castrate resistant prostate cancer. Genome-wide methylation analysis was performed using a methylation cancer panel in 10 normal prostates and 45 tumor samples from patients placed on androgen deprivation who were followed until castrate resistant disease developed. Castrate resistant disease was defined according to EAU (European Association of Urology) guideline criteria. Two pathologists reviewed the Gleason score, Ki-67 index and neuroendocrine differentiation. Hierarchical clustering analysis was performed and relationships with outcome were investigated by Cox regression and log rank analysis. We found 61 genes that were significantly hypermethylated in greater than 20% of tumors analyzed. Three clusters of patients were characterized by a DNA methylation profile, including 1 at risk for earlier castrate resistant disease (log rank p = 0.019) and specific mortality (log rank p = 0.002). Hypermethylation of ETV1 (HR 3.75) and ZNF215 (HR 2.89) predicted disease progression despite androgen deprivation. Hypermethylation of IRAK3 (HR 13.72), ZNF215 (HR 4.81) and SEPT9 (HR 7.64) were independent markers of prognosis. Prostate specific antigen greater than 25 ng/ml, Gleason pattern 5, Ki-67 index greater than 12% and metastasis at diagnosis also predicted a negative response to androgen deprivation. Study limitations included the retrospective design and limited number of cases. Epigenetic silencing of the mentioned genes could be novel molecular markers for the prognosis of advanced prostate cancer. It might predict castrate resistance during hormone deprivation and, thus, disease specific mortality. Gene hypermethylation is associated with disease progression in patients who receive hormone therapy. It

  4. Early gross motor skills predict the subsequent development of language in children with autism spectrum disorder.

    Science.gov (United States)

    Bedford, Rachael; Pickles, Andrew; Lord, Catherine

    2016-09-01

    Motor milestones such as the onset of walking are important developmental markers, not only for later motor skills but also for more widespread social-cognitive development. The aim of the current study was to test whether gross motor abilities, specifically the onset of walking, predicted the subsequent rate of language development in a large cohort of children with autism spectrum disorder (ASD). We ran growth curve models for expressive and receptive language measured at 2, 3, 5 and 9 years in 209 autistic children. Measures of gross motor, visual reception and autism symptoms were collected at the 2 year visit. In Model 1, walking onset was included as a predictor of the slope of language development. Model 2 included a measure of non-verbal IQ and autism symptom severity as covariates. The final model, Model 3, additionally covaried for gross motor ability. In the first model, parent-reported age of walking onset significantly predicted the subsequent rate of language development although the relationship became non-significant when gross motor skill, non-verbal ability and autism severity scores were included (Models 2 & 3). Gross motor score, however, did remain a significant predictor of both expressive and receptive language development. Taken together, the model results provide some evidence that early motor abilities in young children with ASD can have longitudinal cross-domain influences, potentially contributing, in part, to the linguistic difficulties that characterise ASD. Autism Res 2016, 9: 993-1001. © 2015 The Authors Autism Research published by Wiley Periodicals, Inc. on behalf of International Society for Autism Research. © 2015 The Authors Autism Research published by Wiley Periodicals, Inc. on behalf of International Society for Autism Research.

  5. Predictive Factors for Developing Venous Thrombosis during Cisplatin-Based Chemotherapy in Testicular Cancer.

    Science.gov (United States)

    Heidegger, Isabel; Porres, Daniel; Veek, Nica; Heidenreich, Axel; Pfister, David

    2017-01-01

    Malignancies and cisplatin-based chemotherapy are both known to correlate with a high risk of venous thrombotic events (VTT). In testicular cancer, the information regarding the incidence and reason of VTT in patients undergoing cisplatin-based chemotherapy is still discussed controversially. Moreover, no risk factors for developing a VTT during cisplatin-based chemotherapy have been elucidated so far. We retrospectively analyzed 153 patients with testicular cancer undergoing cisplatin-based chemotherapy at our institution for the development of a VTT during or after chemotherapy. Clinical and pathological parameters for identifying possible risk factors for VTT were analyzed. The Khorana risk score was used to calculate the risk of VTT. Student t test was applied for calculating the statistical significance of differences between the treatment groups. Twenty-six out of 153 patients (17%) developed a VTT during chemotherapy. When we analyzed the risk factors for developing a VTT, we found that Lugano stage ≥IIc was significantly (p = 0.0006) correlated with the risk of developing a VTT during chemotherapy. On calculating the VTT risk using the Khorana risk score model, we found that only 2 out of 26 patients (7.7%) were in the high-risk Khorana group (≥3). Patients with testicular cancer with a high tumor volume have a significant risk of developing a VTT with cisplatin-based chemotherapy. The Khorana risk score is not an accurate tool for predicting VTT in testicular cancer. © 2017 S. Karger AG, Basel.

  6. The predictive and discriminant validity of the zone of proximal development.

    Science.gov (United States)

    Meijer, J; Elshout, J J

    2001-03-01

    Dynamic measurement procedures are supposed to uncover the zone of proximal development and to increase predictive validity in comparison to conventional, static measurement procedures. Two alternative explanations for the discrepancies between static and dynamic measurements were investigated. The first focuses on Vygotsky's learning potential theory, the second considers the role of anxiety tendency during test taking. If test anxious tendencies are mitigated by dynamic testing procedures, in particular the availability of assistance, the concept of the zone of proximal development may be superfluous in explaining the differences between the outcomes of static and dynamic measurement. Participants were students from secondary education in the Netherlands. They were tested repeatedly in grade three as well as in grade four. Participants were between 14 and 17 years old; their average age was 15.4 years with a standard deviation of .52. Two types of mathematics tests were used in a longitudinal experiment. The first type of test consisted of open-ended items, which participants had to solve completely on their own. With the second type of test, assistance was available to participants during the test. The latter so-called learning test was conceived of as a dynamic testing procedure. Furthermore, a test anxiety questionnaire was administered repeatedly. Structural equation modelling was used to analyse the data. Apart from emotionality and worry, lack of self-confidence appears to be an important constituent of test anxiety. The learning test appears to contribute to the predictive validity of conventional tests and thus a part of Vygotsky's claims were substantiated. Moreover, the mere inclusion of a test anxiety factor into an explanatory model for the gathered data is not sufficient. Apart from test anxiety and mathematical ability it is necessary to assume a factor which may be construed as mathematics learning potential. The results indicate that the observed

  7. Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries.

    Science.gov (United States)

    Jochems, Arthur; Deist, Timo M; El Naqa, Issam; Kessler, Marc; Mayo, Chuck; Reeves, Jackson; Jolly, Shruti; Matuszak, Martha; Ten Haken, Randall; van Soest, Johan; Oberije, Cary; Faivre-Finn, Corinne; Price, Gareth; de Ruysscher, Dirk; Lambin, Philippe; Dekker, Andre

    2017-10-01

    Tools for survival prediction for non-small cell lung cancer (NSCLC) patients treated with chemoradiation or radiation therapy are of limited quality. In this work, we developed a predictive model of survival at 2 years. The model is based on a large volume of historical patient data and serves as a proof of concept to demonstrate the distributed learning approach. Clinical data from 698 lung cancer patients, treated with curative intent with chemoradiation or radiation therapy alone, were collected and stored at 2 different cancer institutes (559 patients at Maastro clinic (Netherlands) and 139 at Michigan university [United States]). The model was further validated on 196 patients originating from The Christie (United Kingdon). A Bayesian network model was adapted for distributed learning (the animation can be viewed at https://www.youtube.com/watch?v=ZDJFOxpwqEA). Two-year posttreatment survival was chosen as the endpoint. The Maastro clinic cohort data are publicly available at https://www.cancerdata.org/publication/developing-and-validating-survival-prediction-model-nsclc-patients-through-distributed, and the developed models can be found at www.predictcancer.org. Variables included in the final model were T and N category, age, performance status, and total tumor dose. The model has an area under the curve (AUC) of 0.66 on the external validation set and an AUC of 0.62 on a 5-fold cross validation. A model based on the T and N category performed with an AUC of 0.47 on the validation set, significantly worse than our model (PLearning the model in a centralized or distributed fashion yields a minor difference on the probabilities of the conditional probability tables (0.6%); the discriminative performance of the models on the validation set is similar (P=.26). Distributed learning from federated databases allows learning of predictive models on data originating from multiple institutions while avoiding many of the data-sharing barriers. We believe that

  8. Development of technique for estimating primary cooling system break diameter in predicting nuclear emergency event sequence

    International Nuclear Information System (INIS)

    Tatebe, Yasumasa; Yoshida, Yoshitaka

    2012-01-01

    If an emergency event occurs in a nuclear power plant, appropriate action is selected and taken in accordance with the plant status, which changes from time to time, in order to prevent escalation and mitigate the event consequences. It is thus important to predict the event sequence and identify the plant behavior resulting from the action taken. In predicting the event sequence during a loss-of-coolant accident (LOCA), it is necessary to estimate break diameter. The conventional method for this estimation is time-consuming, since it involves multiple sensitivity analyses to determine the break diameter that is consistent with the plant behavior. To speed up the process of predicting the nuclear emergency event sequence, a new break diameter estimation technique that is applicable to pressurized water reactors was developed in this study. This technique enables the estimation of break diameter using the plant data sent from the safety parameter display system (SPDS), with focus on the depressurization rate in the reactor cooling system (RCS) during LOCA. The results of LOCA analysis, performed by varying the break diameter using the MAAP4 and RELAP5/MOD3.2 codes, confirmed that the RCS depressurization rate could be expressed by the log linear function of break diameter, except in the case of a small leak, in which RCS depressurization is affected by the coolant charging system and the high-pressure injection system. A correlation equation for break diameter estimation was developed from this function and tested for accuracy. Testing verified that the correlation equation could estimate break diameter accurately within an error of approximately 16%, even if the leak increases gradually, changing the plant status. (author)

  9. Does specific psychopathology predict development of psychosis in ultra high-risk (UHR) patients?

    Science.gov (United States)

    Thompson, Andrew; Nelson, Barnaby; Bruxner, Annie; O'Connor, Karen; Mossaheb, Nilufar; Simmons, Magenta B; Yung, Alison

    2013-04-01

    Studies have attempted to identify additional risk factors within the group identified as 'ultra high risk' (UHR) for developing psychotic disorders in order to characterise those at highest risk. However, these studies have often neglected clinical symptom types as additional risk factors. We aimed to investigate the relationship between baseline clinical psychotic or psychotic-like symptoms and the subsequent transition to a psychotic disorder in a UHR sample. A retrospective 'case-control' methodology was used. We identified all individuals from a UHR clinic who had subsequently developed a psychotic disorder (cases) and compared these to a random sample of individuals from the clinic who did not become psychotic within the sampling time frame (controls). The sample consisted of 120 patients (60 cases, 60 controls). An audit tool was used to identify clinical symptoms reported at entry to the clinic (baseline) using the clinical file. Diagnosis at transition was assessed using the Operational Criteria for Psychotic Illness (OPCRIT) computer program. The relationship between transition to a psychotic disorder and baseline symptoms was explored using survival analysis. Presence of thought disorder, any delusions and elevated mood significantly predicted transition to a psychotic disorder. When other symptoms were adjusted for, only the presence of elevated mood significantly predicted subsequent transition (hazard ratio 2.69, p = 0.002). Thought disorder was a predictor of transition to a schizophrenia-like psychotic disorder (hazard ratio 3.69, p = 0.008). Few individual clinical symptoms appear to be predictive of transition to a psychotic disorder in the UHR group. Clinicians should be cautious about the use of clinical profile alone in such individuals when determining who is at highest risk.

  10. Developing a predictive model for the chemical composition of soot nanoparticles

    Energy Technology Data Exchange (ETDEWEB)

    Violi, Angela [Univ. of Michigan, Ann Arbor, MI (United States); Michelsen, Hope [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Hansen, Nils [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Wilson, Kevin [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

    2017-04-07

    In order to provide the scientific foundation to enable technology breakthroughs in transportation fuel, it is important to develop a combustion modeling capability to optimize the operation and design of evolving fuels in advanced engines for transportation applications. The goal of this proposal is to develop a validated predictive model to describe the chemical composition of soot nanoparticles in premixed and diffusion flames. Atomistic studies in conjunction with state-of-the-art experiments are the distinguishing characteristics of this unique interdisciplinary effort. The modeling effort has been conducted at the University of Michigan by Prof. A. Violi. The experimental work has entailed a series of studies using different techniques to analyze gas-phase soot precursor chemistry and soot particle production in premixed and diffusion flames. Measurements have provided spatial distributions of polycyclic aromatic hydrocarbons and other gas-phase species and size and composition of incipient soot nanoparticles for comparison with model results. The experimental team includes Dr. N. Hansen and H. Michelsen at Sandia National Labs' Combustion Research Facility, and Dr. K. Wilson as collaborator at Lawrence Berkeley National Lab's Advanced Light Source. Our results show that the chemical and physical properties of nanoparticles affect the coagulation behavior in soot formation, and our results on an experimentally validated, predictive model for the chemical composition of soot nanoparticles will not only enhance our understanding of soot formation since but will also allow the prediction of particle size distributions under combustion conditions. These results provide a novel description of soot formation based on physical and chemical properties of the particles for use in the next generation of soot models and an enhanced capability for facilitating the design of alternative fuels and the engines they will power.

  11. [Preliminary evaluation on self-developed dentin porcelain color prediction system].

    Science.gov (United States)

    Chen, L; Lu, C; Li, X L; Zhu, X M; Zhang, S; Tan, J G

    2016-09-01

    To apply the self-developed dentin porcelain color prediction system in the fabrication of porcelain-fused-to-metal-crown(PFMC), and to evaluate its accuracy in color-matching. Twenty upper central incisors were recruited according to preset criteria, and three PFMC were made for each tooth using three shade-matching techniques. Group A: PFMC were made according to the result of visual color selection; Group B: an spectrophotometer-based color-matching technique was used; Group C: PFMC were fabricated with dentin porcelain powder calculated by the prediction system according to the L(*), a(*), b(*) value measured by a spectrophotometer. Color differences(ΔE) (measured by spectrophotometer) of three groups of crowns were calculated in the cervical, middle, and incisal regions. The results were analyzed using one-way ANOVA. Mean color differences in body regions were: Group A: 3.53±1.80, Group B: 2.86±1.63, Group C: 3.77±1.40(P>0.05), and those in incisal regions were: Group A: 2.70 ± 1.13, Group B: 2.80 ± 0.90, Group C: 3.04 ± 1.03(P>0.05). In cervical region, Group C had greater color difference than Group B(2.78±1.14)(P0.05). PFMC fabricated using self-developed dentin porcelain color prediction system had similar color matching compared with conventional and instrument-based methods.

  12. Predicting development of undrained shear strength in soft oil sands tailings

    Energy Technology Data Exchange (ETDEWEB)

    Masala, S. [Klohn Crippen Berger, Calgary, AB (Canada); Matthews, J. [Shell Canada Ltd., Calgary, AB (Canada)

    2010-07-01

    This PowerPoint presentation discussed a method of predicting the development of undrained shear strength in soft oil sands tailings. Phenomenology charts of oil sands tailings ponds were used to present the suspension, density, stresses and hydrostatic behaviour of tailings. Sedimentation and consolidation processes were discussed. The charts demonstrated how the tailings slurry settles and consolidates, releases water and dissipates pore pressures. The slurry then develops intergranular stresses and increases in density. The increases correlate with increased resistance to deformation and decreased compressibility and hydraulic conductivity. A critical state soil mechanics (CSSM) was used to characterize the soft oil sands tailings. Undrained strength was determined using the concept of the undrained strength ratio (USR). The USR was determined using traditional geotechnical investigation methods. Settling of the non-consolidated (NC) soil deposits was simulated using the finite strain consolidation theory. The model was based on the premise that current effective stresses control undrained shear strength in the NC deposits. Case studies were used to demonstrate the predictive framework. tabs, figs.

  13. A CBR-Based and MAHP-Based Customer Value Prediction Model for New Product Development

    Science.gov (United States)

    Zhao, Yu-Jie; Luo, Xin-xing; Deng, Li

    2014-01-01

    In the fierce market environment, the enterprise which wants to meet customer needs and boost its market profit and share must focus on the new product development. To overcome the limitations of previous research, Chan et al. proposed a dynamic decision support system to predict the customer lifetime value (CLV) for new product development. However, to better meet the customer needs, there are still some deficiencies in their model, so this study proposes a CBR-based and MAHP-based customer value prediction model for a new product (C&M-CVPM). CBR (case based reasoning) can reduce experts' workload and evaluation time, while MAHP (multiplicative analytic hierarchy process) can use actual but average influencing factor's effectiveness in stimulation, and at same time C&M-CVPM uses dynamic customers' transition probability which is more close to reality. This study not only introduces the realization of CBR and MAHP, but also elaborates C&M-CVPM's three main modules. The application of the proposed model is illustrated and confirmed to be sensible and convincing through a stimulation experiment. PMID:25162050

  14. Quantitative Lymphoscintigraphy to Predict the Possibility of Lymphedema Development After Breast Cancer Surgery: Retrospective Clinical Study.

    Science.gov (United States)

    Kim, Paul; Lee, Ju Kang; Lim, Oh Kyung; Park, Heung Kyu; Park, Ki Deok

    2017-12-01

    To predict the probability of lymphedema development in breast cancer patients in the early post-operation stage, we investigated the ability of quantitative lymphoscintigraphic assessment. This retrospective study included 201 patients without lymphedema after unilateral breast cancer surgery. Lymphoscintigraphy was performed between 4 and 8 weeks after surgery to evaluate the lymphatic system in the early postoperative stage. Quantitative lymphoscintigraphy was performed using four methods: ratio of radiopharmaceutical clearance rate of the affected to normal hand; ratio of radioactivity of the affected to normal hand; ratio of radiopharmaceutical uptake rate of the affected to normal axilla (RUA); and ratio of radioactivity of the affected to normal axilla (RRA). During a 1-year follow-up, patients with a circumferential interlimb difference of 2 cm at any measurement location and a 200-mL interlimb volume difference were diagnosed with lymphedema. We investigated the difference in quantitative lymphoscintigraphic assessment between the non-lymphedema and lymphedema groups. Quantitative lymphoscintigraphic assessment revealed that the RUA and RRA were significantly lower in the lymphedema group than in the non-lymphedema group. After adjusting the model for all significant variables (body mass index, N-stage, T-stage, type of surgery, and type of lymph node surgery), RRA was associated with lymphedema (odds ratio=0.14; 95% confidence interval, 0.04-0.46; p=0.001). In patients in the early postoperative stage after unilateral breast cancer surgery, quantitative lymphoscintigraphic assessment can be used to predict the probability of developing lymphedema.

  15. Developing a novel hierarchical approach for multiscale structural reliability predictions for ultra-high consequence applications

    Energy Technology Data Exchange (ETDEWEB)

    Emery, John M. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Coffin, Peter [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Robbins, Brian A. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Carroll, Jay [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Field, Richard V. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Jeremy Yoo, Yung Suk [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Kacher, Josh [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2017-09-01

    Microstructural variabilities are among the predominant sources of uncertainty in structural performance and reliability. We seek to develop efficient algorithms for multiscale calcu- lations for polycrystalline alloys such as aluminum alloy 6061-T6 in environments where ductile fracture is the dominant failure mode. Our approach employs concurrent multiscale methods, but does not focus on their development. They are a necessary but not sufficient ingredient to multiscale reliability predictions. We have focused on how to efficiently use concurrent models for forward propagation because practical applications cannot include fine-scale details throughout the problem domain due to exorbitant computational demand. Our approach begins with a low-fidelity prediction at the engineering scale that is sub- sequently refined with multiscale simulation. The results presented in this report focus on plasticity and damage at the meso-scale, efforts to expedite Monte Carlo simulation with mi- crostructural considerations, modeling aspects regarding geometric representation of grains and second-phase particles, and contrasting algorithms for scale coupling.

  16. External validation of approaches to prediction of falls during hospital rehabilitation stays and development of a new simpler tool

    Directory of Open Access Journals (Sweden)

    Angela Vratsistas-Curto

    2017-12-01

    Full Text Available Objectives: To test the external validity of 4 approaches to fall prediction in a rehabilitation setting (Predict_FIRST, Ontario Modified STRATIFY (OMS, physiotherapists’ judgement of fall risk (PT_Risk, and falls in the past year (Past_Falls, and to develop and test the validity of a simpler tool for fall prediction in rehabilitation (Predict_CM2. Participants: A total of 300 consecutively-admitted rehabilitation inpatients. Methods: Prospective inception cohort study. Falls during the rehabilitation stay were monitored. Potential predictors were extracted from medical records. Results: Forty-one patients (14% fell during their rehabilitation stay. The external validity, area under the receiver operating characteristic curve (AUC, for predicting future fallers was: 0.71 (95% confidence interval (95% CI: 0.61–0.81 for OMS (Total_Score; 0.66 (95% CI: 0.57–0.74 for Predict_FIRST; 0.65 (95% CI 0.57–0.73 for PT_Risk; and 0.52 for Past_Falls (95% CI: 0.46–0.60. A simple 3-item tool (Predict_CM2 was developed from the most predictive individual items (impaired mobility/transfer ability, impaired cognition, and male sex. The accuracy of Predict_CM2 was 0.73 (95% CI: 0.66–0.81, comparable to OMS (Total_Score (p = 0.52, significantly better than Predict_FIRST (p = 0.04, and Past_Falls (p < 0.001, and approaching significantly better than PT_Risk (p = 0.09. Conclusion: Predict_CM2 is a simpler screening tool with similar accuracy for predicting fallers in rehabilitation to OMS (Total_Score and better accuracy than Predict_FIRST or Past_Falls. External validation of Predict_CM2 is required.

  17. Development and Application of Advanced Weather Prediction Technologies for the Wind Energy Industry (Invited)

    Science.gov (United States)

    Mahoney, W. P.; Wiener, G.; Liu, Y.; Myers, W.; Johnson, D.

    2010-12-01

    Wind energy decision makers are required to make critical judgments on a daily basis with regard to energy generation, distribution, demand, storage, and integration. Accurate knowledge of the present and future state of the atmosphere is vital in making these decisions. As wind energy portfolios expand, this forecast problem is taking on new urgency because wind forecast inaccuracies frequently lead to substantial economic losses and constrain the national expansion of renewable energy. Improved weather prediction and precise spatial analysis of small-scale weather events are crucial for renewable energy management. In early 2009, the National Center for Atmospheric Research (NCAR) began a collaborative project with Xcel Energy Services, Inc. to perform research and develop technologies to improve Xcel Energy's ability to increase the amount of wind energy in their generation portfolio. The agreement and scope of work was designed to provide highly detailed, localized wind energy forecasts to enable Xcel Energy to more efficiently integrate electricity generated from wind into the power grid. The wind prediction technologies are designed to help Xcel Energy operators make critical decisions about powering down traditional coal and natural gas-powered plants when sufficient wind energy is predicted. The wind prediction technologies have been designed to cover Xcel Energy wind resources spanning a region from Wisconsin to New Mexico. The goal of the project is not only to improve Xcel Energy’s wind energy prediction capabilities, but also to make technological advancements in wind and wind energy prediction, expand our knowledge of boundary layer meteorology, and share the results across the renewable energy industry. To generate wind energy forecasts, NCAR is incorporating observations of current atmospheric conditions from a variety of sources including satellites, aircraft, weather radars, ground-based weather stations, wind profilers, and even wind sensors on

  18. Gender identity outcomes in children with disorders/differences of sex development: Predictive factors.

    Science.gov (United States)

    Bakula, Dana M; Mullins, Alexandria J; Sharkey, Christina M; Wolfe-Christensen, Cortney; Mullins, Larry L; Wisniewski, Amy B

    2017-06-01

    Disorders/differences of sex development (DSD) comprise multiple congenital conditions in which chromosomal, gonadal, and/or anatomical sex are discordant. The prediction of future gender identity (i.e., self-identifying as male, female, or other) in children with DSD can be imprecise, and current knowledge about the development of gender identity in people with, and without DSD, is limited. However, sex of rearing is the strongest predictor of gender identity for the majority of individuals with various DSD conditions. When making decisions regarding sex of rearing biological factors (e.g., possession of a Y chromosome, degree and duration of pre- and postnatal androgen exposure, phenotypic presentation of the external genitalia, and fertility potential), social and cultural factors, as well as quality of life should be considered. Information on gender identity outcomes across a range of DSD diagnoses is presented to aid in sex of rearing assignment. Copyright © 2017 Elsevier Inc. All rights reserved.

  19. Research and development studies for predicting the thermal fatigue; Etudes de R and D pour la prediction de la fatigue thermique

    Energy Technology Data Exchange (ETDEWEB)

    Moulin, D.; Garnier, J.; Fissolo, A.; Lejeail, Y. [CEA, 75 - Paris (France); Stephan, J.M.; Moinereau, D.; Masson, J. [Electricite de France, Les Renardieres, 77 - Moret sur Loing (France). Direction des Etudes et Recherches

    2001-07-01

    This paper presents some studies in development or realized in the EDF and CEA laboratories, concerning the thermal fatigue damage in nuclear reactor components. The first part presents the basic principles and the methods of lifetime prediction. The second part gives some examples on sodium loop, water loop, welded junctions resistance to thermal fatigue and tests on fatigue specimen. (A.L.B.)

  20. Predicting inadequate bowel preparation for colonoscopy in participants receiving split-dose bowel preparation: development and validation of a prediction score

    NARCIS (Netherlands)

    Dik, V.K.; Moons, L.M.; Huyuk, M.; Schaar, P. van der; Cappel, W.H. de Vos Tot Nede; Borg, P.C. ter; Meijssen, M.A.; Ouwendijk, R.J.; Fevre, D.M. Le; Stouten, M.; Galien, O. van der; Hiemstra, T.J.; Monkelbaan, J.F.; Oijen, M.G. van; Siersema, P.D.

    2015-01-01

    BACKGROUND: Adequate bowel preparation is important for optimal colonoscopy. It is important to identify patients at risk for inadequate bowel preparation because this allows taking precautions in this specific group. OBJECTIVE: To develop a prediction score to identify patients at risk for

  1. Predicting inadequate bowel preparation for colonoscopy in participants receiving split-dose bowel preparation : Development and validation of a prediction score

    NARCIS (Netherlands)

    Dik, Vincent K.; Moons, Leon M G; Hüyük, Melek; Van Der Schaar, Peter; De Vos Tot Nederveen Cappel, Wouter H.; Ter Borg, Pieter C J; Meijssen, Maarten A C; Ouwendijk, Rob J T H; Le Fèvre, Doris M.; Stouten, Merijn; Van Der Galiën, Onno; Hiemstra, Theo J.; Monkelbaan, Jan F.; van Oijen, Martijn G. H.; Siersema, Peter D.; Tang, Thjon J.; Ter Borg, Frank; Kuipers, Ernst J.

    2015-01-01

    Background: Adequate bowel preparation is important for optimal colonoscopy. It is important to identify patients at risk for inadequate bowel preparation because this allows taking precautions in this specific group. Objective: To develop a prediction score to identify patients at risk for

  2. Discharge destination following lower limb fracture: development of a prediction model to assist with decision making.

    Science.gov (United States)

    Kimmel, Lara A; Holland, Anne E; Edwards, Elton R; Cameron, Peter A; De Steiger, Richard; Page, Richard S; Gabbe, Belinda

    2012-06-01

    Accurate prediction of the likelihood of discharge to inpatient rehabilitation following lower limb fracture made on admission to hospital may assist patient discharge planning and decrease the burden on the hospital system caused by delays in decision making. To develop a prognostic model for discharge to inpatient rehabilitation. Isolated lower extremity fracture cases (excluding fractured neck of femur), captured by the Victorian Orthopaedic Trauma Outcomes Registry (VOTOR), were extracted for analysis. A training data set was created for model development and validation data set for evaluation. A multivariable logistic regression model was developed based on patient and injury characteristics. Models were assessed using measures of discrimination (C-statistic) and calibration (Hosmer-Lemeshow (H-L) statistic). A total of 1429 patients met the inclusion criteria and were randomly split into training and test data sets. Increasing age, more proximal fracture type, compensation or private fund source for the admission, metropolitan location of residence, not working prior to injury and having a self-reported pre-injury disability were included in the final prediction model. The C-statistic for the model was 0.92 (95% confidence interval (CI) 0.88, 0.95) with an H-L statistic of χ(2)=11.62, p=0.17. For the test data set, the C-statistic was 0.86 (95% CI 0.83, 0.90) with an H-L statistic of χ(2)=37.98, plower limb fracture was developed with excellent discrimination although the calibration was reduced in the test data set. This model requires prospective testing but could form an integral part of decision making in regards to discharge disposition to facilitate timely and accurate referral to rehabilitation and optimise resource allocation. Copyright © 2011 Elsevier Ltd. All rights reserved.

  3. Interaction between striatal volume and DAT1 polymorphism predicts working memory development during adolescence

    Directory of Open Access Journals (Sweden)

    F. Nemmi

    2018-04-01

    Full Text Available There is considerable inter-individual variability in the rate at which working memory (WM develops during childhood and adolescence, but the neural and genetic basis for these differences are poorly understood. Dopamine-related genes, striatal activation and morphology have been associated with increased WM capacity after training. Here we tested the hypothesis that these factors would also explain some of the inter-individual differences in the rate of WM development.We measured WM performance in 487 healthy subjects twice: at age 14 and 19. At age 14 subjects underwent a structural MRI scan, and genotyping of five single nucleotide polymorphisms (SNPs in or close to the dopamine genes DRD2, DAT-1 and COMT, which have previously been associated with gains in WM after WM training. We then analyzed which biological factors predicted the rate of increase in WM between ages 14 and 19.We found a significant interaction between putamen size and DAT1/SLC6A3 rs40184 polymorphism, such that TC heterozygotes with a larger putamen at age 14 showed greater WM improvement at age 19.The effect of the DAT1 polymorphism on WM development was exerted in interaction with striatal morphology. These results suggest that development of WM partially share neuro-physiological mechanism with training-induced plasticity. Keywords: Working memory, Development, Dopamine, Striatum, DAT-1, rs40184

  4. MRI to predict prostate growth and development in children, adolescents and young adults.

    Science.gov (United States)

    Ren, Jing; Liu, Huijia; Wang, He; Wen, Didi; Huang, Xufang; Ren, Fang; Huan, Yi

    2015-02-01

    The purpose of this study was to investigate the use of MRI in predicting prostate growth and development. A total of 1,500 healthy male volunteers who underwent MRI of the pelvis were included in this prospective study. Subjects were divided into five groups according to age (group A, 2-5 years; group B, 6-10 years; group C, 11-15 years; group D, 16-20 years; group E, 21-25 years). Total prostate volume (TPV) as well as prostate central zone (CZ) and peripheral zone (PZ) were measured and evaluated on MRI. Data of the different groups were compared using variance analysis, Scheffé's method, Kruskal-Wallis H-test, and Pearson's correlation. Statistical significance was inferred at P development scores were 0.08, 0.69, 1.56, 2.38, and 2.74, respectively. Both TPVs and zonal anatomy scores varied significantly among the five groups (P = 0.000). TPV and zonal anatomy score increased with increasing age. MRI provides a reliable quantitative reference for prostate growth and development. • When and how the prostate develops after birth remains unclear. • Prostate volume increases rapidly after the age of 10 years. • MRI provides a reliable objective and quantitative reference for prostate growth and development.

  5. Polycystic ovarian morphology in normal women does not predict the development of polycystic ovary syndrome.

    Science.gov (United States)

    Murphy, M K; Hall, J E; Adams, J M; Lee, H; Welt, C K

    2006-10-01

    Polycystic ovarian morphology (PCOM) is present in 25% of normal women in the absence of polycystic ovary syndrome (PCOS); however, the natural history of PCOM is unknown. We hypothesized that the presence of PCOM predisposes the development of PCOS. The study was a longitudinal follow-up study over 8.2 +/- 5.2 yr (mean +/- sd; range 1.7-17.5 yr). The study took place in an outpatient setting. Women who took part in a previous study as a normal control and had an ultrasound examination (n = 40) participated. Subjects underwent an interval menstrual history, physical exam, blood sampling, and repeat ultrasound in the follicular phase. Development of PCOS was diagnosed by irregular menses and hyperandrogenism, in the absence of other disorders. Changes in ovarian morphology over time were evaluated. At the baseline visit, 23 women (57.5%) had PCOM and 17 (42.5%) had normal ovarian morphology. One subject with PCOM developed irregular menses and presumptive PCOS. Eleven subjects with PCOM no longer met the criteria for PCOM at follow-up. There was no factor that predicted the change to normal ovarian morphology at the follow-up visit. These data suggest that PCOM in women with regular ovulatory cycles does not commonly predispose the development of PCOS. Although it is unusual to develop PCOM if the ovaries are normal on first assessment, ovaries in women with PCOM no longer meet the criteria for PCOM in approximately half of cases over time.

  6. Development and validation of equations utilizing lamb vision system output to predict lamb carcass fabrication yields.

    Science.gov (United States)

    Cunha, B C N; Belk, K E; Scanga, J A; LeValley, S B; Tatum, J D; Smith, G C

    2004-07-01

    This study was performed to validate previous equations and to develop and evaluate new regression equations for predicting lamb carcass fabrication yields using outputs from a lamb vision system-hot carcass component (LVS-HCC) and the lamb vision system-chilled carcass LM imaging component (LVS-CCC). Lamb carcasses (n = 149) were selected after slaughter, imaged hot using the LVS-HCC, and chilled for 24 to 48 h at -3 to 1 degrees C. Chilled carcasses yield grades (YG) were assigned on-line by USDA graders and by expert USDA grading supervisors with unlimited time and access to the carcasses. Before fabrication, carcasses were ribbed between the 12th and 13th ribs and imaged using the LVS-CCC. Carcasses were fabricated into bone-in subprimal/primal cuts. Yields calculated included 1) saleable meat yield (SMY); 2) subprimal yield (SPY); and 3) fat yield (FY). On-line (whole-number) USDA YG accounted for 59, 58, and 64%; expert (whole-number) USDA YG explained 59, 59, and 65%; and expert (nearest-tenth) USDA YG accounted for 60, 60, and 67% of the observed variation in SMY, SPY, and FY, respectively. The best prediction equation developed in this trial using LVS-HCC output and hot carcass weight as independent variables explained 68, 62, and 74% of the variation in SMY, SPY, and FY, respectively. Addition of output from LVS-CCC improved predictive accuracy of the equations; the combined output equations explained 72 and 66% of the variability in SMY and SPY, respectively. Accuracy and repeatability of measurement of LM area made with the LVS-CCC also was assessed, and results suggested that use of LVS-CCC provided reasonably accurate (R2 = 0.59) and highly repeatable (repeatability = 0.98) measurements of LM area. Compared with USDA YG, use of the dual-component lamb vision system to predict cut yields of lamb carcasses improved accuracy and precision, suggesting that this system could have an application as an objective means for pricing carcasses in a value

  7. Validation of asphalt mixture pavement skid prediction model and development of skid prediction model for surface treatments.

    Science.gov (United States)

    2017-04-01

    Pavement skid resistance is primarily a function of the surface texture, which includes both microtexture and macrotexture. Earlier, under the Texas Department of Transportation (TxDOT) Research Project 0-5627, the researchers developed a method to p...

  8. Developing predictive insight into changing water systems: use-inspired hydrologic science for the Anthropocene

    Science.gov (United States)

    Thompson, S. E.; Sivapalan, M.; Harman, C. J.; Srinivasan, V.; Hipsey, M. R.; Reed, P.; Montanari, A.; Blöschl, G.

    2013-12-01

    Globally, many different kinds of water resources management issues call for policy- and infrastructure-based responses. Yet responsible decision-making about water resources management raises a fundamental challenge for hydrologists: making predictions about water resources on decadal- to century-long timescales. Obtaining insight into hydrologic futures over 100 yr timescales forces researchers to address internal and exogenous changes in the properties of hydrologic systems. To do this, new hydrologic research must identify, describe and model feedbacks between water and other changing, coupled environmental subsystems. These models must be constrained to yield useful insights, despite the many likely sources of uncertainty in their predictions. Chief among these uncertainties are the impacts of the increasing role of human intervention in the global water cycle - a defining challenge for hydrology in the Anthropocene. Here we present a research agenda that proposes a suite of strategies to address these challenges from the perspectives of hydrologic science research. The research agenda focuses on the development of co-evolutionary hydrologic modeling to explore coupling across systems, and to address the implications of this coupling on the long-time behavior of the coupled systems. Three research directions support the development of these models: hydrologic reconstruction, comparative hydrology and model-data learning. These strategies focus on understanding hydrologic processes and feedbacks over long timescales, across many locations, and through strategic coupling of observational and model data in specific systems. We highlight the value of use-inspired and team-based science that is motivated by real-world hydrologic problems but targets improvements in fundamental understanding to support decision-making and management. Fully realizing the potential of this approach will ultimately require detailed integration of social science and physical science

  9. Development of quantitative structure-activity relationship (QSAR) models to predict the carcinogenic potency of chemicals

    International Nuclear Information System (INIS)

    Venkatapathy, Raghuraman; Wang Chingyi; Bruce, Robert Mark; Moudgal, Chandrika

    2009-01-01

    Determining the carcinogenicity and carcinogenic potency of new chemicals is both a labor-intensive and time-consuming process. In order to expedite the screening process, there is a need to identify alternative toxicity measures that may be used as surrogates for carcinogenic potency. Alternative toxicity measures for carcinogenic potency currently being used in the literature include lethal dose (dose that kills 50% of a study population [LD 50 ]), lowest-observed-adverse-effect-level (LOAEL) and maximum tolerated dose (MTD). The purpose of this study was to investigate the correlation between tumor dose (TD 50 ) and three alternative toxicity measures as an estimator of carcinogenic potency. A second aim of this study was to develop a Classification and Regression Tree (CART) between TD 50 and estimated/experimental predictor variables to predict the carcinogenic potency of new chemicals. Rat TD 50 s of 590 structurally diverse chemicals were obtained from the Cancer Potency Database, and the three alternative toxicity measures considered in this study were estimated using TOPKAT, a toxicity estimation software. Though poor correlations were obtained between carcinogenic potency and the three alternative toxicity (both experimental and TOPKAT) measures for the CPDB chemicals, a CART developed using experimental data with no missing values as predictor variables provided reasonable estimates of TD 50 for nine chemicals that were part of an external validation set. However, if experimental values for the three alternative measures, mutagenicity and logP are not available in the literature, then either the CART developed using missing experimental values or estimated values may be used for making a prediction

  10. Thalamic functional connectivity predicts seizure laterality in individual TLE patients: application of a biomarker development strategy.

    Science.gov (United States)

    Barron, Daniel S; Fox, Peter T; Pardoe, Heath; Lancaster, Jack; Price, Larry R; Blackmon, Karen; Berry, Kristen; Cavazos, Jose E; Kuzniecky, Ruben; Devinsky, Orrin; Thesen, Thomas

    2015-01-01

    Noninvasive markers of brain function could yield biomarkers in many neurological disorders. Disease models constrained by coordinate-based meta-analysis are likely to increase this yield. Here, we evaluate a thalamic model of temporal lobe epilepsy that we proposed in a coordinate-based meta-analysis and extended in a diffusion tractography study of an independent patient population. Specifically, we evaluated whether thalamic functional connectivity (resting-state fMRI-BOLD) with temporal lobe areas can predict seizure onset laterality, as established with intracranial EEG. Twenty-four lesional and non-lesional temporal lobe epilepsy patients were studied. No significant differences in functional connection strength in patient and control groups were observed with Mann-Whitney Tests (corrected for multiple comparisons). Notwithstanding the lack of group differences, individual patient difference scores (from control mean connection strength) successfully predicted seizure onset zone as shown in ROC curves: discriminant analysis (two-dimensional) predicted seizure onset zone with 85% sensitivity and 91% specificity; logistic regression (four-dimensional) achieved 86% sensitivity and 100% specificity. The strongest markers in both analyses were left thalamo-hippocampal and right thalamo-entorhinal cortex functional connection strength. Thus, this study shows that thalamic functional connections are sensitive and specific markers of seizure onset laterality in individual temporal lobe epilepsy patients. This study also advances an overall strategy for the programmatic development of neuroimaging biomarkers in clinical and genetic populations: a disease model informed by coordinate-based meta-analysis was used to anatomically constrain individual patient analyses.

  11. DEVELOPMENT OF THE SOCIAL TENSION RISK PREDICTING ALGORITHM IN THE POPULATION OF CERTAIN REGIONS OF RUSSIA

    Directory of Open Access Journals (Sweden)

    A. B. Mulik

    2017-01-01

    Full Text Available Aim. The aim of the study was development of approaches to predict the risk of social tension for population of the Russian Federation regions.Methods. Theoretical studies based on the analysis of cartographic material from the National Atlas of Russia. The use of geo-information technologies has provided modeling of environmental load in the territory of certain regions of Russia. Experimental studies were performed using standard methods of psycho-physiological testing involving 336 persons 18-23 years old of both sexes.Results. As a fundamental biologically significant factor of the environment, differentiating the Russian Federation territory to areas with discrete actual physical effects, total solar radiation was determined. The subsequent allocation of model regions (Republic of Crimea, Rostov and Saratov regions based on the principle of minimizing regional differences associated factors of environmental pressure per person. Experimental studies have revealed persistent systemic relationships of phenotypic characteristics and tendency of person to neuropsychic tension. The risk of social tension for the study area population is predicted on the condition of finding more than two thirds of the representatives of sample within the borders of a high level of general non-specific reactivity of an organism.Main conclusions. The expediency of using the northern latitude as an integral index of differentiation of areas on the specifics of the severity of the physical factors of environmental impact on human activity is justified. The possibility of the application for the level of general nonspecific reactivity of an organism as a phenotypic trait marker of social tension risk is identified. An algorithm for predicting the risk of social tension among the population, compactly living in certain territories of the Russian Federation is designed. 

  12. Development of in-vehicle noise prediction models for Mumbai Metropolitan Region, India

    Directory of Open Access Journals (Sweden)

    Vishal Konbattulwar

    2016-08-01

    Full Text Available Traffic noise is one of the major sources of noise pollution in metropolitan regions causing various health hazards (e.g., long-term sleep disturbance, increase in blood pressure, physical tension, etc.. In this research, noise prediction models, which can measure the noise level experienced by the commuters while driving or traveling by motorized vehicles in the Mumbai Metropolitan Region, India, were developed. These models were developed by conducting a comprehensive study of various factors (e.g., vehicle speed, traffic volume and road characteristics, etc. affecting the levels of concentration of noise. A widespread data collection was done by conducting road trips of total length of 403.80 km via different modes of transport, such as air-conditioned (A/C car, non A/C car, bus and intermediate public transport (i.e., traditional 3-wheeler autos. Multiple regression analyses were performed to develop a functional relation between equivalent noise levels experienced by passengers while traveling (which was considered as a dependent variable and explanatory variables such as traffic characteristics, vehicle class, vehicle speed, various other location characteristics, etc. Noise levels are generally higher in the vicinity of intersections and signalized junctions. Independent data sets (for each mode of transport were used to validate the developed models. It was noted that maximum differences between observed and estimated values from the model were within the range of ±7.8% of the observed value.

  13. Understanding and predicting climate variations in the Middle East for sustainable water resource management and development

    Science.gov (United States)

    Samuels, Rana

    data from local stations are developed. These are used to develop scenarios for local rainfall statistics such as average annual amounts, dry spells, wet spells and drought persistence. This suite of models can provide information that is not attainable from existing tools in terms of its spatial and temporal resolution. Specifically, the goal is to project the impact of established global climate change scenarios in this region and, how much of the change might be mitigated by proposed CO2 reduction strategies. A major problem in this enterprise is to find the best way to integrate global climatic information with local rainfall data. From the climatologic perspective the problem is to find the right teleconnections. That is, non local or global measurable phenomena that influence local rainfall in a way that could be characterized and quantified statistically. From the computational perspective the challenge is to model these subtle, nonlinear relationships and to downscale the global effects into local predictions. Climate simulations to the year 2100 under selected climate change scenarios are used. Overall, the suite of models developed and presented can be applied to answer most questions from the different water users and planners. Farmers and the irrigation community can ask "What is the probability of rain over the next week?" Policy makers can ask "How much desalination capacity will I need to meet demand 90% of the time in the climate change scenario over the next 20 years?" Aquifer managers can ask "What is the expected recharge rate of the aquifers over the next decade?" The use of climate driven answers to these questions will help the region better prepare and adapt to future shifts in water resources and availability.

  14. The Operational Hydro-meteorological Ensemble Prediction System at Meteo-France and its representation interface for the French Service for Flood Prediction (SCHAPI) : description and undergoing developments.

    Science.gov (United States)

    Rousset-Regimbeau, F.; Martin, E.; Thirel, G.; Habets, F.; Coustau, M.; Roquelaure, S.; De Saint Aubin, C.; Ardilouze, C.

    2012-04-01

    The coupled physically-based hydro-meteorological model SAFRAN-ISBA-MODCOU (SIM) is developed at Meteo-France for many years. This fully distributed catchment model is used in a pre-operational mode since 2005 for producing mid-range ensemble streamflow forecasts based on the 51-member 10-day ECMWF EPS. Improvements have been made during the past few years.. First, a statistical adaptation has been performed to improve the meteorological ensemble predictions from the ECMWF. It has been developped over a 3-year archive, and assessed over a 1-year period. Its impact on the performance of the streamflow forecasts has been calculated over 8 months of predictions. Then, a past discharges assimilation system has been implemented in order to improve the initial states of these ensemble streamflow forecasts. It has been developped in the framework of a Phd thesis, and it is now evaluated in real-time conditions. Moreover, an improvement of the physics of the ISBA model (the exponential profile of the hydraulic conductivity in the soil) was implemented. Finally, this system provides ensemble 10-day streamflow prediction to the French National Service for Flood Prediction (SCHAPI). A collaboration between Meteo-France and SCHAPI led to the development of a new website. This website shows the streamflow predictions for about 200 selected river stations over France (selected regarding their interest for flood warning) , as well as alerts for high flows (two levels of high flows corresponding to the levels of risk of the French flood warning system). It aims at providing to the French hydrological forecaters a real-time tool for mid-range flood awareness.

  15. The Development of Mathematical Prediction Model to Predict Resilient Modulus for Natural Soil Stabilized by Pofa-Opc Additive for the Use in Unpaved Road Design

    Science.gov (United States)

    Gamil, Y. M. R.; Bakar, I. H.

    2016-07-01

    Resilient Modulus (Mr) is considered one of the most important parameters in the design of road structure. This paper describes the development of the mathematical model to predict resilient modulus of organic soil stabilized by the mix of Palm Oil Fuel Ash - Ordinary Portland Cement (POFA-OPC) soil stabilization additives. It aims to optimize the use of the use of POFA in soil stabilization. The optimization models enable to eliminate the arbitrary selection and its associated disadvantages in determination of the optimum additive proportion. The model was developed based on Scheffe regression theory. The mix proportions of the samples in the experiment were adopted from similar studies reported in the literature Twenty five samples were designed, prepared and then characterized for each mix proportion based on the MR in 28 days curing. The results are used to develop the mathematical prediction model. The model was statistically analyzed and verified for its adequacy and validity using F-test.

  16. The necessity for complex long-term predictions while designing systems for disposal of radwaste and role of those predictions in development of programs for environment protection

    International Nuclear Information System (INIS)

    Kedrovsky, O.L.; Schishitz, I.Y.

    1993-01-01

    Development of nuclear power in the future depends on solving two problems: creation of safe reactors; and reliable isolation of radwaste formed during all stages of the nuclear-fuel-cycle. The peculiarity of the second problem consists of the fact that considerable financial expenses are necessary for its decision. The range of the problem is characterized by the predictions of waste accumulation according to which summary activity of those materials (by the year 2000) will come up to 6 x 10 10 mCu. To successfully solve the radwaste isolation problem on the governmental level, it is necessary to formulate the corresponding regulation system. The main task of development of geological aspects of radwaste isolation consists of elimination of dangerous situations, reaching minimum damage effect, and development of a system for hydromonitoring, which includes blocks for search and standard prediction. The paper discusses the activities being carried out in Russia to solve the problems of radwaste disposal

  17. Challenges for the development of a biotic ligand model predicting copper toxicity in estuaries and seas.

    Science.gov (United States)

    de Polo, Anna; Scrimshaw, Mark D

    2012-02-01

    An effort is ongoing to develop a biotic ligand model (BLM) that predicts copper (Cu) toxicity in estuarine and marine environments. At present, the BLM accounts for the effects of water chemistry on Cu speciation, but it does not consider the influence of water chemistry on the physiology of the organisms. We discuss how chemistry affects Cu toxicity not only by controlling its speciation, but also by affecting the osmoregulatory physiology of the organism, which varies according to salinity. In an attempt to understand the mechanisms of Cu toxicity and predict its impacts, we explore the hypothesis that the common factor linking the main toxic effects of Cu is the enzyme carbonic anhydrase (CA), because it is a Cu target with multiple functions and salinity-dependent expression and activity. According to this hypothesis, the site of action of Cu in marine fish may be not only the gill, but also the intestine, because in this tissue CA plays an important role in ion transport and water adsorption. Therefore, the BLM of Cu toxicity to marine fish should also consider the intestine as a biotic ligand. Finally, we underline the need to incorporate the osmotic gradient into the BLM calculations to account for the influence of physiology on Cu toxicity. Copyright © 2011 SETAC.

  18. Development of formulation Q1As method for quadrupole noise prediction around a submerged cylinder

    Directory of Open Access Journals (Sweden)

    Yo-Seb Choi

    2017-09-01

    Full Text Available Recent research has shown that quadrupole noise has a significant influence on the overall characteristics of flow-induced noise and on the performance of underwater appendages such as sonar domes. However, advanced research generally uses the Ffowcs Williams–Hawkings analogy without considering the quadrupole source to reduce computational cost. In this study, flow-induced noise is predicted by using an LES turbulence model and a developed formulation, called the formulation Q1As method to properly take into account the quadrupole source. The noise around a circular cylinder in an underwater environment is examined for two cases with different velocities. The results from the method are compared to those obtained from the experiments and the permeable FW–H method. The results are in good agreement with the experimental data, with a difference of less than 1 dB, which indicates that the formulation Q1As method is suitable for use in predicting quadrupole noise around underwater appendages.

  19. Prediction Model of the Outer Radiation Belt Developed by Chungbuk National University

    Directory of Open Access Journals (Sweden)

    Dae-Kyu Shin

    2014-12-01

    Full Text Available The Earth’s outer radiation belt often suffers from drastic changes in the electron fluxes. Since the electrons can be a potential threat to satellites, efforts have long been made to model and predict electron flux variations. In this paper, we describe a prediction model for the outer belt electrons that we have recently developed at Chungbuk National University. The model is based on a one-dimensional radial diffusion equation with observationally determined specifications of a few major ingredients in the following way. First, the boundary condition of the outer edge of the outer belt is specified by empirical functions that we determine using the THEMIS satellite observations of energetic electrons near the boundary. Second, the plasmapause locations are specified by empirical functions that we determine using the electron density data of THEMIS. Third, the model incorporates the local acceleration effect by chorus waves into the one-dimensional radial diffusion equation. We determine this chorus acceleration effect by first obtaining an empirical formula of chorus intensity as a function of drift shell parameter L*, incorporating it as a source term in the one-dimensional diffusion equation, and lastly calibrating the term to best agree with observations of a certain interval. We present a comparison of the model run results with and without the chorus acceleration effect, demonstrating that the chorus effect has been incorporated into the model to a reasonable degree.

  20. Development and validation of a method to predict coal pillar life in South Africa

    Energy Technology Data Exchange (ETDEWEB)

    Van Der Merwe, J.N. [Pretoria Univ., Dept. of Mining Engineering (South Africa)

    2005-07-01

    One of the most difficult aspects of mine closure procedures, is to predict the long term stability of pillars in the case of coal mining. In South African coal mining, pillars have been designed since the late 1960's with the aid of a pillar strength formula based on statistical analysis of failed pillar cases by the well known team of Salamon and Munro. They developed the widely used power formula for pillar strength. Since that time, however, the data base of failed pillars has effectively doubled in size and re-analysis of the new data indicated that the original pillar strength of small pillars may have been over estimated. The data was then extensively re-analysed and a more effective linear formula for pillar strength was found. The most important differences between the two formulae are that the predicted strength of small pillars are lower and the strength of larger pillars, higher with the new formula. However, neither of the two methods explicitly cater for the prediction of the expected time of stability of coal pillars. No direct correlation between the safety factors and the period of stability of pillars could be found. It was then determined that the most frequent mode of pillar failure was by progressive scaling. Using the new formula to determine a minimum value of pillar safety factor (i.e. the safety factor at which failure can be taken as guaranteed to occur), the final sizes - after scaling - at which the failed pillars had to be in order to fail, were determined. The differences between the original dimensions and the postulated final dimensions were then used to calculate a rate of pillar scaling. The rate was then re-applied to the original data bases of both failed and intact pillar cases and distinct differences were found. The projected lives of the failed pillars were substantially shorter than the projected lives of the intact pillars. While this inspired confidence in the procedure, it was still based on an assumed and unproven

  1. Development and validation of a method to predict coal pillar life in South Africa

    International Nuclear Information System (INIS)

    Van Der Merwe, J.N.

    2005-01-01

    One of the most difficult aspects of mine closure procedures, is to predict the long term stability of pillars in the case of coal mining. In South African coal mining, pillars have been designed since the late 1960's with the aid of a pillar strength formula based on statistical analysis of failed pillar cases by the well known team of Salamon and Munro. They developed the widely used power formula for pillar strength. Since that time, however, the data base of failed pillars has effectively doubled in size and re-analysis of the new data indicated that the original pillar strength of small pillars may have been over estimated. The data was then extensively re-analysed and a more effective linear formula for pillar strength was found. The most important differences between the two formulae are that the predicted strength of small pillars are lower and the strength of larger pillars, higher with the new formula. However, neither of the two methods explicitly cater for the prediction of the expected time of stability of coal pillars. No direct correlation between the safety factors and the period of stability of pillars could be found. It was then determined that the most frequent mode of pillar failure was by progressive scaling. Using the new formula to determine a minimum value of pillar safety factor (i.e. the safety factor at which failure can be taken as guaranteed to occur), the final sizes - after scaling - at which the failed pillars had to be in order to fail, were determined. The differences between the original dimensions and the postulated final dimensions were then used to calculate a rate of pillar scaling. The rate was then re-applied to the original data bases of both failed and intact pillar cases and distinct differences were found. The projected lives of the failed pillars were substantially shorter than the projected lives of the intact pillars. While this inspired confidence in the procedure, it was still based on an assumed and unproven rate

  2. Soft sensor development for Mooney viscosity prediction in rubber mixing process based on GMMDJITGPR algorithm

    Science.gov (United States)

    Yang, Kai; Chen, Xiangguang; Wang, Li; Jin, Huaiping

    2017-01-01

    In rubber mixing process, the key parameter (Mooney viscosity), which is used to evaluate the property of the product, can only be obtained with 4-6h delay offline. It is quite helpful for the industry, if the parameter can be estimate on line. Various data driven soft sensors have been used to prediction in the rubber mixing. However, it always not functions well due to the phase and nonlinear property in the process. The purpose of this paper is to develop an efficient soft sensing algorithm to solve the problem. Based on the proposed GMMD local sample selecting criterion, the phase information is extracted in the local modeling. Using the Gaussian local modeling method within Just-in-time (JIT) learning framework, nonlinearity of the process is well handled. Efficiency of the new method is verified by comparing the performance with various mainstream soft sensors, using the samples from real industrial rubber mixing process.

  3. Development of Test Method for Simple Shear and Prediction of Hardening Behavior Considering the Branchings Effect

    International Nuclear Information System (INIS)

    Kim, Dongwook; Bang, Sungsik; Kim, Minsoo; Lee, Hyungyil; Kim, Naksoo

    2013-01-01

    In this study we establish a process to predict hardening behavior considering the Branchings effect for zircaloy-4 sheets. When a metal is compressed after tension in forming, the yield strength decreases. For this reason, the Branchings effect should be considered in FE simulations of spring-back. We suggested a suitable specimen size and a method for determining the optimum tightening torque for simple shear tests. Shear stress-strain curves are obtained for five materials. We developed a method to convert the shear load-displacement curve to the effective stress-strain curve with Fea. We simulated the simple shear forward/reverse test using the combined isotropic/kinematic hardening model. We also investigated the change of the load-displacement curve by varying the hardening coefficients. We determined the hardening coefficients so that they follow the hardening behavior of zircaloy-4 in experiments

  4. Development of Test Method for Simple Shear and Prediction of Hardening Behavior Considering the Branchings Effect

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Dongwook; Bang, Sungsik; Kim, Minsoo; Lee, Hyungyil; Kim, Naksoo [Sogang Univ., Seoul (Korea, Republic of)

    2013-10-15

    In this study we establish a process to predict hardening behavior considering the Branchings effect for zircaloy-4 sheets. When a metal is compressed after tension in forming, the yield strength decreases. For this reason, the Branchings effect should be considered in FE simulations of spring-back. We suggested a suitable specimen size and a method for determining the optimum tightening torque for simple shear tests. Shear stress-strain curves are obtained for five materials. We developed a method to convert the shear load-displacement curve to the effective stress-strain curve with Fea. We simulated the simple shear forward/reverse test using the combined isotropic/kinematic hardening model. We also investigated the change of the load-displacement curve by varying the hardening coefficients. We determined the hardening coefficients so that they follow the hardening behavior of zircaloy-4 in experiments.

  5. Analysis of the development and prediction of the population movement indicators in the Czech Republic

    Directory of Open Access Journals (Sweden)

    Milan Palát

    2004-01-01

    Full Text Available The paper is aimed at the presentation of findings obtained in the study of the development of time series of the population movement in the defined territorial unit and time interval 1993 – 2001. In addition to the exact evaluation of the dynamics and trend, the analysis of selected indicators is also aimed at a short-term extrapolation prediction. Developmental trends are specified of the population composition according to main age groups, live births according to legitimacy and the order of birth, deaths according to the cause of death, the first marriage according to a bridegroom and bride age, selected indicators of the marriage rate and divorce rate, divorce rate according to the number of underage children and foreign migration according to countries.

  6. Developing and evaluating polygenic risk prediction models for stratified disease prevention.

    Science.gov (United States)

    Chatterjee, Nilanjan; Shi, Jianxin; García-Closas, Montserrat

    2016-07-01

    Knowledge of genetics and its implications for human health is rapidly evolving in accordance with recent events, such as discoveries of large numbers of disease susceptibility loci from genome-wide association studies, the US Supreme Court ruling of the non-patentability of human genes, and the development of a regulatory framework for commercial genetic tests. In anticipation of the increasing relevance of genetic testing for the assessment of disease risks, this Review provides a summary of the methodologies used for building, evaluating and applying risk prediction models that include information from genetic testing and environmental risk factors. Potential applications of models for primary and secondary disease prevention are illustrated through several case studies, and future challenges and opportunities are discussed.

  7. Predictive factors for the development of persistent pain after breast cancer surgery

    DEFF Research Database (Denmark)

    Andersen, Kenneth Geving; Duriaud, Helle Molter; Jensen, Helle Elisabeth

    2015-01-01

    Previous studies have reported that 15% to 25% of patients treated for breast cancer experience long-term moderate-to-severe pain in the area of surgery, potentially lasting for several years. Few prospective studies have included all potential risk factors for the development of persistent pain...... after breast cancer surgery (PPBCS). The aim of this prospective cohort study was to comprehensively identify factors predicting PPBCS. Patients scheduled for primary breast cancer surgery were recruited. Assessments were conducted preoperatively, the first 3 days postoperatively, and 1 week, 6 months...... were included, and 475 (88%) were available for analysis at 1 year. At 1-year follow-up, the prevalence of moderate-to-severe pain at rest was 14% and during movement was 7%. Factors associated with pain at rest were age breast conserving surgery (OR: 2.0, P...

  8. Predicting dermal absorption of gas-phase chemicals: transient model development, evaluation, and application

    DEFF Research Database (Denmark)

    Gong, M.; Zhang, Y.; Weschler, Charles J.

    2014-01-01

    A transient model is developed to predict dermal absorption of gas-phase chemicals via direct air-to-skin-to-blood transport under non-steady-state conditions. It differs from published models in that it considers convective mass-transfer resistance in the boundary layer of air adjacent to the skin....... Results calculated with this transient model are in good agreement with the limited experimental results that are available for comparison. The sensitivity of the modeled estimates to key parameters is examined. The model is then used to estimate air-to-skin-to-blood absorption of six phthalate esters...... and less absorbed into blood than would a steady-state model. In the 7-day scenario, results calculated by the transient and steady-state models converge over a time period that varies between 3 and 4days for all but the largest phthalate (DEHP). Dermal intake is comparable to or larger than inhalation...

  9. Development and Life Prediction of Erosion Resistant Turbine Low Conductivity Thermal Barrier Coatings

    Science.gov (United States)

    Zhu, Dongming; Miller, Robert A.; Kuczmarski, Maria A.

    2010-01-01

    Future rotorcraft propulsion systems are required to operate under highly-loaded conditions and in harsh sand erosion environments, thereby imposing significant material design and durability issues. The incorporation of advanced thermal barrier coatings (TBC) in high pressure turbine systems enables engine designs with higher inlet temperatures, thus improving the engine efficiency, power density and reliability. The impact and erosion resistance of turbine thermal barrier coating systems are crucial to the turbine coating technology application, because a robust turbine blade TBC system is a prerequisite for fully utilizing the potential coating technology benefit in the rotorcraft propulsion. This paper describes the turbine blade TBC development in addressing the coating impact and erosion resistance. Advanced thermal barrier coating systems with improved performance have also been validated in laboratory simulated engine erosion and/or thermal gradient environments. A preliminary life prediction modeling approach to emphasize the turbine blade coating erosion is also presented.

  10. Prognosis of patients with whiplash-associated disorders consulting physiotherapy: development of a predictive model for recovery

    OpenAIRE

    Bohman, Tony; C?t?, Pierre; Boyle, Eleanor; Cassidy, J David; Carroll, Linda J; Skillgate, Eva

    2012-01-01

    Abstract Background Patients with whiplash-associated disorders (WAD) have a generally favourable prognosis, yet some develop longstanding pain and disability. Predicting who will recover from WAD shortly after a traffic collision is very challenging for health care providers such as physical therapists. Therefore, we aimed to develop a prediction model for the recovery of WAD in a cohort of patients who consulted physical therapists within six weeks after the injury. Methods Our cohort inclu...

  11. Clinical audit in gynecological cancer surgery: development of a risk scoring system to predict adverse events.

    Science.gov (United States)

    Kondalsamy-Chennakesavan, Srinivas; Bouman, Chantal; De Jong, Suzanne; Sanday, Karen; Nicklin, Jim; Land, Russell; Obermair, Andreas

    2009-12-01

    Advanced gynecological surgery undertaken in a specialized gynecologic oncology unit may be associated with significant perioperative morbidity. Validated risk prediction models are available for general surgical specialties but currently not for gynecological cancer surgery. The objective of this study was to evaluate risk factors for adverse events (AEs) of patients treated for suspected or proven gynecological cancer and to develop a clinical risk score (RS) to predict such AEs. AEs were prospectively recorded and matched with demographical, clinical and histopathological data on 369 patients who had an abdominal or laparoscopic procedure for proven or suspected gynecological cancer at a tertiary gynecological cancer center. Stepwise multiple logistic regression was used to determine the best predictors of AEs. For the risk score (RS), the coefficients from the model were scaled using a factor of 2 and rounded to the nearest integer to derive the risk points. Sum of all the risk points form the RS. Ninety-five patients (25.8%) had at least one AE. Twenty-nine (7.9%) and 77 (20.9%) patients experienced intra- and postoperative AEs respectively with 11 patients (3.0%) experiencing both. The independent predictors for any AE were complexity of the surgical procedure, elevated SGOT (serum glutamic oxaloacetic transaminase, > or /=35 U/L), higher ASA scores and overweight. The risk score can vary from 0 to 14. The risk for developing any AE is described by the formula 100 / (1 + e((3.697 - (RS /2)))). RS allows for quantification of the risk for AEs. Risk factors are generally not modifiable with the possible exception of obesity.

  12. Environmental prediction, risk assessment and extreme events: adaptation strategies for the developing world

    Science.gov (United States)

    Webster, Peter J.; Jian, Jun

    2011-01-01

    The uncertainty associated with predicting extreme weather events has serious implications for the developing world, owing to the greater societal vulnerability to such events. Continual exposure to unanticipated extreme events is a contributing factor for the descent into perpetual and structural rural poverty. We provide two examples of how probabilistic environmental prediction of extreme weather events can support dynamic adaptation. In the current climate era, we describe how short-term flood forecasts have been developed and implemented in Bangladesh. Forecasts of impending floods with horizons of 10 days are used to change agricultural practices and planning, store food and household items and evacuate those in peril. For the first time in Bangladesh, floods were anticipated in 2007 and 2008, with broad actions taking place in advance of the floods, grossing agricultural and household savings measured in units of annual income. We argue that probabilistic environmental forecasts disseminated to an informed user community can reduce poverty caused by exposure to unanticipated extreme events. Second, it is also realized that not all decisions in the future can be made at the village level and that grand plans for water resource management require extensive planning and funding. Based on imperfect models and scenarios of economic and population growth, we further suggest that flood frequency and intensity will increase in the Ganges, Brahmaputra and Yangtze catchments as greenhouse-gas concentrations increase. However, irrespective of the climate-change scenario chosen, the availability of fresh water in the latter half of the twenty-first century seems to be dominated by population increases that far outweigh climate-change effects. Paradoxically, fresh water availability may become more critical if there is no climate change. PMID:22042897

  13. Development and Validation of Predictive Models of Cardiac Mortality and Transplantation in Resynchronization Therapy

    Directory of Open Access Journals (Sweden)

    Eduardo Arrais Rocha

    2015-01-01

    Full Text Available Abstract Background: 30-40% of cardiac resynchronization therapy cases do not achieve favorable outcomes. Objective: This study aimed to develop predictive models for the combined endpoint of cardiac death and transplantation (Tx at different stages of cardiac resynchronization therapy (CRT. Methods: Prospective observational study of 116 patients aged 64.8 ± 11.1 years, 68.1% of whom had functional class (FC III and 31.9% had ambulatory class IV. Clinical, electrocardiographic and echocardiographic variables were assessed by using Cox regression and Kaplan-Meier curves. Results: The cardiac mortality/Tx rate was 16.3% during the follow-up period of 34.0 ± 17.9 months. Prior to implantation, right ventricular dysfunction (RVD, ejection fraction < 25% and use of high doses of diuretics (HDD increased the risk of cardiac death and Tx by 3.9-, 4.8-, and 5.9-fold, respectively. In the first year after CRT, RVD, HDD and hospitalization due to congestive heart failure increased the risk of death at hazard ratios of 3.5, 5.3, and 12.5, respectively. In the second year after CRT, RVD and FC III/IV were significant risk factors of mortality in the multivariate Cox model. The accuracy rates of the models were 84.6% at preimplantation, 93% in the first year after CRT, and 90.5% in the second year after CRT. The models were validated by bootstrapping. Conclusion: We developed predictive models of cardiac death and Tx at different stages of CRT based on the analysis of simple and easily obtainable clinical and echocardiographic variables. The models showed good accuracy and adjustment, were validated internally, and are useful in the selection, monitoring and counseling of patients indicated for CRT.

  14. Predictive maintenance technology development at G.A. Siwabessy multipurpose reactor

    Energy Technology Data Exchange (ETDEWEB)

    Jupiter Sitorus Pane; Imron, M.; Sapto Hartoko; Sentot Alibasya Harahap [Multipurpose Research Reactor G.A. Siwabessy, National Nuclear Energy Agency (Indonesia)

    1999-10-01

    Safe operation of reactor is certainly influenced by condition of system and component equipped to the reactor's system. In order to maintain the condition of that systems and components, RSG-GAS has arranged maintenance program with time-basis. All 6 (six) groups of reactor systems are maintained within interval of weekly, monthly, three monthly, six-monthly, yearly, five-yearly appropriately. The experience showed that event though the maintenance was performed persistently, the condition of system and component are still not able to determine exactly. The possibility of accidental failure is open since the failure factor are varied and complicated. In order to limit an uncertainty of the component condition a based maintenance shall be introduced. An infrared investigation and manual vibration analysis had been used to diagnose the condition of some RSG-GAS' components. In addition, other alternative technology for predictive maintenance was developed. It is started by computerizing the database maintenance and doing historical review for its aging management, and developing data acquisition and processing equipment using Lab View computer program for collecting and processing signal data from dynamics system. This paper describes briefly the status of those development results. (author)

  15. MRI to predict prostate growth and development in children, adolescents and young adults

    International Nuclear Information System (INIS)

    Ren, Jing; Liu, Huijia; Wen, Didi; Huang, Xufang; Ren, Fang; Huan, Yi; Wang, He

    2015-01-01

    The purpose of this study was to investigate the use of MRI in predicting prostate growth and development. A total of 1,500 healthy male volunteers who underwent MRI of the pelvis were included in this prospective study. Subjects were divided into five groups according to age (group A, 2-5 years; group B, 6-10 years; group C, 11-15 years; group D, 16-20 years; group E, 21-25 years). Total prostate volume (TPV) as well as prostate central zone (CZ) and peripheral zone (PZ) were measured and evaluated on MRI. Data of the different groups were compared using variance analysis, Scheffe's method, Kruskal-Wallis H-test, and Pearson's correlation. Statistical significance was inferred at P 3 , 0.05 cm 3 , 2.83 cm 3 , 8.32 cm 3, and 11.56 cm 3 , respectively, and the median prostate development scores were 0.08, 0.69, 1.56, 2.38, and 2.74, respectively. Both TPVs and zonal anatomy scores varied significantly among the five groups (P = 0.000). TPV and zonal anatomy score increased with increasing age. MRI provides a reliable quantitative reference for prostate growth and development. (orig.)

  16. Corruption, development and governance indicators predict invasive species risk from trade.

    Science.gov (United States)

    Brenton-Rule, Evan C; Barbieri, Rafael F; Lester, Philip J

    2016-06-15

    Invasive species have an enormous global impact, with international trade being the leading pathway for their introduction. Current multinational trade deals under negotiation will dramatically change trading partnerships and pathways. These changes have considerable potential to influence biological invasions and global biodiversity. Using a database of 47 328 interceptions spanning 10 years, we demonstrate how development and governance socio-economic indicators of trading partners can predict exotic species interceptions. For import pathways associated with vegetable material, a significantly higher risk of exotic species interceptions was associated with countries that are poorly regulated, have more forest cover and have surprisingly low corruption. Corruption and indicators such as political stability or adherence to rule of law were important in vehicle or timber import pathways. These results will be of considerable value to policy makers, primarily by shifting quarantine procedures to focus on countries of high risk based on their socio-economic status. Further, using New Zealand as an example, we demonstrate how a ninefold reduction in incursions could be achieved if socio-economic indicators were used to select trade partners. International trade deals that ignore governance and development indicators may facilitate introductions and biodiversity loss. Development and governance within countries clearly have biodiversity implications beyond borders. © 2016 The Author(s).

  17. Development of a viscoplastic dynamic fracture mechanics treatment for crack arrest predictions in a PTS event

    International Nuclear Information System (INIS)

    Kanninen, M.F.; Hudak, S.J. Jr.; Reed, K.W.; Dexter, R.J.; Polch, E.Z.; Cardinal, J.W.; Achenbach, J.D.; Popelar, C.H.

    1986-01-01

    The objective of this research is to develop a fundamentally correct methodology for the prediction of crack arrest at the high upper shelf conditions occurring in a postulated pressurized thermal shock (PTS) event. The effort is aimed at the development of a versatile finite-element method for the solution of time-dependent boundary value problems that admit inertia effects, a prescribed spatial temperature distribution, and viscoplastic constitutive and fracture behavior. Supporting this development are (1) material characterization and fracture experimentation, (2) detailed mathematical analyses of the near-tip region, (3) elastodynamic fracture analysis, and (4) elastic-plastic tearing instability analyses. As a first step, dynamic-viscoplastic analyses are currently being made of the wide plate tests being performed by the National Bureau of Standards in a companion HSST program. Some preliminary conclusions drawn from this work and from the supporting research activities are offered in this paper. The outstanding critical issues that subsequent research must focus on are also described

  18. Predictive maintenance technology development at G.A. Siwabessy multipurpose reactor

    International Nuclear Information System (INIS)

    Jupiter Sitorus Pane; Imron, M.; Sapto Hartoko; Sentot Alibasya Harahap

    1999-01-01

    Safe operation of reactor is certainly influenced by condition of system and component equipped to the reactor's system. In order to maintain the condition of that systems and components, RSG-GAS has arranged maintenance program with time-basis. All 6 (six) groups of reactor systems are maintained within interval of weekly, monthly, three monthly, six-monthly, yearly, five-yearly appropriately. The experience showed that event though the maintenance was performed persistently, the condition of system and component are still not able to determine exactly. The possibility of accidental failure is open since the failure factor are varied and complicated. In order to limit an uncertainty of the component condition a based maintenance shall be introduced. An infrared investigation and manual vibration analysis had been used to diagnose the condition of some RSG-GAS' components. In addition, other alternative technology for predictive maintenance was developed. It is started by computerizing the database maintenance and doing historical review for its aging management, and developing data acquisition and processing equipment using Lab View computer program for collecting and processing signal data from dynamics system. This paper describes briefly the status of those development results. (author)

  19. Theory of Mind Predicts Emotion Knowledge Development in Head Start Children.

    Science.gov (United States)

    Seidenfeld, Adina M; Johnson, Stacy R; Cavadel, Elizabeth Woodburn; Izard, Carroll E

    2014-10-01

    Emotion knowledge (EK) enables children to identify emotions in themselves and others and its development facilitates emotion recognition in complex social situations. Social-cognitive processes, such as theory of mind (ToM), may contribute to developing EK by helping children realize the inherent variability associated with emotion expression across individuals and situations. The present study explored how ToM, particularly false belief understanding, in preschool predicts children's developing EK in kindergarten. Participants were 60 3- to 5-year-old Head Start children. ToM and EK measures were obtained from standardized child tasks. ToM scores were positively related to performance on an EK task in kindergarten after controlling for preschool levels of EK and verbal ability. Exploratory analyses provided preliminary evidence that ToM serves as an indirect effect between verbal ability and EK. Early intervention programs may benefit from including lessons on ToM to help promote socio-emotional learning, specifically EK. This consideration may be the most fruitful when the targeted population is at-risk.

  20. Hypocalcemia development in patients operated for primary hyperparathyroidism: Can it be predicted preoperatively?

    Science.gov (United States)

    Kaya, Cafer; Tam, Abbas Ali; Dirikoç, Ahmet; Kılıçyazgan, Aylin; Kılıç, Mehmet; Türkölmez, Şeyda; Ersoy, Reyhan; Çakır, Bekir

    2016-10-01

    Primary hyperparathyroidism (PHP) is a common endocrine disease, and its most effective treatment is surgery. Postoperative hypocalcemia is a morbidity of parathyroid surgeries, and it may extend hospitalization durations. The purpose of this study is to determine the predictive factors related to the development of hypocalcemia and hungry bone syndrome (HBS) in patients who underwent parathyroidectomy for PHP. Laboratory data comprising parathyroid hormone (PTH), calcium, phosphate, 25-OHD, albumin, magnesium, alkaline phosphatase (ALP), blood urea nitrogen (BUN), and thyroid stimulating hormone (TSH) of the patients were recorded preoperatively, on the 1st and 4th days postoperatively, and in the 6th postoperative month, and their neck ultrasound (US) and bone densitometry data were also recorded. Hypocalcemia was seen in 63 patients (38.4%) on the 1st day after parathyroidectomy. Ten patients (6.1%) had permanent hypocalcemia in the 6th month after surgery. Out of the patients who underwent parathyroidectomy for PHP, 22 (13.4%) had HBS. The incidence of postoperative hypocalcemia was higher in patients who underwent parathyroidectomy for PHP, who had parathyroid hyperplasia, and who had osteoporosis. Preoperative PTH, ALP, and BUN values were higher in those patients who developed HBS. Furthermore, HBS was more common in patients who had osteoporosis, who had parathyroid hyperplasia, and who underwent thyroidectomy simultaneously with parathyroidectomy. As a result, patients who have the risk factors for development of hypocalcemia and HBS should be monitored more attentively during the perioperative period.

  1. Positive parenting predicts the development of adolescent brain structure: A longitudinal study

    Directory of Open Access Journals (Sweden)

    Sarah Whittle

    2014-04-01

    Full Text Available Little work has been conducted that examines the effects of positive environmental experiences on brain development to date. The aim of this study was to prospectively investigate the effects of positive (warm and supportive maternal behavior on structural brain development during adolescence, using longitudinal structural MRI. Participants were 188 (92 female adolescents, who were part of a longitudinal adolescent development study that involved mother–adolescent interactions and MRI scans at approximately 12 years old, and follow-up MRI scans approximately 4 years later. FreeSurfer software was used to estimate the volume of limbic-striatal regions (amygdala, hippocampus, caudate, putamen, pallidum, and nucleus accumbens and the thickness of prefrontal regions (anterior cingulate and orbitofrontal cortices across both time points. Higher frequency of positive maternal behavior during the interactions predicted attenuated volumetric growth in the right amygdala, and accelerated cortical thinning in the right anterior cingulate (males only and left and right orbitofrontal cortices, between baseline and follow up. These results have implications for understanding the biological mediators of risk and protective factors for mental disorders that have onset during adolescence.

  2. Positive parenting predicts the development of adolescent brain structure: a longitudinal study.

    Science.gov (United States)

    Whittle, Sarah; Simmons, Julian G; Dennison, Meg; Vijayakumar, Nandita; Schwartz, Orli; Yap, Marie B H; Sheeber, Lisa; Allen, Nicholas B

    2014-04-01

    Little work has been conducted that examines the effects of positive environmental experiences on brain development to date. The aim of this study was to prospectively investigate the effects of positive (warm and supportive) maternal behavior on structural brain development during adolescence, using longitudinal structural MRI. Participants were 188 (92 female) adolescents, who were part of a longitudinal adolescent development study that involved mother-adolescent interactions and MRI scans at approximately 12 years old, and follow-up MRI scans approximately 4 years later. FreeSurfer software was used to estimate the volume of limbic-striatal regions (amygdala, hippocampus, caudate, putamen, pallidum, and nucleus accumbens) and the thickness of prefrontal regions (anterior cingulate and orbitofrontal cortices) across both time points. Higher frequency of positive maternal behavior during the interactions predicted attenuated volumetric growth in the right amygdala, and accelerated cortical thinning in the right anterior cingulate (males only) and left and right orbitofrontal cortices, between baseline and follow up. These results have implications for understanding the biological mediators of risk and protective factors for mental disorders that have onset during adolescence. Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.

  3. Dispositional mindfulness is predicted by structural development of the insula during late adolescence

    Directory of Open Access Journals (Sweden)

    S. Friedel

    2015-08-01

    Full Text Available Adolescence is a critical period of development, in which the increasing social and cognitive demands of independence need to be met by enhanced self-regulatory abilities. The cultivation of mindfulness has been associated with improved self-regulation in adult populations, and it is theorized that one neurodevelopmental mechanism that supports this capacity is the development of the prefrontal cortex. The current study examined the neurodevelopmental mechanisms associated with dispositional mindfulness in adolescence. Using a longitudinal within-persons design, 82 participants underwent structural magnetic resonance imaging (MRI assessments at approximately ages 16 and 19, and also completed self-reported measurements of mindfulness at age 19. It was hypothesized that adolescents who demonstrated greater thinning of frontal cortical regions between the age of 16 and 19 would exhibit higher dispositional mindfulness levels at age 19. Results indicated that, contrary to predictions, adolescents with higher levels of mindfulness demonstrated less thinning in the left anterior insula. By contrast, higher IQ was associated with greater thinning of the right caudal middle frontal and right superior frontal regions. The involvement of insula development in mindfulness is consistent with a direct role for this structure in managing self-regulation, and in doing so concords with recent models of self-referential interoceptive awareness.

  4. Heterogeneity in development of aspects of working memory predicts longitudinal attention deficit hyperactivity disorder symptom change.

    Science.gov (United States)

    Karalunas, Sarah L; Gustafsson, Hanna C; Dieckmann, Nathan F; Tipsord, Jessica; Mitchell, Suzanne H; Nigg, Joel T

    2017-08-01

    The role of cognitive mechanisms in the clinical course of neurodevelopmental disorders is poorly understood. Attention Deficit Hyperactivity Disorder (ADHD) is emblematic in that numerous alterations in cognitive development are apparent, yet how they relate to changes in symptom expression with age is unclear. To resolve the role of cognitive mechanisms in ADHD, a developmental perspective that takes into account expected within-group heterogeneity is needed. The current study uses an accelerated longitudinal design and latent trajectory growth mixture models in a sample of children ages 7-13 years carefully characterized as with (n = 437) and without (n = 297) ADHD to (a) identify heterogeneous developmental trajectories for response inhibition, visual spatial working memory maintenance, and delayed reward discounting and (b) to assess the relationships between these cognitive trajectories and ADHD symptom change. Best-fitting models indicated multiple trajectory classes in both the ADHD and typically developing samples, as well as distinct relationships between each cognitive process and ADHD symptom change. Developmental change in response inhibition and delayed reward discounting were unrelated to ADHD symptom change, while individual differences in the rate of visual spatial working memory maintenance improvement predicted symptom remission in ADHD. Characterizing heterogeneity in cognitive development will be crucial for clarifying mechanisms of symptom persistence and recovery. Results here suggest working memory maintenance may be uniquely related to ADHD symptom improvement. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  5. Grand challenges in developing a predictive understanding of global fire dynamics

    Science.gov (United States)

    Randerson, J. T.; Chen, Y.; Wiggins, E. B.; Andela, N.; Morton, D. C.; Veraverbeke, S.; van der Werf, G.

    2017-12-01

    High quality satellite observations of burned area and fire thermal anomalies over the past two decades have transformed our understanding of climate, ecosystem, and human controls on the spatial and temporal distribution of landscape fires. The satellite observations provide evidence for a rapid and widespread loss of fire from grassland and savanna ecosystems worldwide. Continued expansion of industrial agriculture suggests that observed declines in global burned area are likely to continue in future decades, with profound consequences for ecosystem function and the habitat of many endangered species. Satellite time series also highlight the importance of El Niño-Southern Oscillation and other climate modes as drivers of interannual variability. In many regions, lead times between climate indices and fire activity are considerable, enabling the development of early warning prediction systems for fire season severity. With the recent availability of high-resolution observations from Suomi NPP, Landsat 8, and Sentinel 2, the field of global fire ecology is poised to make even more significant breakthroughs over the next decade. With these new observations, it may be possible to reduce uncertainties in the spatial pattern of burned area by several fold. It is difficult to overstate the importance of these new data constraints for improving our understanding of fire impacts on human health and radiative forcing of climate change. A key research challenge in this context is to understand how the loss of global burned area will affect magnitude of the terrestrial carbon sink and trends in atmospheric composition. Advances in prognostic fire modeling will require new approaches linking agriculture with landscape fire dynamics. A critical need in this context is the development of predictive models of road networks and other drivers of land fragmentation, and a closer integration of fragmentation information with algorithms predicting fire spread. Concurrently, a better

  6. Development and validation of prediction models for endometrial cancer in postmenopausal bleeding.

    Science.gov (United States)

    Wong, Alyssa Sze-Wai; Cheung, Chun Wai; Fung, Linda Wen-Ying; Lao, Terence Tzu-Hsi; Mol, Ben Willem J; Sahota, Daljit Singh

    2016-08-01

    To develop and assess the accuracy of risk prediction models to diagnose endometrial cancer in women having postmenopausal bleeding (PMB). A retrospective cohort study of 4383 women in a One-stop PMB clinic from a university teaching hospital in Hong Kong. Clinical risk factors, transvaginal ultrasonic measurement of endometrial thickness (ET) and endometrial histology were obtained from consecutive women between 2002 and 2013. Two models to predict risk of endometrial cancer were developed and assessed, one based on patient characteristics alone and a second incorporated ET with patient characteristics. Endometrial histology was used as the reference standard. The split-sample internal validation and bootstrapping technique were adopted. The optimal threshold for prediction of endometrial cancer by the final models was determined using a receiver-operating characteristics (ROC) curve and Youden Index. The diagnostic gain was compared to a reference strategy of measuring ET only by comparing the AUC using the Delong test. Out of 4383 women with PMB, 168 (3.8%) were diagnosed with endometrial cancer. ET alone had an area under curve (AUC) of 0.92 (95% confidence intervals [CIs] 0.89-0.94). In the patient characteristics only model, independent predictors of cancer were age at presentation, age at menopause, body mass index, nulliparity and recurrent vaginal bleeding. The AUC and Youdens Index of the patient characteristic only model were respectively 0.73 (95% CI 0.67-0.80) and 0.72 (Sensitivity=66.5%; Specificity=68.9%; +ve LR=2.14; -ve LR=0.49). ET, age at presentation, nulliparity and recurrent vaginal bleeding were independent predictors in the patient characteristics plus ET model. The AUC and Youdens Index of the patient characteristic plus ET model where respectively 0.92 (95% CI 0.88-0.96) and 0.71 (Sensitivity=82.7%; Specificity=88.3%; +ve LR=6.38; -ve LR=0.2). Comparison of AUC indicated that a history alone model was inferior to a model using ET alone

  7. Development of a Simple Clinical Risk Score for Early Prediction of Severe Dengue in Adult Patients.

    Directory of Open Access Journals (Sweden)

    Ing-Kit Lee

    Full Text Available We aimed to develop and validate a risk score to aid in the early identification of laboratory-confirmed dengue patients at high risk of severe dengue (SD (i.e. severe plasma leakage with shock or respiratory distress, or severe bleeding or organ impairment. We retrospectively analyzed data of 1184 non-SD patients at hospital presentation and 69 SD patients before SD onset. We fit a logistic regression model using 85% of the population and converted the model coefficients to a numeric risk score. Subsequently, we validated the score using the remaining 15% of patients. Using the derivation cohort, two scoring algorithms for predicting SD were developed: models 1 (dengue illness ≤4 days and 2 (dengue illness >4 days. In model 1, we identified four variables: age ≥65 years, minor gastrointestinal bleeding, leukocytosis, and platelet count ≥100×109 cells/L. Model 1 (ranging from -2 to +6 points showed good discrimination between SD and non-SD, with an area under the receiver operating characteristic curve (AUC of 0.848 (95% confidence interval [CI], 0.771-0.924. The optimal cutoff value for model 1 was 1 point, with a sensitivity and specificity for predicting SD of 70.3% and 90.6%, respectively. In model 2 (ranging from 0 to +3 points, significant predictors were age ≥65 years and leukocytosis. Model 2 showed an AUC of 0.859 (95% CI, 0.756-0.963, with an optimal cutoff value of 1 point (sensitivity, 80.3%; specificity, 85.8%. The median interval from hospital presentation to SD was 1 day. This finding underscores the importance of close monitoring, timely resuscitation of shock including intravenous fluid adjustment and early correction of dengue-related complications to prevent the progressive dengue severity. In the validation data, AUCs of 0.904 (95% CI, 0.825-0.983 and 0.917 (95% CI, 0.833-1.0 in models 1 and 2, respectively, were achieved. The observed SD rates (in both cohorts were 50% for those with a score of ≥2 points

  8. Development of equations, based on milk intake, to predict starter feed intake of preweaned dairy calves.

    Science.gov (United States)

    Silva, A L; DeVries, T J; Tedeschi, L O; Marcondes, M I

    2018-04-16

    There is a lack of studies that provide models or equations capable of predicting starter feed intake (SFI) for milk-fed dairy calves. Therefore, a multi-study analysis was conducted to identify variables that influence SFI, and to develop equations to predict SFI in milk-fed dairy calves up to 64 days of age. The database was composed of individual data of 176 calves from eight experiments, totaling 6426 daily observations of intake. The information collected from the studies were: birth BW (kg), SFI (kg/day), fluid milk or milk replacer intake (MI; l/day), sex (male or female), breed (Holstein or Holstein×Gyr crossbred) and age (days). Correlations between SFI and the quantitative variables MI, birth BW, metabolic birth BW, fat intake, CP intake, metabolizable energy intake, and age were calculated. Subsequently, data were graphed, and based on a visual appraisal of the pattern of the data, an exponential function was chosen. Data were evaluated using a meta-analysis approach to estimate fixed and random effects of the experiments using nonlinear mixed coefficient statistical models. A negative correlation between SFI and MI was observed (r=-0.39), but age was positively correlated with SFI (r=0.66). No effect of liquid feed source (milk or milk replacer) was observed in developing the equation. Two equations, significantly different for all parameters, were fit to predict SFI for calves that consume less than 5 (SFI5) l/day of milk or milk replacer: ${\\rm SFI}_{{\\,\\lt\\,5}} {\\equals}0.1839_{{\\,\\pm\\,0.0581}} {\\times}{\\rm MI}{\\times}{\\rm exp}^{{\\left( {\\left( {0.0333_{{\\,\\pm\\,0.0021 }} {\\minus}0.0040_{{\\,\\pm\\,0.0011}} {\\times}{\\rm MI}} \\right){\\times}\\left( {{\\rm A}{\\minus}{\\rm }\\left( {0.8302_{{\\,\\pm\\,0.5092}} {\\plus}6.0332_{{\\,\\pm\\,0.3583}} {\\times}{\\rm MI}} \\right)} \\right)} \\right)}} {\\minus}\\left( {0.12{\\times}{\\rm MI}} \\right)$ ; ${\\rm SFI}_{{\\,\\gt\\,5}} {\\equals}0.1225_{{\\,\\pm\\,0.0005 }} {\\times

  9. Maternal Weight Predicts Children's Psychosocial Development via Parenting Stress and Emotional Availability.

    Science.gov (United States)

    Bergmann, Sarah; Schlesier-Michel, Andrea; Wendt, Verena; Grube, Matthias; Keitel-Korndörfer, Anja; Gausche, Ruth; von Klitzing, Kai; Klein, Annette M

    2016-01-01

    Maternal obesity has been shown to be a risk factor for obesity in children and may also affect children's psychosocial outcomes. It is not yet clear whether there are also psycho-emotional mechanisms explaining the effects of maternal weight on young children's weight and psychosocial development. We aimed to evaluate whether maternal body mass index (BMI), mother-child emotional availability (EA), and maternal parenting stress are associated with children's weight and psychosocial development (i.e., internalizing/externalizing symptoms and social competence) and whether these predictors interact with each other. This longitudinal study included three assessment points (~11 months apart). The baseline sample consisted of N = 194 mothers and their children aged 5-47 months (M = 28.18, SD = 8.44, 99 girls). At t 1, we measured maternal weight and height to calculate maternal BMI. We videotaped mother-child interactions, coding them with the EA Scales (fourth edition). We assessed maternal parenting stress with the Parenting Stress Index (PSI) short form. At t 1 to t 3, we measured height and weight of children and calculated BMI-SDS scores. Children's externalizing and internalizing problems (t 1-t 3) and social competence (t 3, N = 118) were assessed using questionnaires: Child Behavior Checklist (CBCL 1.5-5), Strengths and Difficulties Questionnaire (SDQ: prosocial behavior), and a checklist for behavioral problems at preschool age (VBV 3-6: social-emotional competence). By applying structural equation modeling (SEM) and a latent regression analysis, we found maternal BMI to predict higher BMI-SDS and a poorer psychosocial development (higher externalizing symptoms, lower social competence) in children. Higher parenting stress predicted higher levels of externalizing and internalizing symptoms and lower social competence. Better maternal EA was associated with higher social competence. We found parenting stress to serve as a mediator in the association between

  10. Maternal Weight Predicts Children's Psychosocial Development via Parenting Stress and Emotional Availability

    Science.gov (United States)

    Bergmann, Sarah; Schlesier-Michel, Andrea; Wendt, Verena; Grube, Matthias; Keitel-Korndörfer, Anja; Gausche, Ruth; von Klitzing, Kai; Klein, Annette M.

    2016-01-01

    Introduction: Maternal obesity has been shown to be a risk factor for obesity in children and may also affect children's psychosocial outcomes. It is not yet clear whether there are also psycho-emotional mechanisms explaining the effects of maternal weight on young children's weight and psychosocial development. We aimed to evaluate whether maternal body mass index (BMI), mother–child emotional availability (EA), and maternal parenting stress are associated with children's weight and psychosocial development (i.e., internalizing/externalizing symptoms and social competence) and whether these predictors interact with each other. Methods: This longitudinal study included three assessment points (~11 months apart). The baseline sample consisted of N = 194 mothers and their children aged 5–47 months (M = 28.18, SD = 8.44, 99 girls). At t1, we measured maternal weight and height to calculate maternal BMI. We videotaped mother–child interactions, coding them with the EA Scales (fourth edition). We assessed maternal parenting stress with the Parenting Stress Index (PSI) short form. At t1 to t3, we measured height and weight of children and calculated BMI–SDS scores. Children's externalizing and internalizing problems (t1–t3) and social competence (t3, N = 118) were assessed using questionnaires: Child Behavior Checklist (CBCL 1.5–5), Strengths and Difficulties Questionnaire (SDQ: prosocial behavior), and a checklist for behavioral problems at preschool age (VBV 3–6: social-emotional competence). Results: By applying structural equation modeling (SEM) and a latent regression analysis, we found maternal BMI to predict higher BMI–SDS and a poorer psychosocial development (higher externalizing symptoms, lower social competence) in children. Higher parenting stress predicted higher levels of externalizing and internalizing symptoms and lower social competence. Better maternal EA was associated with higher social competence. We found parenting stress to serve as

  11. Maternal weight predicts children’s psychosocial development via parenting stress and emotional availability

    Directory of Open Access Journals (Sweden)

    Sarah Bergmann

    2016-08-01

    Full Text Available Introduction: Maternal obesity has been shown to be a risk factor for obesity in children and may also affect children’s psychosocial outcomes. It is not yet clear whether there are also psycho-emotional mechanisms explaining the effects of maternal weight on young children’s weight and psychosocial development. We aimed to evaluate whether maternal body mass index (BMI, mother-child emotional availability (EA and maternal parenting stress are associated with children’s weight and psychosocial development (i.e. internalizing/externalizing symptoms and social competence and whether these predictors interact with each other. Methods: This longitudinal study included 3 assessment points (approx. 11 months apart. The baseline sample consisted of N=194 mothers and their children aged 5 to 47 months (M=28.18, SD=8.44, 99 girls. At t1, we measured maternal weight and height to calculate maternal BMI. We videotaped mother-child interactions, coding them with the Emotional Availability Scales (4th edition. We assessed maternal parenting stress with the Parenting Stress Index (PSI short form. At t1 to t3, we measured height and weight of children and calculated BMI-SDS scores. Children’s externalizing and internalizing problems (t1-t3 and social competence (t3, N=118 were assessed using questionnaires: Child Behavior Checklist (CBCL1, 5-5, Strength and Difficulties Questionnaire (SDQ: prosocial behavior and a checklist for behavioral problems at preschool age (VBV 3-6: social-emotional competence. Results: By applying structural equation modeling (SEM and a latent regression analysis, we found maternal BMI to predict higher BMI-SDS and a poorer psychosocial development (higher externalizing symptoms, lower social competence in children. Higher parenting stress predicted higher levels of externalizing and internalizing symptoms and lower social competence. Better maternal EA was associated with higher social competence. We found parenting stress to

  12. Development and Validation of a Clinically Based Risk Calculator for the Transdiagnostic Prediction of Psychosis

    Science.gov (United States)

    Rutigliano, Grazia; Stahl, Daniel; Davies, Cathy; Bonoldi, Ilaria; Reilly, Thomas; McGuire, Philip

    2017-01-01

    Importance The overall effect of At Risk Mental State (ARMS) services for the detection of individuals who will develop psychosis in secondary mental health care is undetermined. Objective To measure the proportion of individuals with a first episode of psychosis detected by ARMS services in secondary mental health services, and to develop and externally validate a practical web-based individualized risk calculator tool for the transdiagnostic prediction of psychosis in secondary mental health care. Design, Setting, and Participants Clinical register-based cohort study. Patients were drawn from electronic, real-world, real-time clinical records relating to 2008 to 2015 routine secondary mental health care in the South London and the Maudsley National Health Service Foundation Trust. The study included all patients receiving a first index diagnosis of nonorganic and nonpsychotic mental disorder within the South London and the Maudsley National Health Service Foundation Trust in the period between January 1, 2008, and December 31, 2015. Data analysis began on September 1, 2016. Main Outcomes and Measures Risk of development of nonorganic International Statistical Classification of Diseases and Related Health Problems, Tenth Revision psychotic disorders. Results A total of 91 199 patients receiving a first index diagnosis of nonorganic and nonpsychotic mental disorder within South London and the Maudsley National Health Service Foundation Trust were included in the derivation (n = 33 820) or external validation (n = 54 716) data sets. The mean age was 32.97 years, 50.88% were men, and 61.05% were white race/ethnicity. The mean follow-up was 1588 days. The overall 6-year risk of psychosis in secondary mental health care was 3.02 (95% CI, 2.88-3.15), which is higher than the 6-year risk in the local general population (0.62). Compared with the ARMS designation, all of the International Statistical Classification of Diseases and Related Health Problems

  13. Interfacing models of wildlife habitat and human development to predict the future distribution of puma habitat

    Science.gov (United States)

    Burdett, Christopher L.; Crooks, Kevin R.; Theobald, David M.; Wilson, Kenneth R.; Boydston, Erin E.; Lyren, Lisa A.; Fisher, Robert N.; Vickers, T. Winston; Morrison, Scott A.; Boyce, Walter M.

    2010-01-01

    The impact of human land uses on ecological systems typically differ relative to how extensively natural conditions are modified. Exurban development is intermediate-intensity residential development that often occurs in natural landscapes. Most species-habitat models do not evaluate the effects of such intermediate levels of human development and even fewer predict how future development patterns might affect the amount and configuration of habitat. We addressed these deficiencies by interfacing a habitat model with a spatially-explicit housing-density model to study the effect of human land uses on the habitat of pumas (Puma concolor) in southern California. We studied the response of pumas to natural and anthropogenic features within their home ranges and how mortality risk varied across a gradient of human development. We also used our housing-density model to estimate past and future housing densities and model the distribution of puma habitat in 1970, 2000, and 2030. The natural landscape for pumas in our study area consisted of riparian areas, oak woodlands, and open, conifer forests embedded in a chaparral matrix. Pumas rarely incorporated suburban or urban development into their home ranges, which is consistent with the hypothesis that the behavioral decisions of individuals can be collectively manifested as population-limiting factors at broader spatial scales. Pumas incorporated rural and exurban development into their home ranges, apparently perceiving these areas as modified, rather than non-habitat. Overall, pumas used exurban areas less than expected and showed a neutral response to rural areas. However, individual pumas that selected for or showed a neutral response to exurban areas had a higher risk of mortality than pumas that selected against exurban habitat. Exurban areas are likely hotspots for puma-human conflict in southern California. Approximately 10% of our study area will transform from exurban, rural, or undeveloped areas to suburban or

  14. Validity of Predictive Equations for Resting Energy Expenditure Developed for Obese Patients: Impact of Body Composition Method

    Science.gov (United States)

    Achamrah, Najate; Jésus, Pierre; Grigioni, Sébastien; Rimbert, Agnès; Petit, André; Déchelotte, Pierre; Folope, Vanessa; Coëffier, Moïse

    2018-01-01

    Predictive equations have been specifically developed for obese patients to estimate resting energy expenditure (REE). Body composition (BC) assessment is needed for some of these equations. We assessed the impact of BC methods on the accuracy of specific predictive equations developed in obese patients. REE was measured (mREE) by indirect calorimetry and BC assessed by bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA). mREE, percentages of prediction accuracy (±10% of mREE) were compared. Predictive equations were studied in 2588 obese patients. Mean mREE was 1788 ± 6.3 kcal/24 h. Only the Müller (BIA) and Harris & Benedict (HB) equations provided REE with no difference from mREE. The Huang, Müller, Horie-Waitzberg, and HB formulas provided a higher accurate prediction (>60% of cases). The use of BIA provided better predictions of REE than DXA for the Huang and Müller equations. Inversely, the Horie-Waitzberg and Lazzer formulas provided a higher accuracy using DXA. Accuracy decreased when applied to patients with BMI ≥ 40, except for the Horie-Waitzberg and Lazzer (DXA) formulas. Müller equations based on BIA provided a marked improvement of REE prediction accuracy than equations not based on BC. The interest of BC to improve REE predictive equations accuracy in obese patients should be confirmed. PMID:29320432

  15. A new predictive indicator for development of pressure ulcers in bedridden patients based on common laboratory tests results.

    Science.gov (United States)

    Hatanaka, N; Yamamoto, Y; Ichihara, K; Mastuo, S; Nakamura, Y; Watanabe, M; Iwatani, Y

    2008-04-01

    Various scales have been devised to predict development of pressure ulcers on the basis of clinical and laboratory data, such as the Braden Scale (Braden score), which is used to monitor activity and skin conditions of bedridden patients. However, none of these scales facilitates clinically reliable prediction. To develop a clinical laboratory data-based predictive equation for the development of pressure ulcers. Subjects were 149 hospitalised patients with respiratory disorders who were monitored for the development of pressure ulcers over a 3-month period. The proportional hazards model (Cox regression) was used to analyse the results of 12 basic laboratory tests on the day of hospitalisation in comparison with Braden score. Pressure ulcers developed in 38 patients within the study period. A Cox regression model consisting solely of Braden scale items showed that none of these items contributed to significantly predicting pressure ulcers. Rather, a combination of haemoglobin (Hb), C-reactive protein (CRP), albumin (Alb), age, and gender produced the best model for prediction. Using the set of explanatory variables, we created a new indicator based on a multiple logistic regression equation. The new indicator showed high sensitivity (0.73) and specificity (0.70), and its diagnostic power was higher than that of Alb, Hb, CRP, or the Braden score alone. The new indicator may become a more useful clinical tool for predicting presser ulcers than Braden score. The new indicator warrants verification studies to facilitate its clinical implementation in the future.

  16. Predictive value of serum sST2 in preschool wheezers for development of asthma with high FeNO.

    Science.gov (United States)

    Ketelaar, M E; van de Kant, K D; Dijk, F N; Klaassen, E M; Grotenboer, N S; Nawijn, M C; Dompeling, E; Koppelman, G H

    2017-11-01

    Wheezing is common in childhood. However, current prediction models of pediatric asthma have only modest accuracy. Novel biomarkers and definition of subphenotypes may improve asthma prediction. Interleukin-1-receptor-like-1 (IL1RL1 or ST2) is a well-replicated asthma gene and associates with eosinophilia. We investigated whether serum sST2 predicts asthma and asthma with elevated exhaled NO (FeNO), compared to the commonly used Asthma Prediction Index (API). Using logistic regression modeling, we found that serum sST2 levels in 2-3 years-old wheezers do not predict doctors' diagnosed asthma at age 6 years. Instead, sST2 predicts a subphenotype of asthma characterized by increased levels of FeNO, a marker for eosinophilic airway inflammation. Herein, sST2 improved the predictive value of the API (AUC=0.70, 95% CI 0.56-0.84), but had also significant predictive value on its own (AUC=0.65, 95% CI 0.52-0.79). Our study indicates that sST2 in preschool wheezers has predictive value for the development of eosinophilic airway inflammation in asthmatic children at school age. © 2017 EAACI and John Wiley and Sons A/S. Published by John Wiley and Sons Ltd.

  17. [As cardioprotective and angiogenic biomarker, can ghrelin predict coronary collateral development and severity of coronary atherosclerosis?

    Science.gov (United States)

    Akboğa, Mehmet Kadri; Taçoy, Gülten; Yılmaz Demirtaş, Canan; Türkoğlu, Sedat; Boyacı, Bülent; Çengel, Atiye

    2017-06-01

    Ghrelin exerts protective effects on cardiovascular system by inhibiting progression of atherosclerosis, supression of vascular inflammation, and stimulating angiogenesis. Thus, the aim of this study was to investigate the effect of serum ghrelin on coronary collateral development and SYNTAX score in patients with severe coronary artery disease. Total of 91 patients who had ≥90% stenosis in at least one major coronary artery were prospectively included in this cross-sectional, observational study. Collateral degree was graded according to Rentrop-Cohen classification. Patients with grade 2 or 3 collateral degree were allocated to Good Collateral Group and patients with grade 0 or 1 collateral degree were included in Poor Collateral Group. Ghrelin and vascular endothelial growth factor A (VEGF-A) levels were measured using radioimmunoassay and ELISA kits. Serum ghrelin and VEGF-A levels were significantly higher in Good Collateral Group. Furthermore, ghrelin level showed significant inverse correlation with SYNTAX score (r=0.348; p=0.001). In multivariable regression analysis, ghrelin (Odds ratio, 1.013; 95% confidence interval, 1.011-1.017; p=0.013), VEGF-A, fasting plasma glucose and presence of chronic total occlusion were independent predictors of good collateral development. In receiver operating characteristic curve analysis, ghrelin value cut-off point of ≥781 pg/mL predicted good collateral development with sensitivity of 73.1% and specificity of 67.7%. Findings suggested that ghrelin has antioxidant and antiinflammatory properties that protect endothelial functions and also stimulate angiogenesis, which results in development of good coronary collateral and inhibition of progression of coronary atherosclerosis.

  18. A Circulating microRNA Signature Predicts Age-Based Development of Lymphoma.

    Directory of Open Access Journals (Sweden)

    Afshin Beheshti

    Full Text Available Extensive epidemiological data have demonstrated an exponential rise in the incidence of non-Hodgkin lymphoma (NHL that is associated with increasing age. The molecular etiology of this remains largely unknown, which impacts the effectiveness of treatment for patients. We proposed that age-dependent circulating microRNA (miRNA signatures in the host influence diffuse large B cell lymphoma (DLBCL development. Our objective was to examine tumor development in an age-based DLBCL system using an inventive systems biology approach. We harnessed a novel murine model of spontaneous DLBCL initiation (Smurf2-deficient at two age groups: 3 and 15 months old. All Smurf2-deficient mice develop visible DLBCL tumor starting at 15 months of age. Total miRNA was isolated from serum, bone marrow and spleen and were collected for all age groups for Smurf2-deficient mice and age-matched wild-type C57BL/6 mice. Using systems biology techniques, we identified a list of 10 circulating miRNAs being regulated in both the spleen and bone marrow that were present in DLBCL forming mice starting at 3 months of age that were not present in the control mice. Furthermore, this miRNA signature was found to occur circulating in the blood and it strongly impacted JUN and MYC oncogenic signaling. In addition, quantification of the miRNA signature was performed via Droplet Digital PCR technology. It was discovered that a key miRNA signature circulates throughout a host prior to the formation of a tumor starting at 3 months old, which becomes further modulated by age and yielded calculation of a 'carcinogenic risk score'. This novel age-based circulating miRNA signature may potentially be leveraged as a DLBCL risk profile at a young age to predict future lymphoma development or disease progression as well as for potential innovative miRNA-based targeted therapeutic strategies in lymphoma.

  19. Readmissions and death after ICU discharge: development and validation of two predictive models.

    Directory of Open Access Journals (Sweden)

    Omar Badawi

    Full Text Available INTRODUCTION: Early discharge from the ICU is desirable because it shortens time in the ICU and reduces care costs, but can also increase the likelihood of ICU readmission and post-discharge unanticipated death if patients are discharged before they are stable. We postulated that, using eICU® Research Institute (eRI data from >400 ICUs, we could develop robust models predictive of post-discharge death and readmission that may be incorporated into future clinical information systems (CIS to assist ICU discharge planning. METHODS: Retrospective, multi-center, exploratory cohort study of ICU survivors within the eRI database between 1/1/2007 and 3/31/2011. EXCLUSION CRITERIA: DNR or care limitations at ICU discharge and discharge to location external to hospital. Patients were randomized (2∶1 to development and validation cohorts. Multivariable logistic regression was performed on a broad range of variables including: patient demographics, ICU admission diagnosis, admission severity of illness, laboratory values and physiologic variables present during the last 24 hours of the ICU stay. Multiple imputation was used to address missing data. The primary outcomes were the area under the receiver operator characteristic curves (auROC in the validation cohorts for the models predicting readmission and death within 48 hours of ICU discharge. RESULTS: 469,976 and 234,987 patients representing 219 hospitals were in the development and validation cohorts. Early ICU readmission and death was experienced by 2.54% and 0.92% of all patients, respectively. The relationship between predictors and outcomes (death vs readmission differed, justifying the need for separate models. The models for early readmission and death produced auROCs of 0.71 and 0.92, respectively. Both models calibrated well across risk groups. CONCLUSIONS: Our models for death and readmission after ICU discharge showed good to excellent discrimination and good calibration. Although

  20. DIAGNOSIS AND PREDICTION OF CHOLECYSTITIS DEVELOPMENT ON THE BASIS OF NEURAL NETWORK ANALYSIS OF RISK FACTORS

    Directory of Open Access Journals (Sweden)

    V. A. Lazarenko

    2017-01-01

    Full Text Available Purpose. To develop an artificial neural network for diagnosing and predicting the development of cholecystitis based on an analysis of data on risk factors, and to explore the possibilities of its application in real clinical practice.Materials and methods. The collection of materials was held in at the hospitals of the city of Kursk and included a survey of 488 patients with hepatopancreatoduodenal diseases. 203 patients were suffering from cholecystitis, in 285 patients the diagnosis of cholecystitis was excluded. Analysis of risk factors’ data (such as sex, age, bad habits, profession, family relationships, etc. was carried out using an internally developed artificial neural network (multilayer perceptron with hyperbolic tangent as the activation function. The computer program “System of Intellectual Analysis and Diagnosis of Diseases” was registered in accordance with established procedure (Certificate No. 2017613090.Results. The use of neural network analysis of data on risk factors in comparison with the processing of information that forms a clinical picture allows the diagnosis of a potential disease with cholecystitis before the onset of symptoms. The training of the artificial neural network with a quantitative output coding the age of probable hospitalization made it possible to generate an array of values, signifficantly (α ≤ 0.001 not differing from the empirical data. The difference between the mean calculated and mean empirical values was 0.45 for the training set and 1.75 for the clinical approbation group. The mean absolute error was within the range of 1.87–2.07 years.Conclusion. 1. The proposed new approach to the diagnosis and prognosis of cholecystitis has demonstrated its effectiveness, which is confirmed in clinical approbation by the levels of sensitivity (94.44%, m = 2.26 and specificity (80.6%, m = 3.9.2. The error in predicting the age of probable hospitalization of patients with cholecystitis did not

  1. Development of a model to predict flow oscillations in low-flow sodium boiling

    International Nuclear Information System (INIS)

    Levin, A.E.; Griffith, P.

    1980-04-01

    Tests performed in a small scale water loop showed that voiding oscillations, similar to those observed in sodium, were present in water, as well. An analytical model, appropriate for either sodium or water, was developed and used to describe the water flow behavior. The experimental results indicate that water can be successfully employed as a sodium simulant, and further, that the condensation heat transfer coefficient varies significantly during the growth and collapse of vapor slugs during oscillations. It is this variation, combined with the temperature profile of the unheated zone above the heat source, which determines the oscillatory behavior of the system. The analytical program has produced a model which qualitatively does a good job in predicting the flow behavior in the wake experiment. The amplitude discrepancies are attributable to experimental uncertainties and model inadequacies. Several parameters (heat transfer coefficient, unheated zone temperature profile, mixing between hot and cold fluids during oscillations) are set by the user. Criteria for the comparison of water and sodium experiments have been developed

  2. USE OF DASHBOARDS IN PREDICTING THE DEVELOPMENT OF THE COMPANY USING NEURAL NETWORKS IN HOTEL MANAGEMENT

    Directory of Open Access Journals (Sweden)

    Peter GALLO

    2018-04-01

    Full Text Available Tourism development currently represents a very important part of national economics and its development and growth. To ensure growth, managers are looking for new effective tools to optimize decision making. This paper addresses the issue of dashboards based on neural networks and their utilization in managerial decision-making processes. Dashboard based reporting is oriented towards the tourism sector in Slovakia. The result of the research is the proposed balanced ranking and prediction model using financial and nonfinancial indicators with the application of artificial intelligence which allows to reach high level of efficiency and accuracy in evaluation of financial and nonfinancial health of companies operating in the hospitality sector. The proposed model also brings a new managerial and scientific point of view on the in-depth analysis of performance of these facilities. The main function of the proposed model is to classify health of a hotel. For this purpose, the MLP (Multi-Layer Perceptron feedforward artificial neural network using backward propagation of errors was chosen as a training method.

  3. The Essential Resilience Scale: Instrument Development and Prediction of Perceived Health and Behaviour.

    Science.gov (United States)

    Chen, Xinguang; Wang, Yan; Yan, Yaqiong

    2016-12-01

    Further advancement in stress and health research calls for better tools to assess resilience. In this study, we developed the Essential Resilience Scale (ERS) and investigated the association between ERS scores and several health and behaviour measures. We developed the ERS with an operationalized definition of resilience-an individual's capability to anticipate, be flexible with and bounce back from three types of traumatic and adverse events (physical, emotional and social). The 15-item ERS was assessed using survey data from a diverse sample (n = 238, aged 18-45 years, 76 rural-to-urban migrants, 85 rural residents, 77 urban residents) recruited in Wuhan, China. Results showed a high reliability of the ERS (α = 0.94). Confirmatory factor analysis demonstrated a satisfactory fit of the proposed second-order ERS measurement model (goodness-of-fit index = 0.94, comparative fit index = 0.98, root mean square error of approximation = 0.06, chi-square/df = 1.75). Multivariate regression analysis indicated that ERS scores significantly predicted perceived health status, stress, anxiety, depression and cigarette smoking after controlling for important covariates. Findings of this study indicate high reliability and validity of the scale and its potential use in advancing stress and health research. Further studies are implied to provide additional support for the ERS and its relations with other health outcomes. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

  4. Uncertainty quantification of cinematic imaging for development of predictive simulations of turbulent combustion.

    Energy Technology Data Exchange (ETDEWEB)

    Lawson, Matthew; Debusschere, Bert J.; Najm, Habib N.; Sargsyan, Khachik; Frank, Jonathan H.

    2010-09-01

    Recent advances in high frame rate complementary metal-oxide-semiconductor (CMOS) cameras coupled with high repetition rate lasers have enabled laser-based imaging measurements of the temporal evolution of turbulent reacting flows. This measurement capability provides new opportunities for understanding the dynamics of turbulence-chemistry interactions, which is necessary for developing predictive simulations of turbulent combustion. However, quantitative imaging measurements using high frame rate CMOS cameras require careful characterization of the their noise, non-linear response, and variations in this response from pixel to pixel. We develop a noise model and calibration tools to mitigate these problems and to enable quantitative use of CMOS cameras. We have demonstrated proof of principle for image de-noising using both wavelet methods and Bayesian inference. The results offer new approaches for quantitative interpretation of imaging measurements from noisy data acquired with non-linear detectors. These approaches are potentially useful in many areas of scientific research that rely on quantitative imaging measurements.

  5. Irregular menses predicts ovarian cancer: Prospective evidence from the Child Health and Development Studies.

    Science.gov (United States)

    Cirillo, Piera M; Wang, Erica T; Cedars, Marcelle I; Chen, Lee-May; Cohn, Barbara A

    2016-09-01

    We tested the hypothesis that irregular menstruation predicts lower risk for ovarian cancer, possibly due to less frequent ovulation. We conducted a 50-year prospective study of 15,528 mothers in the Child Health and Development Studies cohort recruited from the Kaiser Foundation Health Plan from 1959 to 1966. Irregular menstruation was classified via medical record and self-report at age 26. We identified 116 cases and 84 deaths due to ovarian cancer through 2011 via linkage to the California Cancer Registry and Vital Statistics. Contrary to expectation, women with irregular menstrual cycles had a higher risk of ovarian cancer incidence and mortality over the 50-year follow-up. Associations increased with age (p irregular menstruation and ovarian cancer-we unexpectedly found higher risk for women with irregular cycles. These women are easy to identify and many may have polycystic ovarian syndrome. Classifying high-risk phenotypes such as irregular menstruation creates opportunities to find novel early biomarkers, refine clinical screening protocols and potentially develop new risk reduction strategies. These efforts can lead to earlier detection and better survival for ovarian cancer. © 2016 UICC.

  6. Development and Validation of a Predictive Model for Functional Outcome After Stroke Rehabilitation: The Maugeri Model.

    Science.gov (United States)

    Scrutinio, Domenico; Lanzillo, Bernardo; Guida, Pietro; Mastropasqua, Filippo; Monitillo, Vincenzo; Pusineri, Monica; Formica, Roberto; Russo, Giovanna; Guarnaschelli, Caterina; Ferretti, Chiara; Calabrese, Gianluigi

    2017-12-01

    Prediction of outcome after stroke rehabilitation may help clinicians in decision-making and planning rehabilitation care. We developed and validated a predictive tool to estimate the probability of achieving improvement in physical functioning (model 1) and a level of independence requiring no more than supervision (model 2) after stroke rehabilitation. The models were derived from 717 patients admitted for stroke rehabilitation. We used multivariable logistic regression analysis to build each model. Then, each model was prospectively validated in 875 patients. Model 1 included age, time from stroke occurrence to rehabilitation admission, admission motor and cognitive Functional Independence Measure scores, and neglect. Model 2 included age, male gender, time since stroke onset, and admission motor and cognitive Functional Independence Measure score. Both models demonstrated excellent discrimination. In the derivation cohort, the area under the curve was 0.883 (95% confidence intervals, 0.858-0.910) for model 1 and 0.913 (95% confidence intervals, 0.884-0.942) for model 2. The Hosmer-Lemeshow χ 2 was 4.12 ( P =0.249) and 1.20 ( P =0.754), respectively. In the validation cohort, the area under the curve was 0.866 (95% confidence intervals, 0.840-0.892) for model 1 and 0.850 (95% confidence intervals, 0.815-0.885) for model 2. The Hosmer-Lemeshow χ 2 was 8.86 ( P =0.115) and 34.50 ( P =0.001), respectively. Both improvement in physical functioning (hazard ratios, 0.43; 0.25-0.71; P =0.001) and a level of independence requiring no more than supervision (hazard ratios, 0.32; 0.14-0.68; P =0.004) were independently associated with improved 4-year survival. A calculator is freely available for download at https://goo.gl/fEAp81. This study provides researchers and clinicians with an easy-to-use, accurate, and validated predictive tool for potential application in rehabilitation research and stroke management. © 2017 American Heart Association, Inc.

  7. Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View.

    Science.gov (United States)

    Luo, Wei; Phung, Dinh; Tran, Truyen; Gupta, Sunil; Rana, Santu; Karmakar, Chandan; Shilton, Alistair; Yearwood, John; Dimitrova, Nevenka; Ho, Tu Bao; Venkatesh, Svetha; Berk, Michael

    2016-12-16

    As more and more researchers are turning to big data for new opportunities of biomedical discoveries, machine learning models, as the backbone of big data analysis, are mentioned more often in biomedical journals. However, owing to the inherent complexity of machine learning methods, they are prone to misuse. Because of the flexibility in specifying machine learning models, the results are often insufficiently reported in research articles, hindering reliable assessment of model validity and consistent interpretation of model outputs. To attain a set of guidelines on the use of machine learning predictive models within clinical settings to make sure the models are correctly applied and sufficiently reported so that true discoveries can be distinguished from random coincidence. A multidisciplinary panel of machine learning experts, clinicians, and traditional statisticians were interviewed, using an iterative process in accordance with the Delphi method. The process produced a set of guidelines that consists of (1) a list of reporting items to be included in a research article and (2) a set of practical sequential steps for developing predictive models. A set of guidelines was generated to enable correct application of machine learning models and consistent reporting of model specifications and results in biomedical research. We believe that such guidelines will accelerate the adoption of big data analysis, particularly with machine learning methods, in the biomedical research community. ©Wei Luo, Dinh Phung, Truyen Tran, Sunil Gupta, Santu Rana, Chandan Karmakar, Alistair Shilton, John Yearwood, Nevenka Dimitrova, Tu Bao Ho, Svetha Venkatesh, Michael Berk. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 16.12.2016.

  8. Development of a prediction model for residual disease in newly diagnosed advanced ovarian cancer.

    Science.gov (United States)

    Janco, Jo Marie Tran; Glaser, Gretchen; Kim, Bohyun; McGree, Michaela E; Weaver, Amy L; Cliby, William A; Dowdy, Sean C; Bakkum-Gamez, Jamie N

    2015-07-01

    To construct a tool, using computed tomography (CT) imaging and preoperative clinical variables, to estimate successful primary cytoreduction for advanced epithelial ovarian cancer (EOC). Women who underwent primary cytoreductive surgery for stage IIIC/IV EOC at Mayo Clinic between 1/2/2003 and 12/30/2011 and had preoperative CT images of the abdomen and pelvis within 90days prior to their surgery available for review were included. CT images were reviewed for large-volume ascites, diffuse peritoneal thickening (DPT), omental cake, lymphadenopathy (LP), and spleen or liver involvement. Preoperative factors included age, body mass index (BMI), Eastern Cooperative Oncology Group performance status (ECOG PS), American Society of Anesthesiologists (ASA) score, albumin, CA-125, and thrombocytosis. Two prediction models were developed to estimate the probability of (i) complete and (ii) suboptimal cytoreduction (residual disease (RD) >1cm) using multivariable logistic analysis with backward and stepwise variable selection methods. Internal validation was assessed using bootstrap resampling to derive an optimism-corrected estimate of the c-index. 279 patients met inclusion criteria: 143 had complete cytoreduction, 26 had suboptimal cytoreduction (RD>1cm), and 110 had measurable RD ≤1cm. On multivariable analysis, age, absence of ascites, omental cake, and DPT on CT imaging independently predicted complete cytoreduction (c-index=0.748). Conversely, predictors of suboptimal cytoreduction were ECOG PS, DPT, and LP on preoperative CT imaging (c-index=0.685). The generated models serve as preoperative evaluation tools that may improve counseling and selection for primary surgery, but need to be externally validated. Copyright © 2015 Elsevier Inc. All rights reserved.

  9. Computerized prediction of intensive care unit discharge after cardiac surgery: development and validation of a Gaussian processes model

    Directory of Open Access Journals (Sweden)

    Meyfroidt Geert

    2011-10-01

    Full Text Available Abstract Background The intensive care unit (ICU length of stay (LOS of patients undergoing cardiac surgery may vary considerably, and is often difficult to predict within the first hours after admission. The early clinical evolution of a cardiac surgery patient might be predictive for his LOS. The purpose of the present study was to develop a predictive model for ICU discharge after non-emergency cardiac surgery, by analyzing the first 4 hours of data in the computerized medical record of these patients with Gaussian processes (GP, a machine learning technique. Methods Non-interventional study. Predictive modeling, separate development (n = 461 and validation (n = 499 cohort. GP models were developed to predict the probability of ICU discharge the day after surgery (classification task, and to predict the day of ICU discharge as a discrete variable (regression task. GP predictions were compared with predictions by EuroSCORE, nurses and physicians. The classification task was evaluated using aROC for discrimination, and Brier Score, Brier Score Scaled, and Hosmer-Lemeshow test for calibration. The regression task was evaluated by comparing median actual and predicted discharge, loss penalty function (LPF ((actual-predicted/actual and calculating root mean squared relative errors (RMSRE. Results Median (P25-P75 ICU length of stay was 3 (2-5 days. For classification, the GP model showed an aROC of 0.758 which was significantly higher than the predictions by nurses, but not better than EuroSCORE and physicians. The GP had the best calibration, with a Brier Score of 0.179 and Hosmer-Lemeshow p-value of 0.382. For regression, GP had the highest proportion of patients with a correctly predicted day of discharge (40%, which was significantly better than the EuroSCORE (p Conclusions A GP model that uses PDMS data of the first 4 hours after admission in the ICU of scheduled adult cardiac surgery patients was able to predict discharge from the ICU as a

  10. In-hospital risk prediction for post-stroke depression: development and validation of the Post-stroke Depression Prediction Scale.

    Science.gov (United States)

    de Man-van Ginkel, Janneke M; Hafsteinsdóttir, Thóra B; Lindeman, Eline; Ettema, Roelof G A; Grobbee, Diederick E; Schuurmans, Marieke J

    2013-09-01

    The timely detection of post-stroke depression is complicated by a decreasing length of hospital stay. Therefore, the Post-stroke Depression Prediction Scale was developed and validated. The Post-stroke Depression Prediction Scale is a clinical prediction model for the early identification of stroke patients at increased risk for post-stroke depression. The study included 410 consecutive stroke patients who were able to communicate adequately. Predictors were collected within the first week after stroke. Between 6 to 8 weeks after stroke, major depressive disorder was diagnosed using the Composite International Diagnostic Interview. Multivariable logistic regression models were fitted. A bootstrap-backward selection process resulted in a reduced model. Performance of the model was expressed by discrimination, calibration, and accuracy. The model included a medical history of depression or other psychiatric disorders, hypertension, angina pectoris, and the Barthel Index item dressing. The model had acceptable discrimination, based on an area under the receiver operating characteristic curve of 0.78 (0.72-0.85), and calibration (P value of the U-statistic, 0.96). Transforming the model to an easy-to-use risk-assessment table, the lowest risk category (sum score, depression, which increased to 82% in the highest category (sum score, >21). The clinical prediction model enables clinicians to estimate the degree of the depression risk for an individual patient within the first week after stroke.

  11. MRI to predict prostate growth and development in children, adolescents and young adults

    Energy Technology Data Exchange (ETDEWEB)

    Ren, Jing; Liu, Huijia; Wen, Didi; Huang, Xufang; Ren, Fang; Huan, Yi [Fourth Military Medical University, Department of Radiology, Xijing Hospital, Xi' an City (China); Wang, He [Fourth Military Medical University, Department of Urology, Tangdu Hospital, Xi' an City (China)

    2014-08-06

    The purpose of this study was to investigate the use of MRI in predicting prostate growth and development. A total of 1,500 healthy male volunteers who underwent MRI of the pelvis were included in this prospective study. Subjects were divided into five groups according to age (group A, 2-5 years; group B, 6-10 years; group C, 11-15 years; group D, 16-20 years; group E, 21-25 years). Total prostate volume (TPV) as well as prostate central zone (CZ) and peripheral zone (PZ) were measured and evaluated on MRI. Data of the different groups were compared using variance analysis, Scheffe's method, Kruskal-Wallis H-test, and Pearson's correlation. Statistical significance was inferred at P < 0.05. In groups A and B, the prostates were barely visible. In group C, although TPV was measured, it was hard to distinguish CZ and PZ. In group D, 136 CZ and PZ were clearly visible. In group E, 377 CZ and PZ were clearly visible on T2-weighted imaging (T2WI). The median TPVs of groups A, B, C, D, and E were 0.00 cm{sup 3}, 0.05 cm{sup 3}, 2.83 cm{sup 3}, 8.32 cm{sup 3,} and 11.56 cm{sup 3}, respectively, and the median prostate development scores were 0.08, 0.69, 1.56, 2.38, and 2.74, respectively. Both TPVs and zonal anatomy scores varied significantly among the five groups (P = 0.000). TPV and zonal anatomy score increased with increasing age. MRI provides a reliable quantitative reference for prostate growth and development. (orig.)

  12. A Cellular Automaton / Finite Element model for predicting grain texture development in galvanized coatings

    Science.gov (United States)

    Guillemot, G.; Avettand-Fènoël, M.-N.; Iosta, A.; Foct, J.

    2011-01-01

    Hot-dipping galvanizing process is a widely used and efficient way to protect steel from corrosion. We propose to master the microstructure of zinc grains by investigating the relevant process parameters. In order to improve the texture of this coating, we model grain nucleation and growth processes and simulate the zinc solid phase development. A coupling scheme model has been applied with this aim. This model improves a previous two-dimensional model of the solidification process. It couples a cellular automaton (CA) approach and a finite element (FE) method. CA grid and FE mesh are superimposed on the same domain. The grain development is simulated at the micro-scale based on the CA grid. A nucleation law is defined using a Gaussian probability and a random set of nucleating cells. A crystallographic orientation is defined for each one with a choice of Euler's angle (Ψ,θ,φ). A small growing shape is then associated to each cell in the mushy domain and a dendrite tip kinetics is defined using the model of Kurz [2]. The six directions of basal plane and the two perpendicular directions develop in each mushy cell. During each time step, cell temperature and solid fraction are then determined at micro-scale using the enthalpy conservation relation and variations are reassigned at macro-scale. This coupling scheme model enables to simulate the three-dimensional growing kinetics of the zinc grain in a two-dimensional approach. Grain structure evolutions for various cooling times have been simulated. Final grain structure has been compared to EBSD measurements. We show that the preferentially growth of dendrite arms in the basal plane of zinc grains is correctly predicted. The described coupling scheme model could be applied for simulated other product or manufacturing processes. It constitutes an approach gathering both micro and macro scale models.

  13. Developing a theoretical predictive model for cellular response to combined actions of low radiation and hyperthermia

    International Nuclear Information System (INIS)

    Jin Kyu Kim; Petin, V.G.; Mishra, K.P.

    2007-01-01

    Complete text of publication follows. Background: Organisms in their living environment are not exposed to merely a single stress agent. Several factors such as radiation and heat may simultaneously exert their stressful effect to the organisms. The combined exposure to two stressors can result in an enhanced effect that would be expected from the addition of the separate exposures to individual agents. Objective: This study has been undertaken to develop a theoretical model for assessment of combined effects of low dose radiation and mild heat for predictive cellular response assay. Rationale: Present study was motivated from the belief that synergism may occur in terms of lethal lesions arising from the interaction of non-lethal sub-lesions induced by individual agents. The sub-lesions induced by each agent may be negligible or undetectable. But, there exists a possibility of some cross talk between sublesions produced by radiation and heat. These processes may reflect the real mechanisms for inflicting the lethal damage by otherwise ignorable or undetectable insults to exposed organisms. Results: A theoretically developed mathematical model of the synergy was formulated which was tested for validation on the experimental data. The model predictions fairly closely corresponded with several experimental results. .The significance of synergistic effects for radiation biology has been demonstrated. A number of common peculiarities of synergistic interactions were found to play their roles. A unified biophysical concept for synergistic interaction has been suggested. Conclusions: For a constant dose rate, synergistic interaction between radiation and hyperthermia especially at low intensity is realized only within a certain range of temperature, independently of the target object analyzed. For temperatures below the range, the synergistic effect was not observed and cell killing was mainly determined by the damage induced by ionizing radiation. On the contrary, the

  14. PSA predicts development of incident lower urinary tract symptoms: results from the REDUCE study.

    Science.gov (United States)

    Patel, Devin N; Feng, Tom; Simon, Ross M; Howard, Lauren E; Vidal, Adriana C; Moreira, Daniel M; Castro-Santamaria, Ramiro; Roehrborn, Claus; Andriole, Gerald L; Freedland, Stephen J

    2018-05-23

    The relationship between baseline prostate-specific antigen (PSA) and development of lower urinary tract symptoms (LUTS) in asymptomatic and mildly symptomatic men is unclear. We sought to determine if PSA predicts incident LUTS in these men. A post-hoc analysis of the 4-year REDUCE study was performed to assess for incident LUTS in 1534 men with mild to no LUTS at baseline. The primary aim was to determine whether PSA independently predicted incident LUTS after adjusting for the key clinical variables of age, prostate size, and baseline International prostate symptom score (IPSS). Incident LUTS was defined as the first report of medical treatment, surgery, or sustained clinically significant symptoms (two IPSS >14). Cox proportional hazards, cumulative incidence curves, and the log-rank test were used to test our hypothesis. A total of 1534 men with baseline IPSS PSA 2.5-4 ng/mL, 589 with PSA 4.1-6 ng/mL, and 610 with PSA 6-10 ng/mL. During the 4-year study, 196 men progressed to incident LUTS (50.5% medical treatment, 9% surgery, and 40.5% new symptoms). As a continuous variable, higher PSA was associated with increased incident LUTS on univariable (HR 1.09, p = 0.019) and multivariable (HR 1.08, p = 0.040) analysis. Likewise, baseline PSA 6-10 ng/mL was associated with increased incident LUTS vs. PSA 2.5-4 ng/mL in adjusted models (HR 1.68, p = 0.016). This association was also observed in men with PSA 4.1-6 ng/mL vs. PSA 2.5-4 ng/mL (HR 1.60, p = 0.032). Men with mild to no LUTS but increased baseline PSA are at increased risk of developing incident LUTS presumed due to benign prostatic hyperplasia.

  15. Prediction in a visual language: real-time sentence processing in American Sign Language across development.

    Science.gov (United States)

    Lieberman, Amy M; Borovsky, Arielle; Mayberry, Rachel I

    2018-01-01

    Prediction during sign language comprehension may enable signers to integrate linguistic and non-linguistic information within the visual modality. In two eyetracking experiments, we investigated American Sign language (ASL) semantic prediction in deaf adults and children (aged 4-8 years). Participants viewed ASL sentences in a visual world paradigm in which the sentence-initial verb was either neutral or constrained relative to the sentence-final target noun. Adults and children made anticipatory looks to the target picture before the onset of the target noun in the constrained condition only, showing evidence for semantic prediction. Crucially, signers alternated gaze between the stimulus sign and the target picture only when the sentential object could be predicted from the verb. Signers therefore engage in prediction by optimizing visual attention between divided linguistic and referential signals. These patterns suggest that prediction is a modality-independent process, and theoretical implications are discussed.

  16. Prediction and Factor Extraction of Drug Function by Analyzing Medical Records in Developing Countries.

    Science.gov (United States)

    Hu, Min; Nohara, Yasunobu; Nakamura, Masafumi; Nakashima, Naoki

    2017-01-01

    The World Health Organization has declared Bangladesh one of 58 countries facing acute Human Resources for Health (HRH) crisis. Artificial intelligence in healthcare has been shown to be successful for diagnostics. Using machine learning to predict pharmaceutical prescriptions may solve HRH crises. In this study, we investigate a predictive model by analyzing prescription data of 4,543 subjects in Bangladesh. We predict the function of prescribed drugs, comparing three machine-learning approaches. The approaches compare whether a subject shall be prescribed medicine from the 21 most frequently prescribed drug functions. Receiver Operating Characteristics (ROC) were selected as a way to evaluate and assess prediction models. The results show the drug function with the best prediction performance was oral hypoglycemic drugs, which has an average AUC of 0.962. To understand how the variables affect prediction, we conducted factor analysis based on tree-based algorithms and natural language processing techniques.

  17. A system identification approach for developing model predictive controllers of antibody quality attributes in cell culture processes.

    Science.gov (United States)

    Downey, Brandon; Schmitt, John; Beller, Justin; Russell, Brian; Quach, Anthony; Hermann, Elizabeth; Lyon, David; Breit, Jeffrey

    2017-11-01

    As the biopharmaceutical industry evolves to include more diverse protein formats and processes, more robust control of Critical Quality Attributes (CQAs) is needed to maintain processing flexibility without compromising quality. Active control of CQAs has been demonstrated using model predictive control techniques, which allow development of processes which are robust against disturbances associated with raw material variability and other potentially flexible operating conditions. Wide adoption of model predictive control in biopharmaceutical cell culture processes has been hampered, however, in part due to the large amount of data and expertise required to make a predictive model of controlled CQAs, a requirement for model predictive control. Here we developed a highly automated, perfusion apparatus to systematically and efficiently generate predictive models using application of system identification approaches. We successfully created a predictive model of %galactosylation using data obtained by manipulating galactose concentration in the perfusion apparatus in serialized step change experiments. We then demonstrated the use of the model in a model predictive controller in a simulated control scenario to successfully achieve a %galactosylation set point in a simulated fed-batch culture. The automated model identification approach demonstrated here can potentially be generalized to many CQAs, and could be a more efficient, faster, and highly automated alternative to batch experiments for developing predictive models in cell culture processes, and allow the wider adoption of model predictive control in biopharmaceutical processes. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 33:1647-1661, 2017. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers.

  18. Predicting the onset of hazardous alcohol drinking in primary care: development and validation of a simple risk algorithm.

    Science.gov (United States)

    Bellón, Juan Ángel; de Dios Luna, Juan; King, Michael; Nazareth, Irwin; Motrico, Emma; GildeGómez-Barragán, María Josefa; Torres-González, Francisco; Montón-Franco, Carmen; Sánchez-Celaya, Marta; Díaz-Barreiros, Miguel Ángel; Vicens, Catalina; Moreno-Peral, Patricia

    2017-04-01

    Little is known about the risk of progressing to hazardous alcohol use in abstinent or low-risk drinkers. To develop and validate a simple brief risk algorithm for the onset of hazardous alcohol drinking (HAD) over 12 months for use in primary care. Prospective cohort study in 32 health centres from six Spanish provinces, with evaluations at baseline, 6 months, and 12 months. Forty-one risk factors were measured and multilevel logistic regression and inverse probability weighting were used to build the risk algorithm. The outcome was new occurrence of HAD during the study, as measured by the AUDIT. From the lists of 174 GPs, 3954 adult abstinent or low-risk drinkers were recruited. The 'predictAL-10' risk algorithm included just nine variables (10 questions): province, sex, age, cigarette consumption, perception of financial strain, having ever received treatment for an alcohol problem, childhood sexual abuse, AUDIT-C, and interaction AUDIT-C*Age. The c-index was 0.886 (95% CI = 0.854 to 0.918). The optimal cutoff had a sensitivity of 0.83 and specificity of 0.80. Excluding childhood sexual abuse from the model (the 'predictAL-9'), the c-index was 0.880 (95% CI = 0.847 to 0.913), sensitivity 0.79, and specificity 0.81. There was no statistically significant difference between the c-indexes of predictAL-10 and predictAL-9. The predictAL-10/9 is a simple and internally valid risk algorithm to predict the onset of hazardous alcohol drinking over 12 months in primary care attendees; it is a brief tool that is potentially useful for primary prevention of hazardous alcohol drinking. © British Journal of General Practice 2017.

  19. Development and performance evaluation of a prototype system for prediction of the group error at the maintenance work

    International Nuclear Information System (INIS)

    Yoshino, Kenji; Hirotsu, Yuko

    2000-01-01

    In order to attain zero-izing of much more error rather than it can set to a nuclear power plant, Authors development and its system-izing of the error prediction causal model which predicts group error action at the time of maintenance work were performed. This prototype system has the following feature. (1) When a user inputs the existence and the grade of the existence of the 'feature factor of the maintenance work' as a prediction object, 'an organization and an organization factor', and a 'group PSF (Performance Shaping Factor) factor' into this system. The maintenance group error to target can be predicted through the prediction model which consists of a class of seven stages. (2) This system by utilizing the information on a prediction result database, it can use not only for prediction of a maintenance group error but for various safe activity, such as KYT (dangerous forecast training) and TBM (Tool Box Meeting). (3) This system predicts a cooperation error' at highest rate, and, subsequently predicts the detection error' at a high rate. And to the 'decision-making error', the transfer error' and the 'state cognitive error', it has the characteristic predicted at almost same rate. (4) If it has full knowledge even of the features, such as the enforcement conditions of maintenance work, and organization, even if the user has neither the knowledge about a human factor, nor experience, anyone of this system is slight about the existence, its extent, etc. of generating of a maintenance group error made difficult from the former logically and systematically easily, it can predict in business time for about 15 minutes. (author)

  20. Development of Multiple Sclerosis in Patients with Optic Neuritis: Analysis of Predictive Factors

    Directory of Open Access Journals (Sweden)

    Hacer Durmuş

    2009-09-01

    Full Text Available OBJECTIVE: Optic neuritis (ON is inflammation of the optic nerve that generally leads to transient loss of vision. Unilateral optic neuritis is quite common upon first presentation in multiple sclerosis (MS patients, but it may also remain as a clinically isolated syndrome. In this study we aimed to determine what factors are associated with the development of MS in isolated ON patients. METHODS: Medical charts of patients followed-up at Istanbul University, Faculty of Medicine, Department of Neurology between 1987 and 2003 were screened for patients with isolated ON at first presentation. A cohort of 90 patients was thusly obtained. Clinical and demographic features, visual evoked potential, cerebrospinal fluid (CSF, and magnetic resonance imaging findings, and time to definitive MS according to McDonald’s criteria were recorded. RESULTS: In all, 50% of the patients developed definitive MS after 13 months (95% CI: 4.4-19.6. Two of the patients developed neuromyelitis optica during the course of their follow-up. The development of MS was significantly associated with the presence of a T2 lesion (p= 0.001, oligoclonal bands (OCBs in the CSF (p= 0.002, absence of papilledema (p= 0.027, absence of severe visual impairment (p= 0.016, and subacute (> 1 day visual impairment (p= 0.005, as per log rank testing. According to the Cox proportional hazard regression model, the presence of a T2 lesion (hazard ratio: 4.8; 95% CI: 1.5-15.4 and OCBs (hazard ratio: 3.6; 95% CI: 1.1-11.5 are strongly predictive of the progression to MS. CONCLUSION: As some previous studies have noted, the risk of developing MS after ON is significantly higher in the presence of a T2 lesion and OCBs in the CSF. We think that this should be taken into account before starting early treatment for MS. Recent studies on the pathogenesis of MS have suggested that early treatment of MS reduces neurological disability in the long term. Our results might aid patient selection for early

  1. Development of a software for predicting the effects of nuclear and radiological terrorism events in city areas

    International Nuclear Information System (INIS)

    Luo Lijuan; Chen Bo; Zhuo Weihai; Lu Shuyu

    2011-01-01

    Objective: To develop a new software system that can directly display the predicted results on an electronic map, in order to get a directly perceived understanding of the affected areas of nuclear and radiological terrorism events in city areas. Methods: Three scenarios of events including spreading radioactive materials, dirty bomb attack, and explosion or arson attacks on the radiation facilities were assumed. Gaussian diffusion model was employed to predict the spread and deposition of radioactive pollutants, and both the internal and external doses were estimated for the representative person by using the corresponding dose conversion factors. Through integration of the computing system and Mapinfo geographic information system (GIS), the predicted results were visually displayed on the electronic maps of a city. Results: The new software system could visually display the predicted results on the electronic map of a city, and the predicted results were consistent with those calculated by the similar software Hotspot®. The deviation between this system and Hotspot was less than 0.2 km for predicted isoplethic curves of dose rate downwind. Conclusions: The newly developed software system is of the practical value in predicting the effects of nuclear and radiological terrorism events in city areas. (authors)

  2. Predictions on the Development Dimensions of Provincial Tourism Discipline Based on the Artificial Neural Network BP Model

    Science.gov (United States)

    Yang, Yang; Hu, Jun; Lv, Yingchun; Zhang, Mu

    2013-01-01

    As the tourism industry has gradually become the strategic mainstay industry of the national economy, the scope of the tourism discipline has developed rigorously. This paper makes a predictive study on the development of the scope of Guangdong provincial tourism discipline based on the artificial neural network BP model in order to find out how…

  3. Developing Predictive Approaches to Characterize Adaptive Responses of the Reproductive Endocrine Axis to Aromatase Inhibition: Computational Modeling

    Science.gov (United States)

    Exposure to endocrine disrupting chemicals can affect reproduction and development in both humans and wildlife. We developed a mechanistic mathematical model of the hypothalamic-pituitary-gonadal (HPG) axis in female fathead minnows to predict dose-response and time-course (DRTC)...

  4. GENERAL SCIENTIFIC PRECONDITIONS AND PROSPECTS OF PREDICTION OF GEOSPACE PROCESSES FOR THE BENEFIT OF THE SUSTAINABLE DEVELOPMENT OF TERRITORIES

    Directory of Open Access Journals (Sweden)

    T. P. Varshanina

    2016-01-01

    Full Text Available This work substantiates the need to ontologically couple methods of prediction of geospace processes and fundamental bases of the modern epistemological picture of the world. The method of a structural mask of power geographical fields is offered. On its basis a way of a solution of the problem of indeterminacy and overcoming influence of nonlinearity of geospace processes, as well as the methods of their dot prediction are developed.

  5. Development and validation of a CFD model predicting the backfill process of a nuclear waste gallery

    International Nuclear Information System (INIS)

    Gopala, Vinay Ramohalli; Lycklama a Nijeholt, Jan-Aiso; Bakker, Paul; Haverkate, Benno

    2011-01-01

    Research highlights: → This work presents the CFD simulation of the backfill process of Supercontainers with nuclear waste emplaced in a disposal gallery. → The cement-based material used for backfill is grout and the flow of grout is modelled as a Bingham fluid. → The model is verified against an analytical solution and validated against the flowability tests for concrete. → Comparison between backfill plexiglas experiment and simulation shows a distinct difference in the filling pattern. → The numerical model needs to be further developed to include segregation effects and thixotropic behavior of grout. - Abstract: Nuclear waste material may be stored in underground tunnels for long term storage. The example treated in this article is based on the current Belgian disposal concept for High-Level Waste (HLW), in which the nuclear waste material is packed in concrete shielded packages, called Supercontainers, which are inserted into these tunnels. After placement of the packages in the underground tunnels, the remaining voids between the packages and the tunnel lining is filled-up with a cement-based material called grout in order to encase the stored containers into the underground spacing. This encasement of the stored containers inside the tunnels is known as the backfill process. A good backfill process is necessary to stabilize the waste gallery against ground settlements. A numerical model to simulate the backfill process can help to improve and optimize the process by ensuring a homogeneous filling with no air voids and also optimization of the injection positions to achieve a homogeneous filling. The objective of the present work is to develop such a numerical code that can predict the backfill process well and validate the model against the available experiments and analytical solutions. In the present work the rheology of Grout is modelled as a Bingham fluid which is implemented in OpenFOAM - a finite volume-based open source computational fluid

  6. Developing predictive systems models to address complexity and relevance for ecological risk assessment.

    Science.gov (United States)

    Forbes, Valery E; Calow, Peter

    2013-07-01

    Ecological risk assessments (ERAs) are not used as well as they could be in risk management. Part of the problem is that they often lack ecological relevance; that is, they fail to grasp necessary ecological complexities. Adding realism and complexity can be difficult and costly. We argue that predictive systems models (PSMs) can provide a way of capturing complexity and ecological relevance cost-effectively. However, addressing complexity and ecological relevance is only part of the problem. Ecological risk assessments often fail to meet the needs of risk managers by not providing assessments that relate to protection goals and by expressing risk in ratios that cannot be weighed against the costs of interventions. Once more, PSMs can be designed to provide outputs in terms of value-relevant effects that are modulated against exposure and that can provide a better basis for decision making than arbitrary ratios or threshold values. Recent developments in the modeling and its potential for implementation by risk assessors and risk managers are beginning to demonstrate how PSMs can be practically applied in risk assessment and the advantages that doing so could have. Copyright © 2013 SETAC.

  7. Development of a Summarized Health Index (SHI for use in predicting survival in sea turtles.

    Directory of Open Access Journals (Sweden)

    Tsung-Hsien Li

    Full Text Available Veterinary care plays an influential role in sea turtle rehabilitation, especially in endangered species. Physiological characteristics, hematological and plasma biochemistry profiles, are useful references for clinical management in animals, especially when animals are during the convalescence period. In this study, these factors associated with sea turtle surviving were analyzed. The blood samples were collected when sea turtles remained alive, and then animals were followed up for surviving status. The results indicated that significantly negative correlation was found between buoyancy disorders (BD and sea turtle surviving (p < 0.05. Furthermore, non-surviving sea turtles had significantly higher levels of aspartate aminotranspherase (AST, creatinine kinase (CK, creatinine and uric acid (UA than surviving sea turtles (all p < 0.05. After further analysis by multiple logistic regression model, only factors of BD, creatinine and UA were included in the equation for calculating summarized health index (SHI for each individual. Through evaluation by receiver operating characteristic (ROC curve, the result indicated that the area under curve was 0.920 ± 0.037, and a cut-off SHI value of 2.5244 showed 80.0% sensitivity and 86.7% specificity in predicting survival. Therefore, the developed SHI could be a useful index to evaluate health status of sea turtles and to improve veterinary care at rehabilitation facilities.

  8. The development and verification of a highly accurate collision prediction model for automated noncoplanar plan delivery

    International Nuclear Information System (INIS)

    Yu, Victoria Y.; Tran, Angelia; Nguyen, Dan; Cao, Minsong; Ruan, Dan; Low, Daniel A.; Sheng, Ke

    2015-01-01

    attributed to phantom setup errors due to the slightly deformable and flexible phantom extremities. The estimated site-specific safety buffer distance with 0.001% probability of collision for (gantry-to-couch, gantry-to-phantom) was (1.23 cm, 3.35 cm), (1.01 cm, 3.99 cm), and (2.19 cm, 5.73 cm) for treatment to the head, lung, and prostate, respectively. Automated delivery to all three treatment sites was completed in 15 min and collision free using a digital Linac. Conclusions: An individualized collision prediction model for the purpose of noncoplanar beam delivery was developed and verified. With the model, the study has demonstrated the feasibility of predicting deliverable beams for an individual patient and then guiding fully automated noncoplanar treatment delivery. This work motivates development of clinical workflows and quality assurance procedures to allow more extensive use and automation of noncoplanar beam geometries

  9. The development and verification of a highly accurate collision prediction model for automated noncoplanar plan delivery

    Energy Technology Data Exchange (ETDEWEB)

    Yu, Victoria Y.; Tran, Angelia; Nguyen, Dan; Cao, Minsong; Ruan, Dan; Low, Daniel A.; Sheng, Ke, E-mail: ksheng@mednet.ucla.edu [Department of Radiation Oncology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California 90024 (United States)

    2015-11-15

    attributed to phantom setup errors due to the slightly deformable and flexible phantom extremities. The estimated site-specific safety buffer distance with 0.001% probability of collision for (gantry-to-couch, gantry-to-phantom) was (1.23 cm, 3.35 cm), (1.01 cm, 3.99 cm), and (2.19 cm, 5.73 cm) for treatment to the head, lung, and prostate, respectively. Automated delivery to all three treatment sites was completed in 15 min and collision free using a digital Linac. Conclusions: An individualized collision prediction model for the purpose of noncoplanar beam delivery was developed and verified. With the model, the study has demonstrated the feasibility of predicting deliverable beams for an individual patient and then guiding fully automated noncoplanar treatment delivery. This work motivates development of clinical workflows and quality assurance procedures to allow more extensive use and automation of noncoplanar beam geometries.

  10. The development and verification of a highly accurate collision prediction model for automated noncoplanar plan delivery.

    Science.gov (United States)

    Yu, Victoria Y; Tran, Angelia; Nguyen, Dan; Cao, Minsong; Ruan, Dan; Low, Daniel A; Sheng, Ke

    2015-11-01

    errors due to the slightly deformable and flexible phantom extremities. The estimated site-specific safety buffer distance with 0.001% probability of collision for (gantry-to-couch, gantry-to-phantom) was (1.23 cm, 3.35 cm), (1.01 cm, 3.99 cm), and (2.19 cm, 5.73 cm) for treatment to the head, lung, and prostate, respectively. Automated delivery to all three treatment sites was completed in 15 min and collision free using a digital Linac. An individualized collision prediction model for the purpose of noncoplanar beam delivery was developed and verified. With the model, the study has demonstrated the feasibility of predicting deliverable beams for an individual patient and then guiding fully automated noncoplanar treatment delivery. This work motivates development of clinical workflows and quality assurance procedures to allow more extensive use and automation of noncoplanar beam geometries.

  11. HIV-specific Fc effector function early in infection predicts the development of broadly neutralizing antibodies.

    Directory of Open Access Journals (Sweden)

    Simone I Richardson

    2018-04-01

    Full Text Available While the induction of broadly neutralizing antibodies (bNAbs is a major goal of HIV vaccination strategies, there is mounting evidence to suggest that antibodies with Fc effector function also contribute to protection against HIV infection. Here we investigated Fc effector functionality of HIV-specific IgG plasma antibodies over 3 years of infection in 23 individuals, 13 of whom developed bNAbs. Antibody-dependent cellular phagocytosis (ADCP, complement deposition (ADCD, cellular cytotoxicity (ADCC and cellular trogocytosis (ADCT were detected in almost all individuals with levels of activity increasing over time. At 6 months post-infection, individuals with bNAbs had significantly higher levels of ADCD and ADCT that correlated with antibody binding to C1q and FcγRIIa respectively. In addition, antibodies from individuals with bNAbs showed more IgG subclass diversity to multiple HIV antigens which also correlated with Fc polyfunctionality. Germinal center activity represented by CXCL13 levels and expression of activation-induced cytidine deaminase (AID was found to be associated with neutralization breadth, Fc polyfunctionality and IgG subclass diversity. Overall, multivariate analysis by random forest classification was able to group bNAb individuals with 85% sensitivity and 80% specificity based on the properties of their antibody Fc early in HIV infection. Thus, the Fc effector function profile predicted the development of neutralization breadth in this cohort, suggesting that intrinsic immune factors within the germinal center provide a mechanistic link between the Fc and Fab of HIV-specific antibodies.

  12. Soil Properties Predict Plant Community Development of Mitigation Wetlands Created in the Virginia Piedmont, USA

    Science.gov (United States)

    Dee, Suzanne M.; Ahn, Changwoo

    2012-05-01

    The study investigated vegetative and soil properties in four created mitigation wetlands, ranging in age from three to ten years, all created in the Virginia Piedmont. Vegetation attributes included percent cover, richness (S), diversity (H'), floristic quality assessment index (FQAI), prevalence index (PI), and productivity [i.e., peak above-ground biomass (AGB) and below-ground biomass]. Soil attributes included soil organic matter (SOM), gravimetric soil moisture (GSM), pH, and bulk density (Db) for the top 10 cm. Species dominance (e.g., Juncus effusus, Scirpus cyperinus, Arthraxon hispidus) led to a lack of differences in vegetative attributes between sites. However, site-based differences were found for GSM, pH, and SOM ( P analysis, resulting in four soil condition (SC) categories where plots were grouped based on common attribute levels (i.e., SC1 > SC2 > SC3 > SC4, trended more to less developed). When vegetation attributes were compared between SC groups, greater SOM, lower Db, more circumneutral pH, and higher GSM, all indicative of maturation, were associated with higher H' ( P < 0.05), FQAI ( P < 0.05), and total and volunteer percent cover ( P < 0.05), and lower AGB ( P < 0.001), PI ( P < 0.05), and seeded percent cover ( P < 0.05). The outcome of the study shows that site age does not necessarily equate with site development with soil and vegetation developmental rates varying both within and among sites. The inclusion of soil attributes in post-construction monitoring should be required to enhance our understanding and prediction of developmental trajectory of created mitigation wetlands.

  13. Knowing right from wrong in mental arithmetic judgments: calibration of confidence predicts the development of accuracy.

    Science.gov (United States)

    Rinne, Luke F; Mazzocco, Michèle M M

    2014-01-01

    Does knowing when mental arithmetic judgments are right--and when they are wrong--lead to more accurate judgments over time? We hypothesize that the successful detection of errors (and avoidance of false alarms) may contribute to the development of mental arithmetic performance. Insight into error detection abilities can be gained by examining the "calibration" of mental arithmetic judgments-that is, the alignment between confidence in judgments and the accuracy of those judgments. Calibration may be viewed as a measure of metacognitive monitoring ability. We conducted a developmental longitudinal investigation of the relationship between the calibration of children's mental arithmetic judgments and their performance on a mental arithmetic task. Annually between Grades 5 and 8, children completed a problem verification task in which they rapidly judged the accuracy of arithmetic expressions (e.g., 25 + 50 = 75) and rated their confidence in each judgment. Results showed that calibration was strongly related to concurrent mental arithmetic performance, that calibration continued to develop even as mental arithmetic accuracy approached ceiling, that poor calibration distinguished children with mathematics learning disability from both low and typically achieving children, and that better calibration in Grade 5 predicted larger gains in mental arithmetic accuracy between Grades 5 and 8. We propose that good calibration supports the implementation of cognitive control, leading to long-term improvement in mental arithmetic accuracy. Because mental arithmetic "fluency" is critical for higher-level mathematics competence, calibration of confidence in mental arithmetic judgments may represent a novel and important developmental predictor of future mathematics performance.

  14. The TyG index may predict the development of cardiovascular events.

    Science.gov (United States)

    Sánchez-Íñigo, Laura; Navarro-González, David; Fernández-Montero, Alejandro; Pastrana-Delgado, Juan; Martínez, Jose Alfredo

    2016-02-01

    Cardiovascular disease (CVD) is the worldwide leading cause of morbidity and mortality. An early risk detection of apparently healthy people before CVD onset has clinical relevance in the prevention of cardiovascular events. We evaluated the association between the product of fasting plasma glucose and triglycerides (TyG index) and CVD. A total of 5014 patients of the Vascular Metabolic CUN cohort (VMCUN cohort) were followed up during a median period of 10 years. We used a Cox proportional-hazard ratio with repeated measures to estimate the risk of incidence of CVD across quintiles of the TyG index, calculated as ln[fasting triglycerides (mg/dL) × fasting plasma glucose (mg(dL)/2], and plotted a receiver-operating characteristics (ROC) curve to compare a prediction model fitted on the variables used in the Framingham risk score, a new model containing the Framingham variables with the TyG index, and the risk of coronary heart disease. A higher level of TyG index was significantly associated with an increased risk of developing CVD independent of confounding factors with a value of 2·32 (95% CI: 1·65-3·26) for those in the highest quintile and 1·52 (95% CI: 1·07-2·16) for those in the fourth quintile. The areas under the curve (AUC) of the ROC plots were 0·708 (0·68-0·73) for the Framingham model and 0·719 (0·70-0·74) for the Framingham + TyG index model (P = 0·014). The TyG index, a simple measure reflecting insulin resistance, might be useful to early identify individuals at a high risk of developing a cardiovascular event. © 2015 Stichting European Society for Clinical Investigation Journal Foundation.

  15. HIV-specific Fc effector function early in infection predicts the development of broadly neutralizing antibodies.

    Science.gov (United States)

    Richardson, Simone I; Chung, Amy W; Natarajan, Harini; Mabvakure, Batsirai; Mkhize, Nonhlanhla N; Garrett, Nigel; Abdool Karim, Salim; Moore, Penny L; Ackerman, Margaret E; Alter, Galit; Morris, Lynn

    2018-04-01

    While the induction of broadly neutralizing antibodies (bNAbs) is a major goal of HIV vaccination strategies, there is mounting evidence to suggest that antibodies with Fc effector function also contribute to protection against HIV infection. Here we investigated Fc effector functionality of HIV-specific IgG plasma antibodies over 3 years of infection in 23 individuals, 13 of whom developed bNAbs. Antibody-dependent cellular phagocytosis (ADCP), complement deposition (ADCD), cellular cytotoxicity (ADCC) and cellular trogocytosis (ADCT) were detected in almost all individuals with levels of activity increasing over time. At 6 months post-infection, individuals with bNAbs had significantly higher levels of ADCD and ADCT that correlated with antibody binding to C1q and FcγRIIa respectively. In addition, antibodies from individuals with bNAbs showed more IgG subclass diversity to multiple HIV antigens which also correlated with Fc polyfunctionality. Germinal center activity represented by CXCL13 levels and expression of activation-induced cytidine deaminase (AID) was found to be associated with neutralization breadth, Fc polyfunctionality and IgG subclass diversity. Overall, multivariate analysis by random forest classification was able to group bNAb individuals with 85% sensitivity and 80% specificity based on the properties of their antibody Fc early in HIV infection. Thus, the Fc effector function profile predicted the development of neutralization breadth in this cohort, suggesting that intrinsic immune factors within the germinal center provide a mechanistic link between the Fc and Fab of HIV-specific antibodies.

  16. A hybrid discrete-continuum mathematical model of pattern prediction in the developing retinal vasculature.

    Science.gov (United States)

    McDougall, S R; Watson, M G; Devlin, A H; Mitchell, C A; Chaplain, M A J

    2012-10-01

    by means of a system of five coupled partial differential equations (Aubert et al. in Bull. Math. Biol. 73:2430-2451, 2011). However, this approach was unable to generate spatial information and structural detail for the entire retinal surface. Building upon this earlier work, a more realistic two-dimensional hybrid PDE-discrete model is derived here that tracks the migration of individual astrocytes and endothelial tip cells towards the outer retinal boundary. Blood perfusion is included throughout plexus development and the emergent retinal architectures adapt and remodel in response to various biological factors. The resulting in silico RVP structures are compared with whole-mounted retinal vasculatures at various stages of development, and the agreement is found to be excellent. Having successfully benchmarked the model against wild-type data, the effect of transgenic over-expression of various genes is predicted, based on the ocular-specific expression of VEGF-A during murine development. These results can be used to help inform future experimental investigations of signalling pathways in ocular conditions characterised by aberrant angiogenesis.

  17. Development and Evaluation of a Mobile Personalized Blood Glucose Prediction System for Patients With Gestational Diabetes Mellitus.

    Science.gov (United States)

    Pustozerov, Evgenii; Popova, Polina; Tkachuk, Aleksandra; Bolotko, Yana; Yuldashev, Zafar; Grineva, Elena

    2018-01-09

    Personalized blood glucose (BG) prediction for diabetes patients is an important goal that is pursued by many researchers worldwide. Despite many proposals, only a few projects are dedicated to the development of complete recommender system infrastructures that incorporate BG prediction algorithms for diabetes patients. The development and implementation of such a system aided by mobile technology is of particular interest to patients with gestational diabetes mellitus (GDM), especially considering the significant importance of quickly achieving adequate BG control for these patients in a short period (ie, during pregnancy) and a typically higher acceptance rate for mobile health (mHealth) solutions for short- to midterm usage. This study was conducted with the objective of developing infrastructure comprising data processing algorithms, BG prediction models, and an appropriate mobile app for patients' electronic record management to guide BG prediction-based personalized recommendations for patients with GDM. A mobile app for electronic diary management was developed along with data exchange and continuous BG signal processing software. Both components were coupled to obtain the necessary data for use in the personalized BG prediction system. Necessary data on meals, BG measurements, and other events were collected via the implemented mobile app and continuous glucose monitoring (CGM) system processing software. These data were used to tune and evaluate the BG prediction model, which included an algorithm for dynamic coefficients tuning. In the clinical study, 62 participants (GDM: n=49; control: n=13) took part in a 1-week monitoring trial during which they used the mobile app to track their meals and self-measurements of BG and CGM system for continuous BG monitoring. The data on 909 food intakes and corresponding postprandial BG curves as well as the set of patients' characteristics (eg, glycated hemoglobin, body mass index [BMI], age, and lifestyle parameters

  18. A study on the development of advanced models to predict the critical heat flux for water and liquid metals

    International Nuclear Information System (INIS)

    Lee, Yong Bum

    1994-02-01

    The critical heat flux (CHF) phenomenon in the two-phase convective flows has been an important issue in the fields of design and safety analysis of light water reactor (LWR) as well as sodium cooled liquid metal fast breeder reactor (LMFBR). Especially in the LWR application many physical aspects of the CHF phenomenon are understood and reliable correlations and mechanistic models to predict the CHF condition have been proposed. However, there are few correlations and models which are applicable to liquid metals. Compared with water, liquid metals show a divergent picture for boiling pattern. Therefore, the CHF conditions obtained from investigations with water cannot be applied to liquid metals. In this work a mechanistic model to predict the CHF of water and a correlation for liquid metals are developed. First, a mechanistic model to predict the CHF in flow boiling at low quality was developed based on the liquid sublayer dryout mechanism. In this approach the CHF is assumed to occur when a vapor blanket isolates the liquid sublayer from bulk liquid and then the liquid entering the sublayer falls short of balancing the rate of sublayer dryout by vaporization. Therefore, the vapor blanket velocity is the key parameter. In this work the vapor blanket velocity is theoretically determined based on mass, energy, and momentum balance and finally the mechanistic model to predict the CHF in flow boiling at low quality is developed. The accuracy of the present model is evaluated by comparing model predictions with the experimental data and tabular data of look-up tables. The predictions of the present model agree well with extensive CHF data. In the latter part a correlation to predict the CHF for liquid metals is developed based on the flow excursion mechanism. By using Baroczy two-phase frictional pressure drop correlation and Ledinegg instability criterion, the relationship between the CHF of liquid metals and the principal parameters is derived and finally the

  19. Development of a prediction model for the cost saving potentials in implementing the building energy efficiency rating certification

    International Nuclear Information System (INIS)

    Jeong, Jaewook; Hong, Taehoon; Ji, Changyoon; Kim, Jimin; Lee, Minhyun; Jeong, Kwangbok; Koo, Choongwan

    2017-01-01

    Highlights: • This study evaluates the building energy efficiency rating (BEER) certification. • Prediction model was developed for cost saving potentials by the BEER certification. • Prediction model was developed using LCC analysis, ROV, and Monte Carlo simulation. • Cost saving potential was predicted to be 2.78–3.77% of the construction cost. • Cost saving potential can be used for estimating the investment value of BEER. - Abstract: Building energy efficiency rating (BEER) certification is an energy performance certificates (EPCs) in South Korea. It is critical to examine the cost saving potentials of the BEER-certification in advance. This study aimed to develop a prediction model for the cost saving potentials in implementing the BEER-certification, in which the cost saving potentials included the energy cost savings of the BEER-certification and the relevant CO_2 emissions reduction as well as the additional construction cost for the BEER-certification. The prediction model was developed by using data mining, life cycle cost analysis, real option valuation, and Monte Carlo simulation. The database were established with 437 multi-family housing complexes (MFHCs), including 116 BEER-certified MFHCs and 321 non-certified MFHCs. The case study was conducted to validate the developed prediction model using 321 non-certified MFHCs, which considered 20-year life cycle. As a result, compared to the additional construction cost, the average cost saving potentials of the 1st-BEER-certified MFHCs in Groups 1, 2, and 3 were predicted to be 3.77%, 2.78%, and 2.87%, respectively. The cost saving potentials can be used as a guideline for the additional construction cost of the BEER-certification in the early design phase.

  20. Development, external validation and clinical usefulness of a practical prediction model for radiation-induced dysphagia in lung cancer patients

    International Nuclear Information System (INIS)

    Dehing-Oberije, Cary; De Ruysscher, Dirk; Petit, Steven; Van Meerbeeck, Jan; Vandecasteele, Katrien; De Neve, Wilfried; Dingemans, Anne Marie C.; El Naqa, Issam; Deasy, Joseph; Bradley, Jeff; Huang, Ellen; Lambin, Philippe

    2010-01-01

    Introduction: Acute dysphagia is a distressing dose-limiting toxicity occurring frequently during concurrent chemo-radiation or high-dose radiotherapy for lung cancer. It can lead to treatment interruptions and thus jeopardize survival. Although a number of predictive factors have been identified, it is still not clear how these could offer assistance for treatment decision making in daily clinical practice. Therefore, we have developed and validated a nomogram to predict this side-effect. In addition, clinical usefulness was assessed by comparing model predictions to physicians' predictions. Materials and methods: Clinical data from 469 inoperable lung cancer patients, treated with curative intent, were collected prospectively. A prediction model for acute radiation-induced dysphagia was developed. Model performance was evaluated by the c-statistic and assessed using bootstrapping as well as two external datasets. In addition, a prospective study was conducted comparing model to physicians' predictions in 138 patients. Results: The final multivariate model consisted of age, gender, WHO performance status, mean esophageal dose (MED), maximum esophageal dose (MAXED) and overall treatment time (OTT). The c-statistic, assessed by bootstrapping, was 0.77. External validation yielded an AUC of 0.94 on the Ghent data and 0.77 on the Washington University St. Louis data for dysphagia ≥ grade 3. Comparing model predictions to the physicians' predictions resulted in an AUC of 0.75 versus 0.53, respectively. Conclusions: The proposed model performed well was successfully validated and demonstrated the ability to predict acute severe dysphagia remarkably better than the physicians. Therefore, this model could be used in clinical practice to identify patients at high or low risk.

  1. Development and validation of an ICD-10-based disability predictive index for patients admitted to hospitals with trauma.

    Science.gov (United States)

    Wada, Tomoki; Yasunaga, Hideo; Yamana, Hayato; Matsui, Hiroki; Fushimi, Kiyohide; Morimura, Naoto

    2018-03-01

    There was no established disability predictive measurement for patients with trauma that could be used in administrative claims databases. The aim of the present study was to develop and validate a diagnosis-based disability predictive index for severe physical disability at discharge using the International Classification of Diseases, 10th revision (ICD-10) coding. This retrospective observational study used the Diagnosis Procedure Combination database in Japan. Patients who were admitted to hospitals with trauma and discharged alive from 01 April 2010 to 31 March 2015 were included. Pediatric patients under 15 years old were excluded. Data for patients admitted to hospitals from 01 April 2010 to 31 March 2013 was used for development of a disability predictive index (derivation cohort), while data for patients admitted to hospitals from 01 April 2013 to 31 March 2015 was used for the internal validation (validation cohort). The outcome of interest was severe physical disability defined as the Barthel Index score of predictive index for each patient was defined as the sum of the scores. The predictive performance of the index was validated using the receiver operating characteristic curve analysis in the validation cohort. The derivation cohort included 1,475,158 patients, while the validation cohort included 939,659 patients. Of the 939,659 patients, 235,382 (25.0%) were discharged with severe physical disability. The c-statistics of the disability predictive index was 0.795 (95% confidence interval [CI] 0.794-0.795), while that of a model using the disability predictive index and patient baseline characteristics was 0.856 (95% CI 0.855-0.857). Severe physical disability at discharge may be well predicted with patient age, sex, CCI score, and the diagnosis-based disability predictive index in patients admitted to hospitals with trauma. Copyright © 2018 Elsevier Ltd. All rights reserved.

  2. Effect of average diurnal barn airspace temperatures on prediction of their development during the day

    Directory of Open Access Journals (Sweden)

    Gustav Chládek

    2011-01-01

    Full Text Available A year-round (i.e. 365 days experiment was performed at the Mendel University Training Farm in Žabčice, Czech Republic (GPS 49°0’51.967”N and 16°36’14.614”E, the altitude 179 m with the aim to quantify the effect of the variation of average diurnal barn airspace temperatures on prediction of their changes during the day. Barn airspace temperatures were monitored daily in one-hour intervals and the recorded values were used for calculations of average diurnal temperatures. These were classified into 7 categories (i.e. below 0 °C; 0.1 to 5 °C; 5.1 to 10 °C; 10.1 to 15 °C; 15.1 to 20 °C; 20.1 to 25 °C and above 25 °C. Regarding this classification system, all differences between temperatures measured at identical hours but within various limits were statistically highly significant. The statistical analysis involved also the calculation of the third degree polynomial regression equations, which enabled to characterise the relationship between the temperature and the hour of measurement within the aforementioned categories of diurnal temperatures. Individual equations were markedly different and ranged from y = − 0.0019x3 + 0.0596x2 − 0.3797x − 1.2169 (for temperatures below 0 °C to y = − 0.0108x3 + 0.3297x2 − 1.9367x + 24.3931 (for temperatures above 25 °C. Correlation coefficients (r and coefficients of determination (R2 of these regression equations were generally very high and ranged from 0.872 to 0.976 and from 0.760 to 0.953, respectively. Regarding high values of both coefficients it can be concluded that the calculated equations enable a good and reliable prediction of the diurnal development of barn airspace temperatures.

  3. The development of techniques for determining the residual life time prediction on NPP equipment

    International Nuclear Information System (INIS)

    Antonov, Alexander V.; Dagaev, Alexander V.; Volnikov, Ivan S.

    1999-01-01

    The problem of determining the residual life prediction of NPP equipment is presently highly pressing. NPP residual life resources are 30 years, but for particular equipment it is much less. Thus, residual life resource for equipment of control and protection system of NPP unit is 5-10 years. The NPP equipment is expensive and its replacing requires much expense. Hence an urgent problem is to study residual life resources of equipment on the basis of statistic information obtained during operation. Deterministic approach of determining residual life resources for particular equipment is widely known in the literature. Physical and statistical models are also being developed for determining the residual life, e.g. the model (loading-bearing capability). The present work offers the techniques of the residual life determination reasoning from statistic information of functioning objects in the process of operation. To put the techniques into effect it is necessary to have information about the time of operation of a group of objects of the same type, the number of failures; it is desirable to know failure operating time, order of the object replacement and the reason which caused the replacement (failure or planned preventive maintenance). Metrics is based on studying the parameters for the series of failures resulted from real statistic data. Then we can proceed to distribution density of the failure working time. For this purpose the Voltarra's equation of the second order is solved f(t) = ω(t) + ∫ 0 t f(t - τ)ω(τ)dτ. Since statistics of data sampling related to failure is small due to difficulties in solution of Voltaire's equation, the authors offer moderate method of solution for the above equation. After distribution density of the failure working time is determined the calculation of equipment residual life is made by the following formula: T τ (t) 1/P(τ)∫ 0 ∞ P(t)dt. The proposed techniques are realised as the software. In the course of working

  4. Biomarkers for predicting type 2 diabetes development-Can metabolomics improve on existing biomarkers?

    Directory of Open Access Journals (Sweden)

    Otto Savolainen

    Full Text Available The aim was to determine if metabolomics could be used to build a predictive model for type 2 diabetes (T2D risk that would improve prediction of T2D over current risk markers.Gas chromatography-tandem mass spectrometry metabolomics was used in a nested case-control study based on a screening sample of 64-year-old Caucasian women (n = 629. Candidate metabolic markers of T2D were identified in plasma obtained at baseline and the power to predict diabetes was tested in 69 incident cases occurring during 5.5 years follow-up. The metabolomics results were used as a standalone prediction model and in combination with established T2D predictive biomarkers for building eight T2D prediction models that were compared with each other based on their sensitivity and selectivity for predicting T2D.Established markers of T2D (impaired fasting glucose, impaired glucose tolerance, insulin resistance (HOMA, smoking, serum adiponectin alone, and in combination with metabolomics had the largest areas under the curve (AUC (0.794 (95% confidence interval [0.738-0.850] and 0.808 [0.749-0.867] respectively, with the standalone metabolomics model based on nine fasting plasma markers having a lower predictive power (0.657 [0.577-0.736]. Prediction based on non-blood based measures was 0.638 [0.565-0.711].Established measures of T2D risk remain the best predictor of T2D risk in this population. Additional markers detected using metabolomics are likely related to these measures as they did not enhance the overall prediction in a combined model.

  5. Development and evaluation of a regression-based model to predict cesium concentration ratios for freshwater fish

    International Nuclear Information System (INIS)

    Pinder, John E.; Rowan, David J.; Rasmussen, Joseph B.; Smith, Jim T.; Hinton, Thomas G.; Whicker, F.W.

    2014-01-01

    Data from published studies and World Wide Web sources were combined to produce and test a regression model to predict Cs concentration ratios for freshwater fish species. The accuracies of predicted concentration ratios, which were computed using 1) species trophic levels obtained from random resampling of known food items and 2) K concentrations in the water for 207 fish from 44 species and 43 locations, were tested against independent observations of ratios for 57 fish from 17 species from 25 locations. Accuracy was assessed as the percent of observed to predicted ratios within factors of 2 or 3. Conservatism, expressed as the lack of under prediction, was assessed as the percent of observed to predicted ratios that were less than 2 or less than 3. The model's median observed to predicted ratio was 1.26, which was not significantly different from 1, and 50% of the ratios were between 0.73 and 1.85. The percentages of ratios within factors of 2 or 3 were 67 and 82%, respectively. The percentages of ratios that were <2 or <3 were 79 and 88%, respectively. An example for Perca fluviatilis demonstrated that increased prediction accuracy could be obtained when more detailed knowledge of diet was available to estimate trophic level. - Highlights: • We developed a model to predict Cs concentration ratios for freshwater fish species. • The model uses only two variables to predict a species CR for any location. • One variable is the K concentration in the freshwater. • The other is a species mean trophic level measure easily obtained from (fishbase.org). • The median observed to predicted ratio for 57 independent test cases was 1.26

  6. Prognosis of patients with whiplash-associated disorders consulting physiotherapy: development of a predictive model for recovery

    Directory of Open Access Journals (Sweden)

    Bohman Tony

    2012-12-01

    Full Text Available Abstract Background Patients with whiplash-associated disorders (WAD have a generally favourable prognosis, yet some develop longstanding pain and disability. Predicting who will recover from WAD shortly after a traffic collision is very challenging for health care providers such as physical therapists. Therefore, we aimed to develop a prediction model for the recovery of WAD in a cohort of patients who consulted physical therapists within six weeks after the injury. Methods Our cohort included 680 adult patients with WAD who were injured in Saskatchewan, Canada, between 1997 and 1999. All patients had consulted a physical therapist as a result of the injury. Baseline prognostic factors were collected from an injury questionnaire administered by Saskatchewan Government Insurance. The outcome, global self-perceived recovery, was assessed by telephone interviews six weeks, three and six months later. Twenty-five possible baseline prognostic factors were considered in the analyses. A prediction model was built using Cox regression. The predictive ability of the model was estimated with concordance statistics (c-index. Internal validity was checked using bootstrapping. Results Our final prediction model included: age, number of days to reporting the collision, neck pain intensity, low back pain intensity, pain other than neck and back pain, headache before collision and recovery expectations. The model had an acceptable level of predictive ability with a c-index of 0.68 (95% CI: 0.65, 0.71. Internal validation showed that our model was robust and had a good fit. Conclusions We developed a model predicting recovery from WAD, in a cohort of patients who consulted physical therapists. Our model has adequate predictive ability. However, to be fully incorporated in clinical practice the model needs to be validated in other populations and tested in clinical settings.

  7. Prognosis of patients with whiplash-associated disorders consulting physiotherapy: development of a predictive model for recovery

    Science.gov (United States)

    2012-01-01

    Background Patients with whiplash-associated disorders (WAD) have a generally favourable prognosis, yet some develop longstanding pain and disability. Predicting who will recover from WAD shortly after a traffic collision is very challenging for health care providers such as physical therapists. Therefore, we aimed to develop a prediction model for the recovery of WAD in a cohort of patients who consulted physical therapists within six weeks after the injury. Methods Our cohort included 680 adult patients with WAD who were injured in Saskatchewan, Canada, between 1997 and 1999. All patients had consulted a physical therapist as a result of the injury. Baseline prognostic factors were collected from an injury questionnaire administered by Saskatchewan Government Insurance. The outcome, global self-perceived recovery, was assessed by telephone interviews six weeks, three and six months later. Twenty-five possible baseline prognostic factors were considered in the analyses. A prediction model was built using Cox regression. The predictive ability of the model was estimated with concordance statistics (c-index). Internal validity was checked using bootstrapping. Results Our final prediction model included: age, number of days to reporting the collision, neck pain intensity, low back pain intensity, pain other than neck and back pain, headache before collision and recovery expectations. The model had an acceptable level of predictive ability with a c-index of 0.68 (95% CI: 0.65, 0.71). Internal validation showed that our model was robust and had a good fit. Conclusions We developed a model predicting recovery from WAD, in a cohort of patients who consulted physical therapists. Our model has adequate predictive ability. However, to be fully incorporated in clinical practice the model needs to be validated in other populations and tested in clinical settings. PMID:23273330

  8. Developing Lightning Prediction Tools for the CCAFS Dual-Polarimetric Radar

    Science.gov (United States)

    Petersen, W. A.; Carey, L. D.; Deierling, W.; Johnson, E.; Bateman, M.

    2009-01-01

    NASA Marshall Space Flight Center and the University of Alabama Huntsville are collaborating with the 45th Weather Squadron (45WS) to develop improved lightning prediction capabilities for the new C-band dual-polarimetric weather radar being acquired for use by 45WS and launch weather forecasters at Cape Canaveral Air Force Station (CCAFS). In particular, these algorithms will focus on lightning onset, cessation and combined lightning-radar applications for convective winds assessment. Research using radar reflectivity (Z) data for prediction of lightning onset has been extensively discussed in the literature and subsequently applied by launch weather forecasters as it pertains to lightning nowcasting. Currently the forecasters apply a relatively straight forward but effective temperature-Z threshold algorithm for assessing the likelihood of lightning onset in a given storm. In addition, a layered VIL above the freezing level product is used as automated guidance for the onset of lightning. Only limited research and field work has been conducted on lightning cessation using Z and vertically-integrated Z for determining cessation. Though not used operationally vertically-integrated Z (basis for VIL) has recently shown promise as a tool for use in nowcasting lightning cessation. The work discussed herein leverages and expands upon these and similar reflectivity-threshold approaches via the application/addition of over two decades of polarimetric radar research focused on distinct multi-parameter radar signatures of ice/mixed-phase initiation and ice-crystal orientation in highly electrified convective clouds. Specifically, our approach is based on numerous previous studies that have observed repeatable patterns in the behavior of the vertical hydrometeor column as it relates to the temporal evolution of differential reflectivity and depolarization (manifested in either LDR or p(sub hv)), development of in-situ mixed and ice phase microphysics, electric fields, and

  9. Multivariable prediction model for suspected giant cell arteritis: development and validation

    Directory of Open Access Journals (Sweden)

    Ing EB

    2017-11-01

    Full Text Available Edsel B Ing,1 Gabriela Lahaie Luna,2 Andrew Toren,3 Royce Ing,4 John J Chen,5 Nitika Arora,6 Nurhan Torun,7 Otana A Jakpor,8 J Alexander Fraser,9 Felix J Tyndel,10 Arun NE Sundaram,10 Xinyang Liu,11 Cindy TY Lam,1 Vivek Patel,12 Ezekiel Weis,13 David Jordan,14 Steven Gilberg,14 Christian Pagnoux,15 Martin ten Hove21Department of Ophthalmology and Vision Sciences, University of Toronto Medical School, Toronto, 2Department of Ophthalmology, Queen’s University, Kingston, ON, 3Department of Ophthalmology, University of Laval, Quebec, QC, 4Toronto Eyelid, Strabismus and Orbit Surgery Clinic, Toronto, ON, Canada; 5Mayo Clinic, Department of Ophthalmology and Neurology, 6Mayo Clinic, Department of Ophthalmology, Rochester, MN, 7Department of Surgery, Division of Ophthalmology, Harvard Medical School, Boston, MA, 8Harvard Medical School, Boston, MA, USA; 9Department of Clinical Neurological Sciences and Ophthalmology, Western University, London, 10Department of Medicine, University of Toronto Medical School, Toronto, ON, Canada; 11Department of Medicine, Fudan University Shanghai Medical College, Shanghai, People’s Republic of China; 12Roski Eye Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; 13Departments of Ophthalmology, Universities of Alberta and Calgary, Edmonton and Calgary, AB, 14Department of Ophthalmology, University of Ottawa, Ottawa, ON, 15Vasculitis Clinic, Mount Sinai Hospital, Toronto, ON, CanadaPurpose: To develop and validate a diagnostic prediction model for patients with suspected giant cell arteritis (GCA.Methods: A retrospective review of records of consecutive adult patients undergoing temporal artery biopsy (TABx for suspected GCA was conducted at seven university centers. The pathologic diagnosis was considered the final diagnosis. The predictor variables were age, gender, new onset headache, clinical temporal artery abnormality, jaw claudication, ischemic vision loss (VL, diplopia

  10. Development of Reliability Based Life Prediction Methods for Thermal and Environmental Barrier Coatings in Ceramic Matrix Composites

    Science.gov (United States)

    Shah, Ashwin

    2001-01-01

    Literature survey related to the EBC/TBC (environmental barrier coating/thermal barrier coating) fife models, failure mechanisms in EBC/TBC and the initial work plan for the proposed EBC/TBC life prediction methods development was developed as well as the finite element model for the thermal/stress analysis of the GRC-developed EBC system was prepared. Technical report for these activities is given in the subsequent sections.

  11. Development and evaluation of a regression-based model to predict cesium-137 concentration ratios for saltwater fish

    International Nuclear Information System (INIS)

    Pinder, John E.; Rowan, David J.; Smith, Jim T.

    2016-01-01

    Data from published studies and World Wide Web sources were combined to develop a regression model to predict "1"3"7Cs concentration ratios for saltwater fish. Predictions were developed from 1) numeric trophic levels computed primarily from random resampling of known food items and 2) K concentrations in the saltwater for 65 samplings from 41 different species from both the Atlantic and Pacific Oceans. A number of different models were initially developed and evaluated for accuracy which was assessed as the ratios of independently measured concentration ratios to those predicted by the model. In contrast to freshwater systems, were K concentrations are highly variable and are an important factor in affecting fish concentration ratios, the less variable K concentrations in saltwater were relatively unimportant in affecting concentration ratios. As a result, the simplest model, which used only trophic level as a predictor, had comparable accuracies to more complex models that also included K concentrations. A test of model accuracy involving comparisons of 56 published concentration ratios from 51 species of marine fish to those predicted by the model indicated that 52 of the predicted concentration ratios were within a factor of 2 of the observed concentration ratios. - Highlights: • We developed a model to predict concentration ratios (C_r) for saltwater fish. • The model requires only a single input variable to predict C_r. • That variable is a mean numeric trophic level available at (fishbase.org). • The K concentrations in seawater were not an important predictor variable. • The median-to observed ratio for 56 independently measured C_r was 0.83.

  12. Predictive value of developmental testing in the second year for cognitive development at five years of age

    Directory of Open Access Journals (Sweden)

    Alastair G Sutcliffe

    2010-09-01

    Full Text Available There is mixed evidence about the predictive validity of the Griffiths mental developmental scales. This study aimed to assess the predictive value of developmental assessments of children in their second year using the Griffiths mental development scales for neuro-developmental status at five years using the Wechsler preschool and primary scale of intelligence, revised (WPPSI-R. In a longitudinal study 253 children were assessed in their second year of life using the Griffiths scales and again at five years using the WPPSI-R. The scores were compared and the predictability of the WPPSI-R outcome on the basis of Griffiths scores was assessed. The WPPSI-R full scale IQ and the performance IQ at age five could be predicted moderately by the Griffiths general quotient (GQ and by the personal/social scale. The Griffiths GQ was not a significant predictor of verbal IQ at age 5. The Griffiths performance scale predicted subsequent WPPSI-R performance IQ, and marginally the Full Scale IQ. For the early identification of children at risk for language delay, the Griffiths scales may not be suitable. However, a shortened form would be useful to predict overall cognitive development from the second year to school entry, focussing on the personal-social and performance scales.

  13. Predictive value of clinical risk indicators in child development: final results of a study based on psychoanalytic theory

    Directory of Open Access Journals (Sweden)

    Maria Cristina Machado Kupfer

    2010-03-01

    Full Text Available We present the final results of a study using the IRDI (Clinical Risk Indicators in Child Development. Based on a psychoanalytic approach, 31 risk signs for child development were constructed and applied to 726 children between the ages of 0 and 18 months. One sub-sample was evaluated at the age of three. The results showed a predictive capacity of IRDIs to indicate developmental problems; 15 indicators for the IRDI were also highlighted that predict psychic risk for the constitution of the subject.

  14. SCALE DEVELOPMENT FOR MEASURING AND PREDICTING ADOLESCENTS' LEISURE TIME PHYSICAL ACTIVITY BEHAVIOR

    Directory of Open Access Journals (Sweden)

    Silvia Arribas Galarraga

    2009-12-01

    Full Text Available The aim of this study was to develop a scale for assessing and predicting adolescents' physical activity behavior in Spain and Luxembourg using the Theory of Planned Behavior as a framework. The sample was comprised of 613 Spanish (boys = 309, girls = 304; M age =15.28, SD =1.127 and 752 Luxembourgish adolescents (boys = 343, girls = 409; M age = 14.92, SD = 1.198, selected from students of two secondary schools in both countries, with a similar socio-economic status. The initial 43-items were all scored on a 4-point response format using the structured alternative format and translated into Spanish, French and German. In order to ensure the accuracy of the translation, standardized parallel back-translation techniques were employed. Following two pilot tests and subsequent revisions, a second order exploratory factor analysis with oblimin direct rotation was used for factor extraction. Internal consistency and test-retest reliabilities were also tested. The 4-week test-retest correlations confirmed the items' time stability. The same five factors were obtained, explaining 63.76% and 63.64% of the total variance in both samples. Internal consistency for the five factors ranged from α = 0.759 to α = 0. 949 in the Spanish sample and from α = 0.735 to α = 0.952 in the Luxembourgish sample. For both samples, inter-factor correlations were all reported significant and positive, except for Factor 5 where they were significant but negative. The high internal consistency of the subscales, the reported item test-retest reliabilities and the identical factor structure confirm the adequacy of the elaborated questionnaire for assessing the TPB-based constructs when used with a population of adolescents in Spain and Luxembourg. The results give some indication that they may have value in measuring the hypothesized TPB constructs for PA behavior in a cross-cultural context

  15. Prediction of vertical distribution and ambient development temperature of Baltic cod, Gadus morhua L., eggs

    DEFF Research Database (Denmark)

    Wieland, Kai; Jarre, Astrid

    1997-01-01

    An artificial neural network (ANN) model was established to predict the vertical distribution of Baltic cod eggs. Data from vertical distribution sampling in the Bornholm Basin over the period 1986-1995 were used to train and test the network, while data sets from sampling in 1996 were used...... for validation. The model explained 82% of the variance between observed and predicted relative frequencies of occurrence of the eggs in relation to salinity, temperature and oxygen concentration; The ANN fitted all observations satisfactorily except for one sampling date, where an exceptional hydrographic...... situation was observed. Mean ambient temperatures, calculated from the predicted vertical distributions of the eggs and used for the computation of egg developmental times, were overestimated by 0.05 degrees C on average. This corresponds to an error in prediction of egg developmental time of less than 1%...

  16. Innovative Development of Kernel-Based Reduced-Order Models for Predicting LCO Onset, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — Reducing uncertainty in the prediction of limit cycle oscillations (LCO) and other nonlinear aeroservoelastic phenomena is critical to flight safety. To do so...

  17. Predicting short term mood developments among depressed patients using adherence and ecological momentary assessment data

    Directory of Open Access Journals (Sweden)

    Adam Mikus

    2018-06-01

    Full Text Available Technology driven interventions provide us with an increasing amount of fine-grained data about the patient. This data includes regular ecological momentary assessments (EMA but also response times to EMA questions by a user. When observing this data, we see a huge variation between the patterns exhibited by different patients. Some are more stable while others vary a lot over time. This poses a challenging problem for the domain of artificial intelligence and makes on wondering whether it is possible to predict the future mental state of a patient using the data that is available. In the end, these predictions could potentially contribute to interventions that tailor the feedback to the user on a daily basis, for example by warning a user that a fall-back might be expected during the next days, or by applying a strategy to prevent the fall-back from occurring in the first place.In this work, we focus on short term mood prediction by considering the adherence and usage data as an additional predictor. We apply recurrent neural networks to handle the temporal aspects best and try to explore whether individual, group level, or one single predictive model provides the highest predictive performance (measured using the root mean squared error (RMSE. We use data collected from patients from five countries who used the ICT4Depression/MoodBuster platform in the context of the EU E-COMPARED project. In total, we used the data from 143 patients (with between 9 and 425days of EMA data who were diagnosed with a major depressive disorder according to DSM-IV.Results show that we can make predictions of short term mood change quite accurate (ranging between 0.065 and 0.11. The past EMA mood ratings proved to be the most influential while adherence and usage data did not improve prediction accuracy. In general, group level predictions proved to be the most promising, however differences were not significant.Short term mood prediction remains a difficult task

  18. Development of a Unified Dissolution and Precipitation Model and Its Use for the Prediction of Oral Drug Absorption.

    Science.gov (United States)

    Jakubiak, Paulina; Wagner, Björn; Grimm, Hans Peter; Petrig-Schaffland, Jeannine; Schuler, Franz; Alvarez-Sánchez, Rubén

    2016-02-01

    Drug absorption is a complex process involving dissolution and precipitation, along with other kinetic processes. The purpose of this work was to (1) establish an in vitro methodology to study dissolution and precipitation in early stages of drug development where low compound consumption and high throughput are necessary, (2) develop a mathematical model for a mechanistic explanation of generated in vitro dissolution and precipitation data, and (3) extrapolate in vitro data to in vivo situations using physiologically based models to predict oral drug absorption. Small-scale pH-shift studies were performed in biorelevant media to monitor the precipitation of a set of poorly soluble weak bases. After developing a dissolution-precipitation model from this data, it was integrated into a simplified, physiologically based absorption model to predict clinical pharmacokinetic profiles. The model helped explain the consequences of supersaturation behavior of compounds. The predicted human pharmacokinetic profiles closely aligned with the observed clinical data. In summary, we describe a novel approach combining experimental dissolution/precipitation methodology with a mechanistic model for the prediction of human drug absorption kinetics. The approach unifies the dissolution and precipitation theories and enables accurate predictions of in vivo oral absorption by means of physiologically based modeling.

  19. Study on new energy development planning and absorptive capability of Xinjiang in China considering resource characteristics and demand prediction

    Science.gov (United States)

    Shao, Hai; Miao, Xujuan; Liu, Jinpeng; Wu, Meng; Zhao, Xuehua

    2018-02-01

    Xinjiang, as the area where wind energy and solar energy resources are extremely rich, with good resource development characteristics, can provide a support for regional power development and supply protection. This paper systematically analyzes the new energy resource and development characteristics of Xinjiang and carries out the demand prediction and excavation of load characteristics of Xinjiang power market. Combing the development plan of new energy of Xinjiang and considering the construction of transmission channel, it analyzes the absorptive capability of new energy. It provides certain reference for the comprehensive planning of new energy development in Xinjiang and the improvement of absorptive capacity of new energy.

  20. In-operation inspection technology development-4 ''development of degradation prediction technology for motor-operated valves''

    Energy Technology Data Exchange (ETDEWEB)

    Kikuo, Takeshima; Yuichi, Higashikawa [Hitachi Engineering and Production Div., Nuclear Systems Div., Hitachi, Ltd., Ibaraki (Japan); Masahiro, Koike [Power and Industrial Systems R and D Lab., Hitachi, Ltd., (Japan); Kenji, Matsumoto [Tokyo Research and Development Center, Japan Power Engineering and Inspection Corp. (Japan); Eiji, O' shima [Tokyo Institute of Technology (Japan)

    2001-07-01

    A method for degradation predicting technology has been proposed for motor operated valves in nuclear power plants which is based on the concept of condition monitoring for maintenance. This method (degradation prediction technology) eliminates the unnecessary overhaul of valves and realizes high reliability and economy. The degradation mechanism was clarified by long time heating experiments of gasket and gland packing and the wear test for them and stem nut to research valve parts degradation by stress (pressure, temperature, etc) during plant operation. Effective electric power measurements for motor operated valves were confirmed to be useful discovering valve part failures. The motor operated valve degradation prediction system was developed on the basis of the experiment results and mechanism. The system is able to predict the degradation of valve parts (gasket/gland packing, stem, stem nut, etc) utilizing plant data (pressure, temperature, etc) and effective power of the motor. The life of valve parts can be estimated from the experimental results. (authors)

  1. Development of a computer model to predict aortic rupture due to impact loading.

    Science.gov (United States)

    Shah, C S; Yang, K H; Hardy, W; Wang, H K; King, A I

    2001-11-01

    Aortic injuries during blunt thoracic impacts can lead to life threatening hemorrhagic shock and potential exsanguination. Experimental approaches designed to study the mechanism of aortic rupture such as the testing of cadavers is not only expensive and time consuming, but has also been relatively unsuccessful. The objective of this study was to develop a computer model and to use it to predict modes of loading that are most likely to produce aortic ruptures. Previously, a 3D finite element model of the human thorax was developed and validated against data obtained from lateral pendulum tests. The model included a detailed description of the heart, lungs, rib cage, sternum, spine, diaphragm, major blood vessels and intercostal muscles. However, the aorta was modeled as a hollow tube using shell elements with no fluid within, and its material properties were assumed to be linear and isotropic. In this study fluid elements representing blood have been incorporated into the model in order to simulate pressure changes inside the aorta due to impact. The current model was globally validated against experimental data published in the literature for both frontal and lateral pendulum impact tests. Simulations of the validated model for thoracic impacts from a number of directions indicate that the ligamentum arteriosum, subclavian artery, parietal pleura and pressure changes within the aorta are factors that could influence aortic rupture. The model suggests that a right-sided impact to the chest is potentially more hazardous with respect to aortic rupture than any other impact direction simulated in this study. The aortic isthmus was the most likely site of aortic rupture regardless of impact direction. The reader is cautioned that this model could only be validated on a global scale. Validation of the kinematics and dynamics of the aorta at the local level could not be done due to a lack of experimental data. It is hoped that this model will be used to design

  2. The development of a quality prediction system for aluminum laser welding to measure plasma intensity using photodiodes

    Energy Technology Data Exchange (ETDEWEB)

    Yu, Ji Young [Technical Research Center, Hyundai Steel Company, Dangjin (Korea, Republic of); Sohn, Yong Ho [Dept. of Materials Science and Engineering, University of Central Florida, Orlando (United States); Park, Young Whan; Kwak, Jae Seob [Dept. of Mechanical Engineering, Pukyong National University, Busan (Korea, Republic of)

    2016-10-15

    Lightweight metals have been used to manufacture the body panels of cars to reduce the weight of car bodies. Typically, aluminum sheets are welded together, with a focus on weld quality assurance. A weld quality prediction system for the laser welding of aluminum was developed in this research to maximize welding production. The behavior of the plasma was also analyzed, dependent on various welding conditions. The light intensity of the plasma was altered with heat input and wire feed rate conditions, and the strength of the weld and sensor signals correlated closely for this heat input condition. Using these characteristics, a new algorithm and program were developed to evaluate the weld quality. The design involves a combinatory algorithm using a neural network model for the prediction of tensile strength from measured signals and a fuzzy multi-feature pattern recognition algorithm for the weld quality classification to improve predictability of the system.

  3. The development of a quality prediction system for aluminum laser welding to measure plasma intensity using photodiodes

    International Nuclear Information System (INIS)

    Yu, Ji Young; Sohn, Yong Ho; Park, Young Whan; Kwak, Jae Seob

    2016-01-01

    Lightweight metals have been used to manufacture the body panels of cars to reduce the weight of car bodies. Typically, aluminum sheets are welded together, with a focus on weld quality assurance. A weld quality prediction system for the laser welding of aluminum was developed in this research to maximize welding production. The behavior of the plasma was also analyzed, dependent on various welding conditions. The light intensity of the plasma was altered with heat input and wire feed rate conditions, and the strength of the weld and sensor signals correlated closely for this heat input condition. Using these characteristics, a new algorithm and program were developed to evaluate the weld quality. The design involves a combinatory algorithm using a neural network model for the prediction of tensile strength from measured signals and a fuzzy multi-feature pattern recognition algorithm for the weld quality classification to improve predictability of the system

  4. Model development for prediction and mitigation of dissolved oxygen sags in the Athabasca River, Canada

    Energy Technology Data Exchange (ETDEWEB)

    Martin, Nancy, E-mail: nancy@ualberta.ca [Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB, Canada T6G 2W2 (Canada); McEachern, Preston [Tervita Corporation, AB (Canada); Yu, Tong; Zhu, David Z. [Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB, Canada T6G 2W2 (Canada)

    2013-01-15

    Northern rivers exposed to high biochemical oxygen demand (BOD) loads are prone to dissolved oxygen (DO) sags in winter due to re-aeration occurring within limited open water leads. Additionally, photosynthesis is reduced by decreased daylight hours, inability of solar radiation to pass through ice, and slower algal growth in winter. The low volumetric flow decreases point-source dilution while their travel time increases. The Athabasca River in Alberta, Canada, has experienced these sags which may affect the aquatic ecosystem. A water quality model for an 800 km reach of this river was customized, calibrated, and validated specifically for DO and the factors that determine its concentration. After validation, the model was used to assess the assimilative capacity of the river and mitigation measures that could be deployed. The model reproduced the surface elevation and water temperature for the seven years simulated with mean absolute errors of < 15 cm and < 0.9 °C respectively. The ice cover was adequately predicted for all seven winters, and the simulation of nutrients and phytoplankton primary productivity were satisfactory. The DO concentration was very sensitive to the sediment oxygen demand (SOD), which represented about 50% of the DO sink in winter. The DO calibration was improved by implementing an annual SOD based on the BOD load. The model was used to estimate the capacity of the river to assimilate BOD loads in order to maintain a DO concentration of 7 mg/L, which represents the chronic provincial guideline plus a buffer of 0.5 mg/L. The results revealed the maximum assimilative BOD load of 8.9 ton/day at average flow conditions, which is lower than the maximum permitted load. In addition, the model predicted a minimum assimilative flow of about 52 m{sup 3}/s at average BOD load. Climate change scenarios could increase the frequency of this low flow. A three-level warning-system is proposed to manage the BOD load proactively at different river

  5. Development of Multivariable Models to Predict and Benchmark Transfusion in Elective Surgery Supporting Patient Blood Management.

    Science.gov (United States)

    Hayn, Dieter; Kreiner, Karl; Ebner, Hubert; Kastner, Peter; Breznik, Nada; Rzepka, Angelika; Hofmann, Axel; Gombotz, Hans; Schreier, Günter

    2017-06-14

    Blood transfusion is a highly prevalent procedure in hospitalized patients and in some clinical scenarios it has lifesaving potential. However, in most cases transfusion is administered to hemodynamically stable patients with no benefit, but increased odds of adverse patient outcomes and substantial direct and indirect cost. Therefore, the concept of Patient Blood Management has increasingly gained importance to pre-empt and reduce transfusion and to identify the optimal transfusion volume for an individual patient when transfusion is indicated. It was our aim to describe, how predictive modeling and machine learning tools applied on pre-operative data can be used to predict the amount of red blood cells to be transfused during surgery and to prospectively optimize blood ordering schedules. In addition, the data derived from the predictive models should be used to benchmark different hospitals concerning their blood transfusion patterns. 6,530 case records obtained for elective surgeries from 16 centers taking part in two studies conducted in 2004-2005 and 2009-2010 were analyzed. Transfused red blood cell volume was predicted using random forests. Separate models were trained for overall data, for each center and for each of the two studies. Important characteristics of different models were compared with one another. Our results indicate that predictive modeling applied prior surgery can predict the transfused volume of red blood cells more accurately (correlation coefficient cc = 0.61) than state of the art algorithms (cc = 0.39). We found significantly different patterns of feature importance a) in different hospitals and b) between study 1 and study 2. We conclude that predictive modeling can be used to benchmark the importance of different features on the models derived with data from different hospitals. This might help to optimize crucial processes in a specific hospital, even in other scenarios beyond Patient Blood Management.

  6. Acute Kidney Injury in Trauma Patients Admitted to Critical Care: Development and Validation of a Diagnostic Prediction Model.

    Science.gov (United States)

    Haines, Ryan W; Lin, Shih-Pin; Hewson, Russell; Kirwan, Christopher J; Torrance, Hew D; O'Dwyer, Michael J; West, Anita; Brohi, Karim; Pearse, Rupert M; Zolfaghari, Parjam; Prowle, John R

    2018-02-26

    Acute Kidney Injury (AKI) complicating major trauma is associated with increased mortality and morbidity. Traumatic AKI has specific risk factors and predictable time-course facilitating diagnostic modelling. In a single centre, retrospective observational study we developed risk prediction models for AKI after trauma based on data around intensive care admission. Models predicting AKI were developed using data from 830 patients, using data reduction followed by logistic regression, and were independently validated in a further 564 patients. AKI occurred in 163/830 (19.6%) with 42 (5.1%) receiving renal replacement therapy (RRT). First serum creatinine and phosphate, units of blood transfused in first 24 h, age and Charlson score discriminated need for RRT and AKI early after trauma. For RRT c-statistics were good to excellent: development: 0.92 (0.88-0.96), validation: 0.91 (0.86-0.97). Modelling AKI stage 2-3, c-statistics were also good, development: 0.81 (0.75-0.88) and validation: 0.83 (0.74-0.92). The model predicting AKI stage 1-3 performed moderately, development: c-statistic 0.77 (0.72-0.81), validation: 0.70 (0.64-0.77). Despite good discrimination of need for RRT, positive predictive values (PPV) at the optimal cut-off were only 23.0% (13.7-42.7) in development. However, PPV for the alternative endpoint of RRT and/or death improved to 41.2% (34.8-48.1) highlighting death as a clinically relevant endpoint to RRT.

  7. Development of Web tools to predict axillary lymph node metastasis and pathological response to neoadjuvant chemotherapy in breast cancer patients.

    Science.gov (United States)

    Sugimoto, Masahiro; Takada, Masahiro; Toi, Masakazu

    2014-12-09

    Nomograms are a standard computational tool to predict the likelihood of an outcome using multiple available patient features. We have developed a more powerful data mining methodology, to predict axillary lymph node (AxLN) metastasis and response to neoadjuvant chemotherapy (NAC) in primary breast cancer patients. We developed websites to use these tools. The tools calculate the probability of AxLN metastasis (AxLN model) and pathological complete response to NAC (NAC model). As a calculation algorithm, we employed a decision tree-based prediction model known as the alternative decision tree (ADTree), which is an analog development of if-then type decision trees. An ensemble technique was used to combine multiple ADTree predictions, resulting in higher generalization abilities and robustness against missing values. The AxLN model was developed with training datasets (n=148) and test datasets (n=143), and validated using an independent cohort (n=174), yielding an area under the receiver operating characteristic curve (AUC) of 0.768. The NAC model was developed and validated with n=150 and n=173 datasets from a randomized controlled trial, yielding an AUC of 0.787. AxLN and NAC models require users to input up to 17 and 16 variables, respectively. These include pathological features, including human epidermal growth factor receptor 2 (HER2) status and imaging findings. Each input variable has an option of "unknown," to facilitate prediction for cases with missing values. The websites developed facilitate the use of these tools, and serve as a database for accumulating new datasets.

  8. Urine and plasma metabolites predict the development of diabetic nephropathy in individuals with Type 2 diabetes mellitus

    NARCIS (Netherlands)

    Pena, M. J.; Lambers Heerspink, H. J.; Hellemons, M. E.; Friedrich, T.; Dallmann, G.; Lajer, M.; Bakker, S. J. L.; Gansevoort, R. T.; Rossing, P.; de Zeeuw, D.; Roscioni, S. S.

    Aims Early detection of individuals with Type 2 diabetes mellitus or hypertension at risk for micro- or macroalbuminuria may facilitate prevention and treatment of renal disease. We aimed to discover plasma and urine metabolites that predict the development of micro-or macroalbuminuria. Methods

  9. Predictive score for the development or progression of Graves' orbitopathy in patients with newly diagnosed Graves' hyperthyroidism

    DEFF Research Database (Denmark)

    Wiersinga, Wilmar; Žarković, Miloš; Bartalena, Luigi

    2018-01-01

    OBJECTIVE: To construct a predictive score for the development or progression of Graves' orbitopathy (GO) in Graves' hyperthyroidism (GH). DESIGN: Prospective observational study in patients with newly diagnosed GH, treated with antithyroid drugs (ATD) for 18 months at ten participating centers f...

  10. Developing predictions of in vivo developmental toxicity of ToxCast chemicals using mouse embryonic stem cells.

    Science.gov (United States)

    Developing predictions of in vivo developmental toxicity of ToxCast chemicals using mouse embryonic stem cells S. Hunter, M. Rosen, M. Hoopes, H. Nichols, S. Jeffay, K. Chandler1, Integrated Systems Toxicology Division, National Health and Environmental Effects Research Labor...

  11. Current and future perspectives on the development, evaluation and application of in silico approaches for predicting toxicity

    Science.gov (United States)

    Safety-related problems continue to be one of the major reasons of attrition in drug development. Non-testing approaches to predict toxicity could form part of the solution. This review provides a perspective of current status of non-testing approaches available for the predictio...

  12. Prognosis of patients with nonspecific neck pain: development and external validation of a prediction rule for persistence of complaints

    NARCIS (Netherlands)

    Schellingerhout, J.M.; Heijmans, M.W.; Verhagen, A.P.; Lewis, M.; de Vet, H.C.W.; Koes, B.W.

    2010-01-01

    Study Design.: Reanalysis of data from 3 randomized controlled trials. Objective.: Development and validation of a prediction rule that estimates the probability of complaints persisting for at least 6 months in patients presenting with nonspecific neck pain in primary care. Sumary of Background

  13. Development of an On-Line Surgeon-Specific Operating Room Time Prediction System (Experience with the Michigan Surgical Monitors)

    OpenAIRE

    Brown, Allan C.D.; Schmidt, Nancy M.

    1984-01-01

    The development of a micro-computer application for the on-line prediction of surgeon-specific operating room time using an IBM - PCXT is described. The reasons leading to the project, together with an assessment of the Condor 20 relational database management system as the basis for the application are discussed.

  14. Development and validation of a prediction model for long-term sickness absence based on occupational health survey variables

    NARCIS (Netherlands)

    Roelen, Corne; Thorsen, Sannie; Heymans, Martijn; Twisk, Jos; Bultmann, Ute; Bjorner, Jakob

    2018-01-01

    Purpose: The purpose of this study is to develop and validate a prediction model for identifying employees at increased risk of long-term sickness absence (LTSA), by using variables commonly measured in occupational health surveys. Materials and methods: Based on the literature, 15 predictor

  15. A risk score for the prediction of advanced age-related macular degeneration: Development and validation in 2 prospective cohorts

    Science.gov (United States)

    We aimed to develop an eye specific model which used readily available information to predict risk for advanced age-related macular degeneration (AMD). We used the Age-Related Eye Disease Study (AREDS) as our training dataset, which consisted of the 4,507 participants (contributing 1,185 affected v...

  16. Development of the Metacognitive Skills of Prediction and Evaluation in Children With or Without Math Disability

    Science.gov (United States)

    Garrett, Adia J.; Mazzocco, Michèle M. M.; Baker, Linda

    2009-01-01

    Metacognition refers to knowledge about one’s own cognition. The present study was designed to assess metacognitive skills that either precede or follow task engagement, rather than the processes that occur during a task. Specifically, we examined prediction and evaluation skills among children with (n = 17) or without (n = 179) mathematics learning disability (MLD), from grades 2 to 4. Children were asked to predict which of several math problems they could solve correctly; later, they were asked to solve those problems. They were asked to evaluate whether their solution to each of another set of problems was correct. Children’s ability to evaluate their answers to math problems improved from grade 2 to grade 3, whereas there was no change over time in the children’s ability to predict which problems they could solve correctly. Children with MLD were less accurate than children without MLD in evaluating both their correct and incorrect solutions, and they were less accurate at predicting which problems they could solve correctly. However, children with MLD were as accurate as their peers in correctly predicting that they could not solve specific math problems. The findings have implications for the usefulness of children’s self-review during mathematics problem solving. PMID:20084181

  17. Recent development of risk-prediction models for incident hypertension: An updated systematic review.

    Directory of Open Access Journals (Sweden)

    Dongdong Sun

    Full Text Available Hypertension is a leading global health threat and a major cardiovascular disease. Since clinical interventions are effective in delaying the disease progression from prehypertension to hypertension, diagnostic prediction models to identify patient populations at high risk for hypertension are imperative.Both PubMed and Embase databases were searched for eligible reports of either prediction models or risk scores of hypertension. The study data were collected, including risk factors, statistic methods, characteristics of study design and participants, performance measurement, etc.From the searched literature, 26 studies reporting 48 prediction models were selected. Among them, 20 reports studied the established models using traditional risk factors, such as body mass index (BMI, age, smoking, blood pressure (BP level, parental history of hypertension, and biochemical factors, whereas 6 reports used genetic risk score (GRS as the prediction factor. AUC ranged from 0.64 to 0.97, and C-statistic ranged from 60% to 90%.The traditional models are still the predominant risk prediction models for hypertension, but recently, more models have begun to incorporate genetic factors as part of their model predictors. However, these genetic predictors need to be well selected. The current reported models have acceptable to good discrimination and calibration ability, but whether the models can be applied in clinical practice still needs more validation and adjustment.

  18. The Development of Storm Surge Ensemble Prediction System and Case Study of Typhoon Meranti in 2016

    Science.gov (United States)

    Tsai, Y. L.; Wu, T. R.; Terng, C. T.; Chu, C. H.

    2017-12-01

    Taiwan is under the threat of storm surge and associated inundation, which is located at a potentially severe storm generation zone. The use of ensemble prediction can help forecasters to know the characteristic of storm surge under the uncertainty of track and intensity. In addition, it can help the deterministic forecasting. In this study, the kernel of ensemble prediction system is based on COMCOT-SURGE (COrnell Multi-grid COupled Tsunami Model - Storm Surge). COMCOT-SURGE solves nonlinear shallow water equations in Open Ocean and coastal regions with the nested-grid scheme and adopts wet-dry-cell treatment to calculate potential inundation area. In order to consider tide-surge interaction, the global TPXO 7.1 tide model provides the tidal boundary conditions. After a series of validations and case studies, COMCOT-SURGE has become an official operating system of Central Weather Bureau (CWB) in Taiwan. In this study, the strongest typhoon in 2016, Typhoon Meranti, is chosen as a case study. We adopt twenty ensemble members from CWB WRF Ensemble Prediction System (CWB WEPS), which differs from parameters of microphysics, boundary layer, cumulus, and surface. From box-and-whisker results, maximum observed storm surges were located in the interval of the first and third quartile at more than 70 % gauge locations, e.g. Toucheng, Chengkung, and Jiangjyun. In conclusion, the ensemble prediction can effectively help forecasters to predict storm surge especially under the uncertainty of storm track and intensity

  19. Water Pollution Prediction in the Three Gorges Reservoir Area and Countermeasures for Sustainable Development of the Water Environment

    Directory of Open Access Journals (Sweden)

    Yinghui Li

    2017-10-01

    Full Text Available The Three Gorges Project was implemented in 1994 to promote sustainable water resource use and development of the water environment in the Three Gorges Reservoir Area (hereafter “Reservoir Area”. However, massive discharge of wastewater along the river threatens these goals; therefore, this study employs a grey prediction model (GM to predict the annual emissions of primary pollution sources, including industrial wastewater, domestic wastewater, and oily and domestic wastewater from ships, that influence the Three Gorges Reservoir Area water environment. First, we optimize the initial values of a traditional GM (1,1 model, and build a new GM (1,1 model that minimizes the sum of squares of the relative simulation errors. Second, we use the new GM (1,1 model to simulate historical annual emissions data for the four pollution sources and thereby test the effectiveness of the model. Third, we predict the annual emissions of the four pollution sources in the Three Gorges Reservoir Area for a future period. The prediction results reveal the annual emission trends for the major wastewater types, and indicate the primary sources of water pollution in the Three Gorges Reservoir Area. Based on our predictions, we suggest several countermeasures against water pollution and towards the sustainable development of the water environment in the Three Gorges Reservoir Area.

  20. Water Pollution Prediction in the Three Gorges Reservoir Area and Countermeasures for Sustainable Development of the Water Environment

    Science.gov (United States)

    Huang, Shuaijin; Qu, Xuexin

    2017-01-01

    The Three Gorges Project was implemented in 1994 to promote sustainable water resource use and development of the water environment in the Three Gorges Reservoir Area (hereafter “Reservoir Area”). However, massive discharge of wastewater along the river threatens these goals; therefore, this study employs a grey prediction model (GM) to predict the annual emissions of primary pollution sources, including industrial wastewater, domestic wastewater, and oily and domestic wastewater from ships, that influence the Three Gorges Reservoir Area water environment. First, we optimize the initial values of a traditional GM (1,1) model, and build a new GM (1,1) model that minimizes the sum of squares of the relative simulation errors. Second, we use the new GM (1,1) model to simulate historical annual emissions data for the four pollution sources and thereby test the effectiveness of the model. Third, we predict the annual emissions of the four pollution sources in the Three Gorges Reservoir Area for a future period. The prediction results reveal the annual emission trends for the major wastewater types, and indicate the primary sources of water pollution in the Three Gorges Reservoir Area. Based on our predictions, we suggest several countermeasures against water pollution and towards the sustainable development of the water environment in the Three Gorges Reservoir Area. PMID:29077006

  1. Water Pollution Prediction in the Three Gorges Reservoir Area and Countermeasures for Sustainable Development of the Water Environment.

    Science.gov (United States)

    Li, Yinghui; Huang, Shuaijin; Qu, Xuexin

    2017-10-27

    The Three Gorges Project was implemented in 1994 to promote sustainable water resource use and development of the water environment in the Three Gorges Reservoir Area (hereafter "Reservoir Area"). However, massive discharge of wastewater along the river threatens these goals; therefore, this study employs a grey prediction model (GM) to predict the annual emissions of primary pollution sources, including industrial wastewater, domestic wastewater, and oily and domestic wastewater from ships, that influence the Three Gorges Reservoir Area water environment. First, we optimize the initial values of a traditional GM (1,1) model, and build a new GM (1,1) model that minimizes the sum of squares of the relative simulation errors. Second, we use the new GM (1,1) model to simulate historical annual emissions data for the four pollution sources and thereby test the effectiveness of the model. Third, we predict the annual emissions of the four pollution sources in the Three Gorges Reservoir Area for a future period. The prediction results reveal the annual emission trends for the major wastewater types, and indicate the primary sources of water pollution in the Three Gorges Reservoir Area. Based on our predictions, we suggest several countermeasures against water pollution and towards the sustainable development of the water environment in the Three Gorges Reservoir Area.

  2. Developing a NIR multispectral imaging for prediction and visualization of peanut protein content using variable selection algorithms

    Science.gov (United States)

    Cheng, Jun-Hu; Jin, Huali; Liu, Zhiwei

    2018-01-01

    The feasibility of developing a multispectral imaging method using important wavelengths from hyperspectral images selected by genetic algorithm (GA), successive projection algorithm (SPA) and regression coefficient (RC) methods for modeling and predicting protein content in peanut kernel was investigated for the first time. Partial least squares regression (PLSR) calibration model was established between the spectral data from the selected optimal wavelengths and the reference measured protein content ranged from 23.46% to 28.43%. The RC-PLSR model established using eight key wavelengths (1153, 1567, 1972, 2143, 2288, 2339, 2389 and 2446 nm) showed the best predictive results with the coefficient of determination of prediction (R2P) of 0.901, and root mean square error of prediction (RMSEP) of 0.108 and residual predictive deviation (RPD) of 2.32. Based on the obtained best model and image processing algorithms, the distribution maps of protein content were generated. The overall results of this study indicated that developing a rapid and online multispectral imaging system using the feature wavelengths and PLSR analysis is potential and feasible for determination of the protein content in peanut kernels.

  3. The Role of Lipidogram Parameters during Pregnancy in the Prediction of Preeclampsia Development

    Directory of Open Access Journals (Sweden)

    I.B. Ventskovskaya

    2016-08-01

    Full Text Available Aim of investigation. To study the correlation between lipid metabolism during pregnancy and the risk of preeclampsia (PE. Material and methods. We have studied parameters of lipid metabolism in the blood serum of 267 pregnant women with the help of diagnostic kits. Blood sampling was carried out in I and II gestation trimesters. Total cholesterol (TC and triglycerides (TG were determined by colorimetric, enzymatic methods; high density lipoproteins (HDL — with homogeneous method, low density lipoproteins (LDL — with the direct method. Very low density lipoproteins (VLDL concentration was calculated from the Friedwald equation: VLDL = TG / 2.2. Depending on the clinical picture of PE, 43 pregnant women were divided into groups with mild and moderate-to-severe courses of the disease. Results. Among women with PE, we observed significant changes of the lipid profile parameters in the II trimester. These women had elevated TG levels in the blood serum: I group — 1.73 ± 0.14 mM/l, II group — 1.86 ± 0.18 mM/l as compared to the group III (controls — 1.32 ± 0.29 mM/l; decreased HDL indices: I group — 0.79 ± 0.19 mM/l; group II — 0.64 ± 0.04 mM/l in comparison with the control group — 1.17 ± 0.12 mM/l and increased VLDL — 0.78 ± 0.09 mM/l for group I, 0.90 ± 0.06 mM/l in women of group II unlike group III — 0.60 ± 0.16 mM/l. TC and LDL levels among patients with PE did not differ from pregnant women in the control group. Conclusions. It was demonstrated the existence of an imbalance in the synthesis of lipoproteins in women, whose pregnancies were complicated by development of PE that manifested by hypertriglyceridemia with predominance of atherogenic fractions. It was found that the investigation of the parameters of lipid profile in the II trimester of pregnancy allows us to predict the risk of PE and its long-term cardiovascular and metabolic consequences.

  4. Scale Development for Measuring and Predicting Adolescents’ Leisure Time Physical Activity Behavior

    Science.gov (United States)

    Ries, Francis; Romero Granados, Santiago; Arribas Galarraga, Silvia

    2009-01-01

    The aim of this study was to develop a scale for assessing and predicting adolescents’ physical activity behavior in Spain and Luxembourg using the Theory of Planned Behavior as a framework. The sample was comprised of 613 Spanish (boys = 309, girls = 304; M age =15.28, SD =1.127) and 752 Luxembourgish adolescents (boys = 343, girls = 409; M age = 14.92, SD = 1.198), selected from students of two secondary schools in both countries, with a similar socio-economic status. The initial 43-items were all scored on a 4-point response format using the structured alternative format and translated into Spanish, French and German. In order to ensure the accuracy of the translation, standardized parallel back-translation techniques were employed. Following two pilot tests and subsequent revisions, a second order exploratory factor analysis with oblimin direct rotation was used for factor extraction. Internal consistency and test-retest reliabilities were also tested. The 4-week test-retest correlations confirmed the items’ time stability. The same five factors were obtained, explaining 63.76% and 63.64% of the total variance in both samples. Internal consistency for the five factors ranged from α = 0.759 to α = 0. 949 in the Spanish sample and from α = 0.735 to α = 0.952 in the Luxembourgish sample. For both samples, inter-factor correlations were all reported significant and positive, except for Factor 5 where they were significant but negative. The high internal consistency of the subscales, the reported item test-retest reliabilities and the identical factor structure confirm the adequacy of the elaborated questionnaire for assessing the TPB-based constructs when used with a population of adolescents in Spain and Luxembourg. The results give some indication that they may have value in measuring the hypothesized TPB constructs for PA behavior in a cross-cultural context. Key points When using the structured alternative format, weak internal consistency was obtained

  5. Prehospital evaluation and economic analysis of different coronary syndrome treatment strategies - PREDICT - Rationale, Development and Implementation

    Directory of Open Access Journals (Sweden)

    Craig Alan

    2011-03-01

    Full Text Available Abstract Background A standard of prehospital care for patients presenting with ST-segment elevation myocardial infarction (STEMI includes prehospital 12-lead and advance Emergency Department notification or prehospital bypass to percutaneous coronary intervention centres. Implementation of either care strategies is variable across communities and neither may exist in some communities. The main objective is to compare prehospital care strategies for time to treatment and survival outcomes as well as cost effectiveness. Methods/Design PREDICT is a multicentre, prospective population-based cohort study of all chest pain patients 18 years or older presenting within 30 mins to 6 hours of symptom onset and treated with nitroglycerin, transported by paramedics in a number of different urban and rural regions in Ontario. The primary objective of this study is to compare the proportion of study subjects who receive reperfusion within the target door-to-reperfusion times in subjects obtained after four prehospital strategies: 12-lead ECG and advance emergency department (ED notification or 3-lead ECG monitoring and alert to dispatch prior to hospital arrival; either with or without the opportunity to bypass to a PCI centre. Discussion We anticipate four challenges to successful study implementation and have developed strategies for each: 1 diversity in the interpretation of the ethical and privacy issues across 47 research ethics boards/commiittees covering 71 hospitals, 2 remote oversight of data guardian abstraction, 3 timeliness of implementation, and 4 potential interference in the study by concurrent technological advances. Research ethics approvals from academic centres were obtained initially and submitted to non academic centre applications. Data guardians were trained by a single investigator and data entry is informed by a detailed data dictionary including variable definitions and abstraction instrucations and subjected to error and logic

  6. Prediction of vertical distribution and ambient development temperature of Baltic cod, Gadus morhua L., eggs

    DEFF Research Database (Denmark)

    Wieland, Kai; Jarre, Astrid

    1997-01-01

    An artificial neural network (ANN) model was established to predict the vertical distribution of Baltic cod eggs. Data from vertical distribution sampling in the Bornholm Basin over the period 1986-1995 were used to train and test the network, while data sets from sampling in 1996 were used...... for validation. The model explained 82% of the variance between observed and predicted relative frequencies of occurrence of the eggs in relation to salinity, temperature and oxygen concentration; The ANN fitted all observations satisfactorily except for one sampling date, where an exceptional hydrographic...

  7. Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation

    International Nuclear Information System (INIS)

    Li, Xiang; Peng, Ling; Yao, Xiaojing; Cui, Shaolong; Hu, Yuan; You, Chengzeng; Chi, Tianhe

    2017-01-01

    Air pollutant concentration forecasting is an effective method of protecting public health by providing an early warning against harmful air pollutants. However, existing methods of air pollutant concentration prediction fail to effectively model long-term dependencies, and most neglect spatial correlations. In this paper, a novel long short-term memory neural network extended (LSTME) model that inherently considers spatiotemporal correlations is proposed for air pollutant concentration prediction. Long short-term memory (LSTM) layers were used to automatically extract inherent useful features from historical air pollutant data, and auxiliary data, including meteorological data and time stamp data, were merged into the proposed model to enhance the performance. Hourly PM 2.5 (particulate matter with an aerodynamic diameter less than or equal to 2.5 μm) concentration data collected at 12 air quality monitoring stations in Beijing City from Jan/01/2014 to May/28/2016 were used to validate the effectiveness of the proposed LSTME model. Experiments were performed using the spatiotemporal deep learning (STDL) model, the time delay neural network (TDNN) model, the autoregressive moving average (ARMA) model, the support vector regression (SVR) model, and the traditional LSTM NN model, and a comparison of the results demonstrated that the LSTME model is superior to the other statistics-based models. Additionally, the use of auxiliary data improved model performance. For the one-hour prediction tasks, the proposed model performed well and exhibited a mean absolute percentage error (MAPE) of 11.93%. In addition, we conducted multiscale predictions over different time spans and achieved satisfactory performance, even for 13–24 h prediction tasks (MAPE = 31.47%). - Highlights: • Regional air pollutant concentration shows an obvious spatiotemporal correlation. • Our prediction model presents superior performance. • Climate data and metadata can significantly

  8. Rule base system in developing groundwater pollution expert system: predicting model

    International Nuclear Information System (INIS)

    Mongkon Ta-oun; Mohamed Daud; Mohd Zohadie Bardaie; Shamshuddin Jusop

    2000-01-01

    New techniques are now available for use in the protection of the environment. One of these techniques is the use of expert system for prediction groundwater pollution potential. Groundwater Pollution Expert system (GWPES) rules are a collection of principles and procedures used to know the comprehension of groundwater pollution prediction. The rules of groundwater pollution expert system in the form of questions, choice, radio-box, slide rule, button or frame are translated in to IF-THEN rule. The rules including of variables, types, domains and descriptions were used by the function of wxCLIPS (C Language Integrate Production System) expert system shell. (author)

  9. Broadband Fan Noise Prediction System for Turbofan Engines. Volume 1; Setup_BFaNS User's Manual and Developer's Guide

    Science.gov (United States)

    Morin, Bruce L.

    2010-01-01

    Pratt & Whitney has developed a Broadband Fan Noise Prediction System (BFaNS) for turbofan engines. This system computes the noise generated by turbulence impinging on the leading edges of the fan and fan exit guide vane, and noise generated by boundary-layer turbulence passing over the fan trailing edge. BFaNS has been validated on three fan rigs that were tested during the NASA Advanced Subsonic Technology Program (AST). The predicted noise spectra agreed well with measured data. The predicted effects of fan speed, vane count, and vane sweep also agreed well with measurements. The noise prediction system consists of two computer programs: Setup_BFaNS and BFaNS. Setup_BFaNS converts user-specified geometry and flow-field information into a BFaNS input file. From this input file, BFaNS computes the inlet and aft broadband sound power spectra generated by the fan and FEGV. The output file from BFaNS contains the inlet, aft and total sound power spectra from each noise source. This report is the first volume of a three-volume set documenting the Broadband Fan Noise Prediction System: Volume 1: Setup_BFaNS User s Manual and Developer s Guide; Volume 2: BFaNS User's Manual and Developer s Guide; and Volume 3: Validation and Test Cases. The present volume begins with an overview of the Broadband Fan Noise Prediction System, followed by step-by-step instructions for installing and running Setup_BFaNS. It concludes with technical documentation of the Setup_BFaNS computer program.

  10. Broadband Fan Noise Prediction System for Turbofan Engines. Volume 2; BFaNS User's Manual and Developer's Guide

    Science.gov (United States)

    Morin, Bruce L.

    2010-01-01

    Pratt & Whitney has developed a Broadband Fan Noise Prediction System (BFaNS) for turbofan engines. This system computes the noise generated by turbulence impinging on the leading edges of the fan and fan exit guide vane, and noise generated by boundary-layer turbulence passing over the fan trailing edge. BFaNS has been validated on three fan rigs that were tested during the NASA Advanced Subsonic Technology Program (AST). The predicted noise spectra agreed well with measured data. The predicted effects of fan speed, vane count, and vane sweep also agreed well with measurements. The noise prediction system consists of two computer programs: Setup_BFaNS and BFaNS. Setup_BFaNS converts user-specified geometry and flow-field information into a BFaNS input file. From this input file, BFaNS computes the inlet and aft broadband sound power spectra generated by the fan and FEGV. The output file from BFaNS contains the inlet, aft and total sound power spectra from each noise source. This report is the second volume of a three-volume set documenting the Broadband Fan Noise Prediction System: Volume 1: Setup_BFaNS User s Manual and Developer s Guide; Volume 2: BFaNS User s Manual and Developer s Guide; and Volume 3: Validation and Test Cases. The present volume begins with an overview of the Broadband Fan Noise Prediction System, followed by step-by-step instructions for installing and running BFaNS. It concludes with technical documentation of the BFaNS computer program.

  11. ToxiM: A Toxicity Prediction Tool for Small Molecules Developed Using Machine Learning and Chemoinformatics Approaches

    Directory of Open Access Journals (Sweden)

    Ashok K. Sharma

    2017-11-01

    Full Text Available The experimental methods for the prediction of molecular toxicity are tedious and time-consuming tasks. Thus, the computational approaches could be used to develop alternative methods for toxicity prediction. We have developed a tool for the prediction of molecular toxicity along with the aqueous solubility and permeability of any molecule/metabolite. Using a comprehensive and curated set of toxin molecules as a training set, the different chemical and structural based features such as descriptors and fingerprints were exploited for feature selection, optimization and development of machine learning based classification and regression models. The compositional differences in the distribution of atoms were apparent between toxins and non-toxins, and hence, the molecular features were used for the classification and regression. On 10-fold cross-validation, the descriptor-based, fingerprint-based and hybrid-based classification models showed similar accuracy (93% and Matthews's correlation coefficient (0.84. The performances of all the three models were comparable (Matthews's correlation coefficient = 0.84–0.87 on the blind dataset. In addition, the regression-based models using descriptors as input features were also compared and evaluated on the blind dataset. Random forest based regression model for the prediction of solubility performed better (R2 = 0.84 than the multi-linear regression (MLR and partial least square regression (PLSR models, whereas, the partial least squares based regression model for the prediction of permeability (caco-2 performed better (R2 = 0.68 in comparison to the random forest and MLR based regression models. The performance of final classification and regression models was evaluated using the two validation datasets including the known toxins and commonly used constituents of health products, which attests to its accuracy. The ToxiM web server would be a highly useful and reliable tool for the prediction of toxicity

  12. Development and validation of a prediction model for long-term sickness absence based on occupational health survey variables.

    Science.gov (United States)

    Roelen, Corné; Thorsen, Sannie; Heymans, Martijn; Twisk, Jos; Bültmann, Ute; Bjørner, Jakob

    2018-01-01

    The purpose of this study is to develop and validate a prediction model for identifying employees at increased risk of long-term sickness absence (LTSA), by using variables commonly measured in occupational health surveys. Based on the literature, 15 predictor variables were retrieved from the DAnish National working Environment Survey (DANES) and included in a model predicting incident LTSA (≥4 consecutive weeks) during 1-year follow-up in a sample of 4000 DANES participants. The 15-predictor model was reduced by backward stepwise statistical techniques and then validated in a sample of 2524 DANES participants, not included in the development sample. Identification of employees at increased LTSA risk was investigated by receiver operating characteristic (ROC) analysis; the area-under-the-ROC-curve (AUC) reflected discrimination between employees with and without LTSA during follow-up. The 15-predictor model was reduced to a 9-predictor model including age, gender, education, self-rated health, mental health, prior LTSA, work ability, emotional job demands, and recognition by the management. Discrimination by the 9-predictor model was significant (AUC = 0.68; 95% CI 0.61-0.76), but not practically useful. A prediction model based on occupational health survey variables identified employees with an increased LTSA risk, but should be further developed into a practically useful tool to predict the risk of LTSA in the general working population. Implications for rehabilitation Long-term sickness absence risk predictions would enable healthcare providers to refer high-risk employees to rehabilitation programs aimed at preventing or reducing work disability. A prediction model based on health survey variables discriminates between employees at high and low risk of long-term sickness absence, but discrimination was not practically useful. Health survey variables provide insufficient information to determine long-term sickness absence risk profiles. There is a need for

  13. Development of Bundle Position-Wise Linear Model for Predicting the Pressure Tube Diametral Creep in CANDU Reactors

    International Nuclear Information System (INIS)

    Lee, Jae Yong; Na, Man Gyun

    2011-01-01

    Diametral creep of the pressure tube (PT) is one of the principal aging mechanisms governing the heat transfer and hydraulic degradation of a heat transport system. PT diametral creep leads to diametral expansion that affects the thermal hydraulic characteristics of the coolant channels and the critical heat flux. Therefore, it is essential to predict the PT diametral creep in CANDU reactors, which is caused mainly by fast neutron irradiation, reactor coolant temperature and so forth. The currently used PT diametral creep prediction model considers the complex interactions between the effects of temperature and fast neutron flux on the deformation of PT zirconium alloys. The model assumes that long-term steady-state deformation consists of separable, additive components from thermal creep, irradiation creep and irradiation growth. This is a mechanistic model based on measured data. However, this model has high prediction uncertainty. Recently, a statistical error modeling method was developed using plant inspection data from the Bruce B CANDU reactor. The aim of this study was to develop a bundle position-wise linear model (BPLM) to predict PT diametral creep employing previously measured PT diameters and HTS operating conditions. There are twelve bundles in a fuel channel and for each bundle, a linear model was developed by using the dependent variables, such as the fast neutron fluxes and the bundle temperatures. The training data set was selected using the subtractive clustering method. The data of 39 channels that consist of 80 percent of a total of 49 measured channels from Units 2, 3 and 4 were used to develop the BPLM models. The remaining 10 channels' data were used to test the developed BPLM models. The BPLM was optimized by the maximum likelihood estimation method. The developed BPLM to predict PT diametral creep was verified using the operating data gathered from the Units 2,3 and 4 in Korea. Two error components for the BPLM, which are the epistemic

  14. Prediction and validation of pool fire development in enclosures by means of CFD (Poolfire) Report - Year 1

    International Nuclear Information System (INIS)

    van Hees, P.; Wahlqvist, J.; Hostikka, S.; Sikanen, T.; Husted, B.; Magnusson, T.; Joerud, F.

    2012-01-01

    Fires in nuclear power plants can be an important hazard for the overall safety of the facility. One of the typical fire sources is a pool fire. It is therefore important to have good knowledge on the fire behaviour of pool fire and be able to predict the heat release rate by prediction of the mass loss rate. This project envisages developing a pyrolysis model to be used in CFD models. In the this first year report the literature review conducted within the project is reported as well as the first tasks in the evaluation and modelling of the new model. (Author)

  15. Prediction and validation of pool fire development in enclosures by means of CFD (Poolfire) Report - Year 1

    Energy Technology Data Exchange (ETDEWEB)

    van Hees, P.; Wahlqvist, J. (Lund Univ., Lund (Sweden)); Hostikka, S.; Sikanen, T. (VTT Technical Research Centre of Finland (Finland)); Husted, B. (Haugesund College, Stord (Norway)); Magnusson, T. (Ringhals AB, Vaeroebacka (Sweden)); Joerud, F. (Oskarshamn Kraftgrupp AB, Oskarshamn (Sweden))

    2012-02-15

    Fires in nuclear power plants can be an important hazard for the overall safety of the facility. One of the typical fire sources is a pool fire. It is therefore important to have good knowledge on the fire behaviour of pool fire and be able to predict the heat release rate by prediction of the mass loss rate. This project envisages developing a pyrolysis model to be used in CFD models. In the this first year report the literature review conducted within the project is reported as well as the first tasks in the evaluation and modelling of the new model. (Author)

  16. Development and validation of a multivariate prediction model for patients with acute pancreatitis in Intensive Care Medicine.

    Science.gov (United States)

    Zubia-Olaskoaga, Felix; Maraví-Poma, Enrique; Urreta-Barallobre, Iratxe; Ramírez-Puerta, María-Rosario; Mourelo-Fariña, Mónica; Marcos-Neira, María-Pilar; García-García, Miguel Ángel

    2018-03-01

    Development and validation of a multivariate prediction model for patients with acute pancreatitis (AP) admitted in Intensive Care Units (ICU). A prospective multicenter observational study, in 1 year period, in 46 international ICUs (EPAMI study). adults admitted to an ICU with AP and at least one organ failure. Development of a multivariate prediction model, using the worst data of the stay in ICU, based in multivariate analysis, simple imputation in a development cohort. The model was validated in another cohort. 374 patients were included (mortality of 28.9%). Variables with statistical significance in multivariate analysis were age, no alcoholic and no biliary etiology, development of shock, development of respiratory failure, need of continuous renal replacement therapy, and intra-abdominal pressure. The model created with these variables presented an AUC of ROC curve of 0.90 (CI 95% 0.81-0.94) in the validation cohort. We developed a multivariable prediction model, and AP cases could be classified as low mortality risk (between 2 and 9.5 points, mortality of 1.35%), moderate mortality risk (between 10 and 12.5 points, 28.92% of mortality), and high mortality risk (13 points of more, mortality of 88.37%). Our model presented better AUC of ROC curve than APACHE II (0.91 vs 0.80) and SOFA in the first 24 h (0.91 vs 0.79). We developed and validated a multivariate prediction model, which can be applied in any moment of the stay in ICU, with better discriminatory power than APACHE II and SOFA in the first 24 h. Copyright © 2018 IAP and EPC. Published by Elsevier B.V. All rights reserved.

  17. Systematic Review of Health Economic Impact Evaluations of Risk Prediction Models : Stop Developing, Start Evaluating

    NARCIS (Netherlands)

    van Giessen, Anoukh; Peters, Jaime; Wilcher, Britni; Hyde, Chris; Moons, Carl; de Wit, Ardine; Koffijberg, Erik

    2017-01-01

    Background: Although health economic evaluations (HEEs) are increasingly common for therapeutic interventions, they appear to be rare for the use of risk prediction models (PMs). Objectives: To evaluate the current state of HEEs of PMs by performing a comprehensive systematic review. Methods: Four

  18. Developing hybrid approaches to predict pKa values of ionizable groups

    Science.gov (United States)

    Witham, Shawn; Talley, Kemper; Wang, Lin; Zhang, Zhe; Sarkar, Subhra; Gao, Daquan; Yang, Wei

    2011-01-01

    Accurate predictions of pKa values of titratable groups require taking into account all relevant processes associated with the ionization/deionization. Frequently, however, the ionization does not involve significant structural changes and the dominating effects are purely electrostatic in origin allowing accurate predictions to be made based on the electrostatic energy difference between ionized and neutral forms alone using a static structure. On another hand, if the change of the charge state is accompanied by a structural reorganization of the target protein, then the relevant conformational changes have to be taken into account in the pKa calculations. Here we report a hybrid approach that first predicts the titratable groups, which ionization is expected to cause conformational changes, termed “problematic” residues, then applies a special protocol on them, while the rest of the pKa’s are predicted with rigid backbone approach as implemented in multi-conformation continuum electrostatics (MCCE) method. The backbone representative conformations for “problematic” groups are generated with either molecular dynamics simulations with charged and uncharged amino acid or with ab-initio local segment modeling. The corresponding ensembles are then used to calculate the pKa of the “problematic” residues and then the results are averaged. PMID:21744395

  19. Tidal influence on offshore wind fields and resource predictions[Efficient Development of Offshore Windfarms

    Energy Technology Data Exchange (ETDEWEB)

    Khan, D. [Entec UK Ltd., Doherty Innovation Centre, Penicuik (United Kingdom); Infield, D. [Loughborough Univ., Centre for Renewable Energy Systems Tecnology, Loughborough (United Kingdom)

    2002-03-01

    The rise and fall of the sea surface due to tides effectively moves an offshore wind turbine hub through the wind shear profile. This effect is quantified using measured data from 3 offshore UK sites. Statistical evidence of the influence of tide on mean wind speed and turbulence is presented. The implications of this effect for predicting offshore wind resource are outlined. (au)

  20. Developing a Model and Applications for Probabilities of Student Success: A Case Study of Predictive Analytics

    Science.gov (United States)

    Calvert, Carol Elaine

    2014-01-01

    This case study relates to distance learning students on open access courses. It demonstrates the use of predictive analytics to generate a model of the probabilities of success and retention at different points, or milestones, in a student journey. A core set of explanatory variables has been established and their varying relative importance at…

  1. Theory of mind and emotion understanding predict moral development in early childhood.

    Science.gov (United States)

    Lane, Jonathan D; Wellman, Henry M; Olson, Sheryl L; LaBounty, Jennifer; Kerr, David C R

    2010-11-01

    The current study utilized longitudinal data to investigate how theory of mind (ToM) and emotion understanding (EU) concurrently and prospectively predicted young children's moral reasoning and decision making. One hundred twenty-eight children were assessed on measures of ToM and EU at 3.5 and 5.5 years of age. At 5.5 years, children were also assessed on the quality of moral reasoning and decision making they used to negotiate prosocial moral dilemmas, in which the needs of a story protagonist conflict with the needs of another story character. More sophisticated EU predicted greater use of physical- and material-needs reasoning, and a more advanced ToM predicted greater use of psychological-needs reasoning. Most intriguing, ToM and EU jointly predicted greater use of higher-level acceptance-authority reasoning, which is likely a