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.

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.

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)

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

The role of donor characteristics and post-granulocyte colony-stimulating factor white blood cell counts in predicting the adverse events and yields of stem cell mobilization.

Science.gov (United States)

Chen, Shu-Huey; Yang, Shang-Hsien; Chu, Sung-Chao; Su, Yu-Chieh; Chang, Chu-Yu; Chiu, Ya-Wen; Kao, Ruey-Ho; Li, Dian-Kun; Yang, Kuo-Liang; Wang, Tso-Fu

2011-05-01

Granulocyte colony-stimulating factor (G-CSF) is now widely used for stem cell mobilization. We evaluated the role of post-G-CSF white blood cell (WBC) counts and donor factors in predicting adverse events and yields associated with mobilization. WBC counts were determined at baseline, after the third and the fifth dose of G-CSF in 476 healthy donors. Donors with WBC ≥ 50 × 10(3)/μL post the third dose of G-CSF experienced more fatigue, myalgia/arthralgia, and chills, but final post-G-CSF CD34(+) cell counts were similar. Although the final CD34(+) cell count was higher in donors with WBC ≥ 50 × 10(3)/μL post the fifth G-CSF, the incidence of side effects was similar. Females more frequently experienced headache, nausea/anorexia, vomiting, fever, and lower final CD34(+) cell count than did males. Donors with body mass index (BMI) ≥ 25 showed higher incidences of sweat and insomnia as well as higher final CD34(+) cell counts. Donor receiving G-CSF ≥ 10 μg/kg tended to experience bone pain, headache and chills more frequently. Multivariate analysis indicated that female gender is an independent factor predictive of the occurrence of most side effects, except for ECOG > 1 and chills. Higher BMI was also an independent predictor for fatigue, myalgia/arthralgia, and sweat. Higher G-CSF dose was associated with bone pain, while the WBC count post the third G-CSF was associated with fatigue only. In addition, one donor in the study period did not complete the mobilization due to suspected anaphylactoid reaction. Observation for 1 h after the first injection of G-CSF is required to prevent complications from unpredictable side effects.

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)

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.

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.

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.

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.

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.

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.

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.

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)

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)

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

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.

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.

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.

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,

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

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

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.

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 .

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.

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)

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.

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.

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)

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

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

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

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.

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 (...

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

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.

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.

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

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.

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

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)

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.

Primary hyperparathyroidism: Recognition and management

International Nuclear Information System (INIS)

Avioli, L.V.

1987-01-01

Many cases may be missed, particularly those that are normocalcemic. The disease should be considered in all older patients with chronic mental or behavioral disturbances, nonspecific neuromuscular or GI complaints, and arthralgias--not only because hyperparathyroidism is eminently curable but also because it is virtually impossible to predict when the disease will become life threatening. 13 refs

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

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

Nonstructural Proteins of Alphavirus—Potential Targets for Drug Development

Directory of Open Access Journals (Sweden)

Farhana Abu Bakar

2018-02-01

Full Text Available Alphaviruses are enveloped, positive single-stranded RNA viruses, typically transmitted by arthropods. They often cause arthralgia or encephalitic diseases in infected humans and there is currently no targeted antiviral treatment available. The re-emergence of alphaviruses in Asia, Europe, and the Americas over the last decade, including chikungunya and o’nyong’nyong viruses, have intensified the search for selective inhibitors. In this review, we highlight key molecular determinants within the alphavirus replication complex that have been identified as viral targets, focusing on their structure and functionality in viral dissemination. We also summarize recent structural data of these viral targets and discuss how these could serve as templates to facilitate structure-based drug design and development of small molecule inhibitors.

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

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.

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).

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;

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.

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.

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.

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)

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.

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

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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

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.

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)

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

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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.

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

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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.

Musculoskeletal Adverse Events Associated with Adjuvant Aromatase Inhibitors

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Qamar J. Khan

2010-01-01

Full Text Available Musculoskeletal symptoms including arthralgia and myalgia occur frequently in aging women, particularly during the transition to menopause, when plasma estrogens precipitously decline. In postmenopausal women (PMW with breast cancer, third-generation aromatase inhibitors (AIs as adjuvant hormonal therapy have proven to be more effective, and to have a more predictable side effect profile, than tamoxifen. However, AIs further reduce plasma estrogens in PMW, exacerbating musculoskeletal symptoms. Clinical trial data have shown significantly higher incidences of arthralgia and myalgia with AIs compared with women on tamoxifen or placebo. Symptoms may be severe enough to significantly affect quality of life; musculoskeletal symptoms are a frequent reason for discontinuing therapy. In many cases, symptoms can be effectively managed with oral analgesics or other strategies. Early recognition and effective management of musculoskeletal symptoms can help maximize treatment compliance, enabling patients to derive optimal benefit from therapy in terms of preventing recurrence.

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

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

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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.

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

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

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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.

Development of Antimicrobial Peptide Prediction Tool for Aquaculture Industries.

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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.

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.

Developing and Validating a Predictive Model for Stroke Progression

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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

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

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

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.

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

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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.

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)

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.

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.

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.

Observant, Nonaggressive Temperament Predicts Theory of Mind Development

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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

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.

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.

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.

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.

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.

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)

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.

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.

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)

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

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.)

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

[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.

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

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.

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...

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.)

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.)

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

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.

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.

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.

Primaer HIV-infektion

DEFF Research Database (Denmark)

Pedersen, C; Pedersen, B K

1996-01-01

Up to 70% of individuals with primary HIV infection will develop symptoms of an acute illness. The most common symptoms reported are fever, generalized lymphadenopathy, arthralgia and myalgia, headache, pharyngitis, enanthema, skin rash, diarrhoea, and mucocutaneous ulcerations. More rarely...

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.

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...

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.

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.

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.

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.

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.

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

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.

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...

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

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

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…

Protein docking prediction using predicted protein-protein interface

Directory of Open Access Journals (Sweden)

Li Bin

2012-01-01

Full Text Available Abstract Background Many important cellular processes are carried out by protein complexes. To provide physical pictures of interacting proteins, many computational protein-protein prediction methods have been developed in the past. However, it is still difficult to identify the correct docking complex structure within top ranks among alternative conformations. Results We present a novel protein docking algorithm that utilizes imperfect protein-protein binding interface prediction for guiding protein docking. Since the accuracy of protein binding site prediction varies depending on cases, the challenge is to develop a method which does not deteriorate but improves docking results by using a binding site prediction which may not be 100% accurate. The algorithm, named PI-LZerD (using Predicted Interface with Local 3D Zernike descriptor-based Docking algorithm, is based on a pair wise protein docking prediction algorithm, LZerD, which we have developed earlier. PI-LZerD starts from performing docking prediction using the provided protein-protein binding interface prediction as constraints, which is followed by the second round of docking with updated docking interface information to further improve docking conformation. Benchmark results on bound and unbound cases show that PI-LZerD consistently improves the docking prediction accuracy as compared with docking without using binding site prediction or using the binding site prediction as post-filtering. Conclusion We have developed PI-LZerD, a pairwise docking algorithm, which uses imperfect protein-protein binding interface prediction to improve docking accuracy. PI-LZerD consistently showed better prediction accuracy over alternative methods in the series of benchmark experiments including docking using actual docking interface site predictions as well as unbound docking cases.

Protein docking prediction using predicted protein-protein interface.

Science.gov (United States)

Li, Bin; Kihara, Daisuke

2012-01-10

Many important cellular processes are carried out by protein complexes. To provide physical pictures of interacting proteins, many computational protein-protein prediction methods have been developed in the past. However, it is still difficult to identify the correct docking complex structure within top ranks among alternative conformations. We present a novel protein docking algorithm that utilizes imperfect protein-protein binding interface prediction for guiding protein docking. Since the accuracy of protein binding site prediction varies depending on cases, the challenge is to develop a method which does not deteriorate but improves docking results by using a binding site prediction which may not be 100% accurate. The algorithm, named PI-LZerD (using Predicted Interface with Local 3D Zernike descriptor-based Docking algorithm), is based on a pair wise protein docking prediction algorithm, LZerD, which we have developed earlier. PI-LZerD starts from performing docking prediction using the provided protein-protein binding interface prediction as constraints, which is followed by the second round of docking with updated docking interface information to further improve docking conformation. Benchmark results on bound and unbound cases show that PI-LZerD consistently improves the docking prediction accuracy as compared with docking without using binding site prediction or using the binding site prediction as post-filtering. We have developed PI-LZerD, a pairwise docking algorithm, which uses imperfect protein-protein binding interface prediction to improve docking accuracy. PI-LZerD consistently showed better prediction accuracy over alternative methods in the series of benchmark experiments including docking using actual docking interface site predictions as well as unbound docking cases.

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...

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.

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

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)

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.

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...

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/

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.

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

First reported chikungunya fever outbreak in the republic of Congo, 2011.

Directory of Open Access Journals (Sweden)

Nanikaly Moyen

Full Text Available Chikungunya is an Aedes -borne disease characterised by febrile arthralgia and responsible for massive outbreaks. We present a prospective clinical cohort study and a retrospective serological study relating to a CHIK outbreak, in the Republic of Congo in 2011.We analysed 317 suspected cases, of which 308 (97.2% lived in the city of Brazzaville (66.6% in the South area. Amongst them, 37 (11.7% were CHIKV+ve patients (i.e., biologically confirmed by a real-time RT-PCR assay, of whom 36 (97.3% had fever, 22 (66.7% myalgia and 32 (86.5% arthralgia. All tested negative for dengue. The distribution of incident cases within Brazzaville districts was compared with CHIKV seroprevalence before the outbreak (34.4% in 517 blood donors, providing evidence for previous circulation of CHIKV. We applied a CHIK clinical score to 126 patients recruited within the two first day of illness (including 28 CHIKV+ves (22.2% with sensitivity (78.6% and specificity (72.4% values comparing with those of the referent study in Reunion Island. The negative predictive value was high (92%, but the positive predictive value (45% indicate poor potential contribution to medical practice to identify CHIKV+ve patients in low prevalence outbreaks. However, the score allowed a slightly more accurate follow-up of the evolution of the outbreak than the criterion "fever+arthralgia". The complete sequencing of a Congolase isolate (Brazza_MRS1 demonstrated belonging to the East/Central/South African lineage and was further used for producing a robust genome-scale CHIKV phylogenetic analysis.We describe the first Chikungunya outbreak declared in the Republic of Congo. The seroprevalence study conducted amongst blood donors before outbreak provided evidence for previous CHIKV circulation. We suggest that a more systematic survey of the entomological situation and of arbovirus circulation is necessary in Central Africa for better understanding the environmental, microbiological and

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

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

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).

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...

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

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.

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

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...

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.

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.

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)

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.

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.

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

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

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.

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.

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.

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

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)

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...

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.”

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

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.

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...

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.

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.

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.

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

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...

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

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.

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

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

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

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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.

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.

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)

Reliability of Degree-Day Models to Predict the Development Time of Plutella xylostella (L.) under Field Conditions.

Science.gov (United States)

Marchioro, C A; Krechemer, F S; de Moraes, C P; Foerster, L A

2015-12-01

The diamondback moth, Plutella xylostella (L.), is a cosmopolitan pest of brassicaceous crops occurring in regions with highly distinct climate conditions. Several studies have investigated the relationship between temperature and P. xylostella development rate, providing degree-day models for populations from different geographical regions. However, there are no data available to date to demonstrate the suitability of such models to make reliable projections on the development time for this species in field conditions. In the present study, 19 models available in the literature were tested regarding their ability to accurately predict the development time of two cohorts of P. xylostella under field conditions. Only 11 out of the 19 models tested accurately predicted the development time for the first cohort of P. xylostella, but only seven for the second cohort. Five models correctly predicted the development time for both cohorts evaluated. Our data demonstrate that the accuracy of the models available for P. xylostella varies widely and therefore should be used with caution for pest management purposes.

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

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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

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.

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.

Hypersensitivity to mechanical and intra-articular electrical stimuli in persons with painful temporomandibular joints

DEFF Research Database (Denmark)

Ayesh, Emad; Jensen, Troels Staehelin; Svensson, P

2007-01-01

This study tested whether persons with TMJ arthralgia have a modality-specific and site-specific hypersensitivity to somatosensory stimuli assessed by quantitative sensory tests (QST). Forty-three healthy persons and 20 with TMJ arthralgia participated. The QST consisted of: sensory and pain dete...... of sensitization of the TMJs as well as central nociceptive pathways. QST may facilitate a mechanism-based classification of temporomandibular disorders. Udgivelsesdato: 2007-Dec...

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.

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)

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.

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

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

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.

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

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)

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...

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.

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

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

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

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.

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.

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

ToxiM: A Toxicity Prediction Tool for Small Molecules Developed Using Machine Learning and Chemoinformatics Approaches

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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

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.

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)

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)

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.

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

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.

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.

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.

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

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.

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.

How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology

Energy Technology Data Exchange (ETDEWEB)

Wittwehr, Clemens; Aladjov, Hristo; Ankley, Gerald; Byrne, Hugh J.; de Knecht, Joop; Heinzle, Elmar; Klambauer, Günter; Landesmann, Brigitte; Luijten, Mirjam; MacKay, Cameron; Maxwell, Gavin; Meek, M. E. (Bette); Paini, Alicia; Perkins, Edward; Sobanski, Tomasz; Villeneuve, Dan; Waters, Katrina M.; Whelan, Maurice

2016-12-19

Efforts are underway to transform regulatory toxicology and chemical safety assessment from a largely empirical science based on direct observation of apical toxicity outcomes in whole organism toxicity tests to a predictive one in which outcomes and risk are inferred from accumulated mechanistic understanding. The adverse outcome pathway (AOP) framework has emerged as a systematic approach for organizing knowledge that supports such inference. We argue that this systematic organization of knowledge can inform and help direct the design and development of computational prediction models that can further enhance the utility of mechanistic and in silico data for chemical safety assessment. Examples of AOP-informed model development and its application to the assessment of chemicals for skin sensitization and multiple modes of endocrine disruption are provided. The role of problem formulation, not only as a critical phase of risk assessment, but also as guide for both AOP and complementary model development described. Finally, a proposal for actively engaging the modeling community in AOP-informed computational model development is made. The contents serve as a vision for how AOPs can be leveraged to facilitate development of computational prediction models needed to support the next generation of chemical safety assessment.

Development of a risk-prediction model for Middle East respiratory syndrome coronavirus infection in dialysis patients.

Science.gov (United States)

Ahmed, Anwar E; Alshukairi, Abeer N; Al-Jahdali, Hamdan; Alaqeel, Mody; Siddiq, Salma S; Alsaab, Hanan A; Sakr, Ezzeldin A; Alyahya, Hamed A; Alandonisi, Munzir M; Subedar, Alaa T; Aloudah, Nouf M; Baharoon, Salim; Alsalamah, Majid A; Al Johani, Sameera; Alghamdi, Mohammed G

2018-04-14

Introduction The Middle East respiratory syndrome coronavirus (MERS-CoV) infection can cause transmission clusters and high mortality in hemodialysis facilities. We attempted to develop a risk-prediction model to assess the early risk of MERS-CoV infection in dialysis patients. Methods This two-center retrospective cohort study included 104 dialysis patients who were suspected of MERS-CoV infection and diagnosed with rRT-PCR between September 2012 and June 2016 at King Fahd General Hospital in Jeddah and King Abdulaziz Medical City in Riyadh. We retrieved data on demographic, clinical, and radiological findings, and laboratory indices of each patient. Findings A risk-prediction model to assess early risk for MERS-CoV in dialysis patients has been developed. Independent predictors of MERS-CoV infection were identified, including chest pain (OR = 24.194; P = 0.011), leukopenia (OR = 6.080; P = 0.049), and elevated aspartate aminotransferase (AST) (OR = 11.179; P = 0.013). The adequacy of this prediction model was good (P = 0.728), with a high predictive utility (area under curve [AUC] = 76.99%; 95% CI: 67.05% to 86.38%). The prediction of the model had optimism-corrected bootstrap resampling AUC of 71.79%. The Youden index yielded a value of 0.439 or greater as the best cut-off for high risk of MERS infection. Discussion This risk-prediction model in dialysis patients appears to depend markedly on chest pain, leukopenia, and elevated AST. The model accurately predicts the high risk of MERS-CoV infection in dialysis patients. This could be clinically useful in applying timely intervention and control measures to prevent clusters of infections in dialysis facilities or other health care settings. The predictive utility of the model warrants further validation in external samples and prospective studies. © 2018 International Society for Hemodialysis.

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.

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...

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.

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

An initial investigation on developing a new method to predict short-term breast cancer risk based on deep learning technology

Science.gov (United States)

Qiu, Yuchen; Wang, Yunzhi; Yan, Shiju; Tan, Maxine; Cheng, Samuel; Liu, Hong; Zheng, Bin

2016-03-01

In order to establish a new personalized breast cancer screening paradigm, it is critically important to accurately predict the short-term risk of a woman having image-detectable cancer after a negative mammographic screening. In this study, we developed and tested a novel short-term risk assessment model based on deep learning method. During the experiment, a number of 270 "prior" negative screening cases was assembled. In the next sequential ("current") screening mammography, 135 cases were positive and 135 cases remained negative. These cases were randomly divided into a training set with 200 cases and a testing set with 70 cases. A deep learning based computer-aided diagnosis (CAD) scheme was then developed for the risk assessment, which consists of two modules: adaptive feature identification module and risk prediction module. The adaptive feature identification module is composed of three pairs of convolution-max-pooling layers, which contains 20, 10, and 5 feature maps respectively. The risk prediction module is implemented by a multiple layer perception (MLP) classifier, which produces a risk score to predict the likelihood of the woman developing short-term mammography-detectable cancer. The result shows that the new CAD-based risk model yielded a positive predictive value of 69.2% and a negative predictive value of 74.2%, with a total prediction accuracy of 71.4%. This study demonstrated that applying a new deep learning technology may have significant potential to develop a new short-term risk predicting scheme with improved performance in detecting early abnormal symptom from the negative mammograms.

Development and validation of a prediction model for insulin-associated hypoglycemia in non-critically ill hospitalized adults.

Science.gov (United States)

Mathioudakis, Nestoras Nicolas; Everett, Estelle; Routh, Shuvodra; Pronovost, Peter J; Yeh, Hsin-Chieh; Golden, Sherita Hill; Saria, Suchi

2018-01-01

To develop and validate a multivariable prediction model for insulin-associated hypoglycemia in non-critically ill hospitalized adults. We collected pharmacologic, demographic, laboratory, and diagnostic data from 128 657 inpatient days in which at least 1 unit of subcutaneous insulin was administered in the absence of intravenous insulin, total parenteral nutrition, or insulin pump use (index days). These data were used to develop multivariable prediction models for biochemical and clinically significant hypoglycemia (blood glucose (BG) of ≤70 mg/dL and model development and validation, respectively. Using predictors of age, weight, admitting service, insulin doses, mean BG, nadir BG, BG coefficient of variation (CV BG ), diet status, type 1 diabetes, type 2 diabetes, acute kidney injury, chronic kidney disease (CKD), liver disease, and digestive disease, our model achieved a c-statistic of 0.77 (95% CI 0.75 to 0.78), positive likelihood ratio (+LR) of 3.5 (95% CI 3.4 to 3.6) and negative likelihood ratio (-LR) of 0.32 (95% CI 0.30 to 0.35) for prediction of biochemical hypoglycemia. Using predictors of sex, weight, insulin doses, mean BG, nadir BG, CV BG , diet status, type 1 diabetes, type 2 diabetes, CKD stage, and steroid use, our model achieved a c-statistic of 0.80 (95% CI 0.78 to 0.82), +LR of 3.8 (95% CI 3.7 to 4.0) and -LR of 0.2 (95% CI 0.2 to 0.3) for prediction of clinically significant hypoglycemia. Hospitalized patients at risk of insulin-associated hypoglycemia can be identified using validated prediction models, which may support the development of real-time preventive interventions.

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.

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.

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...

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

How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology.

Science.gov (United States)

Wittwehr, Clemens; Aladjov, Hristo; Ankley, Gerald; Byrne, Hugh J; de Knecht, Joop; Heinzle, Elmar; Klambauer, Günter; Landesmann, Brigitte; Luijten, Mirjam; MacKay, Cameron; Maxwell, Gavin; Meek, M E Bette; Paini, Alicia; Perkins, Edward; Sobanski, Tomasz; Villeneuve, Dan; Waters, Katrina M; Whelan, Maurice

2017-02-01

Efforts are underway to transform regulatory toxicology and chemical safety assessment from a largely empirical science based on direct observation of apical toxicity outcomes in whole organism toxicity tests to a predictive one in which outcomes and risk are inferred from accumulated mechanistic understanding. The adverse outcome pathway (AOP) framework provides a systematic approach for organizing knowledge that may support such inference. Likewise, computational models of biological systems at various scales provide another means and platform to integrate current biological understanding to facilitate inference and extrapolation. We argue that the systematic organization of knowledge into AOP frameworks can inform and help direct the design and development of computational prediction models that can further enhance the utility of mechanistic and in silico data for chemical safety assessment. This concept was explored as part of a workshop on AOP-Informed Predictive Modeling Approaches for Regulatory Toxicology held September 24-25, 2015. Examples of AOP-informed model development and its application to the assessment of chemicals for skin sensitization and multiple modes of endocrine disruption are provided. The role of problem formulation, not only as a critical phase of risk assessment, but also as guide for both AOP and complementary model development is described. Finally, a proposal for actively engaging the modeling community in AOP-informed computational model development is made. The contents serve as a vision for how AOPs can be leveraged to facilitate development of computational prediction models needed to support the next generation of chemical safety assessment. © The Author 2016. Published by Oxford University Press on behalf of the Society of Toxicology.

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

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)

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...

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.

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)

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.

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.

Computational prediction of protein-protein interactions in Leishmania predicted proteomes.

Directory of Open Access Journals (Sweden)

Antonio M Rezende

Full Text Available The Trypanosomatids parasites Leishmania braziliensis, Leishmania major and Leishmania infantum are important human pathogens. Despite of years of study and genome availability, effective vaccine has not been developed yet, and the chemotherapy is highly toxic. Therefore, it is clear just interdisciplinary integrated studies will have success in trying to search new targets for developing of vaccines and drugs. An essential part of this rationale is related to protein-protein interaction network (PPI study which can provide a better understanding of complex protein interactions in biological system. Thus, we modeled PPIs for Trypanosomatids through computational methods using sequence comparison against public database of protein or domain interaction for interaction prediction (Interolog Mapping and developed a dedicated combined system score to address the predictions robustness. The confidence evaluation of network prediction approach was addressed using gold standard positive and negative datasets and the AUC value obtained was 0.94. As result, 39,420, 43,531 and 45,235 interactions were predicted for L. braziliensis, L. major and L. infantum respectively. For each predicted network the top 20 proteins were ranked by MCC topological index. In addition, information related with immunological potential, degree of protein sequence conservation among orthologs and degree of identity compared to proteins of potential parasite hosts was integrated. This information integration provides a better understanding and usefulness of the predicted networks that can be valuable to select new potential biological targets for drug and vaccine development. Network modularity which is a key when one is interested in destabilizing the PPIs for drug or vaccine purposes along with multiple alignments of the predicted PPIs were performed revealing patterns associated with protein turnover. In addition, around 50% of hypothetical protein present in the networks

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.

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.)

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...

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

Prediction of gas volume fraction in fully-developed gas-liquid flow in a vertical pipe

International Nuclear Information System (INIS)

Islam, A.S.M.A.; Adoo, N.A.; Bergstrom, D.J.; Wang, D.F.

2015-01-01

An Eulerian-Eulerian two-fluid model has been implemented for the prediction of the gas volume fraction profile in turbulent upward gas-liquid flow in a vertical pipe. The two-fluid transport equations are discretized using the finite volume method and a low Reynolds number κ-ε turbulence model is used to predict the turbulence field for the liquid phase. The contribution to the effective turbulence by the gas phase is modeled by a bubble induced turbulent viscosity. For the fully-developed flow being considered, the gas volume fraction profile is calculated using the radial momentum balance for the bubble phase. The model potentially includes the effect of bubble size on the interphase forces and turbulence model. The results obtained are in good agreement with experimental data from the literature. The one-dimensional formulation being developed allows for the efficient assessment and further development of both turbulence and two-fluid models for multiphase flow applications in the nuclear industry. (author)

Prediction of gas volume fraction in fully-developed gas-liquid flow in a vertical pipe

Energy Technology Data Exchange (ETDEWEB)

Islam, A.S.M.A.; Adoo, N.A.; Bergstrom, D.J., E-mail: nana.adoo@usask.ca [University of Saskatchewan, Department of Mechanical Engineering, Saskatoon, SK (Canada); Wang, D.F. [Canadian Nuclear Laboratories, Chalk River, ON (Canada)

2015-07-01

An Eulerian-Eulerian two-fluid model has been implemented for the prediction of the gas volume fraction profile in turbulent upward gas-liquid flow in a vertical pipe. The two-fluid transport equations are discretized using the finite volume method and a low Reynolds number κ-ε turbulence model is used to predict the turbulence field for the liquid phase. The contribution to the effective turbulence by the gas phase is modeled by a bubble induced turbulent viscosity. For the fully-developed flow being considered, the gas volume fraction profile is calculated using the radial momentum balance for the bubble phase. The model potentially includes the effect of bubble size on the interphase forces and turbulence model. The results obtained are in good agreement with experimental data from the literature. The one-dimensional formulation being developed allows for the efficient assessment and further development of both turbulence and two-fluid models for multiphase flow applications in the nuclear industry. (author)

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

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.

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.

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.

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.

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.

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.

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)

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.

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.

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

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.

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)

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)

Development of a Data-Driven Predictive Model of Supply Air Temperature in an Air-Handling Unit for Conserving Energy

Directory of Open Access Journals (Sweden)

Goopyo Hong

2018-02-01

Full Text Available The purpose of this study was to develop a data-driven predictive model that can predict the supply air temperature (SAT in an air-handling unit (AHU by using a neural network. A case study was selected, and AHU operational data from December 2015 to November 2016 was collected. A data-driven predictive model was generated through an evolving process that consisted of an initial model, an optimal model, and an adaptive model. In order to develop the optimal model, input variables, the number of neurons and hidden layers, and the period of the training data set were considered. Since AHU data changes over time, an adaptive model, which has the ability to actively cope with constantly changing data, was developed. This adaptive model determined the model with the lowest mean square error (MSE of the 91 models, which had two hidden layers and sets up a 12-hour test set at every prediction. The adaptive model used recently collected data as training data and utilized the sliding window technique rather than the accumulative data method. Furthermore, additional testing was performed to validate the adaptive model using AHU data from another building. The final adaptive model predicts SAT to a root mean square error (RMSE of less than 0.6 °C.

MRI of the wrist and finger joints in inflammatory joint diseases at 1-year interval: MRI features to predict bone erosions

International Nuclear Information System (INIS)

Savnik, Anette; Malmskov, Hanne; Graff, Lykke B.; Danneskiold-Samsoee, Bente; Bliddal, Henning; Thomsen, Henrik S.; Nielsen, Henrik; Boesen, Jens

2002-01-01

The aim of this study was to assess the ability of MRI determined synovial volumes and bone marrow oedema to predict progressions in bone erosions after 1 year in patients with different types of inflammatory joint diseases. Eighty-four patients underwent MRI, laboratory and clinical examination at baseline and 1 year later. Magnetic resonance imaging of the wrist and finger joints was performed in 22 patients with rheumatoid arthritis less than 3 years (group 1) who fulfilled the American College of Rheumatology (ACR) criteria for rheumatoid arthritis, 18 patients with reactive arthritis or psoriatic arthritis (group 2), 22 patients with more than 3 years duration of rheumatoid arthritis, who fulfilled the ACR criteria for rheumatoid arthritis (group 3), and 20 patients with arthralgia (group 4). The volume of the synovial membrane was outlined manually before and after gadodiamide injection on the T1-weighted sequences in the finger joints. Bones with marrow oedema were summed up in the wrist and fingers on short-tau inversion recovery sequences. These MRI features was compared with the number of bone erosions 1 year later. The MR images were scored independently under masked conditions. The synovial volumes in the finger joints assessed on pre-contrast images was highly predictive of bone erosions 1 year later in patients with rheumatoid arthritis (groups 1 and 3). The strongest individual predictor of bone erosions at 1-year follow-up was bone marrow oedema, if present at the wrist at baseline. Bone erosions on baseline MRI were in few cases reversible at follow-up MRI. The total synovial volume in the finger joints, and the presence of bone oedema in the wrist bones, seems to be predictive for the number of bone erosions 1 year later and may be used in screening. The importance of very early bone changes on MRI and the importance of the reversibility of these findings remain to be clarified. (orig.)

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.

Vaccination with Dendritic Cell Myeloma Fusions in Conjuction with Stem Cell Transplantation and PD-1 Blockade

Science.gov (United States)

2015-07-01

Resolved PM19 Arthralgia, hands 11/2012 1 Possible None Resolved PM23 Hypothyroidism 10/9/13 1 Possible None Resolved PM44 Arthralgia 3/1/2014 1...a brief episode of muscle spasms) 7/29/2013 1 Unrelated Probable None Resolved PM32 Injection site reaction 7/29/2013 1 Unrelated Definite...GM- CSF) Ibuprofen Resolved PM32 Pain, joint 8/5/2013 1 Definite Definite None Resolved PM32 Pain, muscle 8/5/2013 1 Definite Definite None

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.

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.

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.

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

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.

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)...

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

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...

Pediatric morphea (localized scleroderma): review of 136 patients.

Science.gov (United States)

Christen-Zaech, Stéphanie; Hakim, Miriam D; Afsar, F Sule; Paller, Amy S

2008-09-01

Morphea is an autoimmune inflammatory sclerosing disorder that may cause permanent functional disability and disfigurement. We sought to determine the clinical features of morphea in a large pediatric cohort. We conducted a retrospective chart review of 136 pediatric patients with morphea from one center, 1989 to 2006. Most children showed linear morphea, with a disproportionately high number of Caucasian and female patients. Two patients with rapidly progressing generalized or extensive linear morphea and arthralgias developed restrictive pulmonary disease. Initial oral corticosteroid treatment and long-term methotrexate administration stabilized and/or led to disease improvement in most patients with aggressive disease. Retrospective analysis, relatively small sample size, and risk of a selected referral population to the single site are limitations. These data suggest an increased prevalence of morphea in Caucasian girls, and support methotrexate as treatment for problematic forms. Visceral manifestations rarely occur; the presence of progressive problematic cutaneous disease and arthralgias should trigger closer patient monitoring.

First Imported Case of Chikungunya Virus Infection in a Travelling Canadian Returning from the Caribbean

Directory of Open Access Journals (Sweden)

Christian Therrien

2016-01-01

Full Text Available This is the first Canadian case of Chikungunya virus (CHIKV infection reported in a traveller returning from the Caribbean. Following multiple mosquito bites in Martinique Island in January 2014, the patient presented with high fever, headaches, arthralgia on both hands and feet, and a rash on the trunk upon his return to Canada. Initial serological testing for dengue virus infection was negative. Support therapy with nonsteroidal anti-inflammatory drugs was administered. The symptoms gradually improved 4 weeks after onset with residual arthralgia and morning joint stiffness. This clinical feature prompted the clinician to request CHIKV virus serology which was found to be positive for the presence of IgM and neutralizing antibodies. In 2014, over four hundred confirmed CHIKV infection cases were diagnosed in Canadian travellers returning from the Caribbean and Central America. Clinical suspicion of CHIKV or dengue virus infections should be considered in febrile patients with arthralgia returning from the recently CHIKV endemic countries of the Americas.

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.

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.

Who will have Sustainable Employment After a Back Injury? The Development of a Clinical Prediction Model in a Cohort of Injured Workers.

Science.gov (United States)

Shearer, Heather M; Côté, Pierre; Boyle, Eleanor; Hayden, Jill A; Frank, John; Johnson, William G

2017-09-01

Purpose Our objective was to develop a clinical prediction model to identify workers with sustainable employment following an episode of work-related low back pain (LBP). Methods We used data from a cohort study of injured workers with incident LBP claims in the USA to predict employment patterns 1 and 6 months following a workers' compensation claim. We developed three sequential models to determine the contribution of three domains of variables: (1) basic demographic/clinical variables; (2) health-related variables; and (3) work-related factors. Multivariable logistic regression was used to develop the predictive models. We constructed receiver operator curves and used the c-index to measure predictive accuracy. Results Seventy-nine percent and 77 % of workers had sustainable employment at 1 and 6 months, respectively. Sustainable employment at 1 month was predicted by initial back pain intensity, mental health-related quality of life, claim litigation and employer type (c-index = 0.77). At 6 months, sustainable employment was predicted by physical and mental health-related quality of life, claim litigation and employer type (c-index = 0.77). Adding health-related and work-related variables to models improved predictive accuracy by 8.5 and 10 % at 1 and 6 months respectively. Conclusion We developed clinically-relevant models to predict sustainable employment in injured workers who made a workers' compensation claim for LBP. Inquiring about back pain intensity, physical and mental health-related quality of life, claim litigation and employer type may be beneficial in developing programs of care. Our models need to be validated in other populations.

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.

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

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.

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

Development and validation of a multilevel model for predicting workload under routine and nonroutine conditions in an air traffic management center.

Science.gov (United States)

Neal, Andrew; Hannah, Sam; Sanderson, Penelope; Bolland, Scott; Mooij, Martijn; Murphy, Sean

2014-03-01

The aim of this study was to develop a model capable of predicting variability in the mental workload experienced by frontline operators under routine and nonroutine conditions. Excess workload is a risk that needs to be managed in safety-critical industries. Predictive models are needed to manage this risk effectively yet are difficult to develop. Much of the difficulty stems from the fact that workload prediction is a multilevel problem. A multilevel workload model was developed in Study I with data collected from an en route air traffic management center. Dynamic density metrics were used to predict variability in workload within and between work units while controlling for variability among raters.The model was cross-validated in Studies 2 and 3 with the use of a high-fidelity simulator. Reported workload generally remained within the bounds of the 90% prediction interval in Studies 2 and 3. Workload crossed the upper bound of the prediction interval only under nonroutine conditions. Qualitative analyses suggest that nonroutine events caused workload to cross the upper bound of the prediction interval because the controllers could not manage their workload strategically. The model performed well under both routine and nonroutine conditions and over different patterns of workload variation. Workload prediction models can be used to support both strategic and tactical workload management. Strategic uses include the analysis of historical and projected workflows and the assessment of staffing needs.Tactical uses include the dynamic reallocation of resources to meet changes in demand.

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…

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.

Development and validation of a predictive risk model for all-cause mortality in type 2 diabetes.

Science.gov (United States)

Robinson, Tom E; Elley, C Raina; Kenealy, Tim; Drury, Paul L

2015-06-01

Type 2 diabetes is common and is associated with an approximate 80% increase in the rate of mortality. Management decisions may be assisted by an estimate of the patient's absolute risk of adverse outcomes, including death. This study aimed to derive a predictive risk model for all-cause mortality in type 2 diabetes. We used primary care data from a large national multi-ethnic cohort of patients with type 2 diabetes in New Zealand and linked mortality records to develop a predictive risk model for 5-year risk of mortality. We then validated this model using information from a separate cohort of patients with type 2 diabetes. 26,864 people were included in the development cohort with a median follow up time of 9.1 years. We developed three models initially using demographic information and then progressively more clinical detail. The final model, which also included markers of renal disease, proved to give best prediction of all-cause mortality with a C-statistic of 0.80 in the development cohort and 0.79 in the validation cohort (7610 people) and was well calibrated. Ethnicity was a major factor with hazard ratios of 1.37 for indigenous Maori, 0.41 for East Asian and 0.55 for Indo Asian compared with European (P<0.001). We have developed a model using information usually available in primary care that provides good assessment of patient's risk of death. Results are similar to models previously published from smaller cohorts in other countries and apply to a wider range of patient ethnic groups. Copyright © 2015. Published by Elsevier Ireland Ltd.

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.

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.

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.

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

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

Development and Evaluation of Season-ahead Precipitation and Streamflow Predictions for Sectoral Management in Western Ethiopia

Science.gov (United States)

Block, P. J.; Alexander, S.; WU, S.

2017-12-01

Skillful season-ahead predictions conditioned on local and large-scale hydro-climate variables can provide valuable knowledge to farmers and reservoir operators, enabling informed water resource allocation and management decisions. In Ethiopia, the potential for advancing agriculture and hydropower management, and subsequently economic growth, is substantial, yet evidence suggests a weak adoption of prediction information by sectoral audiences. To address common critiques, including skill, scale, and uncertainty, probabilistic forecasts are developed at various scales - temporally and spatially - for the Finchaa hydropower dam and the Koga agricultural scheme in an attempt to promote uptake and application. Significant prediction skill is evident across scales, particularly for statistical models. This raises questions regarding other potential barriers to forecast utilization at community scales, which are also addressed.

Development and application of a statistical methodology to evaluate the predictive accuracy of building energy baseline models

Energy Technology Data Exchange (ETDEWEB)

Granderson, Jessica [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). Energy Technologies Area Div.; Price, Phillip N. [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). Energy Technologies Area Div.

2014-03-01

This paper documents the development and application of a general statistical methodology to assess the accuracy of baseline energy models, focusing on its application to Measurement and Verification (M&V) of whole-­building energy savings. The methodology complements the principles addressed in resources such as ASHRAE Guideline 14 and the International Performance Measurement and Verification Protocol. It requires fitting a baseline model to data from a ``training period’’ and using the model to predict total electricity consumption during a subsequent ``prediction period.’’ We illustrate the methodology by evaluating five baseline models using data from 29 buildings. The training period and prediction period were varied, and model predictions of daily, weekly, and monthly energy consumption were compared to meter data to determine model accuracy. Several metrics were used to characterize the accuracy of the predictions, and in some cases the best-­performing model as judged by one metric was not the best performer when judged by another metric.

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.

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.

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

Quantification by qPCR of Pathobionts in Chronic Periodontitis: Development of Predictive Models of Disease Severity at Site-Specific Level

OpenAIRE

Tomás, Inmaculada; Regueira-Iglesias, Alba; López, Maria; Arias-Bujanda, Nora; Novoa, Lourdes; Balsa-Castro, Carlos; Tomás, Maria

2017-01-01

Currently, there is little evidence available on the development of predictive models for the diagnosis or prognosis of chronic periodontitis based on the qPCR quantification of subgingival pathobionts. Our objectives were to: (1) analyze and internally validate pathobiont-based models that could be used to distinguish different periodontal conditions at site-specific level within the same patient with chronic periodontitis; (2) develop nomograms derived from predictive models. Subgingival pl...

Archaeological predictive model set.

Science.gov (United States)

2015-03-01

This report is the documentation for Task 7 of the Statewide Archaeological Predictive Model Set. The goal of this project is to : develop a set of statewide predictive models to assist the planning of transportation projects. PennDOT is developing t...

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)

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.

Predictive medicine

NARCIS (Netherlands)

Boenink, Marianne; ten Have, Henk

2015-01-01

In the last part of the twentieth century, predictive medicine has gained currency as an important ideal in biomedical research and health care. Research in the genetic and molecular basis of disease suggested that the insights gained might be used to develop tests that predict the future health

Earthquake prediction

International Nuclear Information System (INIS)

Ward, P.L.

1978-01-01

The state of the art of earthquake prediction is summarized, the possible responses to such prediction are examined, and some needs in the present prediction program and in research related to use of this new technology are reviewed. Three basic aspects of earthquake prediction are discussed: location of the areas where large earthquakes are most likely to occur, observation within these areas of measurable changes (earthquake precursors) and determination of the area and time over which the earthquake will occur, and development of models of the earthquake source in order to interpret the precursors reliably. 6 figures

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...

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.

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.

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.

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.

Developing EHR-driven heart failure risk prediction models using CPXR(Log) with the probabilistic loss function.

Science.gov (United States)

Taslimitehrani, Vahid; Dong, Guozhu; Pereira, Naveen L; Panahiazar, Maryam; Pathak, Jyotishman

2016-04-01

Computerized survival prediction in healthcare identifying the risk of disease mortality, helps healthcare providers to effectively manage their patients by providing appropriate treatment options. In this study, we propose to apply a classification algorithm, Contrast Pattern Aided Logistic Regression (CPXR(Log)) with the probabilistic loss function, to develop and validate prognostic risk models to predict 1, 2, and 5year survival in heart failure (HF) using data from electronic health records (EHRs) at Mayo Clinic. The CPXR(Log) constructs a pattern aided logistic regression model defined by several patterns and corresponding local logistic regression models. One of the models generated by CPXR(Log) achieved an AUC and accuracy of 0.94 and 0.91, respectively, and significantly outperformed prognostic models reported in prior studies. Data extracted from EHRs allowed incorporation of patient co-morbidities into our models which helped improve the performance of the CPXR(Log) models (15.9% AUC improvement), although did not improve the accuracy of the models built by other classifiers. We also propose a probabilistic loss function to determine the large error and small error instances. The new loss function used in the algorithm outperforms other functions used in the previous studies by 1% improvement in the AUC. This study revealed that using EHR data to build prediction models can be very challenging using existing classification methods due to the high dimensionality and complexity of EHR data. The risk models developed by CPXR(Log) also reveal that HF is a highly heterogeneous disease, i.e., different subgroups of HF patients require different types of considerations with their diagnosis and treatment. Our risk models provided two valuable insights for application of predictive modeling techniques in biomedicine: Logistic risk models often make systematic prediction errors, and it is prudent to use subgroup based prediction models such as those given by CPXR

Who will have Sustainable Employment After a Back Injury? The Development of a Clinical Prediction Model in a Cohort of Injured Workers

DEFF Research Database (Denmark)

Shearer, Heather M.; Côté, Pierre; Boyle, Eleanor

2017-01-01

to develop the predictive models. We constructed receiver operator curves and used the c-index to measure predictive accuracy. Results Seventy-nine percent and 77 % of workers had sustainable employment at 1 and 6 months, respectively. Sustainable employment at 1 month was predicted by initial back pain...... intensity, mental health-related quality of life, claim litigation and employer type (c-index = 0.77). At 6 months, sustainable employment was predicted by physical and mental health-related quality of life, claim litigation and employer type (c-index = 0.77). Adding health-related and work......-related variables to models improved predictive accuracy by 8.5 and 10 % at 1 and 6 months respectively. Conclusion We developed clinically-relevant models to predict sustainable employment in injured workers who made a workers’ compensation claim for LBP. Inquiring about back pain intensity, physical and mental...

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

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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.

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.

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

Post-bronchoscopy pneumonia in patients suffering from lung cancer: Development and validation of a risk prediction score.

Science.gov (United States)

Takiguchi, Hiroto; Hayama, Naoki; Oguma, Tsuyoshi; Harada, Kazuki; Sato, Masako; Horio, Yukihiro; Tanaka, Jun; Tomomatsu, Hiromi; Tomomatsu, Katsuyoshi; Takihara, Takahisa; Niimi, Kyoko; Nakagawa, Tomoki; Masuda, Ryota; Aoki, Takuya; Urano, Tetsuya; Iwazaki, Masayuki; Asano, Koichiro

2017-05-01

The incidence, risk factors, and consequences of pneumonia after flexible bronchoscopy in patients with lung cancer have not been studied in detail. We retrospectively analyzed the data from 237 patients with lung cancer who underwent diagnostic bronchoscopy between April 2012 and July 2013 (derivation sample) and 241 patients diagnosed between August 2013 and July 2014 (validation sample) in a tertiary referral hospital in Japan. A score predictive of post-bronchoscopy pneumonia was developed in the derivation sample and tested in the validation sample. Pneumonia developed after bronchoscopy in 6.3% and 4.1% of patients in the derivation and validation samples, respectively. Patients who developed post-bronchoscopy pneumonia needed to change or cancel their planned cancer therapy more frequently than those without pneumonia (56% vs. 6%, ppneumonia, which we added to develop our predictive score. The incidence of pneumonia associated with scores=0, 1, and ≥2 was 0, 3.7, and 13.4% respectively in the derivation sample (p=0.003), and 0, 2.9, and 9.7% respectively in the validation sample (p=0.016). The incidence of post-bronchoscopy pneumonia in patients with lung cancer was not rare and associated with adverse effects on the clinical course. A simple 3-point predictive score identified patients with lung cancer at high risk of post-bronchoscopy pneumonia prior to the procedure. Copyright © 2017 The Japanese Respiratory Society. Published by Elsevier B.V. All rights reserved.

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.

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.

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

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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.

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.

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

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

Application of the predicted heat strain model in development of localized, threshold-based heat stress management guidelines for the construction industry.

Science.gov (United States)

Rowlinson, Steve; Jia, Yunyan Andrea

2014-04-01

Existing heat stress risk management guidelines recommended by international standards are not practical for the construction industry which needs site supervision staff to make instant managerial decisions to mitigate heat risks. The ability of the predicted heat strain (PHS) model [ISO 7933 (2004). Ergonomics of the thermal environment analytical determination and interpretation of heat stress using calculation of the predicted heat strain. Geneva: International Standard Organisation] to predict maximum allowable exposure time (D lim) has now enabled development of localized, action-triggering and threshold-based guidelines for implementation by lay frontline staff on construction sites. This article presents a protocol for development of two heat stress management tools by applying the PHS model to its full potential. One of the tools is developed to facilitate managerial decisions on an optimized work-rest regimen for paced work. The other tool is developed to enable workers' self-regulation during self-paced work.

Developing predictions of in vivo developmental toxicity of ToxCast chemicals using mouse embryonic stem cells.

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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...

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)

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…

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

Development of a generally applicable morphokinetic algorithm capable of predicting the implantation potential of embryos transferred on Day 3

Science.gov (United States)

Petersen, Bjørn Molt; Boel, Mikkel; Montag, Markus; Gardner, David K.

2016-01-01

STUDY QUESTION Can a generally applicable morphokinetic algorithm suitable for Day 3 transfers of time-lapse monitored embryos originating from different culture conditions and fertilization methods be developed for the purpose of supporting the embryologist's decision on which embryo to transfer back to the patient in assisted reproduction? SUMMARY ANSWER The algorithm presented here can be used independently of culture conditions and fertilization method and provides predictive power not surpassed by other published algorithms for ranking embryos according to their blastocyst formation potential. WHAT IS KNOWN ALREADY Generally applicable algorithms have so far been developed only for predicting blastocyst formation. A number of clinics have reported validated implantation prediction algorithms, which have been developed based on clinic-specific culture conditions and clinical environment. However, a generally applicable embryo evaluation algorithm based on actual implantation outcome has not yet been reported. STUDY DESIGN, SIZE, DURATION Retrospective evaluation of data extracted from a database of known implantation data (KID) originating from 3275 embryos transferred on Day 3 conducted in 24 clinics between 2009 and 2014. The data represented different culture conditions (reduced and ambient oxygen with various culture medium strategies) and fertilization methods (IVF, ICSI). The capability to predict blastocyst formation was evaluated on an independent set of morphokinetic data from 11 218 embryos which had been cultured to Day 5. PARTICIPANTS/MATERIALS, SETTING, METHODS The algorithm was developed by applying automated recursive partitioning to a large number of annotation types and derived equations, progressing to a five-fold cross-validation test of the complete data set and a validation test of different incubation conditions and fertilization methods. The results were expressed as receiver operating characteristics curves using the area under the

Data-Based Predictive Control with Multirate Prediction Step

Science.gov (United States)

Barlow, Jonathan S.

2010-01-01

Data-based predictive control is an emerging control method that stems from Model Predictive Control (MPC). MPC computes current control action based on a prediction of the system output a number of time steps into the future and is generally derived from a known model of the system. Data-based predictive control has the advantage of deriving predictive models and controller gains from input-output data. Thus, a controller can be designed from the outputs of complex simulation code or a physical system where no explicit model exists. If the output data happens to be corrupted by periodic disturbances, the designed controller will also have the built-in ability to reject these disturbances without the need to know them. When data-based predictive control is implemented online, it becomes a version of adaptive control. One challenge of MPC is computational requirements increasing with prediction horizon length. This paper develops a closed-loop dynamic output feedback controller that minimizes a multi-step-ahead receding-horizon cost function with multirate prediction step. One result is a reduced influence of prediction horizon and the number of system outputs on the computational requirements of the controller. Another result is an emphasis on portions of the prediction window that are sampled more frequently. A third result is the ability to include more outputs in the feedback path than in the cost function.

Predicting the Extent of Inundation due to Sea-Level Rise: Al Hamra Development, Ras Al Khaimah, UAE. A Pilot Project

Directory of Open Access Journals (Sweden)

Arthur Robert M.

2016-06-01

Full Text Available As new information is received, predictions of sea-level rise resulting from global warming continue to be revised upwards. Measurements indicate that the rise in sea-level is continuing at, or close to, the worst case forecasts (Kellet et al. 2014. Coastal areas are coming under increasing risk of inundation and flooding as storms are predicted to increase in frequency and severity, adding to the risk of inundation due to higher sea levels. Stakeholders, government agencies, developers and land owners require accurate, up to date information to be able to protect coastal areas. Geographic Information Systems (GIS along with accurate remote sensing technologies such as LiDAR provides the best means for delivering this information. Using these technologies, this paper predicts the risk posed to a large multi-use development in the emirate of Ras Al Khaimah, UAE. This development, Al Hamra Village, is situated on the coast of the Arabian Gulf. Al Hamra’s physical relationship to the Gulf is in common with other developments in Ras Al Khaimah in its and for this reason has been used as a pilot project. The resulting GIS model shows that Al Hamra is indeed at risk from predicted flood events. How this information can be used as a planning tool for numerous strategies is discussed in this paper.

Novel Uses of In Vitro Data to Develop Quantitative Biological Activity Relationship Models for in Vivo Carcinogenicity Prediction.

Science.gov (United States)

Pradeep, Prachi; Povinelli, Richard J; Merrill, Stephen J; Bozdag, Serdar; Sem, Daniel S

2015-04-01

The availability of large in vitro datasets enables better insight into the mode of action of chemicals and better identification of potential mechanism(s) of toxicity. Several studies have shown that not all in vitro assays can contribute as equal predictors of in vivo carcinogenicity for development of hybrid Quantitative Structure Activity Relationship (QSAR) models. We propose two novel approaches for the use of mechanistically relevant in vitro assay data in the identification of relevant biological descriptors and development of Quantitative Biological Activity Relationship (QBAR) models for carcinogenicity prediction. We demonstrate that in vitro assay data can be used to develop QBAR models for in vivo carcinogenicity prediction via two case studies corroborated with firm scientific rationale. The case studies demonstrate the similarities between QBAR and QSAR modeling in: (i) the selection of relevant descriptors to be used in the machine learning algorithm, and (ii) the development of a computational model that maps chemical or biological descriptors to a toxic endpoint. The results of both the case studies show: (i) improved accuracy and sensitivity which is especially desirable under regulatory requirements, and (ii) overall adherence with the OECD/REACH guidelines. Such mechanism based models can be used along with QSAR models for prediction of mechanistically complex toxic endpoints. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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 ...

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

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

Development of a semi-automated method for subspecialty case distribution and prediction of intraoperative consultations in surgical pathology

Directory of Open Access Journals (Sweden)

Raul S Gonzalez

2015-01-01

Full Text Available Background: In many surgical pathology laboratories, operating room schedules are prospectively reviewed to determine specimen distribution to different subspecialty services and to predict the number and nature of potential intraoperative consultations for which prior medical records and slides require review. At our institution, such schedules were manually converted into easily interpretable, surgical pathology-friendly reports to facilitate these activities. This conversion, however, was time-consuming and arguably a non-value-added activity. Objective: Our goal was to develop a semi-automated method of generating these reports that improved their readability while taking less time to perform than the manual method. Materials and Methods: A dynamic Microsoft Excel workbook was developed to automatically convert published operating room schedules into different tabular formats. Based on the surgical procedure descriptions in the schedule, a list of linked keywords and phrases was utilized to sort cases by subspecialty and to predict potential intraoperative consultations. After two trial-and-optimization cycles, the method was incorporated into standard practice. Results: The workbook distributed cases to appropriate subspecialties and accurately predicted intraoperative requests. Users indicated that they spent 1-2 h fewer per day on this activity than before, and team members preferred the formatting of the newer reports. Comparison of the manual and semi-automatic predictions showed that the mean daily difference in predicted versus actual intraoperative consultations underwent no statistically significant changes before and after implementation for most subspecialties. Conclusions: A well-designed, lean, and simple information technology solution to determine subspecialty case distribution and prediction of intraoperative consultations in surgical pathology is approximately as accurate as the gold standard manual method and requires less

Intergenerational Consequences: Women's Experiences of Discrimination in Pregnancy Predict Infant Social-Emotional Development at 6 Months and 1 Year.

Science.gov (United States)

Rosenthal, Lisa; Earnshaw, Valerie A; Moore, Joan M; Ferguson, Darrah N; Lewis, Tené T; Reid, Allecia E; Lewis, Jessica B; Stasko, Emily C; Tobin, Jonathan N; Ickovics, Jeannette R

2018-04-01

Racial/ethnic and socioeconomic disparities in infant development in the United States have lifelong consequences. Discrimination predicts poorer health and academic outcomes. This study explored for the first time intergenerational consequences of women's experiences of discrimination reported during pregnancy for their infants' social-emotional development in the first year of life. Data come from a longitudinal study with predominantly Black and Latina, socioeconomically disadvantaged, urban young women (N = 704, Mage = 18.53) across pregnancy through 1 year postpartum. Women were recruited from community hospitals and health centers in a Northeastern US city. Linear regression analyses examined whether women's experiences of everyday discrimination reported during pregnancy predicted social-emotional development outcomes among their infants at 6 months and 1 year of age, controlling for potentially confounding medical and sociodemographic factors. Path analyses tested if pregnancy distress, anxiety, or depressive symptoms mediated significant associations. Everyday discrimination reported during pregnancy prospectively predicted greater inhibition/separation problems and greater negative emotionality, but did not predict attention skills or positive emotionality, at 6 months and 1 year. Depressive symptoms mediated the association of discrimination with negative emotionality at 6 months, and pregnancy distress, anxiety, and depressive symptoms mediated the association of discrimination with negative emotionality at 1 year. Findings support that there are intergenerational consequences of discrimination, extending past findings to infant social-emotional development outcomes in the first year of life. It may be important to address discrimination before and during pregnancy and enhance support to mothers and infants exposed to discrimination to promote health equity across the life span.

Development of a Program for Predicting Flow Instability in a Once-through Sodium-Heated Steam Generator (III)

Energy Technology Data Exchange (ETDEWEB)

Kim, Eui Kwang; Yoon, Jung; Kim, Jong Bum; Jeong, Jiyoung [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

2016-10-15

Two-phase flow systems can be subjected to several types of instability problems. Density-wave oscillation is the most common and important type of instability in boiling channels. Such instability gives difficulties in predictions of system performance and system control, and component failure due to thermal fatigue. A computer program developed for predicting two-phase flow instability in a steam generator heated by liquid sodium was presented in the previous works. Limit cycle was predicted even in a fixed node system. The amplitude of inlet flow rate is larger than that of outlet flow rate. The amplitude of phase change location oscillation within boiling-to-vapor boundary node is larger than that of liquid-to-boiling boundary node. Sodium and steam temperature are invariant at tube exit.

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 K_ow, 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.

PCTFPeval: a web tool for benchmarking newly developed algorithms for predicting cooperative transcription factor pairs in yeast.

Science.gov (United States)

Lai, Fu-Jou; Chang, Hong-Tsun; Wu, Wei-Sheng

2015-01-01

Computational identification of cooperative transcription factor (TF) pairs helps understand the combinatorial regulation of gene expression in eukaryotic cells. Many advanced algorithms have been proposed to predict cooperative TF pairs in yeast. However, it is still difficult to conduct a comprehensive and objective performance comparison of different algorithms because of lacking sufficient performance indices and adequate overall performance scores. To solve this problem, in our previous study (published in BMC Systems Biology 2014), we adopted/proposed eight performance indices and designed two overall performance scores to compare the performance of 14 existing algorithms for predicting cooperative TF pairs in yeast. Most importantly, our performance comparison framework can be applied to comprehensively and objectively evaluate the performance of a newly developed algorithm. However, to use our framework, researchers have to put a lot of effort to construct it first. To save researchers time and effort, here we develop a web tool to implement our performance comparison framework, featuring fast data processing, a comprehensive performance comparison and an easy-to-use web interface. The developed tool is called PCTFPeval (Predicted Cooperative TF Pair evaluator), written in PHP and Python programming languages. The friendly web interface allows users to input a list of predicted cooperative TF pairs from their algorithm and select (i) the compared algorithms among the 15 existing algorithms, (ii) the performance indices among the eight existing indices, and (iii) the overall performance scores from two possible choices. The comprehensive performance comparison results are then generated in tens of seconds and shown as both bar charts and tables. The original comparison results of each compared algorithm and each selected performance index can be downloaded as text files for further analyses. Allowing users to select eight existing performance indices and 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

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

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

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

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

Development of a simple tool to predict the risk of postpartum diabetes in women with gestational diabetes mellitus.

Science.gov (United States)

Köhler, M; Ziegler, A G; Beyerlein, A

2016-06-01

Women with gestational diabetes mellitus (GDM) have an increased risk of diabetes postpartum. We developed a score to predict the long-term risk of postpartum diabetes using clinical and anamnestic variables recorded during or shortly after delivery. Data from 257 GDM women who were prospectively followed for diabetes outcome over 20 years of follow-up were used to develop and validate the risk score. Participants were divided into training and test sets. The risk score was calculated using Lasso Cox regression and divided into four risk categories, and its prediction performance was assessed in the test set. Postpartum diabetes developed in 110 women. The computed training set risk score of 5 × body mass index in early pregnancy (per kg/m(2)) + 132 if GDM was treated with insulin (otherwise 0) + 44 if the woman had a family history of diabetes (otherwise 0) - 35 if the woman lactated (otherwise 0) had R (2) values of 0.23, 0.25, and 0.33 at 5, 10, and 15 years postpartum, respectively, and a C-Index of 0.75. Application of the risk score in the test set resulted in observed risk of postpartum diabetes at 5 years of 11 % for low risk scores ≤140, 29 % for scores 141-220, 64 % for scores 221-300, and 80 % for scores >300. The derived risk score is easy to calculate, allows accurate prediction of GDM-related postpartum diabetes, and may thus be a useful prediction tool for clinicians and general practitioners.

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.

Development of a deep convolutional neural network to predict grading of canine meningiomas from magnetic resonance images.

Science.gov (United States)

Banzato, T; Cherubini, G B; Atzori, M; Zotti, A

2018-05-01

An established deep neural network (DNN) based on transfer learning and a newly designed DNN were tested to predict the grade of meningiomas from magnetic resonance (MR) images in dogs and to determine the accuracy of classification of using pre- and post-contrast T1-weighted (T1W), and T2-weighted (T2W) MR images. The images were randomly assigned to a training set, a validation set and a test set, comprising 60%, 10% and 30% of images, respectively. The combination of DNN and MR sequence displaying the highest discriminating accuracy was used to develop an image classifier to predict the grading of new cases. The algorithm based on transfer learning using the established DNN did not provide satisfactory results, whereas the newly designed DNN had high classification accuracy. On the basis of classification accuracy, an image classifier built on the newly designed DNN using post-contrast T1W images was developed. This image classifier correctly predicted the grading of 8 out of 10 images not included in the data set. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Development and evaluation of mathematical model to predict disintegration time of fast disintegrating tablets using powder characteristics.

Science.gov (United States)

Goel, H; Arora, A; Tiwary, A K; Rana, V

2011-02-01

The objective of the study was to develop a mathematical model for predicting the disintegration time of fast disintegrating tablets (FDTs) by estimating the powder characteristics of powder blend prior to compression. A combination of chitosan-alginate complex and glycine in the ratio of 50:50 was used for preparing FDTs. The developed mathematical model allowed water sorption time (WST), effective pore radius (R(eff.p)) and swelling Index (SI) of powder mixture as well as tablet crushing strength to be successfully correlated with disintegration time (DT) of FDTs. The predicted model showed that disintegration time of FDTs to be directly correlated with powder characteristics and inversely correlated with tablet crushing strength. Furthermore, a correlation of 0.97 was obtained when DT of FDTs was compared with SI/(WST * R(eff.p)). This correlation was not affected by inclusion of water soluble (ondansetron hydrochloride or metaclopramide hydrochloride) or water insoluble (domperidone) drugs in the powder blend or FDTs. These observations indicated the versatility of the mathematical model in predicting the disintegration time of FDTs by evaluating the selected characteristics of the powder blends without actually preparing the FDTs.

Predicting infant cortical surface development using a 4D varifold-based learning framework and local topography-based shape morphing.

Science.gov (United States)

Rekik, Islem; Li, Gang; Lin, Weili; Shen, Dinggang

2016-02-01

Longitudinal neuroimaging analysis methods have remarkably advanced our understanding of early postnatal brain development. However, learning predictive models to trace forth the evolution trajectories of both normal and abnormal cortical shapes remains broadly absent. To fill this critical gap, we pioneered the first prediction model for longitudinal developing cortical surfaces in infants using a spatiotemporal current-based learning framework solely from the baseline cortical surface. In this paper, we detail this prediction model and even further improve its performance by introducing two key variants. First, we use the varifold metric to overcome the limitations of the current metric for surface registration that was used in our preliminary study. We also extend the conventional varifold-based surface registration model for pairwise registration to a spatiotemporal surface regression model. Second, we propose a morphing process of the baseline surface using its topographic attributes such as normal direction and principal curvature sign. Specifically, our method learns from longitudinal data both the geometric (vertices positions) and dynamic (temporal evolution trajectories) features of the infant cortical surface, comprising a training stage and a prediction stage. In the training stage, we use the proposed varifold-based shape regression model to estimate geodesic cortical shape evolution trajectories for each training subject. We then build an empirical mean spatiotemporal surface atlas. In the prediction stage, given an infant, we select the best learnt features from training subjects to simultaneously predict the cortical surface shapes at all later timepoints, based on similarity metrics between this baseline surface and the learnt baseline population average surface atlas. We used a leave-one-out cross validation method to predict the inner cortical surface shape at 3, 6, 9 and 12 months of age from the baseline cortical surface shape at birth. Our

Risk Factors and Predictive Model Development of Thirty-Day Post-Operative Surgical Site Infection in the Veterans Administration Surgical Population.

Science.gov (United States)

Li, Xinli; Nylander, William; Smith, Tracy; Han, Soonhee; Gunnar, William

2018-04-01

Surgical site infection (SSI) complicates approximately 2% of surgeries in the Veterans Affairs (VA) hospitals. Surgical site infections are responsible for increased morbidity, length of hospital stay, cost, and mortality. Surgical site infection can be minimized by modifying risk factors. In this study, we identified risk factors and developed accurate predictive surgical specialty-specific SSI risk prediction models for the Veterans Health Administration (VHA) surgery population. In a retrospective observation study, surgical patients who underwent surgery from October 2013 to September 2016 from 136 VA hospitals were included. The Veteran Affairs Surgical Quality Improvement Program (VASQIP) database was used for the pre-operative demographic and clinical characteristics, intra-operative characteristics, and 30-day post-operative outcomes. The study population represents 11 surgical specialties: neurosurgery, urology, podiatry, otolaryngology, general, orthopedic, plastic, thoracic, vascular, cardiac coronary artery bypass graft (CABG), and cardiac valve/other surgery. Multivariable logistic regression models were developed for the 30-day post-operative SSIs. Among 354,528 surgical procedures, 6,538 (1.8%) had SSIs within 30 days. Surgical site infection rates varied among surgical specialty (0.7%-3.0%). Surgical site infection rates were higher in emergency procedures, procedures with long operative duration, greater complexity, and higher relative value units. Other factors associated with increased SSI risk were high level of American Society of Anesthesiologists (ASA) classification (level 4 and 5), dyspnea, open wound/infection, wound classification, ascites, bleeding disorder, chemotherapy, smoking, history of severe chronic obstructive pulmonary disease (COPD), radiotherapy, steroid use for chronic conditions, and weight loss. Each surgical specialty had a distinct combination of risk factors. Accurate SSI risk-predictive surgery specialty

Development of a Late-Life Dementia Prediction Index with Supervised Machine Learning in the Population-Based CAIDE Study.

Science.gov (United States)

Pekkala, Timo; Hall, Anette; Lötjönen, Jyrki; Mattila, Jussi; Soininen, Hilkka; Ngandu, Tiia; Laatikainen, Tiina; Kivipelto, Miia; Solomon, Alina

2017-01-01

This study aimed to develop a late-life dementia prediction model using a novel validated supervised machine learning method, the Disease State Index (DSI), in the Finnish population-based CAIDE study. The CAIDE study was based on previous population-based midlife surveys. CAIDE participants were re-examined twice in late-life, and the first late-life re-examination was used as baseline for the present study. The main study population included 709 cognitively normal subjects at first re-examination who returned to the second re-examination up to 10 years later (incident dementia n = 39). An extended population (n = 1009, incident dementia 151) included non-participants/non-survivors (national registers data). DSI was used to develop a dementia index based on first re-examination assessments. Performance in predicting dementia was assessed as area under the ROC curve (AUC). AUCs for DSI were 0.79 and 0.75 for main and extended populations. Included predictors were cognition, vascular factors, age, subjective memory complaints, and APOE genotype. The supervised machine learning method performed well in identifying comprehensive profiles for predicting dementia development up to 10 years later. DSI could thus be useful for identifying individuals who are most at risk and may benefit from dementia prevention interventions.

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.

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).

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

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)

Predictability and Prediction for an Experimental Cultural Market

Science.gov (United States)

Colbaugh, Richard; Glass, Kristin; Ormerod, Paul

Individuals are often influenced by the behavior of others, for instance because they wish to obtain the benefits of coordinated actions or infer otherwise inaccessible information. In such situations this social influence decreases the ex ante predictability of the ensuing social dynamics. We claim that, interestingly, these same social forces can increase the extent to which the outcome of a social process can be predicted very early in the process. This paper explores this claim through a theoretical and empirical analysis of the experimental music market described and analyzed in [1]. We propose a very simple model for this music market, assess the predictability of market outcomes through formal analysis of the model, and use insights derived through this analysis to develop algorithms for predicting market share winners, and their ultimate market shares, in the very early stages of the market. The utility of these predictive algorithms is illustrated through analysis of the experimental music market data sets [2].

A prediction model of drug-induced ototoxicity developed by an optimal support vector machine (SVM) method.

Science.gov (United States)

Zhou, Shu; Li, Guo-Bo; Huang, Lu-Yi; Xie, Huan-Zhang; Zhao, Ying-Lan; Chen, Yu-Zong; Li, Lin-Li; Yang, Sheng-Yong

2014-08-01

Drug-induced ototoxicity, as a toxic side effect, is an important issue needed to be considered in drug discovery. Nevertheless, current experimental methods used to evaluate drug-induced ototoxicity are often time-consuming and expensive, indicating that they are not suitable for a large-scale evaluation of drug-induced ototoxicity in the early stage of drug discovery. We thus, in this investigation, established an effective computational prediction model of drug-induced ototoxicity using an optimal support vector machine (SVM) method, GA-CG-SVM. Three GA-CG-SVM models were developed based on three training sets containing agents bearing different risk levels of drug-induced ototoxicity. For comparison, models based on naïve Bayesian (NB) and recursive partitioning (RP) methods were also used on the same training sets. Among all the prediction models, the GA-CG-SVM model II showed the best performance, which offered prediction accuracies of 85.33% and 83.05% for two independent test sets, respectively. Overall, the good performance of the GA-CG-SVM model II indicates that it could be used for the prediction of drug-induced ototoxicity in the early stage of drug discovery. Copyright © 2014 Elsevier Ltd. All rights reserved.

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.

Perioperative prediction of agitated (hyperactive) delirium after cardiac surgery in adults - The development of a practical scorecard.

Science.gov (United States)

Mufti, Hani N; Hirsch, Gregory M

2017-12-01

Delirium is a temporary mental disorder that occurs frequently among hospitalized patients. In this study we sought to develop a user-friendly scorecard based on perioperative features to identify patients at risk of developing agitated delirium after cardiac surgery. Retrospective analysis was performed on adult patients undergoing cardiac surgery in a single center. A parsimonious predictive model was created, with subsequent internal validation. Then a simple scorecard was developed that can be used to predict the probability of agitated delirium. Among the 5584 patients who met the study criteria, 614 (11.4%) developed postoperative agitated delirium. Independent predictors of postoperative agitated delirium were age, male gender, history of cerebrovascular disease, procedure other than isolated Coronary Arteries Bypass Surgery, transfusion of blood products within the first 48h, mechanical ventilation for >24h, length of stay in the Intensive Care Unit. The scorecard stratified patients into 4 categories at risk of postoperative agitated delirium ranging from 30%. Using a large cohort of adult patient's undergoing cardiac surgery, a user-friendly scorecard was developed and validated, which will facilitate the implementation of timely interventions to mitigate adverse effects of agitated delirium in this high risk population. Copyright © 2017 Elsevier Inc. All rights reserved.

U.S. Army Armament Research, Development and Engineering Center Grain Evaluation Software to Numerically Predict Linear Burn Regression for Solid Propellant Grain Geometries

Science.gov (United States)

2017-10-01

ENGINEERING CENTER GRAIN EVALUATION SOFTWARE TO NUMERICALLY PREDICT LINEAR BURN REGRESSION FOR SOLID PROPELLANT GRAIN GEOMETRIES Brian...distribution is unlimited. AD U.S. ARMY ARMAMENT RESEARCH, DEVELOPMENT AND ENGINEERING CENTER Munitions Engineering Technology Center Picatinny...U.S. ARMY ARMAMENT RESEARCH, DEVELOPMENT AND ENGINEERING CENTER GRAIN EVALUATION SOFTWARE TO NUMERICALLY PREDICT LINEAR BURN REGRESSION FOR SOLID

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.

Cull sow knife-separable lean content evaluation at harvest and lean mass content prediction equation development.

Science.gov (United States)

Abell, Caitlyn E; Stalder, Kenneth J; Hendricks, Haven B; Fitzgerald, Robert F

2012-07-01

The objectives of this study were to develop a prediction equation for carcass knife-separable lean within and across USDA cull sow market weight classes (MWC) and to determine carcass and individual primal cut knife separable lean content from cull sows. There were significant percent lean and fat differences in the primal cuts across USDA MWC. The two lighter USDA MWC had a greater percent carcass lean and lower percent fat compared to the two heavier MWC. In general, hot carcass weight explained the majority of carcass lean variation. Additionally, backfat was a significant variation source when predicting cull sow carcass lean. The findings support using a single lean prediction equation across MWC to assist processors when making cull sow purchasing decisions and determine the mix of animals from various USDA MWC that will meet their needs when making pork products with defined lean:fat content. Copyright © 2012 Elsevier Ltd. All rights reserved.

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)

Peritraumatic startle response predicts the vulnerability to develop PTSD-like behaviors in rats: a model for peritraumatic dissociation

Directory of Open Access Journals (Sweden)

Xinwen eDong

2014-01-01

Full Text Available Peritraumatic dissociation, a state characterized by alteration in perception and reduced awareness of surroundings, is considered to be a risk factor for the development of post-traumatic stress disorder (PTSD. However, the predictive ability of peritraumatic dissociation is questioned for the inconsistent results in different time points of assessment. The startle reflex is an objective behavioral measurement of defensive response to abrupt and intense sensory stimulus of surroundings, with potential to be used as an assessment on the dissociative status in both humans and rodents. The present study examined the predictive effect of acoustic startle response (ASR in different time points around the traumatic event in an animal model of PTSD. The PTSD-like symptoms, including hyperarousal, avoidance, and contextual fear, were assessed 2-3 weeks post-trauma. The results showed that 1 the startle amplitude attenuated immediate after intense footshock in almost half of the stress animals, 2 the attenuated startle responses at 1 hour but not 24 hours after stress predicted the development of severe PTSD-like symptoms. These data indicate that the startle alteration at the immediate period after trauma, including 1 hour, is more important in PTSD prediction than 24 hours after trauma. Our study also suggests that the startle attenuation immediate after intense stress may serve as an objective measurement of peritraumatic dissociation in rats.

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.

Predicting the onset of hazardous alcohol drinking in primary care: development and validation of a simple risk algorithm.

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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.

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

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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…

Development of cytomegalovirus (CMV) disease may be predicted in HIV-infected patients by CMV polymerase chain reaction and the antigenemia test

DEFF Research Database (Denmark)

Dodt, K K; Jacobsen, P H; Hofmann, B

1997-01-01

; OR: CMV PCR 10.0, antigenemia test 4.4 and CMV cultures 4.3. No clinical parameters had any significant predictive value in the stepwise multivariate model. CONCLUSIONS: The CMV PCR and the CMV antigenemia tests are both sensitive methods that may predict development of CMV disease up to several...... evaluated PCR and the antigenemia tests as methods for early detection of CMV disease. METHODS: Two-hundred HIV-seropositive subjects with CD4 T-cell counts below 100 x 10(6)/l were monitored with CMV polymerase chain reaction (PCR), the antigenemia test, blood cultures and CMV immunoglobulin (Ig) G and Ig...... showed that the CMV PCR, the antigenemia test and blood cultures all had significant predictive values for subsequent development of CMV disease with odds ratios (OR) of 30, 22 and 20. CMV serology had no predictive value. Multivariate analysis showed that the PCR method was superior to the other tests...

Prediction of Positions of Active Compounds Makes It Possible To Increase Activity in Fragment-Based Drug Development

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Yoshifumi Fukunishi

2011-05-01

Full Text Available We have developed a computational method that predicts the positions of active compounds, making it possible to increase activity as a fragment evolution strategy. We refer to the positions of these compounds as the active position. When an active fragment compound is found, the following lead generation process is performed, primarily to increase activity. In the current method, to predict the location of the active position, hydrogen atoms are replaced by small side chains, generating virtual compounds. These virtual compounds are docked to a target protein, and the docking scores (affinities are examined. The hydrogen atom that gives the virtual compound with good affinity should correspond to the active position and it should be replaced to generate a lead compound. This method was found to work well, with the prediction of the active position being 2 times more efficient than random synthesis. In the current study, 15 examples of lead generation were examined. The probability of finding active positions among all hydrogen atoms was 26%, and the current method accurately predicted 60% of the active positions.

Applied predictive control

CERN Document Server

Sunan, Huang; Heng, Lee Tong

2002-01-01

The presence of considerable time delays in the dynamics of many industrial processes, leading to difficult problems in the associated closed-loop control systems, is a well-recognized phenomenon. The performance achievable in conventional feedback control systems can be significantly degraded if an industrial process has a relatively large time delay compared with the dominant time constant. Under these circumstances, advanced predictive control is necessary to improve the performance of the control system significantly. The book is a focused treatment of the subject matter, including the fundamentals and some state-of-the-art developments in the field of predictive control. Three main schemes for advanced predictive control are addressed in this book: • Smith Predictive Control; • Generalised Predictive Control; • a form of predictive control based on Finite Spectrum Assignment. A substantial part of the book addresses application issues in predictive control, providing several interesting case studie...

Emerging approaches in predictive toxicology.

Science.gov (United States)

Zhang, Luoping; McHale, Cliona M; Greene, Nigel; Snyder, Ronald D; Rich, Ivan N; Aardema, Marilyn J; Roy, Shambhu; Pfuhler, Stefan; Venkatactahalam, Sundaresan

2014-12-01

Predictive toxicology plays an important role in the assessment of toxicity of chemicals and the drug development process. While there are several well-established in vitro and in vivo assays that are suitable for predictive toxicology, recent advances in high-throughput analytical technologies and model systems are expected to have a major impact on the field of predictive toxicology. This commentary provides an overview of the state of the current science and a brief discussion on future perspectives for the field of predictive toxicology for human toxicity. Computational models for predictive toxicology, needs for further refinement and obstacles to expand computational models to include additional classes of chemical compounds are highlighted. Functional and comparative genomics approaches in predictive toxicology are discussed with an emphasis on successful utilization of recently developed model systems for high-throughput analysis. The advantages of three-dimensional model systems and stem cells and their use in predictive toxicology testing are also described. © 2014 Wiley Periodicals, Inc.

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

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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.

[Development and Application of a Performance Prediction Model for Home Care Nursing Based on a Balanced Scorecard using the Bayesian Belief Network].

Science.gov (United States)

Noh, Wonjung; Seomun, Gyeongae

2015-06-01

This study was conducted to develop key performance indicators (KPIs) for home care nursing (HCN) based on a balanced scorecard, and to construct a performance prediction model of strategic objectives using the Bayesian Belief Network (BBN). This methodological study included four steps: establishment of KPIs, performance prediction modeling, development of a performance prediction model using BBN, and simulation of a suggested nursing management strategy. An HCN expert group and a staff group participated. The content validity index was analyzed using STATA 13.0, and BBN was analyzed using HUGIN 8.0. We generated a list of KPIs composed of 4 perspectives, 10 strategic objectives, and 31 KPIs. In the validity test of the performance prediction model, the factor with the greatest variance for increasing profit was maximum cost reduction of HCN services. The factor with the smallest variance for increasing profit was a minimum image improvement for HCN. During sensitivity analysis, the probability of the expert group did not affect the sensitivity. Furthermore, simulation of a 10% image improvement predicted the most effective way to increase profit. KPIs of HCN can estimate financial and non-financial performance. The performance prediction model for HCN will be useful to improve performance.

Combining PubMed knowledge and EHR data to develop a weighted bayesian network for pancreatic cancer prediction.

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Zhao, Di; Weng, Chunhua

2011-10-01

In this paper, we propose a novel method that combines PubMed knowledge and Electronic Health Records to develop a weighted Bayesian Network Inference (BNI) model for pancreatic cancer prediction. We selected 20 common risk factors associated with pancreatic cancer and used PubMed knowledge to weigh the risk factors. A keyword-based algorithm was developed to extract and classify PubMed abstracts into three categories that represented positive, negative, or neutral associations between each risk factor and pancreatic cancer. Then we designed a weighted BNI model by adding the normalized weights into a conventional BNI model. We used this model to extract the EHR values for patients with or without pancreatic cancer, which then enabled us to calculate the prior probabilities for the 20 risk factors in the BNI. The software iDiagnosis was designed to use this weighted BNI model for predicting pancreatic cancer. In an evaluation using a case-control dataset, the weighted BNI model significantly outperformed the conventional BNI and two other classifiers (k-Nearest Neighbor and Support Vector Machine). We conclude that the weighted BNI using PubMed knowledge and EHR data shows remarkable accuracy improvement over existing representative methods for pancreatic cancer prediction. Copyright © 2011 Elsevier Inc. All rights reserved.

Predicting Rib Fracture Risk With Whole-Body Finite Element Models: Development and Preliminary Evaluation of a Probabilistic Analytical Framework

Science.gov (United States)

Forman, Jason L.; Kent, Richard W.; Mroz, Krystoffer; Pipkorn, Bengt; Bostrom, Ola; Segui-Gomez, Maria

2012-01-01

This study sought to develop a strain-based probabilistic method to predict rib fracture risk with whole-body finite element (FE) models, and to describe a method to combine the results with collision exposure information to predict injury risk and potential intervention effectiveness in the field. An age-adjusted ultimate strain distribution was used to estimate local rib fracture probabilities within an FE model. These local probabilities were combined to predict injury risk and severity within the whole ribcage. The ultimate strain distribution was developed from a literature dataset of 133 tests. Frontal collision simulations were performed with the THUMS (Total HUman Model for Safety) model with four levels of delta-V and two restraints: a standard 3-point belt and a progressive 3.5–7 kN force-limited, pretensioned (FL+PT) belt. The results of three simulations (29 km/h standard, 48 km/h standard, and 48 km/h FL+PT) were compared to matched cadaver sled tests. The numbers of fractures predicted for the comparison cases were consistent with those observed experimentally. Combining these results with field exposure informantion (ΔV, NASS-CDS 1992–2002) suggests a 8.9% probability of incurring AIS3+ rib fractures for a 60 year-old restrained by a standard belt in a tow-away frontal collision with this restraint, vehicle, and occupant configuration, compared to 4.6% for the FL+PT belt. This is the first study to describe a probabilistic framework to predict rib fracture risk based on strains observed in human-body FE models. Using this analytical framework, future efforts may incorporate additional subject or collision factors for multi-variable probabilistic injury prediction. PMID:23169122

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)

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)

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

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...

Positive parenting predicts the development of adolescent brain structure: a longitudinal study.

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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.

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.

Prediction of persistent post-surgery pain by preoperative cold pain sensitivity: biomarker development with machine-learning-derived analysis.

Science.gov (United States)

Lötsch, J; Ultsch, A; Kalso, E

2017-10-01

To prevent persistent post-surgery pain, early identification of patients at high risk is a clinical need. Supervised machine-learning techniques were used to test how accurately the patients' performance in a preoperatively performed tonic cold pain test could predict persistent post-surgery pain. We analysed 763 patients from a cohort of 900 women who were treated for breast cancer, of whom 61 patients had developed signs of persistent pain during three yr of follow-up. Preoperatively, all patients underwent a cold pain test (immersion of the hand into a water bath at 2-4 °C). The patients rated the pain intensity using a numerical ratings scale (NRS) from 0 to 10. Supervised machine-learning techniques were used to construct a classifier that could predict patients at risk of persistent pain. Whether or not a patient rated the pain intensity at NRS=10 within less than 45 s during the cold water immersion test provided a negative predictive value of 94.4% to assign a patient to the "persistent pain" group. If NRS=10 was never reached during the cold test, the predictive value for not developing persistent pain was almost 97%. However, a low negative predictive value of 10% implied a high false positive rate. Results provide a robust exclusion of persistent pain in women with an accuracy of 94.4%. Moreover, results provide further support for the hypothesis that the endogenous pain inhibitory system may play an important role in the process of pain becoming persistent. © The Author 2017. Published by Oxford University Press on behalf of the British Journal of Anaesthesia.

Screening strategies and predictive diagnostic tools for the development of new-onset diabetes mellitus after transplantation: an overview

Directory of Open Access Journals (Sweden)

Pham PT

2012-10-01

Full Text Available Phuong-Thu T Pham,1 Kari L Edling,2 Harini A Chakkera,3 Phuong-Chi T Pham,4 Phuong-Mai T Pham51Department of Medicine, Nephrology Division, Kidney Transplant Program, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; 2Department of Medicine, Division of Endocrinology, Diabetes and Hypertension, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; 3Department of Medicine, Nephrology Division Kidney Transplant Program, Mayo Clinic Hospital, Phoenix, AZ, USA; 4Department of Medicine, Nephrology Division, UCLA-Olive View Medical Center, Sylmar, CA, USA; 5Department of Medicine, Greater Los Angeles, Veterans Administration Health Care System, CA, USAAbstract: New-onset diabetes mellitus after transplantation (NODAT is a serious and common complication following solid organ transplantation. NODAT has been reported in 2% to 53% of all solid organ transplants. Kidney transplant recipients who develop NODAT have variably been reported to be at increased risk of fatal and nonfatal cardiovascular events and other adverse outcomes including infection, reduced patient survival, graft rejection, and accelerated graft loss compared with those who do not develop diabetes. Limited clinical studies in liver, heart, and lung transplants similarly suggested that NODAT has an adverse impact on patient and graft outcomes. Early detection and management of NODAT must, therefore, be integrated into the treatment of transplant recipients. Studies investigating the best screening or predictive tool for identifying patients at risk for developing NODAT early after transplantation, however, are lacking. We review the clinical predictive values of fasting plasma glucose, oral glucose tolerance test, and A1C in assessing the risk for NODAT development and as a screening tool. Simple diabetes prediction models that incorporate clinical and/or metabolic risk factors (such as age, body mass index, hypertriglyceridemia, or metabolic syndrome are also

Novel CNS drug discovery and development approach: model-based integration to predict neuro-pharmacokinetics and pharmacodynamics.

Science.gov (United States)

de Lange, Elizabeth C M; van den Brink, Willem; Yamamoto, Yumi; de Witte, Wilhelmus E A; Wong, Yin Cheong

2017-12-01

CNS drug development has been hampered by inadequate consideration of CNS pharmacokinetic (PK), pharmacodynamics (PD) and disease complexity (reductionist approach). Improvement is required via integrative model-based approaches. Areas covered: The authors summarize factors that have played a role in the high attrition rate of CNS compounds. Recent advances in CNS research and drug discovery are presented, especially with regard to assessment of relevant neuro-PK parameters. Suggestions for further improvements are also discussed. Expert opinion: Understanding time- and condition dependent interrelationships between neuro-PK and neuro-PD processes is key to predictions in different conditions. As a first screen, it is suggested to use in silico/in vitro derived molecular properties of candidate compounds and predict concentration-time profiles of compounds in multiple compartments of the human CNS, using time-course based physiology-based (PB) PK models. Then, for selected compounds, one can include in vitro drug-target binding kinetics to predict target occupancy (TO)-time profiles in humans. This will improve neuro-PD prediction. Furthermore, a pharmaco-omics approach is suggested, providing multilevel and paralleled data on systems processes from individuals in a systems-wide manner. Thus, clinical trials will be better informed, using fewer animals, while also, needing fewer individuals and samples per individual for proof of concept in humans.

Development of a Korean Fracture Risk Score (KFRS for Predicting Osteoporotic Fracture Risk: Analysis of Data from the Korean National Health Insurance Service.

Directory of Open Access Journals (Sweden)

Ha Young Kim

Full Text Available Asian-specific prediction models for estimating individual risk of osteoporotic fractures are rare. We developed a Korean fracture risk prediction model using clinical risk factors and assessed validity of the final model.A total of 718,306 Korean men and women aged 50-90 years were followed for 7 years in a national system-based cohort study. In total, 50% of the subjects were assigned randomly to the development dataset and 50% were assigned to the validation dataset. Clinical risk factors for osteoporotic fracture were assessed at the biennial health check. Data on osteoporotic fractures during the follow-up period were identified by ICD-10 codes and the nationwide database of the National Health Insurance Service (NHIS.During the follow-up period, 19,840 osteoporotic fractures were reported (4,889 in men and 14,951 in women in the development dataset. The assessment tool called the Korean Fracture Risk Score (KFRS is comprised of a set of nine variables, including age, body mass index, recent fragility fracture, current smoking, high alcohol intake, lack of regular exercise, recent use of oral glucocorticoid, rheumatoid arthritis, and other causes of secondary osteoporosis. The KFRS predicted osteoporotic fractures over the 7 years. This score was validated using an independent dataset. A close relationship with overall fracture rate was observed when we compared the mean predicted scores after applying the KFRS with the observed risks after 7 years within each 10th of predicted risk.We developed a Korean specific prediction model for osteoporotic fractures. The KFRS was able to predict risk of fracture in the primary population without bone mineral density testing and is therefore suitable for use in both clinical setting and self-assessment. The website is available at http://www.nhis.or.kr.

Relationship of Predicted Risk of Developing Invasive Breast Cancer, as Assessed with Three Models, and Breast Cancer Mortality among Breast Cancer Patients.

Directory of Open Access Journals (Sweden)

Mark E Sherman

Full Text Available Breast cancer risk prediction models are used to plan clinical trials and counsel women; however, relationships of predicted risks of breast cancer incidence and prognosis after breast cancer diagnosis are unknown.Using largely pre-diagnostic information from the Breast Cancer Surveillance Consortium (BCSC for 37,939 invasive breast cancers (1996-2007, we estimated 5-year breast cancer risk (<1%; 1-1.66%; ≥1.67% with three models: BCSC 1-year risk model (BCSC-1; adapted to 5-year predictions; Breast Cancer Risk Assessment Tool (BCRAT; and BCSC 5-year risk model (BCSC-5. Breast cancer-specific mortality post-diagnosis (range: 1-13 years; median: 5.4-5.6 years was related to predicted risk of developing breast cancer using unadjusted Cox proportional hazards models, and in age-stratified (35-44; 45-54; 55-69; 70-89 years models adjusted for continuous age, BCSC registry, calendar period, income, mode of presentation, stage and treatment. Mean age at diagnosis was 60 years.Of 6,021 deaths, 2,993 (49.7% were ascribed to breast cancer. In unadjusted case-only analyses, predicted breast cancer risk ≥1.67% versus <1.0% was associated with lower risk of breast cancer death; BCSC-1: hazard ratio (HR = 0.82 (95% CI = 0.75-0.90; BCRAT: HR = 0.72 (95% CI = 0.65-0.81 and BCSC-5: HR = 0.84 (95% CI = 0.75-0.94. Age-stratified, adjusted models showed similar, although mostly non-significant HRs. Among women ages 55-69 years, HRs approximated 1.0. Generally, higher predicted risk was inversely related to percentages of cancers with unfavorable prognostic characteristics, especially among women 35-44 years.Among cases assessed with three models, higher predicted risk of developing breast cancer was not associated with greater risk of breast cancer death; thus, these models would have limited utility in planning studies to evaluate breast cancer mortality reduction strategies. Further, when offering women counseling, it may be useful to note that high

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...

Development and validation of a prediction algorithm for the onset of common mental disorders in a working population.

Science.gov (United States)

Fernandez, Ana; Salvador-Carulla, Luis; Choi, Isabella; Calvo, Rafael; Harvey, Samuel B; Glozier, Nicholas

2018-01-01

Common mental disorders are the most common reason for long-term sickness absence in most developed countries. Prediction algorithms for the onset of common mental disorders may help target indicated work-based prevention interventions. We aimed to develop and validate a risk algorithm to predict the onset of common mental disorders at 12 months in a working population. We conducted a secondary analysis of the Household, Income and Labour Dynamics in Australia Survey, a longitudinal, nationally representative household panel in Australia. Data from the 6189 working participants who did not meet the criteria for a common mental disorders at baseline were non-randomly split into training and validation databases, based on state of residence. Common mental disorders were assessed with the mental component score of 36-Item Short Form Health Survey questionnaire (score ⩽45). Risk algorithms were constructed following recommendations made by the Transparent Reporting of a multivariable prediction model for Prevention Or Diagnosis statement. Different risk factors were identified among women and men for the final risk algorithms. In the training data, the model for women had a C-index of 0.73 and effect size (Hedges' g) of 0.91. In men, the C-index was 0.76 and the effect size was 1.06. In the validation data, the C-index was 0.66 for women and 0.73 for men, with positive predictive values of 0.28 and 0.26, respectively Conclusion: It is possible to develop an algorithm with good discrimination for the onset identifying overall and modifiable risks of common mental disorders among working men. Such models have the potential to change the way that prevention of common mental disorders at the workplace is conducted, but different models may be required for women.

Maternal Weight Predicts Children's Psychosocial Development via Parenting Stress and Emotional Availability

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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

[Bowel-associated dermatosis-arthritis syndrome during ulcerative colitis: A rare extra-intestinal sign of inflammatory bowel disease].

Science.gov (United States)

Aounallah, A; Zerriaa, S; Ksiaa, M; Jaziri, H; Boussofara, L; Ghariani, N; Mokni, S; Saidi, W; Sriha, B; Belajouza, C; Denguezli, M; Nouira, R

2016-05-01

Bowel-associated dermatosis-arthritis syndrome (BADAS) is characterized by combined pustular skin eruption and arthralgia. It may be associated with inflammatory bowel disease or bowel bypass surgery. We report a case of BADAS in a patient with ulcerative colitis. A 39-year-old woman was being treated for a severe flare-up of ulcerative colitis present over the preceding 2 months and treated with prednisone, azathioprine and cyclosporine. She was also presenting a cutaneous eruption and arthralgia that had begun three days earlier. Dermatological examination revealed profuse vesicular and pustular lesions. Biopsy specimens showed mature neutrophilic infiltrate within the dermis. A diagnosis of BADAS was made and the same treatment was maintained. Systemic symptoms were resolved but the vesicular lesions were superseded by hypertrophic scars. Bowel-associated dermatosis-arthritis syndrome consists of a vesiculopustular eruption associated with arthralgia and/or arthritis and fever, as was the case in our patient. The histological picture is characterized by abundant neutrophilic infiltrate in the superficial dermis. The clinical and histological features and the course of BADAS allow this entity to be classified within the spectrum of neutrophilic dermatoses. Treatment chiefly involves systemic corticosteroids. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

The development of an affinity evaluation and prediction system by using protein–protein docking simulations and parameter tuning

Directory of Open Access Journals (Sweden)

Koki Tsukamoto

2009-01-01

Full Text Available Koki Tsukamoto1, Tatsuya Yoshikawa1,2, Kiyonobu Yokota1, Yuichiro Hourai1, Kazuhiko Fukui11Computational Biology Research Center (CBRC, National Institute of Advanced Industrial Science and Technology (AIST, Koto-ku, Tokyo, Japan; 2Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, Toyonaka, Osaka, JapanAbstract: A system was developed to evaluate and predict the interaction between protein pairs by using the widely used shape complementarity search method as the algorithm for docking simulations between the proteins. We used this system, which we call the affinity evaluation and prediction (AEP system, to evaluate the interaction between 20 protein pairs. The system first executes a “round robin” shape complementarity search of the target protein group, and evaluates the interaction between the complex structures obtained by the search. These complex structures are selected by using a statistical procedure that we developed called ‘grouping’. At a prevalence of 5.0%, our AEP system predicted protein–protein interactions with a 50.0% recall, 55.6% precision, 95.5% accuracy, and an F-measure of 0.526. By optimizing the grouping process, our AEP system successfully predicted 10 protein pairs (among 20 pairs that were biologically relevant combinations. Our ultimate goal is to construct an affinity database that will provide cell biologists and drug designers with crucial information obtained using our AEP system.Keywords: protein–protein interaction, affinity analysis, protein–protein docking, FFT, massive parallel computing

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

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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.

Validation of a Previously Developed Geospatial Model That Predicts the Prevalence of Listeria monocytogenes in New York State Produce Fields

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Weller, Daniel; Shiwakoti, Suvash; Bergholz, Peter; Grohn, Yrjo; Wiedmann, Martin

2015-01-01

Technological advancements, particularly in the field of geographic information systems (GIS), have made it possible to predict the likelihood of foodborne pathogen contamination in produce production environments using geospatial models. Yet, few studies have examined the validity and robustness of such models. This study was performed to test and refine the rules associated with a previously developed geospatial model that predicts the prevalence of Listeria monocytogenes in produce farms in New York State (NYS). Produce fields for each of four enrolled produce farms were categorized into areas of high or low predicted L. monocytogenes prevalence using rules based on a field's available water storage (AWS) and its proximity to water, impervious cover, and pastures. Drag swabs (n = 1,056) were collected from plots assigned to each risk category. Logistic regression, which tested the ability of each rule to accurately predict the prevalence of L. monocytogenes, validated the rules based on water and pasture. Samples collected near water (odds ratio [OR], 3.0) and pasture (OR, 2.9) showed a significantly increased likelihood of L. monocytogenes isolation compared to that for samples collected far from water and pasture. Generalized linear mixed models identified additional land cover factors associated with an increased likelihood of L. monocytogenes isolation, such as proximity to wetlands. These findings validated a subset of previously developed rules that predict L. monocytogenes prevalence in produce production environments. This suggests that GIS and geospatial models can be used to accurately predict L. monocytogenes prevalence on farms and can be used prospectively to minimize the risk of preharvest contamination of produce. PMID:26590280

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

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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.

Development of ε-insensitive smooth support vector regression for predicting minimum miscibility pressure in CO2 flooding

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Shahram Mollaiy-Berneti

2018-02-01

Full Text Available Successful design of a carbon dioxide (CO2 flooding in enhanced oil recovery projects mostly depends on accurate determination of CO2-crude oil minimum miscibility pressure (MMP. Due to the high expensive and time-consuming of experimental determination of MMP, developing a fast and robust method to predict MMP is necessary. In this study, a new method based on ε-insensitive smooth support vector regression (ε-SSVR is introduced to predict MMP for both pure and impure CO2 gas injection cases. The proposed ε-SSVR is developed using dataset of reservoir temperature, crude oil composition and composition of injected CO2. To serve better understanding of the proposed, feed-forward neural network and radial basis function network applied to denoted dataset. The results show that the suggested ε-SSVR has acceptable reliability and robustness in comparison with two other models. Thus, the proposed method can be considered as an alternative way to monitor the MMP in miscible flooding process.

GENERAL SCIENTIFIC PRECONDITIONS AND PROSPECTS OF PREDICTION OF GEOSPACE PROCESSES FOR THE BENEFIT OF THE SUSTAINABLE DEVELOPMENT OF TERRITORIES

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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.

Predictability of the future development of aggressive behavior of cranial dural arteriovenous fistulas based on decision tree analysis.

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Satomi, Junichiro; Ghaibeh, A Ammar; Moriguchi, Hiroki; Nagahiro, Shinji

2015-07-01

The severity of clinical signs and symptoms of cranial dural arteriovenous fistulas (DAVFs) are well correlated with their pattern of venous drainage. Although the presence of cortical venous drainage can be considered a potential predictor of aggressive DAVF behaviors, such as intracranial hemorrhage or progressive neurological deficits due to venous congestion, accurate statistical analyses are currently not available. Using a decision tree data mining method, the authors aimed at clarifying the predictability of the future development of aggressive behaviors of DAVF and at identifying the main causative factors. Of 266 DAVF patients, 89 were eligible for analysis. Under observational management, 51 patients presented with intracranial hemorrhage/infarction during the follow-up period. The authors created a decision tree able to assess the risk for the development of aggressive DAVF behavior. Evaluated by 10-fold cross-validation, the decision tree's accuracy, sensitivity, and specificity were 85.28%, 88.33%, and 80.83%, respectively. The tree shows that the main factor in symptomatic patients was the presence of cortical venous drainage. In its absence, the lesion location determined the risk of a DAVF developing aggressive behavior. Decision tree analysis accurately predicts the future development of aggressive DAVF behavior.

The predictive ability of six pharmacokinetic models of rocuronium developed using a single bolus: evaluation with bolus and continuous infusion regimen.

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Sasakawa, Tomoki; Masui, Kenichi; Kazama, Tomiei; Iwasaki, Hiroshi

2016-08-01

Rocuronium concentration prediction using pharmacokinetic (PK) models would be useful for controlling rocuronium effects because neuromuscular monitoring throughout anesthesia can be difficult. This study assessed whether six different compartmental PK models developed from data obtained after bolus administration only could predict the measured plasma concentration (Cp) values of rocuronium delivered by bolus followed by continuous infusion. Rocuronium Cp values from 19 healthy subjects who received a bolus dose followed by continuous infusion in a phase III multicenter trial in Japan were used retrospectively as evaluation datasets. Six different compartmental PK models of rocuronium were used to simulate rocuronium Cp time course values, which were compared with measured Cp values. Prediction error (PE) derivatives of median absolute PE (MDAPE), median PE (MDPE), wobble, divergence absolute PE, and divergence PE were used to assess inaccuracy, bias, intra-individual variability, and time-related trends in APE and PE values. MDAPE and MDPE values were acceptable only for the Magorian and Kleijn models. The divergence PE value for the Kleijn model was lower than -10 %/h, indicating unstable prediction over time. The Szenohradszky model had the lowest divergence PE (-2.7 %/h) and wobble (5.4 %) values with negative bias (MDPE = -25.9 %). These three models were developed using the mixed-effects modeling approach. The Magorian model showed the best PE derivatives among the models assessed. A PK model developed from data obtained after single-bolus dosing can predict Cp values during bolus and continuous infusion. Thus, a mixed-effects modeling approach may be preferable in extrapolating such data.

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

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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.

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.

MHC Class II epitope predictive algorithms

DEFF Research Database (Denmark)

Nielsen, Morten; Lund, Ole; Buus, S

2010-01-01

Major histocompatibility complex class II (MHC-II) molecules sample peptides from the extracellular space, allowing the immune system to detect the presence of foreign microbes from this compartment. To be able to predict the immune response to given pathogens, a number of methods have been...... developed to predict peptide-MHC binding. However, few methods other than the pioneering TEPITOPE/ProPred method have been developed for MHC-II. Despite recent progress in method development, the predictive performance for MHC-II remains significantly lower than what can be obtained for MHC-I. One reason...

Short-term wind power prediction

DEFF Research Database (Denmark)

Joensen, Alfred K.

2003-01-01

, and to implement these models and methods in an on-line software application. The economical value of having predictions available is also briefly considered. The summary report outlines the background and motivation for developing wind power prediction models. The meteorological theory which is relevant......The present thesis consists of 10 research papers published during the period 1997-2002 together with a summary report. The objective of the work described in the thesis is to develop models and methods for calculation of high accuracy predictions of wind power generated electricity...

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...

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

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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.

Risk prediction model: Statistical and artificial neural network approach

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Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim

2017-04-01

Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.

Predictive systems ecology.

Science.gov (United States)

Evans, Matthew R; Bithell, Mike; Cornell, Stephen J; Dall, Sasha R X; Díaz, Sandra; Emmott, Stephen; Ernande, Bruno; Grimm, Volker; Hodgson, David J; Lewis, Simon L; Mace, Georgina M; Morecroft, Michael; Moustakas, Aristides; Murphy, Eugene; Newbold, Tim; Norris, K J; Petchey, Owen; Smith, Matthew; Travis, Justin M J; Benton, Tim G

2013-11-22

Human societies, and their well-being, depend to a significant extent on the state of the ecosystems that surround them. These ecosystems are changing rapidly usually in response to anthropogenic changes in the environment. To determine the likely impact of environmental change on ecosystems and the best ways to manage them, it would be desirable to be able to predict their future states. We present a proposal to develop the paradigm of predictive systems ecology, explicitly to understand and predict the properties and behaviour of ecological systems. We discuss the necessary and desirable features of predictive systems ecology models. There are places where predictive systems ecology is already being practised and we summarize a range of terrestrial and marine examples. Significant challenges remain but we suggest that ecology would benefit both as a scientific discipline and increase its impact in society if it were to embrace the need to become more predictive.

Accurate and dynamic predictive model for better prediction in medicine and healthcare.

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Alanazi, H O; Abdullah, A H; Qureshi, K N; Ismail, A S

2018-05-01

Information and communication technologies (ICTs) have changed the trend into new integrated operations and methods in all fields of life. The health sector has also adopted new technologies to improve the systems and provide better services to customers. Predictive models in health care are also influenced from new technologies to predict the different disease outcomes. However, still, existing predictive models have suffered from some limitations in terms of predictive outcomes performance. In order to improve predictive model performance, this paper proposed a predictive model by classifying the disease predictions into different categories. To achieve this model performance, this paper uses traumatic brain injury (TBI) datasets. TBI is one of the serious diseases worldwide and needs more attention due to its seriousness and serious impacts on human life. The proposed predictive model improves the predictive performance of TBI. The TBI data set is developed and approved by neurologists to set its features. The experiment results show that the proposed model has achieved significant results including accuracy, sensitivity, and specificity.

TMJ response to mandibular advancement surgery: an overview of risk factors

Science.gov (United States)

VALLADARES-NETO, José; CEVIDANES, Lucia Helena; ROCHA, Wesley Cabral; ALMEIDA, Guilherme de Araújo; de PAIVA, João Batista; RINO-NETO, José

2014-01-01

Objective In order to understand the conflicting information on temporomandibular joint (TMJ) pathophysiologic responses after mandibular advancement surgery, an overview of the literature was proposed with a focus on certain risk factors. Methods A literature search was carried out in the Cochrane, PubMed, Scopus and Web of Science databases in the period from January 1980 through March 2013. Various combinations of keywords related to TMJ changes [disc displacement, arthralgia, condylar resorption (CR)] and aspects of surgical intervention (fixation technique, amount of advancement) were used. A hand search of these papers was also carried out to identify additional articles. Results A total of 148 articles were considered for this overview and, although methodological troubles were common, this review identified relevant findings which the practitioner can take into consideration during treatment planning: 1- Surgery was unable to influence TMJ with preexisting displaced disc and crepitus; 2- Clicking and arthralgia were not predictable after surgery, although there was greater likelihood of improvement rather than deterioration; 3- The amount of mandibular advancement and counterclockwise rotation, and the rigidity of the fixation technique seemed to influence TMJ position and health; 4- The risk of CR increased, especially in identified high-risk cases. Conclusions Young adult females with mandibular retrognathism and increased mandibular plane angle are susceptible to painful TMJ, and are subject to less improvement after surgery and prone to CR. Furthermore, thorough evidenced-based studies are required to understand the response of the TMJ after mandibular advancement surgery. PMID:24626243

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

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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

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

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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.

Basis of predictive mycology.

Science.gov (United States)

Dantigny, Philippe; Guilmart, Audrey; Bensoussan, Maurice

2005-04-15

For over 20 years, predictive microbiology focused on food-pathogenic bacteria. Few studies concerned modelling fungal development. On one hand, most of food mycologists are not familiar with modelling techniques; on the other hand, people involved in modelling are developing tools dedicated to bacteria. Therefore, there is a tendency to extend the use of models that were developed for bacteria to moulds. However, some mould specificities should be taken into account. The use of specific models for predicting germination and growth of fungi was advocated previously []. This paper provides a short review of fungal modelling studies.

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...

Biomarkers for predicting type 2 diabetes development-Can metabolomics improve on existing biomarkers?

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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.

Readmissions and death after ICU discharge: development and validation of two predictive models.

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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

Validation of a Previously Developed Geospatial Model That Predicts the Prevalence of Listeria monocytogenes in New York State Produce Fields.

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Weller, Daniel; Shiwakoti, Suvash; Bergholz, Peter; Grohn, Yrjo; Wiedmann, Martin; Strawn, Laura K

2016-02-01

Technological advancements, particularly in the field of geographic information systems (GIS), have made it possible to predict the likelihood of foodborne pathogen contamination in produce production environments using geospatial models. Yet, few studies have examined the validity and robustness of such models. This study was performed to test and refine the rules associated with a previously developed geospatial model that predicts the prevalence of Listeria monocytogenes in produce farms in New York State (NYS). Produce fields for each of four enrolled produce farms were categorized into areas of high or low predicted L. monocytogenes prevalence using rules based on a field's available water storage (AWS) and its proximity to water, impervious cover, and pastures. Drag swabs (n = 1,056) were collected from plots assigned to each risk category. Logistic regression, which tested the ability of each rule to accurately predict the prevalence of L. monocytogenes, validated the rules based on water and pasture. Samples collected near water (odds ratio [OR], 3.0) and pasture (OR, 2.9) showed a significantly increased likelihood of L. monocytogenes isolation compared to that for samples collected far from water and pasture. Generalized linear mixed models identified additional land cover factors associated with an increased likelihood of L. monocytogenes isolation, such as proximity to wetlands. These findings validated a subset of previously developed rules that predict L. monocytogenes prevalence in produce production environments. This suggests that GIS and geospatial models can be used to accurately predict L. monocytogenes prevalence on farms and can be used prospectively to minimize the risk of preharvest contamination of produce. Copyright © 2016, American Society for Microbiology. All Rights Reserved.

Development of a tool for prediction of ovarian cancer in patients with adnexal masses: Value of plasma fibrinogen.

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Veronika Seebacher

Full Text Available To develop a tool for individualized risk estimation of presence of cancer in women with adnexal masses, and to assess the added value of plasma fibrinogen.We performed a retrospective analysis of a prospectively maintained database of 906 patients with adnexal masses who underwent cystectomy or oophorectomy. Uni- and multivariate logistic regression analyses including pre-operative plasma fibrinogen levels and established predictors were performed. A nomogram was generated to predict the probability of ovarian cancer. Internal validation with split-sample analysis was performed. Decision curve analysis (DCA was then used to evaluate the clinical net benefit of the prediction model.Ovarian cancer including borderline tumours was found in 241 (26.6% patients. In multivariate analysis, elevated plasma fibrinogen, elevated CA-125, suspicion for malignancy on ultrasound, and postmenopausal status were associated with ovarian cancer and formed the basis for the nomogram. The overall predictive accuracy of the model, as measured by AUC, was 0.91 (95% CI 0.87-0.94. DCA revealed a net benefit for using this model for predicting ovarian cancer presence compared to a strategy of treat all or treat none.We confirmed the value of plasma fibrinogen as a strong predictor for ovarian cancer in a large cohort of patients with adnexal masses. We developed a highly accurate multivariable model to help in the clinical decision-making regarding the presence of ovarian cancer. This model provided net benefit for a wide range of threshold probabilities. External validation is needed before a recommendation for its use in routine practice can be given.

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)

Stargardt disease: towards developing a model to predict phenotype.

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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.

Environmental prediction, risk assessment and extreme events: adaptation strategies for the developing world

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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

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

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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

Predicting Ideological Prejudice.

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Brandt, Mark J

2017-06-01

A major shortcoming of current models of ideological prejudice is that although they can anticipate the direction of the association between participants' ideology and their prejudice against a range of target groups, they cannot predict the size of this association. I developed and tested models that can make specific size predictions for this association. A quantitative model that used the perceived ideology of the target group as the primary predictor of the ideology-prejudice relationship was developed with a representative sample of Americans ( N = 4,940) and tested against models using the perceived status of and choice to belong to the target group as predictors. In four studies (total N = 2,093), ideology-prejudice associations were estimated, and these observed estimates were compared with the models' predictions. The model that was based only on perceived ideology was the most parsimonious with the smallest errors.

Development of Risk Score for Predicting 3-Year Incidence of Type 2 Diabetes: Japan Epidemiology Collaboration on Occupational Health Study.

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Akiko Nanri

Full Text Available Risk models and scores have been developed to predict incidence of type 2 diabetes in Western populations, but their performance may differ when applied to non-Western populations. We developed and validated a risk score for predicting 3-year incidence of type 2 diabetes in a Japanese population.Participants were 37,416 men and women, aged 30 or older, who received periodic health checkup in 2008-2009 in eight companies. Diabetes was defined as fasting plasma glucose (FPG ≥ 126 mg/dl, random plasma glucose ≥ 200 mg/dl, glycated hemoglobin (HbA1c ≥ 6.5%, or receiving medical treatment for diabetes. Risk scores on non-invasive and invasive models including FPG and HbA1c were developed using logistic regression in a derivation cohort and validated in the remaining cohort.The area under the curve (AUC for the non-invasive model including age, sex, body mass index, waist circumference, hypertension, and smoking status was 0.717 (95% CI, 0.703-0.731. In the invasive model in which both FPG and HbA1c were added to the non-invasive model, AUC was increased to 0.893 (95% CI, 0.883-0.902. When the risk scores were applied to the validation cohort, AUCs (95% CI for the non-invasive and invasive model were 0.734 (0.715-0.753 and 0.882 (0.868-0.895, respectively. Participants with a non-invasive score of ≥ 15 and invasive score of ≥ 19 were projected to have >20% and >50% risk, respectively, of developing type 2 diabetes within 3 years.The simple risk score of the non-invasive model might be useful for predicting incident type 2 diabetes, and its predictive performance may be markedly improved by incorporating FPG and HbA1c.

Predicting dementia risk in primary care: development and validation of the Dementia Risk Score using routinely collected data.

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Walters, K; Hardoon, S; Petersen, I; Iliffe, S; Omar, R Z; Nazareth, I; Rait, G

2016-01-21

Existing dementia risk scores require collection of additional data from patients, limiting their use in practice. Routinely collected healthcare data have the potential to assess dementia risk without the need to collect further information. Our objective was to develop and validate a 5-year dementia risk score derived from primary healthcare data. We used data from general practices in The Health Improvement Network (THIN) database from across the UK, randomly selecting 377 practices for a development cohort and identifying 930,395 patients aged 60-95 years without a recording of dementia, cognitive impairment or memory symptoms at baseline. We developed risk algorithm models for two age groups (60-79 and 80-95 years). An external validation was conducted by validating the model on a separate cohort of 264,224 patients from 95 randomly chosen THIN practices that did not contribute to the development cohort. Our main outcome was 5-year risk of first recorded dementia diagnosis. Potential predictors included sociodemographic, cardiovascular, lifestyle and mental health variables. Dementia incidence was 1.88 (95% CI, 1.83-1.93) and 16.53 (95% CI, 16.15-16.92) per 1000 PYAR for those aged 60-79 (n = 6017) and 80-95 years (n = 7104), respectively. Predictors for those aged 60-79 included age, sex, social deprivation, smoking, BMI, heavy alcohol use, anti-hypertensive drugs, diabetes, stroke/TIA, atrial fibrillation, aspirin, depression. The discrimination and calibration of the risk algorithm were good for the 60-79 years model; D statistic 2.03 (95% CI, 1.95-2.11), C index 0.84 (95% CI, 0.81-0.87), and calibration slope 0.98 (95% CI, 0.93-1.02). The algorithm had a high negative predictive value, but lower positive predictive value at most risk thresholds. Discrimination and calibration were poor for the 80-95 years model. Routinely collected data predicts 5-year risk of recorded diagnosis of dementia for those aged 60-79, but not those aged 80+. This

The IntFOLD server: an integrated web resource for protein fold recognition, 3D model quality assessment, intrinsic disorder prediction, domain prediction and ligand binding site prediction.

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Roche, Daniel B; Buenavista, Maria T; Tetchner, Stuart J; McGuffin, Liam J

2011-07-01

The IntFOLD server is a novel independent server that integrates several cutting edge methods for the prediction of structure and function from sequence. Our guiding principles behind the server development were as follows: (i) to provide a simple unified resource that makes our prediction software accessible to all and (ii) to produce integrated output for predictions that can be easily interpreted. The output for predictions is presented as a simple table that summarizes all results graphically via plots and annotated 3D models. The raw machine readable data files for each set of predictions are also provided for developers, which comply with the Critical Assessment of Methods for Protein Structure Prediction (CASP) data standards. The server comprises an integrated suite of five novel methods: nFOLD4, for tertiary structure prediction; ModFOLD 3.0, for model quality assessment; DISOclust 2.0, for disorder prediction; DomFOLD 2.0 for domain prediction; and FunFOLD 1.0, for ligand binding site prediction. Predictions from the IntFOLD server were found to be competitive in several categories in the recent CASP9 experiment. The IntFOLD server is available at the following web site: http://www.reading.ac.uk/bioinf/IntFOLD/.

Forecasting the Water Demand in Chongqing, China Using a Grey Prediction Model and Recommendations for the Sustainable Development of Urban Water Consumption.

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Wu, Hua'an; Zeng, Bo; Zhou, Meng

2017-11-15

High accuracy in water demand predictions is an important basis for the rational allocation of city water resources and forms the basis for sustainable urban development. The shortage of water resources in Chongqing, the youngest central municipality in Southwest China, has significantly increased with the population growth and rapid economic development. In this paper, a new grey water-forecasting model (GWFM) was built based on the data characteristics of water consumption. The parameter estimation and error checking methods of the GWFM model were investigated. Then, the GWFM model was employed to simulate the water demands of Chongqing from 2009 to 2015 and forecast it in 2016. The simulation and prediction errors of the GWFM model was checked, and the results show the GWFM model exhibits better simulation and prediction precisions than those of the classical Grey Model with one variable and single order equation GM(1,1) for short and the frequently-used Discrete Grey Model with one variable and single order equation, DGM(1,1) for short. Finally, the water demand in Chongqing from 2017 to 2022 was forecasted, and some corresponding control measures and recommendations were provided based on the prediction results to ensure a viable water supply and promote the sustainable development of the Chongqing economy.

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

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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

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.

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

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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