The simultaneous mass and energy evaporation (SM2E) model.
Choudhary, Rehan; Klauda, Jeffery B
2016-01-01
In this article, the Simultaneous Mass and Energy Evaporation (SM2E) model is presented. The SM2E model is based on theoretical models for mass and energy transfer. The theoretical models systematically under or over predicted at various flow conditions: laminar, transition, and turbulent. These models were harmonized with experimental measurements to eliminate systematic under or over predictions; a total of 113 measured evaporation rates were used. The SM2E model can be used to estimate evaporation rates for pure liquids as well as liquid mixtures at laminar, transition, and turbulent flow conditions. However, due to limited availability of evaporation data, the model has so far only been tested against data for pure liquids and binary mixtures. The model can take evaporative cooling into account and when the temperature of the evaporating liquid or liquid mixture is known (e.g., isothermal evaporation), the SM2E model reduces to a mass transfer-only model.
Zanith, Caroline C; Pliego, Josefredo R
2015-03-01
The continuum solvation models SMD and SM8 were developed using 2,346 solvation free energy values for 318 neutral molecules in 91 solvents as reference. However, no solvation data of neutral solutes in methanol was used in the parametrization, while only few solvation free energy values of solutes in dimethyl sulfoxide and acetonitrile were used. In this report, we have tested the performance of the models for these important solvents. Taking data from literature, we have generated solvation free energy, enthalpy and entropy values for 37 solutes in methanol, 21 solutes in dimethyl sulfoxide and 19 solutes in acetonitrile. Both SMD and SM8 models have presented a good performance in methanol and acetonitrile, with mean unsigned error equal or less than 0.66 and 0.55 kcal mol(-1) in methanol and acetonitrile, respectively. However, the correlation is worse in dimethyl sulfoxide, where the SMD and SM8 methods present mean unsigned error of 1.02 and 0.95 kcal mol(-1), respectively. Our results point out the SMx family of models need be improved for dimethyl sulfoxide solvent.
Zanith, Caroline C.; Pliego, Josefredo R.
2015-03-01
The continuum solvation models SMD and SM8 were developed using 2,346 solvation free energy values for 318 neutral molecules in 91 solvents as reference. However, no solvation data of neutral solutes in methanol was used in the parametrization, while only few solvation free energy values of solutes in dimethyl sulfoxide and acetonitrile were used. In this report, we have tested the performance of the models for these important solvents. Taking data from literature, we have generated solvation free energy, enthalpy and entropy values for 37 solutes in methanol, 21 solutes in dimethyl sulfoxide and 19 solutes in acetonitrile. Both SMD and SM8 models have presented a good performance in methanol and acetonitrile, with mean unsigned error equal or less than 0.66 and 0.55 kcal mol-1 in methanol and acetonitrile, respectively. However, the correlation is worse in dimethyl sulfoxide, where the SMD and SM8 methods present mean unsigned error of 1.02 and 0.95 kcal mol-1, respectively. Our results point out the SMx family of models need be improved for dimethyl sulfoxide solvent.
Symmetries for SM Alignment in multi-Higgs Doublet Models
Pilaftsis, Apostolos
2016-01-01
We derive the complete set of maximal symmetries for Standard Model (SM) alignment that may occur in the tree-level scalar potential of multi-Higgs Doublet Models, with $n > 2$ Higgs doublets. Our results generalize the symmetries of SM alignment, without decoupling of large mass scales or fine-tuning, previously obtained in the context of two-Higgs Doublet Models.
Energy Exascale Earth System Model (E3SM) Project Strategy
Energy Technology Data Exchange (ETDEWEB)
Bader, D. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
2017-09-18
The E3SM project will assert and maintain an international scientific leadership position in the development of Earth system and climate models at the leading edge of scientific knowledge and computational capabilities. With its collaborators, it will demonstrate its leadership by using these models to achieve the goal of designing, executing, and analyzing climate and Earth system simulations that address the most critical scientific questions for the nation and DOE.
A model relating remanence and microstructure of SmCo5 magnets
International Nuclear Information System (INIS)
Campos, M.F. de; Machado, R.; Landgraf, F.J.G.; Rodrigues, D.; Romero, S.A.; Neiva, A.C.; Missell, F.P.
1998-01-01
The question of remanence in SmCo 5 sintered magnets is reviewed, giving special attention to the influence of microstructure. In SmCo 5 magnets, the microstructural constituents are the matrix phase SmCo 5 , another ferromagnetic phase (Sm 2 Co 7 ), oxides (Sm 2 O 3 ), a recently reported carbide SmCoC 2 and pores. A method to estimate alignment via the ratio J r /J s from remanence measurements and microstructural analysis is proposed. The results agree very well with the degree of alignment calculated from Schulz pole figures. During sintering it was observed that the nucleation of SmCo 5 on the Sm 2 Co 7 phase is epitaxial. Hexagonal Sm 2 Co 7 is present in our sintered magnets but rhombohedral Sm 2 Co 7 may also be present. The effect of chemical composition (Sm content and oxygen content) on remanence and on the ratio J r /J s was evaluated. In the sintering step, the densification occurs more slowly for compositions with higher samarium content (or higher Sm 2 Co 7 volume fraction). A model able to calculate the best chemical composition to maximize coercivity and remanence is presented and discussed. (orig.)
SM4MQ: A Semantic Model for Multidimensional Queries
DEFF Research Database (Denmark)
Varga, Jovan; Dobrokhotova, Ekaterina; Romero, Oscar
2017-01-01
On-Line Analytical Processing (OLAP) is a data analysis approach to support decision-making. On top of that, Exploratory OLAP is a novel initiative for the convergence of OLAP and the Semantic Web (SW) that enables the use of OLAP techniques on SW data. Moreover, OLAP approaches exploit different...... metadata artifacts (e.g., queries) to assist users with the analysis. However, modeling and sharing of most of these artifacts are typically overlooked. Thus, in this paper we focus on the query metadata artifact in the Exploratory OLAP context and propose an RDF-based vocabulary for its representation......, sharing, and reuse on the SW. As OLAP is based on the underlying multidimensional (MD) data model we denote such queries as MD queries and define SM4MQ: A Semantic Model for Multidimensional Queries. Furthermore, we propose a method to automate the exploitation of queries by means of SPARQL. We apply...
Mossallam, Shereen F; Amer, Eglal I; Ewaisha, Radwa E; Khalil, Amal M; Aboushleib, Hamida M; Bahey-El-Din, Mohammed
2015-01-01
Background Schistosoma mansoni infection represents a major cause of morbidity and mortality in many areas of the developing world. Effective vaccines against schistosomiasis are not available and disease management relies mainly on treatment with the anthelmintic drug praziquantel. Several promising schistosomal antigens have been evaluated for vaccine efficacy such as Sm14, Sm29 and tetraspanins. However, most investigators examine these promising antigens in animal models individually rath...
Mossallam, Shereen F; Amer, Eglal I; Ewaisha, Radwa E; Khalil, Amal M; Aboushleib, Hamida M; Bahey-El-Din, Mohammed
2015-03-24
Schistosoma mansoni infection represents a major cause of morbidity and mortality in many areas of the developing world. Effective vaccines against schistosomiasis are not available and disease management relies mainly on treatment with the anthelmintic drug praziquantel. Several promising schistosomal antigens have been evaluated for vaccine efficacy such as Sm14, Sm29 and tetraspanins. However, most investigators examine these promising antigens in animal models individually rather than in properly adjuvanted antigen combinations. In the present study, we made a recombinant fusion protein comprised of the promising schistosomal antigens Sm14 and Sm29. The fusion protein, FSm14/29, was administered to Swiss albino mice either unadjuvanted or adjuvanted with polyinosinic-polycytidylic acid adjuvant, poly(I:C). Mice were challenged with S. mansoni cercariae and different parasitological/immunological parameters were assessed seven weeks post-challenge. Data were analyzed using the ANOVA test with post-hoc Tukey-Kramer test. Mice pre-immunized with unadjuvanted or poly(I:C)-adjuvanted fusion protein showed reduction of adult worm burden of 44.7 and 48.4%, respectively. In addition, significant reduction of tissue egg burdens was observed in mice immunized with the fusion protein when compared with the infected saline/adjuvant negative control groups and groups immunized with the individual Sm14 and Sm29 antigens. Light microscope and scanning electron microscope (SEM) investigation of adult worms recovered from FSm14/29-immunized mice revealed appreciable morphological damage and tegumental deformities. Histopathological examination of liver sections of immunized mice demonstrated reduced granulomatous and inflammatory reactions when compared with infected unvaccinated mice or mice immunized with the individual Sm14 and Sm29 antigens. The findings presented in this study highlight the importance of the fusion protein FSm14/29 as a potential vaccine candidate that is
Experimental validation of Pu-Sm evolution model for CANDU-6 power transients
International Nuclear Information System (INIS)
Coutsiers, Eduardo E.; Pomerantz, Marcelo E.; Moreno, Carlos A.
2000-01-01
Development of a methodology to evaluate the reactivity produced by Pu-Sm transient, effect displayed after power transients. This methodology allows to predict the behavior of liquid zones with which the fine control of CANDU reactor power is made. With this information, it is easier to foresee the refueling demand after power movements. The comparison with experimental results showed good agreement. (author)
International Nuclear Information System (INIS)
Zhao Kui; Guo Jiyu; Lu Xiuqin; Cheng Yehao; Huang Xiaolin; Ma Yong; Li Shuyuan; Ruan Ming; Li Zhichang; Jiang Chenglie
1997-01-01
A preliminary result was reported for the experiment to determine the mass of the heavier neutron-rich nucleus 158 Sm using the 160 Gd( 18 O, 20 Ne) two proton transfer reaction in last progress report. The average Q-value of (4.046 +- 0.102) MeV for the 160 Gd( 18 O, 20 Ne) 158 Sm reaction is given. A mass excess for 158 Sm of (-65.738 +- 0.102) MeV was derived. This is the first experimentally measured value of the mass of 158 Sm which is about 450 keV higher than the evaluation value from systematic trends listed in the 1993 atomic mass table. The new prediction shows better agreement with the measured values and a significant improvement over the earlier FRDM (finite-range droplet model) value
Modeling hysteresis curves of anisotropic SmCoFeCuZr magnets
Energy Technology Data Exchange (ETDEWEB)
Sampaio da Silva, Fernanda A. [Programa de Pos-Graduacao em Engenharia Metalurgica-PUVR, Universidade Federal Fluminense, Av dos Trabalhadores 420, 27255-125 Volta Redonda, RJ (Brazil); Castro, Nicolau A. [Instituto de Pesquisas Tecnologicas, Sao Paulo, SP (Brazil); Campos, Marcos F. de, E-mail: mcampos@metal.eeimvr.uff.br [Programa de Pos-Graduacao em Engenharia Metalurgica-PUVR, Universidade Federal Fluminense, Av dos Trabalhadores 420, 27255-125 Volta Redonda, RJ (Brazil)
2013-02-15
The hysteresis curves at room temperature and at 630 K of an anisotropic magnet were successfully modeled with the Stoner-Wohlfarth Callen-Liu-Cullen (SW-CLC) model. This implies that coherent rotation of domains is the reversal mechanism in this magnet. The chemical composition of the evaluated magnet is Sm(Co{sub bal}Fe{sub 0.06}Cu{sub 0.108}Zr{sub 0.03}){sub 7.2}. The anisotropy field H{sub A} was estimated with the model, resulting {mu}{sub 0}H{sub A}=7.1 T at the room temperature, and 2.9 T at 630 K. For this sample, the CLC interaction parameter (1/d) is very low (near zero) and, thus, the nanocrystalline 2:17 grains are well 'magnetically decoupled'. The texture analysis using Schulz Pole figure data indicated M{sub r}/M{sub s} ratio=0.96, and this means that the magnet is very well aligned. The excellent alignment of the grains is one of the reasons for the high coercivity of this sample ({approx}4 T at room temperature). - Highlights: Black-Right-Pointing-Pointer The Stoner-Wohlfarth model can describe the hysteresis curves of anisotropic Sm2Co17 magnets, since the Callen-Liu-Cullen correction is applied. Black-Right-Pointing-Pointer The anisotropy field of the hard magnetic phase Sm2Co17 can be estimated from the hysteresis curves of anisotropic magnets, since the crystallographic texture is known. Black-Right-Pointing-Pointer It is presented a texture study of commercial sintered Sm2Co17 type magnets. Black-Right-Pointing-Pointer The texture data can be used for evaluation of the squareness of the 2nd quadrant of the hysteresis curve, in Sm2Co17 hard coercivity magnets.
Energy Technology Data Exchange (ETDEWEB)
Neves, M. E-mail: mneves@itn.pt; Gano, L.; Pereira, N.; Costa, M.C.; Costa, M.R.; Chandia, M.; Rosado, M.; Fausto, R
2002-04-01
Bisphosphonates (BPs) are characterized by a P-C-P backbone structure and two phosphonic acid groups bonded to the same carbon, and are established as osteoclast-mediated bone resorption inhibitors. The nature of the groups attached to the central carbon atom are responsible in determining the potency of bisphosphonates as anti-resorption drugs. However, it is not yet clear the exact relationship between their molecular structure and pharmacologic activities. In this study, molecular geometries of pamidronate, alendronate and neridronate, differing only in the length of the aliphatic chains, were predicted by molecular mechanics and their interactions with hydroxyapatite, the main bone mineral component, were examined. We report the synthesis and radiochemical characterization of {sup 153}Sm complexes with pamidronate, alendronate and neridronate. Hydroxyapatite binding and biodistribution studies of these complexes have shown a good correlation with the theoretical molecular modeling interaction studies. So, it is possible to conclude that computational chemistry techniques are a good approach to evaluate specific interactions and may play a relevant role in determining the relative ability of BPs to mineral bone, and open new perspectives to the design of new BPs with increased pharmacological activity. These techniques could be extended to BPs as ligands to carrier radioactive metals, aiming for new bone therapeutic radiopharmaceuticals.
Kiourkos, S
1999-01-01
One of the potentially accessible decay modes of the Higgs boson in the mass region $100 < m_H < 180$ GeV is the $H^0 \\rightarrow Z^0 \\gamma$ channel. The work presented in this note examines the Standard Model and Minimal Supersymmetric Standard Model predictions for the observability of this channel using particle level simulation as well as the ATLAS fast simulation (ATLFAST). It compares present estimates for the signal observability with previously reported ones in \\cite{unal} specifying the changes arising from the assumed energy of the colliding protons and the improvements in the treatment of theoretical predictions. With the present estimates, the expected significance for the SM Higgs does not exceed, in terms of $\\frac{S}{\\sqrt{B}}$, 1.5 $\\sigma$ (including $Z^0 \\rightarrow e^+ e^-$ and $Z^0 \\rightarrow {\\mu}^+ {\\mu}^-$) for an integrated luminosity of $10^5$ pb$^{-1}$ therefore not favouring this channel for SM Higgs searches. Comparable discovery potential is expected at most for the MSSM $...
SM4MQ: A Semantic Model for Multidimensional Queries
DEFF Research Database (Denmark)
Varga, Jovan; Dobrokhotova, Ekaterina; Romero, Oscar
2017-01-01
metadata artifacts (e.g., queries) to assist users with the analysis. However, modeling and sharing of most of these artifacts are typically overlooked. Thus, in this paper we focus on the query metadata artifact in the Exploratory OLAP context and propose an RDF-based vocabulary for its representation......On-Line Analytical Processing (OLAP) is a data analysis approach to support decision-making. On top of that, Exploratory OLAP is a novel initiative for the convergence of OLAP and the Semantic Web (SW) that enables the use of OLAP techniques on SW data. Moreover, OLAP approaches exploit different...
Zhang, Tao; Li, Yanyan; Zou, Peng; Yu, Jing-yu; McEachern, Donna; Wang, Shaomeng; Sun, Duxin
2013-09-01
The inhibitors of apoptosis proteins (IAPs) are a class of key apoptosis regulators overexpressed or dysregulated in cancer. SM-406/AT-406 is a potent and selective small molecule mimetic of Smac that antagonizes the inhibitor of apoptosis proteins (IAPs). A physiologically based pharmacokinetic and pharmacodynamic (PBPK-PD) model was developed to predict the tissue concentration-time profiles of SM-406, the related onco-protein levels in tumor, and the tumor growth inhibition in a mouse model bearing human breast cancer xenograft. In the whole body physiologically based pharmacokinetic (PBPK) model for pharmacokinetics characterization, a well stirred (perfusion rate-limited) model was used to describe SM-406 pharmacokinetics in the lung, heart, kidney, intestine, liver and spleen, and a diffusion rate-limited (permeability limited) model was used for tumor. Pharmacodynamic (PD) models were developed to correlate the SM-406 concentration in tumor to the cIAP1 degradation, pro-caspase 8 decrease, CL-PARP accumulation and tumor growth inhibition. The PBPK-PD model well described the experimental pharmacokinetic data, the pharmacodynamic biomarker responses and tumor growth. This model may be helpful to predict tumor and plasma SM-406 concentrations in the clinic. Copyright © 2013 John Wiley & Sons, Ltd.
4D GUT (and SM) Model Building from Intersecting D-Branes
Kokorelis, C E
2003-01-01
We provide a general overview of the current state of the art in three generation model building proposals - using intersecting D-brane toroidal compactifications of IIA string theories - which have, only, the SM at low energy. In this context, we focus on these model building directions, where natural non-supersymmetric constructions based on $SU(4)_C \\times SU(2)_L \\times SU(2)_R$, SU(5) and flipped SU(5) GUT groups, have at low energy only the Standard Model. In the flipped SU(5) GUTS, the special build up structure of the models accommodates naturally a see-saw mechanism and a new solution to the doublet-triplet splitting problem.
Cultural Resource Predictive Modeling
2017-10-01
refining formal, inductive predictive models is the quality of the archaeological and environmental data. To build models efficiently, relevant...geomorphology, and historic information . Lessons Learned: The original model was focused on the identification of prehistoric resources. This...system but uses predictive modeling informally . For example, there is no probability for buried archaeological deposits on the Burton Mesa, but there is
Predictive modeling of complications.
Osorio, Joseph A; Scheer, Justin K; Ames, Christopher P
2016-09-01
Predictive analytic algorithms are designed to identify patterns in the data that allow for accurate predictions without the need for a hypothesis. Therefore, predictive modeling can provide detailed and patient-specific information that can be readily applied when discussing the risks of surgery with a patient. There are few studies using predictive modeling techniques in the adult spine surgery literature. These types of studies represent the beginning of the use of predictive analytics in spine surgery outcomes. We will discuss the advancements in the field of spine surgery with respect to predictive analytics, the controversies surrounding the technique, and the future directions.
Directory of Open Access Journals (Sweden)
Edi Suresman, dkk
2015-10-01
Full Text Available Abstrack, A successful education is influenced by various factors, one of which is method or approach to education to build a religious attitude for students. One of Islamic values-based learning methods that have been implemented at UPI in an effort to build religious attitude of PPG Post SM-3T participants is targhib and tarhib learning model and the results are very effective. The focus of problem in this study is on whether targhib - tarhib model was used in other LPTKs in building religious attitude for PPG Post SM-3T participants? The purpose of this study is to understand what learning model is applied in LPTK organizing PPG Post SM-3T in building religious attitude for PPG participants? This study was conducted by using a descriptive approach, qualitative-interpretative. The techniques of data collection used are questionnaire in the form of religious attitude distributed to PPG Post SM-3T participants, religious builder in a dormitory, and manager, and participation observation in the field. The data analysis was performed in five steps: verification of the data, presentation of the serial number for answer sheets, scoring of answer sheet, calculation of religious attitude trends by estimating the population and its percentage, and analysis of trends to differences in the effectiveness of each method used to mold religious attitude in each of LPTK organizing PPG. The results of the study suggest any variation of models used in each of LPTK, i.e., lectures, tutorials, thariqah shufis and targhibs - tarhibs. The targhib - tarhib have the most successful position than other models. Targhib – tarhib are 84%, lectures, 50%, shufis, 80%, tutorials, 70%. Keywords: Construction Model, Religious Attitude, PPG Post SM-3T. Abstrak, Keberhasilan pendidikan dipengaruhi oleh berbagai faktor, salah satu faktor tersebut adalah metode atau pendekatan pendidikan yang dapat membangun sikap religius bagi peserta didik. Salah satu metode
Energy Technology Data Exchange (ETDEWEB)
Lu Jun [Department of Radiotherapy, Xijing Hospital, Fourth Military Medical University, 15 West Changle Road, Xi' an 710032, Shaanxi Province (China); Deng Jinglan, E-mail: dengjinglan@gmail.com [Department of Nuclear Medicine, Xijing Hospital, Fourth Military Medical University, 15 West Changle Road, Xi' an 710032, Shaanxi Province (China); Zhao Haitao [Department of Radiology, Xijing Hospital, Fourth Military Medical University, 15 West Changle Road, Xi' an 710032, Shaanxi Province (China); Shi Mei [Department of Radiotherapy, Xijing Hospital, Fourth Military Medical University, 15 West Changle Road, Xi' an 710032, Shaanxi Province (China); Wang Jing [Department of Nuclear Medicine, Xijing Hospital, Fourth Military Medical University, 15 West Changle Road, Xi' an 710032, Shaanxi Province (China); Zhao Lina [Department of Radiotherapy, Xijing Hospital, Fourth Military Medical University, 15 West Changle Road, Xi' an 710032, Shaanxi Province (China)
2011-05-15
Purpose: We investigated the safety and feasibility of the combination of samarium-153-ethylenediamine tetramethylene phosphonate ({sup 153}Sm-EDTMP)-incorporated bone cement (BC) with percutaneous vertebroplasty (PVP) in dogs. Methods and materials: {sup 153}Sm-EDTMP-incorporated BC was prepared by combining solid {sup 153}Sm-EDTMP and polymethylmethacrylate (PMMA) immediately before PVP. It was then injected into the vertebrae of four healthy mongrel dogs (two males and two females) by PVP under CT guidance. Each dog was subjected to five PVP sessions at a {sup 153}Sm-EDTMP dose of 30-70 mCi. The suppressive effect of local injection of {sup 153}Sm-EDTMP on the hematopoietic system was evaluated through counting of peripheral blood cells. Distribution of {sup 153}Sm-EDTMP-incorporated BC and the status of tissues adjacent to injected vertebrae were evaluated with SPECT, CT and MRI. Histopathology was carried out to assess the influence of PVP on the vertebra and adjacent tissues at the microscopic level. Results: PVP was done successfully, and all dogs exhibited normal behavior and stable physical signs after procedures. {sup 153}Sm-EDTMP-incorporated BC was concentrated mainly in target vertebrae, and the peripheral blood cells remained within normal range. The spinal cord and tissues around BC did not exhibit signs of injury even when the dosage of {sup 153}Sm-EDTMP increased from 30 mCi to 70 mCi. Conclusion: A dose lower than 70 mCi of {sup 153}Sm is safe when it was injected into vertebrae. {sup 153}Sm-EDTMP-incorporated BC did not influence the effect of PVP. This means might strengthen anti-tumor activity locally for vertebra with osseous metastasis without damaging adjacent tissues.
Archaeological predictive model set.
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...
Early Start Denver Model - intervention for de helt små børn med autisme
DEFF Research Database (Denmark)
Brynskov, Cecilia
2015-01-01
Early Start Denver Model (ESDM) er en autismespecifik interventionsmetode, som er udviklet til helt små børn med autisme (0-4 år). Metoden fokuserer på at styrke den tidlige kontakt og barnets motivation, og den arbejder målrettet med de socio-kommunikative forløbere for sprog og med den tidlige...
Levy, R.; Mcginness, H.
1976-01-01
Investigations were performed to predict the power available from the wind at the Goldstone, California, antenna site complex. The background for power prediction was derived from a statistical evaluation of available wind speed data records at this location and at nearby locations similarly situated within the Mojave desert. In addition to a model for power prediction over relatively long periods of time, an interim simulation model that produces sample wind speeds is described. The interim model furnishes uncorrelated sample speeds at hourly intervals that reproduce the statistical wind distribution at Goldstone. A stochastic simulation model to provide speed samples representative of both the statistical speed distributions and correlations is also discussed.
Zephyr - the prediction models
DEFF Research Database (Denmark)
Nielsen, Torben Skov; Madsen, Henrik; Nielsen, Henrik Aalborg
2001-01-01
utilities as partners and users. The new models are evaluated for five wind farms in Denmark as well as one wind farm in Spain. It is shown that the predictions based on conditional parametric models are superior to the predictions obatined by state-of-the-art parametric models.......This paper briefly describes new models and methods for predicationg the wind power output from wind farms. The system is being developed in a project which has the research organization Risø and the department of Informatics and Mathematical Modelling (IMM) as the modelling team and all the Danish...
Experimental and modeling studies on phase stability of nanocrystalline magnetic Sm2Co7
International Nuclear Information System (INIS)
Xu, Wenwu; Song, Xiaoyan; Zhang, Zhexu; Liang, Haining
2013-01-01
Highlights: • Low-Co Sm–Co alloy as new candidate for permanent magnets was proposed. • Relationship between the phase stability and grain size was quantified for Sm 2 Co 7 . • The work contributes to the development of nanostructured Sm–Co permanent magnetic materials. -- Abstract: In contrast to the conventional polycrystalline low-Co Sm–Co alloys that have very weak permanent magnetic properties, the Sm 2 Co 7 alloy has been found to have fairly promising permanent magnetic performance when its grain size is reduced to the nanoscale. It was discovered that the crystal structure of the nanocrystalline Sm 2 Co 7 has a strong nanograin-size-dependent stability. The rhombohedral structure of Sm 2 Co 7 phase which is metastable at temperatures lower than 1435 K in conventional polycrystalline system can exist stably at room temperature in the nanocrystalline system. To understand the phase stability of the nanocrystalline Sm 2 Co 7 , the experimental and nanothermodynamic analyses were combined to describe quantitatively the phase transformation behavior of Sm 2 Co 7 on the nanoscale. The results are important for the development of nanostructured Sm–Co permanent magnets
Wolf, A. S.; Asimow, P. D.; Stevenson, D. J.
2015-12-01
Recent first-principles calculations (e.g. Stixrude, 2009; de Koker, 2013), shock-wave experiments (Mosenfelder, 2009), and diamond-anvil cell investigations (Sanloup, 2013) indicate that silicate melts undergo complex structural evolution at high pressure. The observed increase in cation-coordination (e.g. Karki, 2006; 2007) induces higher compressibilities and lower adiabatic thermal gradients in melts as compared with their solid counterparts. These properties are crucial for understanding the evolution of impact-generated magma oceans, which are dominated by the poorly understood behavior of silicates at mantle pressures and temperatures (e.g. Stixrude et al. 2009). Probing these conditions is difficult for both theory and experiment, especially given the large compositional space (MgO-SiO2-FeO-Al2O3-etc). We develop a new model to understand and predict the behavior of oxide and silicate melts at extreme P-T conditions (Wolf et al., 2015). The Coordinated Hard Sphere Mixture (CHaSM) extends the Hard Sphere mixture model, accounting for the range of coordination states for each cation in the liquid. Using approximate analytic expressions for the hard sphere model, this fast statistical method compliments classical and first-principles methods, providing accurate thermodynamic and structural property predictions for melts. This framework is applied to the MgO system, where model parameters are trained on a collection of crystal polymorphs, producing realistic predictions of coordination evolution and the equation of state of MgO melt over a wide P-T range. Typical Mg-coordination numbers are predicted to evolve continuously from 5.25 (0 GPa) to 8.5 (250 GPa), comparing favorably with first-principles Molecular Dynamics (MD) simulations. We begin extending the model to a simplified mantle chemistry using empirical potentials (generally accurate over moderate pressure ranges, consuming classical MD calculations. This approach also sheds light on the universality
Inverse and Predictive Modeling
Energy Technology Data Exchange (ETDEWEB)
Syracuse, Ellen Marie [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2017-09-27
The LANL Seismo-Acoustic team has a strong capability in developing data-driven models that accurately predict a variety of observations. These models range from the simple – one-dimensional models that are constrained by a single dataset and can be used for quick and efficient predictions – to the complex – multidimensional models that are constrained by several types of data and result in more accurate predictions. Team members typically build models of geophysical characteristics of Earth and source distributions at scales of 1 to 1000s of km, the techniques used are applicable for other types of physical characteristics at an even greater range of scales. The following cases provide a snapshot of some of the modeling work done by the Seismo- Acoustic team at LANL.
The Na(+)/H(+) exchanger SM-20220 attenuates ischemic injury in in vitro and in vivo models.
Horikawa, N; Nishioka, M; Itoh, N; Kuribayashi, Y; Matsui, K; Ohashi, N
2001-01-01
The aim of this study is to clarify whether the activation of a Na(+)/H(+) exchanger (NHE) is tightly concerned with neuronal and glial cell injury induced by ischemia using a selective NHE inhibitor, SM-20220 (N-(aminoiminomethyl)-1-methyl-1H-indole-2-carboxamide methanesulfonate). Two hours of hypoxia followed by 24 h of reoxygenation induced lactate dehydrogenase (LDH) release, a marker of cell membrane damage, in cultured neurons and glia derived from rats. SM-20220 significantly reduced LDH release in both cells in a concentration-dependent manner, and this effect was statistically significant at concentrations of more than 10(-8) mol/l for neurons and 10(-7) mol/l for glia. A standard NHE inhibitor, 5-(N-ethyl-N-isopropyl)-amiloride, also reduced LDH release in neurons at concentrations of more than 10(-7) mol/l. In a rat transient middle cerebral artery occlusion model, intravenous infusion of SM-20220 reduced cerebral infarction when the serum concentration of SM- 20220 was maintained at about 10(-7) mol/l. These results suggest that the activation of the NHE plays an important role in ischemic neuronal and glial cell injury, and NHE inhibitor may have good therapeutic value for the treatment of ischemic stroke. Copyright 2001 S. Karger AG, Basel.
Moving from Theory to Practice: Implementing the Kin Keeper[superscript SM] Cancer Prevention Model
Williams, K. P.; Mullan, P. B.; Todem, D.
2009-01-01
This paper presents the rationale and findings of a feasibility and process study of the Kin Keeper[superscript SM] Cancer Prevention Intervention. An observational cohort study design was implemented with African-American women in synergistic female family relationships. Community health workers (CHWs) from two Michigan public health programs…
Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction.
Bandeira E Sousa, Massaine; Cuevas, Jaime; de Oliveira Couto, Evellyn Giselly; Pérez-Rodríguez, Paulino; Jarquín, Diego; Fritsche-Neto, Roberto; Burgueño, Juan; Crossa, Jose
2017-06-07
Multi-environment trials are routinely conducted in plant breeding to select candidates for the next selection cycle. In this study, we compare the prediction accuracy of four developed genomic-enabled prediction models: (1) single-environment, main genotypic effect model (SM); (2) multi-environment, main genotypic effects model (MM); (3) multi-environment, single variance G×E deviation model (MDs); and (4) multi-environment, environment-specific variance G×E deviation model (MDe). Each of these four models were fitted using two kernel methods: a linear kernel Genomic Best Linear Unbiased Predictor, GBLUP (GB), and a nonlinear kernel Gaussian kernel (GK). The eight model-method combinations were applied to two extensive Brazilian maize data sets (HEL and USP data sets), having different numbers of maize hybrids evaluated in different environments for grain yield (GY), plant height (PH), and ear height (EH). Results show that the MDe and the MDs models fitted with the Gaussian kernel (MDe-GK, and MDs-GK) had the highest prediction accuracy. For GY in the HEL data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 9 to 32%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 9 to 49%. For GY in the USP data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 0 to 7%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 34 to 70%. For traits PH and EH, gains in prediction accuracy of models with GK compared to models with GB were smaller than those achieved in GY. Also, these gains in prediction accuracy decreased when a more difficult prediction problem was studied. Copyright © 2017 Bandeira e Sousa et al.
Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction
Directory of Open Access Journals (Sweden)
Massaine Bandeira e Sousa
2017-06-01
Full Text Available Multi-environment trials are routinely conducted in plant breeding to select candidates for the next selection cycle. In this study, we compare the prediction accuracy of four developed genomic-enabled prediction models: (1 single-environment, main genotypic effect model (SM; (2 multi-environment, main genotypic effects model (MM; (3 multi-environment, single variance G×E deviation model (MDs; and (4 multi-environment, environment-specific variance G×E deviation model (MDe. Each of these four models were fitted using two kernel methods: a linear kernel Genomic Best Linear Unbiased Predictor, GBLUP (GB, and a nonlinear kernel Gaussian kernel (GK. The eight model-method combinations were applied to two extensive Brazilian maize data sets (HEL and USP data sets, having different numbers of maize hybrids evaluated in different environments for grain yield (GY, plant height (PH, and ear height (EH. Results show that the MDe and the MDs models fitted with the Gaussian kernel (MDe-GK, and MDs-GK had the highest prediction accuracy. For GY in the HEL data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 9 to 32%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 9 to 49%. For GY in the USP data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 0 to 7%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 34 to 70%. For traits PH and EH, gains in prediction accuracy of models with GK compared to models with GB were smaller than those achieved in GY. Also, these gains in prediction accuracy decreased when a more difficult prediction problem was studied.
SChiSM2: creating interactive web page annotations of molecular structure models using Jmol.
Cammer, Stephen
2007-02-01
SChiSM2 is a web server-based program for creating web pages that include interactive molecular graphics using the freely-available applet, Jmol, for illustration. The program works with Internet Explorer and Firefox on Windows, Safari and Firefox on Mac OSX and Firefox on Linux. The program can be accessed at the following address: http://ci.vbi.vt.edu/cammer/schism2.html.
Predictive Surface Complexation Modeling
Energy Technology Data Exchange (ETDEWEB)
Sverjensky, Dimitri A. [Johns Hopkins Univ., Baltimore, MD (United States). Dept. of Earth and Planetary Sciences
2016-11-29
Surface complexation plays an important role in the equilibria and kinetics of processes controlling the compositions of soilwaters and groundwaters, the fate of contaminants in groundwaters, and the subsurface storage of CO_{2} and nuclear waste. Over the last several decades, many dozens of individual experimental studies have addressed aspects of surface complexation that have contributed to an increased understanding of its role in natural systems. However, there has been no previous attempt to develop a model of surface complexation that can be used to link all the experimental studies in order to place them on a predictive basis. Overall, my research has successfully integrated the results of the work of many experimentalists published over several decades. For the first time in studies of the geochemistry of the mineral-water interface, a practical predictive capability for modeling has become available. The predictive correlations developed in my research now enable extrapolations of experimental studies to provide estimates of surface chemistry for systems not yet studied experimentally and for natural and anthropogenically perturbed systems.
Chan, Shih-Liang; Huang, Shu-Li
2004-09-01
Corresponding to the concept of 'Think globally, act locally and plan regionally' of sustainable development, this paper discusses the approach of planning a sustainable community in terms of systems thinking. We apply a systems tool, the sensitivity model (SM), to build a model of the development of the community of Ping-Ding, located adjacent to the Yang-Ming-Shan National Park, Taiwan. The major issue in the development of Ping-Ding is the conflict between environmental conservation and the development of a local tourism industry. With the involvement of local residents, planners, and interest groups, a system model of 26 variables was defined to identify characteristics of Ping-Ding through pattern recognition. Two scenarios concerning the sustainable development of Ping-Ding are simulated with interlinked feedbacks from variables. The results of the analysis indicate that the development of Ping-Ding would be better served by the planning of agriculture and the tourism industry. The advantages and shortfalls of applying SM in the current planning environment of Taiwan are also discussed to conclude this paper.
Candidate Prediction Models and Methods
DEFF Research Database (Denmark)
Nielsen, Henrik Aalborg; Nielsen, Torben Skov; Madsen, Henrik
2005-01-01
This document lists candidate prediction models for Work Package 3 (WP3) of the PSO-project called ``Intelligent wind power prediction systems'' (FU4101). The main focus is on the models transforming numerical weather predictions into predictions of power production. The document also outlines...
On the Predictiveness of Single-Field Inflationary Models
Burgess, C.P.; Trott, Michael
2014-01-01
We re-examine the predictiveness of single-field inflationary models and discuss how an unknown UV completion can complicate determining inflationary model parameters from observations, even from precision measurements. Besides the usual naturalness issues associated with having a shallow inflationary potential, we describe another issue for inflation, namely, unknown UV physics modifies the running of Standard Model (SM) parameters and thereby introduces uncertainty into the potential inflationary predictions. We illustrate this point using the minimal Higgs Inflationary scenario, which is arguably the most predictive single-field model on the market, because its predictions for $A_s$, $r$ and $n_s$ are made using only one new free parameter beyond those measured in particle physics experiments, and run up to the inflationary regime. We find that this issue can already have observable effects. At the same time, this UV-parameter dependence in the Renormalization Group allows Higgs Inflation to occur (in prin...
Melanoma risk prediction models
Directory of Open Access Journals (Sweden)
Nikolić Jelena
2014-01-01
Full Text Available Background/Aim. The lack of effective therapy for advanced stages of melanoma emphasizes the importance of preventive measures and screenings of population at risk. Identifying individuals at high risk should allow targeted screenings and follow-up involving those who would benefit most. The aim of this study was to identify most significant factors for melanoma prediction in our population and to create prognostic models for identification and differentiation of individuals at risk. Methods. This case-control study included 697 participants (341 patients and 356 controls that underwent extensive interview and skin examination in order to check risk factors for melanoma. Pairwise univariate statistical comparison was used for the coarse selection of the most significant risk factors. These factors were fed into logistic regression (LR and alternating decision trees (ADT prognostic models that were assessed for their usefulness in identification of patients at risk to develop melanoma. Validation of the LR model was done by Hosmer and Lemeshow test, whereas the ADT was validated by 10-fold cross-validation. The achieved sensitivity, specificity, accuracy and AUC for both models were calculated. The melanoma risk score (MRS based on the outcome of the LR model was presented. Results. The LR model showed that the following risk factors were associated with melanoma: sunbeds (OR = 4.018; 95% CI 1.724- 9.366 for those that sometimes used sunbeds, solar damage of the skin (OR = 8.274; 95% CI 2.661-25.730 for those with severe solar damage, hair color (OR = 3.222; 95% CI 1.984-5.231 for light brown/blond hair, the number of common naevi (over 100 naevi had OR = 3.57; 95% CI 1.427-8.931, the number of dysplastic naevi (from 1 to 10 dysplastic naevi OR was 2.672; 95% CI 1.572-4.540; for more than 10 naevi OR was 6.487; 95%; CI 1.993-21.119, Fitzpatricks phototype and the presence of congenital naevi. Red hair, phototype I and large congenital naevi were
On the predictiveness of single-field inflationary models
Burgess, C. P.; Patil, Subodh P.; Trott, Michael
2014-06-01
We re-examine the predictiveness of single-field inflationary models and discuss how an unknown UV completion can complicate determining inflationary model parameters from observations, even from precision measurements. Besides the usual naturalness issues associated with having a shallow inflationary potential, we describe another issue for inflation, namely, unknown UV physics modifies the running of Standard Model (SM) parameters and thereby introduces uncertainty into the potential inflationary predictions. We illustrate this point using the minimal Higgs Inflationary scenario, which is arguably the most predictive single-field model on the market, because its predictions for A S , r and n s are made using only one new free parameter beyond those measured in particle physics experiments, and run up to the inflationary regime. We find that this issue can already have observable effects. At the same time, this UV-parameter dependence in the Renormalization Group allows Higgs Inflation to occur (in principle) for a slightly larger range of Higgs masses. We comment on the origin of the various UV scales that arise at large field values for the SM Higgs, clarifying cut off scale arguments by further developing the formalism of a non-linear realization of SU L (2) × U(1) in curved space. We discuss the interesting fact that, outside of Higgs Inflation, the effect of a non-minimal coupling to gravity, even in the SM, results in a non-linear EFT for the Higgs sector. Finally, we briefly comment on post BICEP2 attempts to modify the Higgs Inflation scenario.
Confidence scores for prediction models
DEFF Research Database (Denmark)
Gerds, Thomas Alexander; van de Wiel, MA
2011-01-01
In medical statistics, many alternative strategies are available for building a prediction model based on training data. Prediction models are routinely compared by means of their prediction performance in independent validation data. If only one data set is available for training and validation......, then rival strategies can still be compared based on repeated bootstraps of the same data. Often, however, the overall performance of rival strategies is similar and it is thus difficult to decide for one model. Here, we investigate the variability of the prediction models that results when the same...... to distinguish rival prediction models with similar prediction performances. Furthermore, on the subject level a confidence score may provide useful supplementary information for new patients who want to base a medical decision on predicted risk. The ideas are illustrated and discussed using data from cancer...
International Nuclear Information System (INIS)
Shamu, R.; Haouat, G.; Lachkar, J.; Lagrange, C.; McEllistrem, M.; Patin, Y.; Sigaud, J.; Cocu, F.; Bernstein, E.; Ramirez, J.
1975-01-01
The neutron total cross section for 148 Sm and total cross section differences for ( 150 Sm, 148 Sm) and ( 152 Sm, 148 Sm) and ( 154 Sm, 148 Sm) were measured from 0.7 to 15MeV neutron energy and differential cross sections for 148 Sm, 150 Sm, 152 Sm and 154 Sm were measured at 7.0MeV neutron energy. The quadrupole deformation parameter of the nuclear potential, β2, was determined for each of the 148 Sm, 150 Sm, 152 Sm, 154 Sm nuclei from these measurements using nonspherical optical-potential coupled-channel calculations. The β2 values deduced for 152 Sm and 154 Sm are in good agreement with values determined from proton and α-particle scattering, but about 10% lower than β2 values of the nuclear charge distribution obtained from Coulomb excitation, electron scattering, and μ-meson scattering experiments [fr
Bootstrap prediction and Bayesian prediction under misspecified models
Fushiki, Tadayoshi
2005-01-01
We consider a statistical prediction problem under misspecified models. In a sense, Bayesian prediction is an optimal prediction method when an assumed model is true. Bootstrap prediction is obtained by applying Breiman's `bagging' method to a plug-in prediction. Bootstrap prediction can be considered to be an approximation to the Bayesian prediction under the assumption that the model is true. However, in applications, there are frequently deviations from the assumed model. In this paper, bo...
Prediction models in complex terrain
DEFF Research Database (Denmark)
Marti, I.; Nielsen, Torben Skov; Madsen, Henrik
2001-01-01
The objective of the work is to investigatethe performance of HIRLAM in complex terrain when used as input to energy production forecasting models, and to develop a statistical model to adapt HIRLAM prediction to the wind farm. The features of the terrain, specially the topography, influence...... the performance of HIRLAM in particular with respect to wind predictions. To estimate the performance of the model two spatial resolutions (0,5 Deg. and 0.2 Deg.) and different sets of HIRLAM variables were used to predict wind speed and energy production. The predictions of energy production for the wind farms...... are calculated using on-line measurements of power production as well as HIRLAM predictions as input thus taking advantage of the auto-correlation, which is present in the power production for shorter pediction horizons. Statistical models are used to discribe the relationship between observed energy production...
MODEL PREDICTIVE CONTROL FUNDAMENTALS
African Journals Online (AJOL)
2012-07-02
Jul 2, 2012 ... Linear MPC. 1. Uses linear model: ˙x = Ax + Bu. 2. Quadratic cost function: F = xT Qx + uT Ru. 3. Linear constraints: Hx + Gu < 0. 4. Quadratic program. Nonlinear MPC. 1. Nonlinear model: ˙x = f(x, u). 2. Cost function can be nonquadratic: F = (x, u). 3. Nonlinear constraints: h(x, u) < 0. 4. Nonlinear program.
Modelling bankruptcy prediction models in Slovak companies
Directory of Open Access Journals (Sweden)
Kovacova Maria
2017-01-01
Full Text Available An intensive research from academics and practitioners has been provided regarding models for bankruptcy prediction and credit risk management. In spite of numerous researches focusing on forecasting bankruptcy using traditional statistics techniques (e.g. discriminant analysis and logistic regression and early artificial intelligence models (e.g. artificial neural networks, there is a trend for transition to machine learning models (support vector machines, bagging, boosting, and random forest to predict bankruptcy one year prior to the event. Comparing the performance of this with unconventional approach with results obtained by discriminant analysis, logistic regression, and neural networks application, it has been found that bagging, boosting, and random forest models outperform the others techniques, and that all prediction accuracy in the testing sample improves when the additional variables are included. On the other side the prediction accuracy of old and well known bankruptcy prediction models is quiet high. Therefore, we aim to analyse these in some way old models on the dataset of Slovak companies to validate their prediction ability in specific conditions. Furthermore, these models will be modelled according to new trends by calculating the influence of elimination of selected variables on the overall prediction ability of these models.
Melanoma Risk Prediction Models
Developing statistical models that estimate the probability of developing melanoma cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Predictive models of moth development
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...
Predictive Models and Computational Embryology
EPA’s ‘virtual embryo’ project is building an integrative systems biology framework for predictive models of developmental toxicity. One schema involves a knowledge-driven adverse outcome pathway (AOP) framework utilizing information from public databases, standardized ontologies...
Predictions models with neural nets
Directory of Open Access Journals (Sweden)
Vladimír Konečný
2008-01-01
Full Text Available The contribution is oriented to basic problem trends solution of economic pointers, using neural networks. Problems include choice of the suitable model and consequently configuration of neural nets, choice computational function of neurons and the way prediction learning. The contribution contains two basic models that use structure of multilayer neural nets and way of determination their configuration. It is postulate a simple rule for teaching period of neural net, to get most credible prediction.Experiments are executed with really data evolution of exchange rate Kč/Euro. The main reason of choice this time series is their availability for sufficient long period. In carry out of experiments the both given basic kind of prediction models with most frequent use functions of neurons are verified. Achieve prediction results are presented as in numerical and so in graphical forms.
Tsai, Zing Tsung-Yeh; Shiu, Shin-Han; Tsai, Huai-Kuang
2015-08-01
Transcription factor (TF) binding is determined by the presence of specific sequence motifs (SM) and chromatin accessibility, where the latter is influenced by both chromatin state (CS) and DNA structure (DS) properties. Although SM, CS, and DS have been used to predict TF binding sites, a predictive model that jointly considers CS and DS has not been developed to predict either TF-specific binding or general binding properties of TFs. Using budding yeast as model, we found that machine learning classifiers trained with either CS or DS features alone perform better in predicting TF-specific binding compared to SM-based classifiers. In addition, simultaneously considering CS and DS further improves the accuracy of the TF binding predictions, indicating the highly complementary nature of these two properties. The contributions of SM, CS, and DS features to binding site predictions differ greatly between TFs, allowing TF-specific predictions and potentially reflecting different TF binding mechanisms. In addition, a "TF-agnostic" predictive model based on three DNA "intrinsic properties" (in silico predicted nucleosome occupancy, major groove geometry, and dinucleotide free energy) that can be calculated from genomic sequences alone has performance that rivals the model incorporating experiment-derived data. This intrinsic property model allows prediction of binding regions not only across TFs, but also across DNA-binding domain families with distinct structural folds. Furthermore, these predicted binding regions can help identify TF binding sites that have a significant impact on target gene expression. Because the intrinsic property model allows prediction of binding regions across DNA-binding domain families, it is TF agnostic and likely describes general binding potential of TFs. Thus, our findings suggest that it is feasible to establish a TF agnostic model for identifying functional regulatory regions in potentially any sequenced genome.
Directory of Open Access Journals (Sweden)
Zing Tsung-Yeh Tsai
2015-08-01
Full Text Available Transcription factor (TF binding is determined by the presence of specific sequence motifs (SM and chromatin accessibility, where the latter is influenced by both chromatin state (CS and DNA structure (DS properties. Although SM, CS, and DS have been used to predict TF binding sites, a predictive model that jointly considers CS and DS has not been developed to predict either TF-specific binding or general binding properties of TFs. Using budding yeast as model, we found that machine learning classifiers trained with either CS or DS features alone perform better in predicting TF-specific binding compared to SM-based classifiers. In addition, simultaneously considering CS and DS further improves the accuracy of the TF binding predictions, indicating the highly complementary nature of these two properties. The contributions of SM, CS, and DS features to binding site predictions differ greatly between TFs, allowing TF-specific predictions and potentially reflecting different TF binding mechanisms. In addition, a "TF-agnostic" predictive model based on three DNA "intrinsic properties" (in silico predicted nucleosome occupancy, major groove geometry, and dinucleotide free energy that can be calculated from genomic sequences alone has performance that rivals the model incorporating experiment-derived data. This intrinsic property model allows prediction of binding regions not only across TFs, but also across DNA-binding domain families with distinct structural folds. Furthermore, these predicted binding regions can help identify TF binding sites that have a significant impact on target gene expression. Because the intrinsic property model allows prediction of binding regions across DNA-binding domain families, it is TF agnostic and likely describes general binding potential of TFs. Thus, our findings suggest that it is feasible to establish a TF agnostic model for identifying functional regulatory regions in potentially any sequenced genome.
Distinguishing a SM-like MSSM Higgs boson from SM Higgs boson ...
Indian Academy of Sciences (India)
mass for various MSSM parameters tan β and mA. We observe that at fixed CM energy, in the SM, the total cross-section increases with the increase in Higgs boson mass whereas this trend is reversed for the MSSM. The changes that occur for the MSSM in comparison to the SM predictions are quantified in terms of the ...
Provably-secure (Chinese government) SM2 and simplified SM2 key exchange protocols.
Yang, Ang; Nam, Junghyun; Kim, Moonseong; Choo, Kim-Kwang Raymond
2014-01-01
We revisit the SM2 protocol, which is widely used in Chinese commercial applications and by Chinese government agencies. Although it is by now standard practice for protocol designers to provide security proofs in widely accepted security models in order to assure protocol implementers of their security properties, the SM2 protocol does not have a proof of security. In this paper, we prove the security of the SM2 protocol in the widely accepted indistinguishability-based Bellare-Rogaway model under the elliptic curve discrete logarithm problem (ECDLP) assumption. We also present a simplified and more efficient version of the SM2 protocol with an accompanying security proof.
What do saliency models predict?
Koehler, Kathryn; Guo, Fei; Zhang, Sheng; Eckstein, Miguel P.
2014-01-01
Saliency models have been frequently used to predict eye movements made during image viewing without a specified task (free viewing). Use of a single image set to systematically compare free viewing to other tasks has never been performed. We investigated the effect of task differences on the ability of three models of saliency to predict the performance of humans viewing a novel database of 800 natural images. We introduced a novel task where 100 observers made explicit perceptual judgments about the most salient image region. Other groups of observers performed a free viewing task, saliency search task, or cued object search task. Behavior on the popular free viewing task was not best predicted by standard saliency models. Instead, the models most accurately predicted the explicit saliency selections and eye movements made while performing saliency judgments. Observers' fixations varied similarly across images for the saliency and free viewing tasks, suggesting that these two tasks are related. The variability of observers' eye movements was modulated by the task (lowest for the object search task and greatest for the free viewing and saliency search tasks) as well as the clutter content of the images. Eye movement variability in saliency search and free viewing might be also limited by inherent variation of what observers consider salient. Our results contribute to understanding the tasks and behavioral measures for which saliency models are best suited as predictors of human behavior, the relationship across various perceptual tasks, and the factors contributing to observer variability in fixational eye movements. PMID:24618107
Sorption of samarium in soils: influence of soil properties and Sm concentration
Energy Technology Data Exchange (ETDEWEB)
Ramirez-Guinart, Oriol; Salaberria, Aitor; Rigol, Anna; Vidal, Miquel [Analytical Chemistry department, Faculty of Chemistry, University of Barcelona, Marti i Franques 1-11, 08028, Barcelona (Spain)
2014-07-01
Due to the fact that barriers of Deep Geological Repositories (DGR) may lose efficiency before the radioisotopes present in the High Level Radioactive Waste (HLRW) completely decay, it is possible that, in the long-term, radioactive leachates may escape from the DGR and reach the soil and water compartments in the biosphere. Therefore, it is required to examine the interaction and mobility of radionuclides present in the HLRW, or their chemical analogues, to predict the impact of their eventual incorporation in the biosphere and to assess the derived risk. Although relevant data have been recently obtained for a few radionuclides in soils, there are still some important gaps for some radionuclides, such us for samarium (Sm). Sm is a lanthanide that, besides being considered as a natural analogue of actinides, may also be present in HLRW in the form of the radioactive isotope {sup 151}Sm. The main objective of this work was to obtain sorption data (K{sub d}) of {sup 151}Sm gathered from a set of soil samples physicochemical fully-characterized (pH, texture, cationic exchange capacity, soil solution cationic composition, organic matter, carbonate and metallic oxides content, etc.). Additionally, as an alternative for testing sorption capacity of radionuclides in soils is the use of the corresponding stable isotope or a chemical analogue, the influence of Sm concentration was also checked. To evaluate {sup 151}Sm sorption, batch assays were carried out for each soil sample, which consisted in a pre-equilibration step of 2 g of each soil with 50 ml of double deionised water, and a subsequent equilibration step with the same solution, but labelled with {sup 151}Sm. The activity of {sup 151}Sm in initial and final solutions was measured by liquid scintillation and K{sub d} ({sup 151}Sm) data were calculated. The reversibly sorbed fraction was estimated by the application of a single extraction test, with double deionised water, to soil residues coming from the previous
Mesoscale modelling of shock initiation in HMX-based explosives
Energy Technology Data Exchange (ETDEWEB)
Swift, D. C. (Damian C.); Mulford, R. N. R. (Robert N. R.); Winter, R. E. (Ron E.); Taylor, P. (Peter); Salisbury, D. A. (Darren A.); Harris, E. J. (Ernie J.)
2002-01-01
Motivation: predictive capability Want to predict initiation, detonics and performance given: {sm_bullet} Variations in composition {sm_bullet} Variations in morphology {sm_bullet}Different loading conditions Previous work on PBX and ANFO: need physically-based model rather than just mechanical calibrations
Sm-Nd isotope province of Crust of Central Asia
International Nuclear Information System (INIS)
Kovalenko, V.I.; Yarmolyhk, V.V.; Kozakov, I.K.; Kovach, V.P.; Kotov, V.B.; Sal'nikova, E.B.
1996-01-01
Results on the Sm-Nd isotope systematic studies of granitoid rocks in the Central Asia are presented. The Sm-Nd isotope provinces, characterized by various Sm-Nd model ages of intrusive granitoids, are separated. The Doriferian, Caledonian, and Hercynian provinces refer to the main ones. 7 refs., 1 fig
Excited states in 146Sm and 147Sm
International Nuclear Information System (INIS)
Kownacki, J.; Sujkowski, Z.; Hammaren, E.; Liukkonen, E.; Piiparinen, M.; Lindblad, Th.; Ryde, H.
1979-10-01
The sup(144,146)Nd(α,xn) and sup(146,148)Nd( 3 He,xn) reactions with Esub(α) = 20 - 43 MeV and E 3 sub(He) = 19 - 27 MeV are used to investigate excited states in the isotopes 146 Sm and 147 Sm. The experiments involve measurements of singles γ-ray spectra and conversion electron spectra, γ-ray angular distributions and three parameter (E sub(γ)E sub(γ) time) coincidences. From these experiments information is obtained for states with spin up to I = 13 + and I = 27/2 - , respectively, These states are interpeted within the framework of the cluster-vibration model (CVM) as well as the shell model. (author)
Arterial injury promotes medial chondrogenesis in Sm22 knockout mice.
Shen, Jianbin; Yang, Maozhou; Jiang, Hong; Ju, Donghong; Zheng, Jian-Pu; Xu, Zhonghui; Liao, Tang-Dong; Li, Li
2011-04-01
Expression of SM22 (also known as SM22alpha and transgelin), a vascular smooth muscle cells (VSMCs) marker, is down-regulated in arterial diseases involving medial osteochondrogenesis. We investigated the effect of SM22 deficiency in a mouse artery injury model to determine the role of SM22 in arterial chondrogenesis. Sm22 knockout (Sm22(-/-)) mice developed prominent medial chondrogenesis 2 weeks after carotid denudation as evidenced by the enhanced expression of chondrogenic markers including type II collagen, aggrecan, osteopontin, bone morphogenetic protein 2, and SRY-box containing gene 9 (SOX9). This was concomitant with suppression of VSMC key transcription factor myocardin and of VSMC markers such as SM α-actin and myosin heavy chain. The conversion tendency from myogenesis to chondrogenesis was also observed in primary Sm22(-/-) VSMCs and in a VSMC line after Sm22 knockdown: SM22 deficiency altered VSMC morphology with compromised stress fibre formation and increased actin dynamics. Meanwhile, the expression level of Sox9 mRNA was up-regulated while the mRNA levels of myocardin and VSMC markers were down-regulated, indicating a pro-chondrogenic transcriptional switch in SM22-deficient VSMCs. Furthermore, the increased expression of SOX9 was mediated by enhanced reactive oxygen species production and nuclear factor-κB pathway activation. These findings suggest that disruption of SM22 alters the actin cytoskeleton and promotes chondrogenic conversion of VSMCs.
Michalski, Rafał; Zygadło, Jakub
2018-04-01
Recent calculations of properties of TbAl2 GdAl2 and SmAl2 single crystals, performed with our new computation system called ATOMIC MATTERS MFA are presented. We applied localized electron approach to describe the thermal evolution of Fine Electronic Structure of Tb3+, Gd3+ and Sm3+ ions over a wide temperature range and estimate Magnetocaloric Effect (MCE). Thermomagnetic properties of TbAl2, GdAl2 and SmAl2 were calculated based on the fine electronic structure of the 4f8, 4f7 and 4f5 electronic configuration of the Tb3+ and Gd3+ and Sm3+ ions, respectively. Our calculations yielded: magnetic moment value and direction; single-crystalline magnetization curves in zero field and in external magnetic field applied in various directions m(T,Bext); the 4f-electronic components of specific heat c4f(T,Bext); and temperature dependence of the magnetic entropy and isothermal entropy change with external magnetic field - ΔS(T,Bext). The cubic universal CEF parameters values used for all CEF calculations was taken from literature and recalculated for universal cubic parameters set for the RAl2 series: A4 = +7.164 Ka04 and A6 = -1.038 Ka06. Magnetic properties were found to be anisotropic due to cubic Laves phase C15 crystal structure symmetry. These studies reveal the importance of multipolar charge interactions when describing thermomagnetic properties of real 4f electronic systems and the effectiveness of an applied self-consistent molecular field in calculations for magnetic phase transition simulation.
Electronic configurations of Sm2+ in SmS and SmB6, and electrical properties
International Nuclear Information System (INIS)
Oudet, X.
1977-01-01
In Dirac's theory the 4f shell is divided into two subshells: 4f/sub 5/2/ with six quantum states and 4f/sub 7/2/ with eight quantum states. Assuming that these two subshells can be filled up successively, we had successfully obtained the saturation magnetic moment of the garnets Ln 3 Fe 5 O 12 . This study is devoted to establish the role of the subshells in the case of SmS phase transformation under pressure P. Particularly for Sm 2+ the low spin configuration 4f/sub 5/2/ 6 4f/sub 7/2/ 0 which with six 4f electrons has a filled subshell 4f/sub 5/2/ leading to a zero magnetic moment. But as the insulator in equilibrium metal I in equilibrium M transition exhibited with this phase transition, is explained through a valence change, we first consider the localized electron model proposed in the case of Fe 3 O 4 to show that large variations of electrical resistivity of SmS and SmB 6 also can be understood through this model. A result which allows us to consider the ion Sm 2+ in both phases of SmS. 1 figure
Photoreduction of Sm3+ to Sm2+ in alcoholic solution
International Nuclear Information System (INIS)
Kusaba, M.; Tsunawaki, Y.; Nakashima, N.
1998-01-01
Full text: Lanthanide ions in solution display broad and sharp absorption bands in the UV and near UV to near IR spectral regions, respectively. The former bands are called charge-transfer (CT) or 5d 3+ , Eu 3+ and Sm 3+ . Furthermore, the photoreduction of Eu 3+ is taken place via an f' 3+ to Sm 2+ because the photoproduct, Sm 2+ , is unstable in the solution. Donohue added macrocyclic ether, 18-crown-6, to the solution in order to stabilize the Sm 2+ and then suggested the photoreduction of Sm 3+ to Sm 2+ would be induced by a multi-photon process. In this work, the transient absorption spectra of Sm 2+ has been measured by means of a nanosecond laser flash photolysis in order to clarify the photoreduction process of Sm 3+ to Sm 2+ in more detail. A sample containing a concentration of 0.01 M Sm 3+ was prepared by adding a slightly stoichiometric excess of 18-crown-6 to a methanol solution of SmCl 3 .6H 2 O. It was completely degassed by freeze-pump-thaw cycles on a high vacuum line to avoid oxidation of Sm 2+ by dissolved oxygen. The flash photolysis measurements were carried out at room temperature. The linear dependence of the concentration of Sm 2+ on the laser fluence indicated that the photoreduction of Sm 3+ to Sm 2+ in the solution occurs via the one-photon process. The photoreduction yield of Sm 3+ was determined to be 0.34 Angstroms ± 0. 09. The lifetime of Sm 2+ in air saturated sample was estimated to be 4.6 micro second
Surface effects on Sm valence in amorphous Sm alloys
International Nuclear Information System (INIS)
Krill, G.; Durand, J.; Berrada, A.; Hassanain, N.; Ravet, M.F.
1980-01-01
The results are presented of XPS and X-ray absorption measurements performed on amorphous Sm Au and La Sm Au compounds. The XPS Sm 3dsub(5/2) core level spectra in these compounds reveal that at the surface (5 to 7 A) the samarium ions present both the Sm 2+ (4f 6 ) and Sm 3+ (4f 5 ) configurations. When the concentration in samarium decreases it is shown that the Sm 2+ configuration is strongly enhanced at the surface whereas the X-ray absorption measurements indicate on the contrary that in the bulk only the Sm 3+ configuration is present. Comparison is made with similar findings in pure crystalline Sm and various crystalline rare earth compounds. (author)
Direct observation of surface-state thermal oscillations in SmB6 oscillators
Casas, Brian; Stern, Alex; Efimkin, Dmitry K.; Fisk, Zachary; Xia, Jing
2018-01-01
SmB6 is a mixed valence Kondo insulator that exhibits a sharp increase in resistance following an activated behavior that levels off and saturates below 4 K. This behavior can be explained by the proposal of SmB6 representing a new state of matter, a topological Kondo insulator, in which a Kondo gap is developed, and topologically protected surface conduction dominates low-temperature transport. Exploiting its nonlinear dynamics, a tunable SmB6 oscillator device was recently demonstrated, where a small dc current generates large oscillating voltages at frequencies from a few Hz to hundreds of MHz. This behavior was explained by a theoretical model describing the thermal and electronic dynamics of coupled surface and bulk states. However, a crucial aspect of this model, the predicted temperature oscillation in the surface state, has not been experimentally observed to date. This is largely due to the technical difficulty of detecting an oscillating temperature of the very thin surface state. Here we report direct measurements of the time-dependent surface-state temperature in SmB6 with a RuO2 microthermometer. Our results agree quantitatively with the theoretically simulated temperature waveform, and hence support the validity of the oscillator model, which will provide accurate theoretical guidance for developing future SmB6 oscillators at higher frequencies.
Iowa calibration of MEPDG performance prediction models.
2013-06-01
This study aims to improve the accuracy of AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) pavement : performance predictions for Iowa pavement systems through local calibration of MEPDG prediction models. A total of 130 : representative p...
Model complexity control for hydrologic prediction
Schoups, G.; Van de Giesen, N.C.; Savenije, H.H.G.
2008-01-01
A common concern in hydrologic modeling is overparameterization of complex models given limited and noisy data. This leads to problems of parameter nonuniqueness and equifinality, which may negatively affect prediction uncertainties. A systematic way of controlling model complexity is therefore
Octupole deformation in 149,151Sm nuclei
International Nuclear Information System (INIS)
Basu, S.; Chatterjee, J.M.; Banik, D.; Chattopadhyay, R.K.; Sharma, R.P.; Pardha Saradhi, S.K.
1994-01-01
Parity doublet states and alternating parity bands in 149,151 Sm nuclei observed in 148,150 Nd (α,3nγ) reactions at E α =37 and 35 MeV are presented here. Nineteen new transitions in 149 Sm and sixteen in 151 Sm have been observed in singles and γ-γ-t-r.f. coincidence experiments. Four alternating parity bands in 149 Sm and two in 151 Sm characterized by simplex quantum numbers are reported. The observed level schemes are interpreted in terms of octupole deformation. The energy difference due to parity splitting, collective rotational parameters, quasiparticle Routhians, single particle angular momentum alignments of bands with simplex quantum number s=±i in 149 Sm and with s=-i in 151 Sm have been calculated from the experimental results. From the E1, E2 branching ratios, the electric dipole moments are deduced to be left-angle D 0 right-angle=0.19±0.08 e fm for 149 Sm and left-angle D 0 right-angle=0.36±0.11 e fm for 151 Sm. The parameter C 1 , a function of the symmetry energy coefficient, has been estimated for 149 Sm from the liquid drop model of Strutinsky for deformed nucleus, and has been found to be C 1 =0.002003 -0.000159 +0.000087 fm
Staying Power of Churn Prediction Models
Risselada, Hans; Verhoef, Peter C.; Bijmolt, Tammo H. A.
In this paper, we study the staying power of various churn prediction models. Staying power is defined as the predictive performance of a model in a number of periods after the estimation period. We examine two methods, logit models and classification trees, both with and without applying a bagging
La/Sm/Er Cation Doping Induced Thermal Properties of SrTiO3 Perovskite.
Rittiruam, Meena; Seetawan, Tosawat; Yokhasing, Sirakan; Matarat, Korakot; Bach Thang, Phan; Kumar, Manish; Han, Jeon Geon
2016-09-06
The La/Sm/Er cations with different radii doping SrTiO3 (STO) as model Sr0.9R0.1TiO3 (R = La, Sm, Er) were designed to investigate structural characteristics and thermal properties by the molecular dynamics simulation with the Green-Kubo relation at 300-2000 K. The structural characteristics were composed of lattice constant, atoms excursion, and pair correlation function (PCF). The thermal properties consisted of heat capacity and thermal conductivity. The lattice constant of R-doped exhibited less than the STO at 300-1100 K and more than STO at 1500-2000 K, which was encouraged by atom excursion and PCF. The thermal properties was compared with literature data at 300-1100 K. In addition, the thermal properties at 1100-2000 K were predicted. It highlights that thermal conductivity tends to decrease at high temperature, due to perturbation of La, Sm, and Er, respectively.
Little Higgs model effects in γγ→ γγ
Indian Academy of Sciences (India)
Though the predictions of the standard model (SM) are in excellent agreement with experiments, there are still several theoretical problems associated with the Higgs sector of the SM, where it is widely believed that some new physics will take over at the TeV scale. One beyond the SM theory which resolves these problems ...
Comparison of Prediction-Error-Modelling Criteria
DEFF Research Database (Denmark)
Jørgensen, John Bagterp; Jørgensen, Sten Bay
2007-01-01
Single and multi-step prediction-error-methods based on the maximum likelihood and least squares criteria are compared. The prediction-error methods studied are based on predictions using the Kalman filter and Kalman predictors for a linear discrete-time stochastic state space model, which is a r...
International Nuclear Information System (INIS)
Poblete, V.; Acevedo, R.
1998-01-01
In this research work, we report the synthesis and structural characterization of the stoichiometric elpasolite Cs 2 NaSmCl 6 . The synthesis was performed under a solid state reaction in nitrogen atmosphere from the chemicals CsCl, NaCl and SmCl 3 weighted stoichiometrically. The best possible crystallization temperature was obtained using thermal studies of the type DTA/TGA (the thermal treatment was allowed to proceed for 2.5 hours at 755 Centigrade, showing a temperature gradient of 10 Centigrade/minute). The structural characterization by powder X-ray diffraction (XDR) indicates that this elpasolite belongs to the Fm 3m (O h 5 ) space group and the optimized structural parameters are as follows: a 0 = 10.8342 Armstrong, V 1271.72 Armstrong 3 , Z=4, M=651.88, D x =3.406 y D exp=3.41 ± 0.01. The profile refinement, using the Rietveld method, allowed us to fit the experimental and the calculated intensities of a total of 32 lines. The above result indicates that the condition R exp 2+ + 3Cl -1 and the counter ions filling the octahedral holes, in full agreement with anti fluorite type crystal. According to the above description, these elpasolite adopt the form (M 1/3 □ 2/3 ) 4 X 2 , where M labels the central metal, X stand for the chlorine ions and □ represent the vacancies, which may accommodate a significant amount of defects without collapsing. This experimental study provides the necessary input to test theoretical models against experimental data. (Author)
Calibration of PMIS pavement performance prediction models.
2012-02-01
Improve the accuracy of TxDOTs existing pavement performance prediction models through calibrating these models using actual field data obtained from the Pavement Management Information System (PMIS). : Ensure logical performance superiority patte...
Predictive Model Assessment for Count Data
National Research Council Canada - National Science Library
Czado, Claudia; Gneiting, Tilmann; Held, Leonhard
2007-01-01
.... In case studies, we critique count regression models for patent data, and assess the predictive performance of Bayesian age-period-cohort models for larynx cancer counts in Germany. Key words: Calibration...
Modeling and Prediction Using Stochastic Differential Equations
DEFF Research Database (Denmark)
Juhl, Rune; Møller, Jan Kloppenborg; Jørgensen, John Bagterp
2016-01-01
deterministic and can predict the future perfectly. A more realistic approach would be to allow for randomness in the model due to e.g., the model be too simple or errors in input. We describe a modeling and prediction setup which better reflects reality and suggests stochastic differential equations (SDEs......) for modeling and forecasting. It is argued that this gives models and predictions which better reflect reality. The SDE approach also offers a more adequate framework for modeling and a number of efficient tools for model building. A software package (CTSM-R) for SDE-based modeling is briefly described....... that describes the variation between subjects. The ODE setup implies that the variation for a single subject is described by a single parameter (or vector), namely the variance (covariance) of the residuals. Furthermore the prediction of the states is given as the solution to the ODEs and hence assumed...
Predictive models for arteriovenous fistula maturation.
Al Shakarchi, Julien; McGrogan, Damian; Van der Veer, Sabine; Sperrin, Matthew; Inston, Nicholas
2016-05-07
Haemodialysis (HD) is a lifeline therapy for patients with end-stage renal disease (ESRD). A critical factor in the survival of renal dialysis patients is the surgical creation of vascular access, and international guidelines recommend arteriovenous fistulas (AVF) as the gold standard of vascular access for haemodialysis. Despite this, AVFs have been associated with high failure rates. Although risk factors for AVF failure have been identified, their utility for predicting AVF failure through predictive models remains unclear. The objectives of this review are to systematically and critically assess the methodology and reporting of studies developing prognostic predictive models for AVF outcomes and assess them for suitability in clinical practice. Electronic databases were searched for studies reporting prognostic predictive models for AVF outcomes. Dual review was conducted to identify studies that reported on the development or validation of a model constructed to predict AVF outcome following creation. Data were extracted on study characteristics, risk predictors, statistical methodology, model type, as well as validation process. We included four different studies reporting five different predictive models. Parameters identified that were common to all scoring system were age and cardiovascular disease. This review has found a small number of predictive models in vascular access. The disparity between each study limits the development of a unified predictive model.
Model Predictive Control Fundamentals | Orukpe | Nigerian Journal ...
African Journals Online (AJOL)
Model Predictive Control (MPC) has developed considerably over the last two decades, both within the research control community and in industries. MPC strategy involves the optimization of a performance index with respect to some future control sequence, using predictions of the output signal based on a process model, ...
Unreachable Setpoints in Model Predictive Control
DEFF Research Database (Denmark)
Rawlings, James B.; Bonné, Dennis; Jørgensen, John Bagterp
2008-01-01
In this work, a new model predictive controller is developed that handles unreachable setpoints better than traditional model predictive control methods. The new controller induces an interesting fast/slow asymmetry in the tracking response of the system. Nominal asymptotic stability of the optim...
Wessler, Benjamin S; Lai Yh, Lana; Kramer, Whitney; Cangelosi, Michael; Raman, Gowri; Lutz, Jennifer S; Kent, David M
2015-07-01
Clinical prediction models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision making and individualize care. For patients with cardiovascular disease, there are numerous CPMs available although the extent of this literature is not well described. We conducted a systematic review for articles containing CPMs for cardiovascular disease published between January 1990 and May 2012. Cardiovascular disease includes coronary heart disease, heart failure, arrhythmias, stroke, venous thromboembolism, and peripheral vascular disease. We created a novel database and characterized CPMs based on the stage of development, population under study, performance, covariates, and predicted outcomes. There are 796 models included in this database. The number of CPMs published each year is increasing steadily over time. Seven hundred seventeen (90%) are de novo CPMs, 21 (3%) are CPM recalibrations, and 58 (7%) are CPM adaptations. This database contains CPMs for 31 index conditions, including 215 CPMs for patients with coronary artery disease, 168 CPMs for population samples, and 79 models for patients with heart failure. There are 77 distinct index/outcome pairings. Of the de novo models in this database, 450 (63%) report a c-statistic and 259 (36%) report some information on calibration. There is an abundance of CPMs available for a wide assortment of cardiovascular disease conditions, with substantial redundancy in the literature. The comparative performance of these models, the consistency of effects and risk estimates across models and the actual and potential clinical impact of this body of literature is poorly understood. © 2015 American Heart Association, Inc.
Hybrid approaches to physiologic modeling and prediction
Olengü, Nicholas O.; Reifman, Jaques
2005-05-01
This paper explores how the accuracy of a first-principles physiological model can be enhanced by integrating data-driven, "black-box" models with the original model to form a "hybrid" model system. Both linear (autoregressive) and nonlinear (neural network) data-driven techniques are separately combined with a first-principles model to predict human body core temperature. Rectal core temperature data from nine volunteers, subject to four 30/10-minute cycles of moderate exercise/rest regimen in both CONTROL and HUMID environmental conditions, are used to develop and test the approach. The results show significant improvements in prediction accuracy, with average improvements of up to 30% for prediction horizons of 20 minutes. The models developed from one subject's data are also used in the prediction of another subject's core temperature. Initial results for this approach for a 20-minute horizon show no significant improvement over the first-principles model by itself.
Evaluating the Predictive Value of Growth Prediction Models
Murphy, Daniel L.; Gaertner, Matthew N.
2014-01-01
This study evaluates four growth prediction models--projection, student growth percentile, trajectory, and transition table--commonly used to forecast (and give schools credit for) middle school students' future proficiency. Analyses focused on vertically scaled summative mathematics assessments, and two performance standards conditions (high…
Model predictive control classical, robust and stochastic
Kouvaritakis, Basil
2016-01-01
For the first time, a textbook that brings together classical predictive control with treatment of up-to-date robust and stochastic techniques. Model Predictive Control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closed-loop stability and performance. Moving on to robust predictive control, the text explains how similar guarantees may be obtained for cases in which the model describing the system dynamics is subject to additive disturbances and parametric uncertainties. Open- and closed-loop optimization are considered and the state of the art in computationally tractable methods based on uncertainty tubes presented for systems with additive model uncertainty. Finally, the tube framework is also applied to model predictive control problems involving hard or probabilistic constraints for the cases of multiplic...
A Global Model for Bankruptcy Prediction.
Alaminos, David; Del Castillo, Agustín; Fernández, Manuel Ángel
2016-01-01
The recent world financial crisis has increased the number of bankruptcies in numerous countries and has resulted in a new area of research which responds to the need to predict this phenomenon, not only at the level of individual countries, but also at a global level, offering explanations of the common characteristics shared by the affected companies. Nevertheless, few studies focus on the prediction of bankruptcies globally. In order to compensate for this lack of empirical literature, this study has used a methodological framework of logistic regression to construct predictive bankruptcy models for Asia, Europe and America, and other global models for the whole world. The objective is to construct a global model with a high capacity for predicting bankruptcy in any region of the world. The results obtained have allowed us to confirm the superiority of the global model in comparison to regional models over periods of up to three years prior to bankruptcy.
2012-01-01
VISITS The rules and conditions to be followed for visits in the SM18 Hall are laid out in the EDMS 1205328 document. No visit is allowed without prior reservation. ACCESS Special access right is needed ONLY from 7 p.m. to 7 a.m. and during week-ends. From 1 December, the current SM18 access database will be closed and a new one “SM18-OWH outside normal hours” started from scratch. Requests, via EDH SM18-OWH, will have to be duly justified. For further information, please contact Evelyne Delucinge.
Fingerprint verification prediction model in hand dermatitis.
Lee, Chew K; Chang, Choong C; Johor, Asmah; Othman, Puwira; Baba, Roshidah
2015-07-01
Hand dermatitis associated fingerprint changes is a significant problem and affects fingerprint verification processes. This study was done to develop a clinically useful prediction model for fingerprint verification in patients with hand dermatitis. A case-control study involving 100 patients with hand dermatitis. All patients verified their thumbprints against their identity card. Registered fingerprints were randomized into a model derivation and model validation group. Predictive model was derived using multiple logistic regression. Validation was done using the goodness-of-fit test. The fingerprint verification prediction model consists of a major criterion (fingerprint dystrophy area of ≥ 25%) and two minor criteria (long horizontal lines and long vertical lines). The presence of the major criterion predicts it will almost always fail verification, while presence of both minor criteria and presence of one minor criterion predict high and low risk of fingerprint verification failure, respectively. When none of the criteria are met, the fingerprint almost always passes the verification. The area under the receiver operating characteristic curve was 0.937, and the goodness-of-fit test showed agreement between the observed and expected number (P = 0.26). The derived fingerprint verification failure prediction model is validated and highly discriminatory in predicting risk of fingerprint verification in patients with hand dermatitis. © 2014 The International Society of Dermatology.
Massive Predictive Modeling using Oracle R Enterprise
CERN. Geneva
2014-01-01
R is fast becoming the lingua franca for analyzing data via statistics, visualization, and predictive analytics. For enterprise-scale data, R users have three main concerns: scalability, performance, and production deployment. Oracle's R-based technologies - Oracle R Distribution, Oracle R Enterprise, Oracle R Connector for Hadoop, and the R package ROracle - address these concerns. In this talk, we introduce Oracle's R technologies, highlighting how each enables R users to achieve scalability and performance while making production deployment of R results a natural outcome of the data analyst/scientist efforts. The focus then turns to Oracle R Enterprise with code examples using the transparency layer and embedded R execution, targeting massive predictive modeling. One goal behind massive predictive modeling is to build models per entity, such as customers, zip codes, simulations, in an effort to understand behavior and tailor predictions at the entity level. Predictions...
Predictive Model of Systemic Toxicity (SOT)
In an effort to ensure chemical safety in light of regulatory advances away from reliance on animal testing, USEPA and L’Oréal have collaborated to develop a quantitative systemic toxicity prediction model. Prediction of human systemic toxicity has proved difficult and remains a ...
Testicular Cancer Risk Prediction Models
Developing statistical models that estimate the probability of testicular cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Pancreatic Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing pancreatic cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Colorectal Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing colorectal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Prostate Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing prostate cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Bladder Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing bladder cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Esophageal Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing esophageal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Cervical Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Breast Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing breast cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Lung Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing lung cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Liver Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing liver cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Ovarian Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing ovarian cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Posterior Predictive Model Checking in Bayesian Networks
Crawford, Aaron
2014-01-01
This simulation study compared the utility of various discrepancy measures within a posterior predictive model checking (PPMC) framework for detecting different types of data-model misfit in multidimensional Bayesian network (BN) models. The investigated conditions were motivated by an applied research program utilizing an operational complex…
Predicting and Modeling RNA Architecture
Westhof, Eric; Masquida, Benoît; Jossinet, Fabrice
2011-01-01
SUMMARY A general approach for modeling the architecture of large and structured RNA molecules is described. The method exploits the modularity and the hierarchical folding of RNA architecture that is viewed as the assembly of preformed double-stranded helices defined by Watson-Crick base pairs and RNA modules maintained by non-Watson-Crick base pairs. Despite the extensive molecular neutrality observed in RNA structures, specificity in RNA folding is achieved through global constraints like lengths of helices, coaxiality of helical stacks, and structures adopted at the junctions of helices. The Assemble integrated suite of computer tools allows for sequence and structure analysis as well as interactive modeling by homology or ab initio assembly with possibilities for fitting within electronic density maps. The local key role of non-Watson-Crick pairs guides RNA architecture formation and offers metrics for assessing the accuracy of three-dimensional models in a more useful way than usual root mean square deviation (RMSD) values. PMID:20504963
Multiple Steps Prediction with Nonlinear ARX Models
Zhang, Qinghua; Ljung, Lennart
2007-01-01
NLARX (NonLinear AutoRegressive with eXogenous inputs) models are frequently used in black-box nonlinear system identication. Though it is easy to make one step ahead prediction with such models, multiple steps prediction is far from trivial. The main difficulty is that in general there is no easy way to compute the mathematical expectation of an output conditioned by past measurements. An optimal solution would require intensive numerical computations related to nonlinear filltering. The pur...
Predictability of extreme values in geophysical models
Directory of Open Access Journals (Sweden)
A. E. Sterk
2012-09-01
Full Text Available Extreme value theory in deterministic systems is concerned with unlikely large (or small values of an observable evaluated along evolutions of the system. In this paper we study the finite-time predictability of extreme values, such as convection, energy, and wind speeds, in three geophysical models. We study whether finite-time Lyapunov exponents are larger or smaller for initial conditions leading to extremes. General statements on whether extreme values are better or less predictable are not possible: the predictability of extreme values depends on the observable, the attractor of the system, and the prediction lead time.
Model complexity control for hydrologic prediction
Schoups, G.; van de Giesen, N. C.; Savenije, H. H. G.
2008-12-01
A common concern in hydrologic modeling is overparameterization of complex models given limited and noisy data. This leads to problems of parameter nonuniqueness and equifinality, which may negatively affect prediction uncertainties. A systematic way of controlling model complexity is therefore needed. We compare three model complexity control methods for hydrologic prediction, namely, cross validation (CV), Akaike's information criterion (AIC), and structural risk minimization (SRM). Results show that simulation of water flow using non-physically-based models (polynomials in this case) leads to increasingly better calibration fits as the model complexity (polynomial order) increases. However, prediction uncertainty worsens for complex non-physically-based models because of overfitting of noisy data. Incorporation of physically based constraints into the model (e.g., storage-discharge relationship) effectively bounds prediction uncertainty, even as the number of parameters increases. The conclusion is that overparameterization and equifinality do not lead to a continued increase in prediction uncertainty, as long as models are constrained by such physical principles. Complexity control of hydrologic models reduces parameter equifinality and identifies the simplest model that adequately explains the data, thereby providing a means of hydrologic generalization and classification. SRM is a promising technique for this purpose, as it (1) provides analytic upper bounds on prediction uncertainty, hence avoiding the computational burden of CV, and (2) extends the applicability of classic methods such as AIC to finite data. The main hurdle in applying SRM is the need for an a priori estimation of the complexity of the hydrologic model, as measured by its Vapnik-Chernovenkis (VC) dimension. Further research is needed in this area.
Quantifying predictive accuracy in survival models.
Lirette, Seth T; Aban, Inmaculada
2017-12-01
For time-to-event outcomes in medical research, survival models are the most appropriate to use. Unlike logistic regression models, quantifying the predictive accuracy of these models is not a trivial task. We present the classes of concordance (C) statistics and R 2 statistics often used to assess the predictive ability of these models. The discussion focuses on Harrell's C, Kent and O'Quigley's R 2 , and Royston and Sauerbrei's R 2 . We present similarities and differences between the statistics, discuss the software options from the most widely used statistical analysis packages, and give a practical example using the Worcester Heart Attack Study dataset.
Predictive power of nuclear-mass models
Directory of Open Access Journals (Sweden)
Yu. A. Litvinov
2013-12-01
Full Text Available Ten different theoretical models are tested for their predictive power in the description of nuclear masses. Two sets of experimental masses are used for the test: the older set of 2003 and the newer one of 2011. The predictive power is studied in two regions of nuclei: the global region (Z, N ≥ 8 and the heavy-nuclei region (Z ≥ 82, N ≥ 126. No clear correlation is found between the predictive power of a model and the accuracy of its description of the masses.
Return Predictability, Model Uncertainty, and Robust Investment
DEFF Research Database (Denmark)
Lukas, Manuel
Stock return predictability is subject to great uncertainty. In this paper we use the model confidence set approach to quantify uncertainty about expected utility from investment, accounting for potential return predictability. For monthly US data and six representative return prediction models, we...... find that confidence sets are very wide, change significantly with the predictor variables, and frequently include expected utilities for which the investor prefers not to invest. The latter motivates a robust investment strategy maximizing the minimal element of the confidence set. The robust investor...... allocates a much lower share of wealth to stocks compared to a standard investor....
Spatial Economics Model Predicting Transport Volume
Directory of Open Access Journals (Sweden)
Lu Bo
2016-10-01
Full Text Available It is extremely important to predict the logistics requirements in a scientific and rational way. However, in recent years, the improvement effect on the prediction method is not very significant and the traditional statistical prediction method has the defects of low precision and poor interpretation of the prediction model, which cannot only guarantee the generalization ability of the prediction model theoretically, but also cannot explain the models effectively. Therefore, in combination with the theories of the spatial economics, industrial economics, and neo-classical economics, taking city of Zhuanghe as the research object, the study identifies the leading industry that can produce a large number of cargoes, and further predicts the static logistics generation of the Zhuanghe and hinterlands. By integrating various factors that can affect the regional logistics requirements, this study established a logistics requirements potential model from the aspect of spatial economic principles, and expanded the way of logistics requirements prediction from the single statistical principles to an new area of special and regional economics.
Accuracy assessment of landslide prediction models
International Nuclear Information System (INIS)
Othman, A N; Mohd, W M N W; Noraini, S
2014-01-01
The increasing population and expansion of settlements over hilly areas has greatly increased the impact of natural disasters such as landslide. Therefore, it is important to developed models which could accurately predict landslide hazard zones. Over the years, various techniques and models have been developed to predict landslide hazard zones. The aim of this paper is to access the accuracy of landslide prediction models developed by the authors. The methodology involved the selection of study area, data acquisition, data processing and model development and also data analysis. The development of these models are based on nine different landslide inducing parameters i.e. slope, land use, lithology, soil properties, geomorphology, flow accumulation, aspect, proximity to river and proximity to road. Rank sum, rating, pairwise comparison and AHP techniques are used to determine the weights for each of the parameters used. Four (4) different models which consider different parameter combinations are developed by the authors. Results obtained are compared to landslide history and accuracies for Model 1, Model 2, Model 3 and Model 4 are 66.7, 66.7%, 60% and 22.9% respectively. From the results, rank sum, rating and pairwise comparison can be useful techniques to predict landslide hazard zones
2016-12-01
assets . SM-6 December 2015 SAR March 17, 2016 12:06:05 UNCLASSIFIED 6 Executive Summary The SM-6 program was designated as an ACAT 1C program with the...FYDP for higher Navy priorities. (Estimating) -22.6 -30.1 Adjustment for current and prior escalation. (Estimating) +6.1 +7.7 Adjustment for current and
Dancer, Peter L; Kleinplatz, Peggy J; Moser, Charles
2006-01-01
This study describes the nature of 24/7 SM slavery as practiced within the SM (sadomasochistic) community. These SM participants, who attempt to live full-time in owner-slave roles, represent a small proportion of those with SM interests. SM slaves have not been studied systematically to determine if and how they differ from other SM practitioners. An online questionnaire was used to obtain responses from individuals who self-identified as slaves. A total of 146 respondents participated, 53% female and 47% male, ranging in age from 18 to 72. We explored the depth of their relationships, how well they approximated "slavery," and how their relationships were structured to maintain distinct roles. Data showed that in long-term SM slave relationships, a power differential exists which extends beyond time-limited SM or sexual interactions. Owners and slaves often use common, daily life experiences or situations, such as the completion of household chores, money management, and morning or evening routines, to distinguish and maintain their respective roles. In addition, contrary to the perception of total submission, results revealed that slaves exercise free will when it is in their best interests to do so. These relationships were long-lasting and satisfying to the respondents.
Predictive validation of an influenza spread model.
Directory of Open Access Journals (Sweden)
Ayaz Hyder
Full Text Available BACKGROUND: Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. METHODS AND FINDINGS: We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998-1999. Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type. Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. CONCLUSIONS: Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve
Predictive Validation of an Influenza Spread Model
Hyder, Ayaz; Buckeridge, David L.; Leung, Brian
2013-01-01
Background Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. Methods and Findings We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998–1999). Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type). Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. Conclusions Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers) with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve their predictive
Energy Technology Data Exchange (ETDEWEB)
Bos, Adrie J.J., E-mail: a.j.j.bos@tudelft.n [Delft University of Technology, Mekelweg 15, NL 2629 JB Delft (Netherlands); Poolton, Nigel R.J. [Delft University of Technology, Mekelweg 15, NL 2629 JB Delft (Netherlands); Institute of Maths and Physics, Aberystwyth University, Aberystwyth SY23 3BZ (United Kingdom); Wallinga, Jakob [Netherlands Centre for Luminescence Dating, Delft University of Technology, Mekelweg 15, NL 2629 JB Delft (Netherlands); Bessiere, Aurelie [Ecole Nat. Superieure de Chimie de Paris, 11 Rue P et M Curie, 75231 Paris Cedex 05 (France); Dorenbos, Pieter [Delft University of Technology, Mekelweg 15, NL 2629 JB Delft (Netherlands)
2010-03-15
Energy-resolved optically stimulated luminescence (OSL) spectra and thermoluminescence (TL) glow curves of a powder sample of YPO{sub 4}:Ce{sup 3+},Sm{sup 3+} were measured to investigate the nature of the trapping centre and to locate its energy level relative to the valence and conduction bands of the YPO{sub 4} host. The high-temperature glow peak could unequivocally be assigned to Sm{sup 2+} (thus Sm{sup 3+} acts as an electron trap). The trap depth of this centre, as derived from the OSL excitation spectra, is in good agreement with the Dorenbos model prediction. The OSL excitation spectra also reveal excited states of Sm{sup 2+} well below the conduction band. These excited states produce a broadening of the high-temperature TL glow peak and also cause the activation energy determined by the Hoogenstraten method to underestimate the trap depth.
Review of electroweak fits of the SM and beyond, after the Higgs discovery -- with Gfitter
Baak, M
2014-01-01
We present an update of the global Standard Model (SM) fit to electroweak precision data under the assumption that the new particle discovered at the LHC is the SM Higgs boson. In this scenario all parameters entering the calculations of electroweak precision observables are known, allowing to over-constrain the SM at the electroweak scale and to assert its validity. Within the SM the W boson mass and the effective weak mixing angle can now be accurately predicted from the global fit. Their results exceed in precision the direct measurements. A determination of the S , T and U parameters, which parametrize the oblique vacuum corrections, is given. We examine the impact of the STU observables on a model of modified couplings of the Higgs boson to gauge bosons, and compare this with the corresponding analysis of LHC measurements of the signal strength of Higgs channels. Future measurements at the International Linear Collider (ILC) promise to improve significantly the experimental precision of key observables u...
Posterior predictive checking of multiple imputation models.
Nguyen, Cattram D; Lee, Katherine J; Carlin, John B
2015-07-01
Multiple imputation is gaining popularity as a strategy for handling missing data, but there is a scarcity of tools for checking imputation models, a critical step in model fitting. Posterior predictive checking (PPC) has been recommended as an imputation diagnostic. PPC involves simulating "replicated" data from the posterior predictive distribution of the model under scrutiny. Model fit is assessed by examining whether the analysis from the observed data appears typical of results obtained from the replicates produced by the model. A proposed diagnostic measure is the posterior predictive "p-value", an extreme value of which (i.e., a value close to 0 or 1) suggests a misfit between the model and the data. The aim of this study was to evaluate the performance of the posterior predictive p-value as an imputation diagnostic. Using simulation methods, we deliberately misspecified imputation models to determine whether posterior predictive p-values were effective in identifying these problems. When estimating the regression parameter of interest, we found that more extreme p-values were associated with poorer imputation model performance, although the results highlighted that traditional thresholds for classical p-values do not apply in this context. A shortcoming of the PPC method was its reduced ability to detect misspecified models with increasing amounts of missing data. Despite the limitations of posterior predictive p-values, they appear to have a valuable place in the imputer's toolkit. In addition to automated checking using p-values, we recommend imputers perform graphical checks and examine other summaries of the test quantity distribution. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Predicting Protein Secondary Structure with Markov Models
DEFF Research Database (Denmark)
Fischer, Paul; Larsen, Simon; Thomsen, Claus
2004-01-01
we are considering here, is to predict the secondary structure from the primary one. To this end we train a Markov model on training data and then use it to classify parts of unknown protein sequences as sheets, helices or coils. We show how to exploit the directional information contained...... in the Markov model for this task. Classifications that are purely based on statistical models might not always be biologically meaningful. We present combinatorial methods to incorporate biological background knowledge to enhance the prediction performance....
Energy based prediction models for building acoustics
DEFF Research Database (Denmark)
Brunskog, Jonas
2012-01-01
In order to reach robust and simplified yet accurate prediction models, energy based principle are commonly used in many fields of acoustics, especially in building acoustics. This includes simple energy flow models, the framework of statistical energy analysis (SEA) as well as more elaborated...... principles as, e.g., wave intensity analysis (WIA). The European standards for building acoustic predictions, the EN 12354 series, are based on energy flow and SEA principles. In the present paper, different energy based prediction models are discussed and critically reviewed. Special attention is placed...... on underlying basic assumptions, such as diffuse fields, high modal overlap, resonant field being dominant, etc., and the consequences of these in terms of limitations in the theory and in the practical use of the models....
Comparative Study of Bancruptcy Prediction Models
Directory of Open Access Journals (Sweden)
Isye Arieshanti
2013-09-01
Full Text Available Early indication of bancruptcy is important for a company. If companies aware of potency of their bancruptcy, they can take a preventive action to anticipate the bancruptcy. In order to detect the potency of a bancruptcy, a company can utilize a a model of bancruptcy prediction. The prediction model can be built using a machine learning methods. However, the choice of machine learning methods should be performed carefully. Because the suitability of a model depends on the problem specifically. Therefore, in this paper we perform a comparative study of several machine leaning methods for bancruptcy prediction. According to the comparative study, the performance of several models that based on machine learning methods (k-NN, fuzzy k-NN, SVM, Bagging Nearest Neighbour SVM, Multilayer Perceptron(MLP, Hybrid of MLP + Multiple Linear Regression, it can be showed that fuzzy k-NN method achieve the best performance with accuracy 77.5%
Prediction Models for Dynamic Demand Response
Energy Technology Data Exchange (ETDEWEB)
Aman, Saima; Frincu, Marc; Chelmis, Charalampos; Noor, Muhammad; Simmhan, Yogesh; Prasanna, Viktor K.
2015-11-02
As Smart Grids move closer to dynamic curtailment programs, Demand Response (DR) events will become necessary not only on fixed time intervals and weekdays predetermined by static policies, but also during changing decision periods and weekends to react to real-time demand signals. Unique challenges arise in this context vis-a-vis demand prediction and curtailment estimation and the transformation of such tasks into an automated, efficient dynamic demand response (D^{2}R) process. While existing work has concentrated on increasing the accuracy of prediction models for DR, there is a lack of studies for prediction models for D^{2}R, which we address in this paper. Our first contribution is the formal definition of D^{2}R, and the description of its challenges and requirements. Our second contribution is a feasibility analysis of very-short-term prediction of electricity consumption for D^{2}R over a diverse, large-scale dataset that includes both small residential customers and large buildings. Our third, and major contribution is a set of insights into the predictability of electricity consumption in the context of D^{2}R. Specifically, we focus on prediction models that can operate at a very small data granularity (here 15-min intervals), for both weekdays and weekends - all conditions that characterize scenarios for D^{2}R. We find that short-term time series and simple averaging models used by Independent Service Operators and utilities achieve superior prediction accuracy. We also observe that workdays are more predictable than weekends and holiday. Also, smaller customers have large variation in consumption and are less predictable than larger buildings. Key implications of our findings are that better models are required for small customers and for non-workdays, both of which are critical for D^{2}R. Also, prediction models require just few days’ worth of data indicating that small amounts of
Are animal models predictive for humans?
Directory of Open Access Journals (Sweden)
Greek Ray
2009-01-01
Full Text Available Abstract It is one of the central aims of the philosophy of science to elucidate the meanings of scientific terms and also to think critically about their application. The focus of this essay is the scientific term predict and whether there is credible evidence that animal models, especially in toxicology and pathophysiology, can be used to predict human outcomes. Whether animals can be used to predict human response to drugs and other chemicals is apparently a contentious issue. However, when one empirically analyzes animal models using scientific tools they fall far short of being able to predict human responses. This is not surprising considering what we have learned from fields such evolutionary and developmental biology, gene regulation and expression, epigenetics, complexity theory, and comparative genomics.
Evaluation of CASP8 model quality predictions
Cozzetto, Domenico
2009-01-01
The model quality assessment problem consists in the a priori estimation of the overall and per-residue accuracy of protein structure predictions. Over the past years, a number of methods have been developed to address this issue and CASP established a prediction category to evaluate their performance in 2006. In 2008 the experiment was repeated and its results are reported here. Participants were invited to infer the correctness of the protein models submitted by the registered automatic servers. Estimates could apply to both whole models and individual amino acids. Groups involved in the tertiary structure prediction categories were also asked to assign local error estimates to each predicted residue in their own models and their results are also discussed here. The correlation between the predicted and observed correctness measures was the basis of the assessment of the results. We observe that consensus-based methods still perform significantly better than those accepting single models, similarly to what was concluded in the previous edition of the experiment. © 2009 WILEY-LISS, INC.
Model predictive controller design of hydrocracker reactors
GÖKÇE, Dila
2014-01-01
This study summarizes the design of a Model Predictive Controller (MPC) in Tüpraş, İzmit Refinery Hydrocracker Unit Reactors. Hydrocracking process, in which heavy vacuum gasoil is converted into lighter and valuable products at high temperature and pressure is described briefly. Controller design description, identification and modeling studies are examined and the model variables are presented. WABT (Weighted Average Bed Temperature) equalization and conversion increase are simulate...
Multi-Model Ensemble Wake Vortex Prediction
Koerner, Stephan; Holzaepfel, Frank; Ahmad, Nash'at N.
2015-01-01
Several multi-model ensemble methods are investigated for predicting wake vortex transport and decay. This study is a joint effort between National Aeronautics and Space Administration and Deutsches Zentrum fuer Luft- und Raumfahrt to develop a multi-model ensemble capability using their wake models. An overview of different multi-model ensemble methods and their feasibility for wake applications is presented. The methods include Reliability Ensemble Averaging, Bayesian Model Averaging, and Monte Carlo Simulations. The methodologies are evaluated using data from wake vortex field experiments.
Mirmehrabi, Mahmoud; Rohani, Sohrab; Perry, Luisa
2006-04-01
A new activity coefficient model was developed from excess Gibbs free energy in the form G(ex) = cA(a) x(1)(b)...x(n)(b). The constants of the proposed model were considered to be function of solute and solvent dielectric constants, Hildebrand solubility parameters and specific volumes of solute and solvent molecules. The proposed model obeys the Gibbs-Duhem condition for activity coefficient models. To generalize the model and make it as a purely predictive model without any adjustable parameters, its constants were found using the experimental activity coefficient and physical properties of 20 vapor-liquid systems. The predictive capability of the proposed model was tested by calculating the activity coefficients of 41 binary vapor-liquid equilibrium systems and showed good agreement with the experimental data in comparison with two other predictive models, the UNIFAC and Hildebrand models. The only data used for the prediction of activity coefficients, were dielectric constants, Hildebrand solubility parameters, and specific volumes of the solute and solvent molecules. Furthermore, the proposed model was used to predict the activity coefficient of an organic compound, stearic acid, whose physical properties were available in methanol and 2-butanone. The predicted activity coefficient along with the thermal properties of the stearic acid were used to calculate the solubility of stearic acid in these two solvents and resulted in a better agreement with the experimental data compared to the UNIFAC and Hildebrand predictive models.
PREDICTIVE CAPACITY OF ARCH FAMILY MODELS
Directory of Open Access Journals (Sweden)
Raphael Silveira Amaro
2016-03-01
Full Text Available In the last decades, a remarkable number of models, variants from the Autoregressive Conditional Heteroscedastic family, have been developed and empirically tested, making extremely complex the process of choosing a particular model. This research aim to compare the predictive capacity, using the Model Confidence Set procedure, than five conditional heteroskedasticity models, considering eight different statistical probability distributions. The financial series which were used refers to the log-return series of the Bovespa index and the Dow Jones Industrial Index in the period between 27 October 2008 and 30 December 2014. The empirical evidences showed that, in general, competing models have a great homogeneity to make predictions, either for a stock market of a developed country or for a stock market of a developing country. An equivalent result can be inferred for the statistical probability distributions that were used.
Holschneider, M.; Ferrat, K.; Lesur, V.; Stolle, C.
2017-12-01
Ionospheric fields are modelled in terms of random structures taking into account a mean behaviour as well as random fluctuations which are described through two point correlation kernels. These kernels are estimated from long time series of numerical simulations from various models. These correlations are best expressed in SM system of coordinates. For the moment we limit ourselves to spatial correlations only in this coordinate system. We study the influence of various indices as possible predictor parameters for these correlations as well as seasonal effects. The various time series of ionospheric fields are stored in a HDF5 database which is accessible via a web interface. The obtained correlation structures serve as prior information to separate external and internal field components from observatory based measurements. We present a model that predicts the correlations as a function of time and some geomagnetic indices. First results of the inversion from observatory data are presented.
A revised prediction model for natural conception.
Bensdorp, Alexandra J; van der Steeg, Jan Willem; Steures, Pieternel; Habbema, J Dik F; Hompes, Peter G A; Bossuyt, Patrick M M; van der Veen, Fulco; Mol, Ben W J; Eijkemans, Marinus J C
2017-06-01
One of the aims in reproductive medicine is to differentiate between couples that have favourable chances of conceiving naturally and those that do not. Since the development of the prediction model of Hunault, characteristics of the subfertile population have changed. The objective of this analysis was to assess whether additional predictors can refine the Hunault model and extend its applicability. Consecutive subfertile couples with unexplained and mild male subfertility presenting in fertility clinics were asked to participate in a prospective cohort study. We constructed a multivariable prediction model with the predictors from the Hunault model and new potential predictors. The primary outcome, natural conception leading to an ongoing pregnancy, was observed in 1053 women of the 5184 included couples (20%). All predictors of the Hunault model were selected into the revised model plus an additional seven (woman's body mass index, cycle length, basal FSH levels, tubal status,history of previous pregnancies in the current relationship (ongoing pregnancies after natural conception, fertility treatment or miscarriages), semen volume, and semen morphology. Predictions from the revised model seem to concur better with observed pregnancy rates compared with the Hunault model; c-statistic of 0.71 (95% CI 0.69 to 0.73) compared with 0.59 (95% CI 0.57 to 0.61). Copyright © 2017. Published by Elsevier Ltd.
Zheng Wen Chen; Wu Shao Yi; Tang Sheng
2003-01-01
The EPR parameters (g factors g sub p sub a sub r sub a sub l sub l sub e sub l , g sub p sub e sub r sub p sub e sub n sub d sub i sub c sub u sub l sub a sub r sub t sub o and hyperfine structure constants A sub p sub a sub r sub a sub l sub l sub e sub l , A sub p sub e sub r sub p sub e sub n sub d sub i sub c sub u sub l sub a sub r sub t sub o) of a tetragonal (C sub 4 sub v) Sm sup 3 sup + center in CaF sub 2 and SrF sub 2 crystals are calculated by considering the crystal-field J-mixing among the ground sup 6 H sub 5 sub / sub 2 , the first excited sup 6 H sub 7 sub / sub 2 and second excited sup 6 H sub 9 sub / sub 2 state multiplets. In the calculations the free-ion and crystal-field parameters of the tetragonal Sm sup 3 sup + -F sup - center obtained from polarized laser-selective excitation spectroscopy are used. The calculated results suggest that the tetragonal Sm sup 3 sup + -F sup - center is the Sm sup 3 sup + center found by later EPR measurements. The g factors g sub p sub a sub r sub a sub...
People Capability Maturity Model. SM.
1995-09-01
of Defense (DoD) necessary to complete this work. We also thank Arlene Dukanauskas (U.S. Army, DISC4) and Joyce France (Office of the Assistant...SEI-95-MM-02 Acknowledgments include Eduardo Cadena ( Servicios en Informatica, Mexico), Nancy Chauncey (U.S. Army), Pat D. Delohery (Trecom
Modelling the predictive performance of credit scoring
Directory of Open Access Journals (Sweden)
Shi-Wei Shen
2013-07-01
Research purpose: The purpose of this empirical paper was to examine the predictive performance of credit scoring systems in Taiwan. Motivation for the study: Corporate lending remains a major business line for financial institutions. However, in light of the recent global financial crises, it has become extremely important for financial institutions to implement rigorous means of assessing clients seeking access to credit facilities. Research design, approach and method: Using a data sample of 10 349 observations drawn between 1992 and 2010, logistic regression models were utilised to examine the predictive performance of credit scoring systems. Main findings: A test of Goodness of fit demonstrated that credit scoring models that incorporated the Taiwan Corporate Credit Risk Index (TCRI, micro- and also macroeconomic variables possessed greater predictive power. This suggests that macroeconomic variables do have explanatory power for default credit risk. Practical/managerial implications: The originality in the study was that three models were developed to predict corporate firms’ defaults based on different microeconomic and macroeconomic factors such as the TCRI, asset growth rates, stock index and gross domestic product. Contribution/value-add: The study utilises different goodness of fits and receiver operator characteristics during the examination of the robustness of the predictive power of these factors.
Modelling language evolution: Examples and predictions
Gong, Tao; Shuai, Lan; Zhang, Menghan
2014-06-01
We survey recent computer modelling research of language evolution, focusing on a rule-based model simulating the lexicon-syntax coevolution and an equation-based model quantifying the language competition dynamics. We discuss four predictions of these models: (a) correlation between domain-general abilities (e.g. sequential learning) and language-specific mechanisms (e.g. word order processing); (b) coevolution of language and relevant competences (e.g. joint attention); (c) effects of cultural transmission and social structure on linguistic understandability; and (d) commonalities between linguistic, biological, and physical phenomena. All these contribute significantly to our understanding of the evolutions of language structures, individual learning mechanisms, and relevant biological and socio-cultural factors. We conclude the survey by highlighting three future directions of modelling studies of language evolution: (a) adopting experimental approaches for model evaluation; (b) consolidating empirical foundations of models; and (c) multi-disciplinary collaboration among modelling, linguistics, and other relevant disciplines.
Model Predictive Control of Sewer Networks
DEFF Research Database (Denmark)
Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik
2016-01-01
The developments in solutions for management of urban drainage are of vital importance, as the amount of sewer water from urban areas continues to increase due to the increase of the world’s population and the change in the climate conditions. How a sewer network is structured, monitored and cont...... benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control....... and controlled have thus become essential factors for efficient performance of waste water treatment plants. This paper examines methods for simplified modelling and controlling a sewer network. A practical approach to the problem is used by analysing simplified design model, which is based on the Barcelona...
Bayesian Predictive Models for Rayleigh Wind Speed
DEFF Research Database (Denmark)
Shahirinia, Amir; Hajizadeh, Amin; Yu, David C
2017-01-01
predictive model of the wind speed aggregates the non-homogeneous distributions into a single continuous distribution. Therefore, the result is able to capture the variation among the probability distributions of the wind speeds at the turbines’ locations in a wind farm. More specifically, instead of using...... a wind speed distribution whose parameters are known or estimated, the parameters are considered as random whose variations are according to probability distributions. The Bayesian predictive model for a Rayleigh which only has a single model scale parameter has been proposed. Also closed-form posterior......One of the major challenges with the increase in wind power generation is the uncertain nature of wind speed. So far the uncertainty about wind speed has been presented through probability distributions. Also the existing models that consider the uncertainty of the wind speed primarily view...
Comparison of two ordinal prediction models
DEFF Research Database (Denmark)
Kattan, Michael W; Gerds, Thomas A
2015-01-01
system (i.e. old or new), such as the level of evidence for one or more factors included in the system or the general opinions of expert clinicians. However, given the major objective of estimating prognosis on an ordinal scale, we argue that the rival staging system candidates should be compared...... on their ability to predict outcome. We sought to outline an algorithm that would compare two rival ordinal systems on their predictive ability. RESULTS: We devised an algorithm based largely on the concordance index, which is appropriate for comparing two models in their ability to rank observations. We...... demonstrate our algorithm with a prostate cancer staging system example. CONCLUSION: We have provided an algorithm for selecting the preferred staging system based on prognostic accuracy. It appears to be useful for the purpose of selecting between two ordinal prediction models....
Predictive analytics can support the ACO model.
Bradley, Paul
2012-04-01
Predictive analytics can be used to rapidly spot hard-to-identify opportunities to better manage care--a key tool in accountable care. When considering analytics models, healthcare providers should: Make value-based care a priority and act on information from analytics models. Create a road map that includes achievable steps, rather than major endeavors. Set long-term expectations and recognize that the effectiveness of an analytics program takes time, unlike revenue cycle initiatives that may show a quick return.
Predictive modeling in homogeneous catalysis: a tutorial
Maldonado, A.G.; Rothenberg, G.
2010-01-01
Predictive modeling has become a practical research tool in homogeneous catalysis. It can help to pinpoint ‘good regions’ in the catalyst space, narrowing the search for the optimal catalyst for a given reaction. Just like any other new idea, in silico catalyst optimization is accepted by some
Model predictive control of smart microgrids
DEFF Research Database (Denmark)
Hu, Jiefeng; Zhu, Jianguo; Guerrero, Josep M.
2014-01-01
required to realise high-performance of distributed generations and will realise innovative control techniques utilising model predictive control (MPC) to assist in coordinating the plethora of generation and load combinations, thus enable the effective exploitation of the clean renewable energy sources...
Feedback model predictive control by randomized algorithms
Batina, Ivo; Stoorvogel, Antonie Arij; Weiland, Siep
2001-01-01
In this paper we present a further development of an algorithm for stochastic disturbance rejection in model predictive control with input constraints based on randomized algorithms. The algorithm presented in our work can solve the problem of stochastic disturbance rejection approximately but with
A Robustly Stabilizing Model Predictive Control Algorithm
Ackmece, A. Behcet; Carson, John M., III
2007-01-01
A model predictive control (MPC) algorithm that differs from prior MPC algorithms has been developed for controlling an uncertain nonlinear system. This algorithm guarantees the resolvability of an associated finite-horizon optimal-control problem in a receding-horizon implementation.
Hierarchical Model Predictive Control for Resource Distribution
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon; Trangbæk, K; Stoustrup, Jakob
2010-01-01
This paper deals with hierarchichal model predictive control (MPC) of distributed systems. A three level hierachical approach is proposed, consisting of a high level MPC controller, a second level of so-called aggregators, controlled by an online MPC-like algorithm, and a lower level of autonomous...
Model Predictive Control based on Finite Impulse Response Models
DEFF Research Database (Denmark)
Prasath, Guru; Jørgensen, John Bagterp
2008-01-01
We develop a regularized l2 finite impulse response (FIR) predictive controller with input and input-rate constraints. Feedback is based on a simple constant output disturbance filter. The performance of the predictive controller in the face of plant-model mismatch is investigated by simulations ...
Directory of Open Access Journals (Sweden)
Eid S. A.
2016-07-01
Full Text Available The excited positive and negative parity states, potential energy surfaces, V ( ; , electromagnetic transition probabilities, B ( E 1, B ( E 2, electric monopole strength X ( E 0 = E 2 and staggering e ect, I = 1, were calculated successfully using the inter- acting boson approximation model IBA -1. The calculated values are compared to the available experimental data and show reasonable agreement. The energy ratios and contour plot of the potential energy surfaces show that 152 Sm is an X (5 candidate.
Disease prediction models and operational readiness.
Directory of Open Access Journals (Sweden)
Courtney D Corley
Full Text Available The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. We define a disease event to be a biological event with focus on the One Health paradigm. These events are characterized by evidence of infection and or disease condition. We reviewed models that attempted to predict a disease event, not merely its transmission dynamics and we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011. We searched commercial and government databases and harvested Google search results for eligible models, using terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche modeling. After removal of duplications and extraneous material, a core collection of 6,524 items was established, and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. As a result, we systematically reviewed 44 papers, and the results are presented in this analysis. We identified 44 models, classified as one or more of the following: event prediction (4, spatial (26, ecological niche (28, diagnostic or clinical (6, spread or response (9, and reviews (3. The model parameters (e.g., etiology, climatic, spatial, cultural and data sources (e.g., remote sensing, non-governmental organizations, expert opinion, epidemiological were recorded and reviewed. A component of this review is the identification of verification and validation (V&V methods applied to each model, if any V&V method was reported. All models were classified as either having undergone Some Verification or Validation method, or No Verification or Validation. We close by outlining an initial set of operational readiness level guidelines for disease prediction models based upon established Technology
Caries risk assessment models in caries prediction
Directory of Open Access Journals (Sweden)
Amila Zukanović
2013-11-01
Full Text Available Objective. The aim of this research was to assess the efficiency of different multifactor models in caries prediction. Material and methods. Data from the questionnaire and objective examination of 109 examinees was entered into the Cariogram, Previser and Caries-Risk Assessment Tool (CAT multifactor risk assessment models. Caries risk was assessed with the help of all three models for each patient, classifying them as low, medium or high-risk patients. The development of new caries lesions over a period of three years [Decay Missing Filled Tooth (DMFT increment = difference between Decay Missing Filled Tooth Surface (DMFTS index at baseline and follow up], provided for examination of the predictive capacity concerning different multifactor models. Results. The data gathered showed that different multifactor risk assessment models give significantly different results (Friedman test: Chi square = 100.073, p=0.000. Cariogram is the model which identified the majority of examinees as medium risk patients (70%. The other two models were more radical in risk assessment, giving more unfavorable risk –profiles for patients. In only 12% of the patients did the three multifactor models assess the risk in the same way. Previser and CAT gave the same results in 63% of cases – the Wilcoxon test showed that there is no statistically significant difference in caries risk assessment between these two models (Z = -1.805, p=0.071. Conclusions. Evaluation of three different multifactor caries risk assessment models (Cariogram, PreViser and CAT showed that only the Cariogram can successfully predict new caries development in 12-year-old Bosnian children.
Link Prediction via Sparse Gaussian Graphical Model
Directory of Open Access Journals (Sweden)
Liangliang Zhang
2016-01-01
Full Text Available Link prediction is an important task in complex network analysis. Traditional link prediction methods are limited by network topology and lack of node property information, which makes predicting links challenging. In this study, we address link prediction using a sparse Gaussian graphical model and demonstrate its theoretical and practical effectiveness. In theory, link prediction is executed by estimating the inverse covariance matrix of samples to overcome information limits. The proposed method was evaluated with four small and four large real-world datasets. The experimental results show that the area under the curve (AUC value obtained by the proposed method improved by an average of 3% and 12.5% compared to 13 mainstream similarity methods, respectively. This method outperforms the baseline method, and the prediction accuracy is superior to mainstream methods when using only 80% of the training set. The method also provides significantly higher AUC values when using only 60% in Dolphin and Taro datasets. Furthermore, the error rate of the proposed method demonstrates superior performance with all datasets compared to mainstream methods.
Electrostatic ion thrusters - towards predictive modeling
Energy Technology Data Exchange (ETDEWEB)
Kalentev, O.; Matyash, K.; Duras, J.; Lueskow, K.F.; Schneider, R. [Ernst-Moritz-Arndt Universitaet Greifswald, D-17489 (Germany); Koch, N. [Technische Hochschule Nuernberg Georg Simon Ohm, Kesslerplatz 12, D-90489 Nuernberg (Germany); Schirra, M. [Thales Electronic Systems GmbH, Soeflinger Strasse 100, D-89077 Ulm (Germany)
2014-02-15
The development of electrostatic ion thrusters so far has mainly been based on empirical and qualitative know-how, and on evolutionary iteration steps. This resulted in considerable effort regarding prototype design, construction and testing and therefore in significant development and qualification costs and high time demands. For future developments it is anticipated to implement simulation tools which allow for quantitative prediction of ion thruster performance, long-term behavior and space craft interaction prior to hardware design and construction. Based on integrated numerical models combining self-consistent kinetic plasma models with plasma-wall interaction modules a new quality in the description of electrostatic thrusters can be reached. These open the perspective for predictive modeling in this field. This paper reviews the application of a set of predictive numerical modeling tools on an ion thruster model of the HEMP-T (High Efficiency Multi-stage Plasma Thruster) type patented by Thales Electron Devices GmbH. (copyright 2014 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)
Characterizing Attention with Predictive Network Models.
Rosenberg, M D; Finn, E S; Scheinost, D; Constable, R T; Chun, M M
2017-04-01
Recent work shows that models based on functional connectivity in large-scale brain networks can predict individuals' attentional abilities. While being some of the first generalizable neuromarkers of cognitive function, these models also inform our basic understanding of attention, providing empirical evidence that: (i) attention is a network property of brain computation; (ii) the functional architecture that underlies attention can be measured while people are not engaged in any explicit task; and (iii) this architecture supports a general attentional ability that is common to several laboratory-based tasks and is impaired in attention deficit hyperactivity disorder (ADHD). Looking ahead, connectivity-based predictive models of attention and other cognitive abilities and behaviors may potentially improve the assessment, diagnosis, and treatment of clinical dysfunction. Copyright © 2017 Elsevier Ltd. All rights reserved.
Genetic models of homosexuality: generating testable predictions
Gavrilets, Sergey; Rice, William R
2006-01-01
Homosexuality is a common occurrence in humans and other species, yet its genetic and evolutionary basis is poorly understood. Here, we formulate and study a series of simple mathematical models for the purpose of predicting empirical patterns that can be used to determine the form of selection that leads to polymorphism of genes influencing homosexuality. Specifically, we develop theory to make contrasting predictions about the genetic characteristics of genes influencing homosexuality including: (i) chromosomal location, (ii) dominance among segregating alleles and (iii) effect sizes that distinguish between the two major models for their polymorphism: the overdominance and sexual antagonism models. We conclude that the measurement of the genetic characteristics of quantitative trait loci (QTLs) found in genomic screens for genes influencing homosexuality can be highly informative in resolving the form of natural selection maintaining their polymorphism. PMID:17015344
A statistical model for predicting muscle performance
Byerly, Diane Leslie De Caix
The objective of these studies was to develop a capability for predicting muscle performance and fatigue to be utilized for both space- and ground-based applications. To develop this predictive model, healthy test subjects performed a defined, repetitive dynamic exercise to failure using a Lordex spinal machine. Throughout the exercise, surface electromyography (SEMG) data were collected from the erector spinae using a Mega Electronics ME3000 muscle tester and surface electrodes placed on both sides of the back muscle. These data were analyzed using a 5th order Autoregressive (AR) model and statistical regression analysis. It was determined that an AR derived parameter, the mean average magnitude of AR poles, significantly correlated with the maximum number of repetitions (designated Rmax) that a test subject was able to perform. Using the mean average magnitude of AR poles, a test subject's performance to failure could be predicted as early as the sixth repetition of the exercise. This predictive model has the potential to provide a basis for improving post-space flight recovery, monitoring muscle atrophy in astronauts and assessing the effectiveness of countermeasures, monitoring astronaut performance and fatigue during Extravehicular Activity (EVA) operations, providing pre-flight assessment of the ability of an EVA crewmember to perform a given task, improving the design of training protocols and simulations for strenuous International Space Station assembly EVA, and enabling EVA work task sequences to be planned enhancing astronaut performance and safety. Potential ground-based, medical applications of the predictive model include monitoring muscle deterioration and performance resulting from illness, establishing safety guidelines in the industry for repetitive tasks, monitoring the stages of rehabilitation for muscle-related injuries sustained in sports and accidents, and enhancing athletic performance through improved training protocols while reducing
Prediction models : the right tool for the right problem
Kappen, Teus H.; Peelen, Linda M.
2016-01-01
PURPOSE OF REVIEW: Perioperative prediction models can help to improve personalized patient care by providing individual risk predictions to both patients and providers. However, the scientific literature on prediction model development and validation can be quite technical and challenging to
Precise predictions for Higgs physics in the next-to-minimal supersymmetric standard model (NMSSM)
International Nuclear Information System (INIS)
Drechsel, Peter
2016-08-01
Within this thesis a precise mass-prediction for the Higgs fields of the Next-to-Minimal Supersymmetric Standard Model (NMSSM) is obtained with Feynman-diagrammatic methods. The results are studied numerically for sample scenarios that are in agreement with current New Physics searches at the LHC. Furthermore a comparison between the obtained results and different calculations is performed as a first step in order to obtain an estimation for the theoretical uncertainties of the Higgs-mass prediction in the NMSSM. The precise mass-prediction includes the full NMSSM one-loop corrections supplemented with the dominant and sub-dominant two-loop corrections within the Minimal Supersymmetric Standard Model (MSSM). These include contributions at the orders O(α t α s , α b α s , α t 2 , α t α b ), as well as a resummation of leading and subleading logarithms from the top/scalar top sector. Higher-order corrections are essential for the NMSSM in order to provide a Higgs particle that is consistent with the available data, including the observed neutral, CP-even Higgs field with a mass of about 125 GeV. We explored the validity of the applied approximation at the two-loop level and found that it is reliable for a wide range of scenarios within the NMSSM. This is especially true for the mass of the observed (MS)SM-like Higgs field. The result of this work will be included in a future extension of the program FeynHiggs. We also compared our results with the program NMSSMCalc that also performs a Feynman-diagrammatic calculation of the Higgs-masses with a slightly different renormalization scheme. The comparison reveals that for the mass of the (MS)SM-like Higgs field the genuine NMSSM-effects induced by the choice of the renormalization scheme are by far minor compared to similar effects observed in the MSSM.
Neuro-fuzzy modeling in bankruptcy prediction
Directory of Open Access Journals (Sweden)
Vlachos D.
2003-01-01
Full Text Available For the past 30 years the problem of bankruptcy prediction had been thoroughly studied. From the paper of Altman in 1968 to the recent papers in the '90s, the progress of prediction accuracy was not satisfactory. This paper investigates an alternative modeling of the system (firm, combining neural networks and fuzzy controllers, i.e. using neuro-fuzzy models. Classical modeling is based on mathematical models that describe the behavior of the firm under consideration. The main idea of fuzzy control, on the other hand, is to build a model of a human control expert who is capable of controlling the process without thinking in a mathematical model. This control expert specifies his control action in the form of linguistic rules. These control rules are translated into the framework of fuzzy set theory providing a calculus, which can stimulate the behavior of the control expert and enhance its performance. The accuracy of the model is studied using datasets from previous research papers.
Predictive Models for Carcinogenicity and Mutagenicity ...
Mutagenicity and carcinogenicity are endpoints of major environmental and regulatory concern. These endpoints are also important targets for development of alternative methods for screening and prediction due to the large number of chemicals of potential concern and the tremendous cost (in time, money, animals) of rodent carcinogenicity bioassays. Both mutagenicity and carcinogenicity involve complex, cellular processes that are only partially understood. Advances in technologies and generation of new data will permit a much deeper understanding. In silico methods for predicting mutagenicity and rodent carcinogenicity based on chemical structural features, along with current mutagenicity and carcinogenicity data sets, have performed well for local prediction (i.e., within specific chemical classes), but are less successful for global prediction (i.e., for a broad range of chemicals). The predictivity of in silico methods can be improved by improving the quality of the data base and endpoints used for modelling. In particular, in vitro assays for clastogenicity need to be improved to reduce false positives (relative to rodent carcinogenicity) and to detect compounds that do not interact directly with DNA or have epigenetic activities. New assays emerging to complement or replace some of the standard assays include VitotoxTM, GreenScreenGC, and RadarScreen. The needs of industry and regulators to assess thousands of compounds necessitate the development of high-t
Disease Prediction Models and Operational Readiness
Energy Technology Data Exchange (ETDEWEB)
Corley, Courtney D.; Pullum, Laura L.; Hartley, David M.; Benedum, Corey M.; Noonan, Christine F.; Rabinowitz, Peter M.; Lancaster, Mary J.
2014-03-19
INTRODUCTION: The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. One of the primary goals of this research was to characterize the viability of biosurveillance models to provide operationally relevant information for decision makers to identify areas for future research. Two critical characteristics differentiate this work from other infectious disease modeling reviews. First, we reviewed models that attempted to predict the disease event, not merely its transmission dynamics. Second, we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011). Methods: We searched dozens of commercial and government databases and harvested Google search results for eligible models utilizing terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche-modeling, The publication date of search results returned are bound by the dates of coverage of each database and the date in which the search was performed, however all searching was completed by December 31, 2010. This returned 13,767 webpages and 12,152 citations. After de-duplication and removal of extraneous material, a core collection of 6,503 items was established and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. Next, PNNL’s IN-SPIRE visual analytics software was used to cross-correlate these publications with the definition for a biosurveillance model resulting in the selection of 54 documents that matched the criteria resulting Ten of these documents, However, dealt purely with disease spread models, inactivation of bacteria, or the modeling of human immune system responses to pathogens rather than predicting disease events. As a result, we systematically reviewed 44 papers and the
Nonlinear model predictive control theory and algorithms
Grüne, Lars
2017-01-01
This book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different NMPC variants. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. An introduction to nonlinear optimal control algorithms yields essential insights into how the nonlinear optimization routine—the core of any nonlinear model predictive controller—works. Accompanying software in MATLAB® and C++ (downloadable from extras.springer.com/), together with an explanatory appendix in the book itself, enables readers to perform computer experiments exploring the possibilities and limitations of NMPC. T...
A predictive model for dimensional errors in fused deposition modeling
DEFF Research Database (Denmark)
Stolfi, A.
2015-01-01
values of L (0.254 mm, 0.330 mm) was produced by comparing predicted values with external face-to-face measurements. After removing outliers, the results show that the developed two-parameter model can serve as tool for modeling the FDM dimensional behavior in a wide range of deposition angles....
A predictive model for dimensional errors in fused deposition modeling
DEFF Research Database (Denmark)
Stolfi, A.
2015-01-01
This work concerns the effect of deposition angle (a) and layer thickness (L) on the dimensional performance of FDM parts using a predictive model based on the geometrical description of the FDM filament profile. An experimental validation over the whole a range from 0° to 177° at 3° steps and two...... values of L (0.254 mm, 0.330 mm) was produced by comparing predicted values with external face-to-face measurements. After removing outliers, the results show that the developed two-parameter model can serve as tool for modeling the FDM dimensional behavior in a wide range of deposition angles....
Predictive Modeling in Actinide Chemistry and Catalysis
Energy Technology Data Exchange (ETDEWEB)
Yang, Ping [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2016-05-16
These are slides from a presentation on predictive modeling in actinide chemistry and catalysis. The following topics are covered in these slides: Structures, bonding, and reactivity (bonding can be quantified by optical probes and theory, and electronic structures and reaction mechanisms of actinide complexes); Magnetic resonance properties (transition metal catalysts with multi-nuclear centers, and NMR/EPR parameters); Moving to more complex systems (surface chemistry of nanomaterials, and interactions of ligands with nanoparticles); Path forward and conclusions.
Predictive modelling of evidence informed teaching
Zhang, Dell; Brown, C.
2017-01-01
In this paper, we analyse the questionnaire survey data collected from 79 English primary schools about the situation of evidence informed teaching, where the evidences could come from research journals or conferences. Specifically, we build a predictive model to see what external factors could help to close the gap between teachers’ belief and behaviour in evidence informed teaching, which is the first of its kind to our knowledge. The major challenge, from the data mining perspective, is th...
A Predictive Model for Cognitive Radio
2006-09-14
response in a given situation. Vadde et al. interest and produce a model for prediction of the response. have applied response surface methodology and...34 2000. [3] K. K. Vadde and V. R. Syrotiuk, "Factor interaction on service configurations to those that best meet our communication delivery in mobile ad...resulting set of configurations randomly or apply additional 2004. screening criteria. [4] K. K. Vadde , M.-V. R. Syrotiuk, and D. C. Montgomery
Tectonic predictions with mantle convection models
Coltice, Nicolas; Shephard, Grace E.
2018-04-01
Over the past 15 yr, numerical models of convection in Earth's mantle have made a leap forward: they can now produce self-consistent plate-like behaviour at the surface together with deep mantle circulation. These digital tools provide a new window into the intimate connections between plate tectonics and mantle dynamics, and can therefore be used for tectonic predictions, in principle. This contribution explores this assumption. First, initial conditions at 30, 20, 10 and 0 Ma are generated by driving a convective flow with imposed plate velocities at the surface. We then compute instantaneous mantle flows in response to the guessed temperature fields without imposing any boundary conditions. Plate boundaries self-consistently emerge at correct locations with respect to reconstructions, except for small plates close to subduction zones. As already observed for other types of instantaneous flow calculations, the structure of the top boundary layer and upper-mantle slab is the dominant character that leads to accurate predictions of surface velocities. Perturbations of the rheological parameters have little impact on the resulting surface velocities. We then compute fully dynamic model evolution from 30 and 10 to 0 Ma, without imposing plate boundaries or plate velocities. Contrary to instantaneous calculations, errors in kinematic predictions are substantial, although the plate layout and kinematics in several areas remain consistent with the expectations for the Earth. For these calculations, varying the rheological parameters makes a difference for plate boundary evolution. Also, identified errors in initial conditions contribute to first-order kinematic errors. This experiment shows that the tectonic predictions of dynamic models over 10 My are highly sensitive to uncertainties of rheological parameters and initial temperature field in comparison to instantaneous flow calculations. Indeed, the initial conditions and the rheological parameters can be good enough
Cho, S H; Kim, J; Park, B Y; Seong, P N; Kang, G H; Kim, J H; Jung, S G; Im, S K; Kim, D H
2010-09-01
Effects of the Korean carcass-grading system on carcass traits and meat quality parameters for the longissimus dorsi (LD) and semimembranosus (SM) muscles were investigated for Hanwoo steer beef. High quality grade beef cuts had significantly higher intramuscular fat content than those of low quality grade beef cuts. To establish a guaranteed system of eating quality for Hanwoo steer beef, a palatability prediction model was developed. The model development approach used canonical discriminant analysis and multivariate regression to assess effects of muscle type, cooking method, and aging treatment on Hanwoo beef quality grade. The palatability prediction model can provide Korean consumers with detailed information about expected eating quality for an individual cut, as well as provide critical information to the industry for maintaining high-quality beef production. The model is a significant advance in grading compared with the traditional carcass grading system, which applies a single quality grade to an entire carcass.
Predictive Modeling of the CDRA 4BMS
Coker, Robert F.; Knox, James C.
2016-01-01
As part of NASA's Advanced Exploration Systems (AES) program and the Life Support Systems Project (LSSP), fully predictive models of the Four Bed Molecular Sieve (4BMS) of the Carbon Dioxide Removal Assembly (CDRA) on the International Space Station (ISS) are being developed. This virtual laboratory will be used to help reduce mass, power, and volume requirements for future missions. In this paper we describe current and planned modeling developments in the area of carbon dioxide removal to support future crewed Mars missions as well as the resolution of anomalies observed in the ISS CDRA.
SM2RAIN-CCI: a new global long-term rainfall data set derived from ESA CCI soil moisture
Ciabatta, Luca; Massari, Christian; Brocca, Luca; Gruber, Alexander; Reimer, Christoph; Hahn, Sebastian; Paulik, Christoph; Dorigo, Wouter; Kidd, Richard; Wagner, Wolfgang
2018-02-01
Accurate and long-term rainfall estimates are the main inputs for several applications, from crop modeling to climate analysis. In this study, we present a new rainfall data set (SM2RAIN-CCI) obtained from the inversion of the satellite soil moisture (SM) observations derived from the ESA Climate Change Initiative (CCI) via SM2RAIN (Brocca et al., 2014). Daily rainfall estimates are generated for an 18-year long period (1998-2015), with a spatial sampling of 0.25° on a global scale, and are based on the integration of the ACTIVE and the PASSIVE ESA CCI SM data sets.The quality of the SM2RAIN-CCI rainfall data set is evaluated by comparing it with two state-of-the-art rainfall satellite products, i.e. the Tropical Measurement Mission Multi-satellite Precipitation Analysis 3B42 real-time product (TMPA 3B42RT) and the Climate Prediction Center Morphing Technique (CMORPH), and one modeled data set (ERA-Interim). A quality check is carried out on a global scale at 1° of spatial sampling and 5 days of temporal sampling by comparing these products with the gauge-based Global Precipitation Climatology Centre Full Data Daily (GPCC-FDD) product. SM2RAIN-CCI shows relatively good results in terms of correlation coefficient (median value > 0.56), root mean square difference (RMSD, median value https://doi.org/10.5281/zenodo.846259" target="_blank">https://doi.org/10.5281/zenodo.846259.
Model unspecific search for new physics in pp collision at
Indian Academy of Sciences (India)
We present the results of a model-independent analysis, which systematically scans the data taken by CMS for deviations from the Standard Model (SM) predictions. Due to ... A broad scan of three kinematic distributions in those classes is performed by identifying deviations from SM expectations, accounting for systematic ...
Energy Technology Data Exchange (ETDEWEB)
Meza-Rocha, A.N., E-mail: ameza@fis.cinvestav.mx [Departamento de Física, Universidad Autónoma Metropolitana-Iztapalapa, P.O. Box 55-534, 09340 México D.F., México (Mexico); Speghini, A. [Dipartimento di Biotecnologie, Universita di Verona and INSTM, UdR Verona, Strada Le Grazie 15, I-37314 Verona (Italy); IFAC CNR, Nello Carrara Institute of Applied Physics, MDF Lab, I-50019 Sesto Fiorentino, FI (Italy); Bettinelli, M. [Dipartimento di Biotecnologie, Universita di Verona and INSTM, UdR Verona, Strada Le Grazie 15, I-37314 Verona (Italy); Caldiño, U. [Departamento de Física, Universidad Autónoma Metropolitana-Iztapalapa, P.O. Box 55-534, 09340 México D.F., México (Mexico)
2015-11-15
A spectroscopy study of Sm{sup 3+} and Sm{sup 3+}/Eu{sup 3+} doped zinc phosphate glasses is performed through photoluminescence spectra and decay time profile measurements. Under Sm{sup 3+} excitation at 344 nm, the Sm{sup 3+} singly doped glass shows an orange global emission with x=0.579 and y=0.414 CIE1931 chromaticity coordinates, whereas the Sm{sup 3+}/Eu{sup 3+} co-doped sample exhibits orange overall emissions (x=0.581 and y=0.398, and x=0.595 and y=0.387) and reddish-orange overall emission (x=0.634 and y=0.355) upon excitations at 344, 360 and 393 nm, respectively. Such luminescence from the co-doped sample is originated by the simultaneous emission of Sm{sup 3+} and Eu{sup 3+}. Under Sm{sup 3+} excitation at 344 and 360 nm, the Eu{sup 3+} emission is sensitized and enhanced by Sm{sup 3+} through a non-radiative energy transfer process. The non-radiative nature was inferred from the shortening of the Sm{sup 3+} lifetime observed in the Sm{sup 3+}/Eu{sup 3+} co-doped sample. An analysis of the Sm{sup 3+} emission decay time profiles using the Inokuti–Hirayama model suggests that an electric quadrupole–quadrupole interaction into Sm–Eu clusters might dominate the energy transfer process, with an efficiency of 0.17. - Highlights: • Zinc phosphate glasses are optically activated with Sm{sup 3+}/Eu{sup 3+} (ZPOSmEu). • Non-radiative energy transfer Sm{sup 3+}→Eu{sup 3+} takes place in ZPOSmEu. • ZPOSmEu overall emission can be modulated with the excitation wavelength. • ZPOSmEu might be useful as orange/reddish-orange phosphor for UV-white LEDs.
The preparation of 153Sm-glucoheptonate
International Nuclear Information System (INIS)
Pu Manfei; Luo Shunzhong; Liu Yebing; Liu Guoping; Wang Wenjin; Chen Qiping; Liu Zhonglin; Fu Yibei
2003-01-01
Influences of labeling conditions on the formation of glucoheptonate (GH) labelled with 153 Sm and in vitro stability of 153 Sm-GH are investigated. The effective labelling method of GH labelled with 153 Sm is established. The results show that the labelling yield of 153 Sm-GH is more than 98% using an open nitrogen-pouring labelling system heated in boiling water bath with reactive time not less than 30 min, concentration of GH more than 2.4 x 10 - '2 mol/L. The specific activity of 153 Sm-GH is up to 14 GBq/g (GH) using 153 SmCl 3 with the high specific activity of 153 Sm (∼4.5 TBq/g(Sm)). 153 Sm-GH can remain well with its radioactive chemical purity more than 96% within 40 hours at room temperature
Predictive Modeling by the Cerebellum Improves Proprioception
Bhanpuri, Nasir H.; Okamura, Allison M.
2013-01-01
Because sensation is delayed, real-time movement control requires not just sensing, but also predicting limb position, a function hypothesized for the cerebellum. Such cerebellar predictions could contribute to perception of limb position (i.e., proprioception), particularly when a person actively moves the limb. Here we show that human cerebellar patients have proprioceptive deficits compared with controls during active movement, but not when the arm is moved passively. Furthermore, when healthy subjects move in a force field with unpredictable dynamics, they have active proprioceptive deficits similar to cerebellar patients. Therefore, muscle activity alone is likely insufficient to enhance proprioception and predictability (i.e., an internal model of the body and environment) is important for active movement to benefit proprioception. We conclude that cerebellar patients have an active proprioceptive deficit consistent with disrupted movement prediction rather than an inability to generally enhance peripheral proprioceptive signals during action and suggest that active proprioceptive deficits should be considered a fundamental cerebellar impairment of clinical importance. PMID:24005283
Prediction of Chemical Function: Model Development and ...
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
Gamma-Ray Pulsars Models and Predictions
Harding, A K
2001-01-01
Pulsed emission from gamma-ray pulsars originates inside the magnetosphere, from radiation by charged particles accelerated near the magnetic poles or in the outer gaps. In polar cap models, the high energy spectrum is cut off by magnetic pair production above an energy that is dependent on the local magnetic field strength. While most young pulsars with surface fields in the range B = 10^{12} - 10^{13} G are expected to have high energy cutoffs around several GeV, the gamma-ray spectra of old pulsars having lower surface fields may extend to 50 GeV. Although the gamma-ray emission of older pulsars is weaker, detecting pulsed emission at high energies from nearby sources would be an important confirmation of polar cap models. Outer gap models predict more gradual high-energy turnovers at around 10 GeV, but also predict an inverse Compton component extending to TeV energies. Detection of pulsed TeV emission, which would not survive attenuation at the polar caps, is thus an important test of outer gap models. N...
A prediction model for Clostridium difficile recurrence
Directory of Open Access Journals (Sweden)
Francis D. LaBarbera
2015-02-01
Full Text Available Background: Clostridium difficile infection (CDI is a growing problem in the community and hospital setting. Its incidence has been on the rise over the past two decades, and it is quickly becoming a major concern for the health care system. High rate of recurrence is one of the major hurdles in the successful treatment of C. difficile infection. There have been few studies that have looked at patterns of recurrence. The studies currently available have shown a number of risk factors associated with C. difficile recurrence (CDR; however, there is little consensus on the impact of most of the identified risk factors. Methods: Our study was a retrospective chart review of 198 patients diagnosed with CDI via Polymerase Chain Reaction (PCR from February 2009 to Jun 2013. In our study, we decided to use a machine learning algorithm called the Random Forest (RF to analyze all of the factors proposed to be associated with CDR. This model is capable of making predictions based on a large number of variables, and has outperformed numerous other models and statistical methods. Results: We came up with a model that was able to accurately predict the CDR with a sensitivity of 83.3%, specificity of 63.1%, and area under curve of 82.6%. Like other similar studies that have used the RF model, we also had very impressive results. Conclusions: We hope that in the future, machine learning algorithms, such as the RF, will see a wider application.
Artificial Neural Network Model for Predicting Compressive
Directory of Open Access Journals (Sweden)
Salim T. Yousif
2013-05-01
Full Text Available Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS, and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature. The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c is the most significant factor affecting the output of the model. The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.
Evaluating predictive models of software quality
International Nuclear Information System (INIS)
Ciaschini, V; Canaparo, M; Ronchieri, E; Salomoni, D
2014-01-01
Applications from High Energy Physics scientific community are constantly growing and implemented by a large number of developers. This implies a strong churn on the code and an associated risk of faults, which is unavoidable as long as the software undergoes active evolution. However, the necessities of production systems run counter to this. Stability and predictability are of paramount importance; in addition, a short turn-around time for the defect discovery-correction-deployment cycle is required. A way to reconcile these opposite foci is to use a software quality model to obtain an approximation of the risk before releasing a program to only deliver software with a risk lower than an agreed threshold. In this article we evaluated two quality predictive models to identify the operational risk and the quality of some software products. We applied these models to the development history of several EMI packages with intent to discover the risk factor of each product and compare it with its real history. We attempted to determine if the models reasonably maps reality for the applications under evaluation, and finally we concluded suggesting directions for further studies.
A generative model for predicting terrorist incidents
Verma, Dinesh C.; Verma, Archit; Felmlee, Diane; Pearson, Gavin; Whitaker, Roger
2017-05-01
A major concern in coalition peace-support operations is the incidence of terrorist activity. In this paper, we propose a generative model for the occurrence of the terrorist incidents, and illustrate that an increase in diversity, as measured by the number of different social groups to which that an individual belongs, is inversely correlated with the likelihood of a terrorist incident in the society. A generative model is one that can predict the likelihood of events in new contexts, as opposed to statistical models which are used to predict the future incidents based on the history of the incidents in an existing context. Generative models can be useful in planning for persistent Information Surveillance and Reconnaissance (ISR) since they allow an estimation of regions in the theater of operation where terrorist incidents may arise, and thus can be used to better allocate the assignment and deployment of ISR assets. In this paper, we present a taxonomy of terrorist incidents, identify factors related to occurrence of terrorist incidents, and provide a mathematical analysis calculating the likelihood of occurrence of terrorist incidents in three common real-life scenarios arising in peace-keeping operations
PREDICTION MODELS OF GRAIN YIELD AND CHARACTERIZATION
Directory of Open Access Journals (Sweden)
Narciso Ysac Avila Serrano
2009-06-01
Full Text Available With the objective to characterize the grain yield of five cowpea cultivars and to find linear regression models to predict it, a study was developed in La Paz, Baja California Sur, Mexico. A complete randomized blocks design was used. Simple and multivariate analyses of variance were carried out using the canonical variables to characterize the cultivars. The variables cluster per plant, pods per plant, pods per cluster, seeds weight per plant, seeds hectoliter weight, 100-seed weight, seeds length, seeds wide, seeds thickness, pods length, pods wide, pods weight, seeds per pods, and seeds weight per pods, showed significant differences (Pâ‰¤ 0.05 among cultivars. PaceÃ±o and IT90K-277-2 cultivars showed the higher seeds weight per plant. The linear regression models showed correlation coefficients â‰¥0.92. In these models, the seeds weight per plant, pods per cluster, pods per plant, cluster per plant and pods length showed significant correlations (Pâ‰¤ 0.05. In conclusion, the results showed that grain yield differ among cultivars and for its estimation, the prediction models showed determination coefficients highly dependable.
International Nuclear Information System (INIS)
Alieva, O.A.; Aliev, O.M.; Ehjnullaev, A.V.
1993-01-01
Phase equilibria in ternary system Sm-Ga-S over cross sections SmGa 2 -GaS, SmGa 2 -SmS, SmS-GaS, Sm 3 S 4 -Ga 2 S 3 , Sm-GaS have been studied for the first time using the methods of physicochemical analysis, their T-x projections were plotted. The cross sections SmGa 2 -GaS, SmGa 2 -SmS, Sm 3 S 4 -Ga 2 S 3 and SmS-GaS are quasibinary, while Sm-GaS is nonquasibinary one. Formation of two ternary compounds of the composition Sm 4 GaS 5 and Sm 5 Ga 2 S 5 was ascertained by peritectic reactions
Predictive Models for Normal Fetal Cardiac Structures.
Krishnan, Anita; Pike, Jodi I; McCarter, Robert; Fulgium, Amanda L; Wilson, Emmanuel; Donofrio, Mary T; Sable, Craig A
2016-12-01
Clinicians rely on age- and size-specific measures of cardiac structures to diagnose cardiac disease. No universally accepted normative data exist for fetal cardiac structures, and most fetal cardiac centers do not use the same standards. The aim of this study was to derive predictive models for Z scores for 13 commonly evaluated fetal cardiac structures using a large heterogeneous population of fetuses without structural cardiac defects. The study used archived normal fetal echocardiograms in representative fetuses aged 12 to 39 weeks. Thirteen cardiac dimensions were remeasured by a blinded echocardiographer from digitally stored clips. Studies with inadequate imaging views were excluded. Regression models were developed to relate each dimension to estimated gestational age (EGA) by dates, biparietal diameter, femur length, and estimated fetal weight by the Hadlock formula. Dimension outcomes were transformed (e.g., using the logarithm or square root) as necessary to meet the normality assumption. Higher order terms, quadratic or cubic, were added as needed to improve model fit. Information criteria and adjusted R 2 values were used to guide final model selection. Each Z-score equation is based on measurements derived from 296 to 414 unique fetuses. EGA yielded the best predictive model for the majority of dimensions; adjusted R 2 values ranged from 0.72 to 0.893. However, each of the other highly correlated (r > 0.94) biometric parameters was an acceptable surrogate for EGA. In most cases, the best fitting model included squared and cubic terms to introduce curvilinearity. For each dimension, models based on EGA provided the best fit for determining normal measurements of fetal cardiac structures. Nevertheless, other biometric parameters, including femur length, biparietal diameter, and estimated fetal weight provided results that were nearly as good. Comprehensive Z-score results are available on the basis of highly predictive models derived from gestational
Directory of Open Access Journals (Sweden)
Yongsheng Hong
2017-12-01
Full Text Available Soil organic matter (SOM is an important parameter of soil fertility, and visible and near-infrared (VIS–NIR spectroscopy combined with multivariate modeling techniques have provided new possibilities to estimate SOM. However, the spectral signal is strongly influenced by soil moisture (SM in the field. Interest in using spectral classification to predict soils in the moist conditions to minimize the influence of SM is growing. The objective of this study was to investigate the transferability of two approaches, SM–based cluster method with known SM (classifying the VIS–NIR spectra into different SM clusters to develop models separately, the normalized soil moisture index (NSMI–based cluster method with unknown SM (utilizing NSMI to indicate the SM and establish models separately, to predict SOM directly in moist soil spectra. One hundred and twenty one soil samples were collected from Central China, and eight SM levels were obtained for each sample through rewetting experiments. Their reflectance spectra and SOM concentrations were measured in the laboratory. Partial least square-support vector machine (PLS-SVM was employed to construct SOM prediction models. Specifically, prediction models were developed for NSMI–based clusters with unknown SM data. The models were assessed through three statistics in the processes of calibration and validation: the coefficient of determination (R2, root mean square error (RMSE and the ratio of the performance to deviation (RPD. Results showed that the variable SM led to reduced VIS–NIR reflectance nonlinearly across the entire spectral range. NSMI was an effective spectral index to indicate the SM. Classifying the VIS–NIR spectra into different SM clusters in known SM states could improve the performance of PLS-SVM models to acceptable prediction accuracies (R2cv = 0.69–0.77, RPD = 1.79–2.08. The estimation of SOM, when using the NSMI–based cluster method with unknown SM (RPD = 1.95–2
An analytical model for climatic predictions
International Nuclear Information System (INIS)
Njau, E.C.
1990-12-01
A climatic model based upon analytical expressions is presented. This model is capable of making long-range predictions of heat energy variations on regional or global scales. These variations can then be transformed into corresponding variations of some other key climatic parameters since weather and climatic changes are basically driven by differential heating and cooling around the earth. On the basis of the mathematical expressions upon which the model is based, it is shown that the global heat energy structure (and hence the associated climatic system) are characterized by zonally as well as latitudinally propagating fluctuations at frequencies downward of 0.5 day -1 . We have calculated the propagation speeds for those particular frequencies that are well documented in the literature. The calculated speeds are in excellent agreement with the measured speeds. (author). 13 refs
An Anisotropic Hardening Model for Springback Prediction
International Nuclear Information System (INIS)
Zeng, Danielle; Xia, Z. Cedric
2005-01-01
As more Advanced High-Strength Steels (AHSS) are heavily used for automotive body structures and closures panels, accurate springback prediction for these components becomes more challenging because of their rapid hardening characteristics and ability to sustain even higher stresses. In this paper, a modified Mroz hardening model is proposed to capture realistic Bauschinger effect at reverse loading, such as when material passes through die radii or drawbead during sheet metal forming process. This model accounts for material anisotropic yield surface and nonlinear isotropic/kinematic hardening behavior. Material tension/compression test data are used to accurately represent Bauschinger effect. The effectiveness of the model is demonstrated by comparison of numerical and experimental springback results for a DP600 straight U-channel test
Web tools for predictive toxicology model building.
Jeliazkova, Nina
2012-07-01
The development and use of web tools in chemistry has accumulated more than 15 years of history already. Powered by the advances in the Internet technologies, the current generation of web systems are starting to expand into areas, traditional for desktop applications. The web platforms integrate data storage, cheminformatics and data analysis tools. The ease of use and the collaborative potential of the web is compelling, despite the challenges. The topic of this review is a set of recently published web tools that facilitate predictive toxicology model building. The focus is on software platforms, offering web access to chemical structure-based methods, although some of the frameworks could also provide bioinformatics or hybrid data analysis functionalities. A number of historical and current developments are cited. In order to provide comparable assessment, the following characteristics are considered: support for workflows, descriptor calculations, visualization, modeling algorithms, data management and data sharing capabilities, availability of GUI or programmatic access and implementation details. The success of the Web is largely due to its highly decentralized, yet sufficiently interoperable model for information access. The expected future convergence between cheminformatics and bioinformatics databases provides new challenges toward management and analysis of large data sets. The web tools in predictive toxicology will likely continue to evolve toward the right mix of flexibility, performance, scalability, interoperability, sets of unique features offered, friendly user interfaces, programmatic access for advanced users, platform independence, results reproducibility, curation and crowdsourcing utilities, collaborative sharing and secure access.
[Endometrial cancer: Predictive models and clinical impact].
Bendifallah, Sofiane; Ballester, Marcos; Daraï, Emile
2017-12-01
In France, in 2015, endometrial cancer (CE) is the first gynecological cancer in terms of incidence and the fourth cause of cancer of the woman. About 8151 new cases and nearly 2179 deaths have been reported. Treatments (surgery, external radiotherapy, brachytherapy and chemotherapy) are currently delivered on the basis of an estimation of the recurrence risk, an estimation of lymph node metastasis or an estimate of survival probability. This risk is determined on the basis of prognostic factors (clinical, histological, imaging, biological) taken alone or grouped together in the form of classification systems, which are currently insufficient to account for the evolutionary and prognostic heterogeneity of endometrial cancer. For endometrial cancer, the concept of mathematical modeling and its application to prediction have developed in recent years. These biomathematical tools have opened a new era of care oriented towards the promotion of targeted therapies and personalized treatments. Many predictive models have been published to estimate the risk of recurrence and lymph node metastasis, but a tiny fraction of them is sufficiently relevant and of clinical utility. The optimization tracks are multiple and varied, suggesting the possibility in the near future of a place for these mathematical models. The development of high-throughput genomics is likely to offer a more detailed molecular characterization of the disease and its heterogeneity. Copyright © 2017 Société Française du Cancer. Published by Elsevier Masson SAS. All rights reserved.
Predictive Capability Maturity Model for computational modeling and simulation.
Energy Technology Data Exchange (ETDEWEB)
Oberkampf, William Louis; Trucano, Timothy Guy; Pilch, Martin M.
2007-10-01
The Predictive Capability Maturity Model (PCMM) is a new model that can be used to assess the level of maturity of computational modeling and simulation (M&S) efforts. The development of the model is based on both the authors experience and their analysis of similar investigations in the past. The perspective taken in this report is one of judging the usefulness of a predictive capability that relies on the numerical solution to partial differential equations to better inform and improve decision making. The review of past investigations, such as the Software Engineering Institute's Capability Maturity Model Integration and the National Aeronautics and Space Administration and Department of Defense Technology Readiness Levels, indicates that a more restricted, more interpretable method is needed to assess the maturity of an M&S effort. The PCMM addresses six contributing elements to M&S: (1) representation and geometric fidelity, (2) physics and material model fidelity, (3) code verification, (4) solution verification, (5) model validation, and (6) uncertainty quantification and sensitivity analysis. For each of these elements, attributes are identified that characterize four increasing levels of maturity. Importantly, the PCMM is a structured method for assessing the maturity of an M&S effort that is directed toward an engineering application of interest. The PCMM does not assess whether the M&S effort, the accuracy of the predictions, or the performance of the engineering system satisfies or does not satisfy specified application requirements.
Predictions of models for environmental radiological assessment
International Nuclear Information System (INIS)
Peres, Sueli da Silva; Lauria, Dejanira da Costa; Mahler, Claudio Fernando
2011-01-01
In the field of environmental impact assessment, models are used for estimating source term, environmental dispersion and transfer of radionuclides, exposure pathway, radiation dose and the risk for human beings Although it is recognized that the specific information of local data are important to improve the quality of the dose assessment results, in fact obtaining it can be very difficult and expensive. Sources of uncertainties are numerous, among which we can cite: the subjectivity of modelers, exposure scenarios and pathways, used codes and general parameters. The various models available utilize different mathematical approaches with different complexities that can result in different predictions. Thus, for the same inputs different models can produce very different outputs. This paper presents briefly the main advances in the field of environmental radiological assessment that aim to improve the reliability of the models used in the assessment of environmental radiological impact. The intercomparison exercise of model supplied incompatible results for 137 Cs and 60 Co, enhancing the need for developing reference methodologies for environmental radiological assessment that allow to confront dose estimations in a common comparison base. The results of the intercomparison exercise are present briefly. (author)
African Journals Online (AJOL)
Dr Ahmed
The tap root with branches and root hairs were buried to a depth of about 99.7cm. FULL LENGTH RESEARCH ARTICLE. STUDIES ON THE GERMINATION AND SEEDLING. CHARACTERISTICS OF THE SAVANNA. PALM TREES. *TAHIR, S.M.1 MU'AZU, S.2, KHAN, A.U2 & IORTSUUN, D.N.2. Department of Biological ...
Radiation damage in SmS, SmSsub(1-x)Psub(x) and SmB6
International Nuclear Information System (INIS)
Morillo, J.; Bordier, G.; de Novion, C.H.; Senateur, J.P.; Jun, J.
1984-08-01
Large conductivity increases under 21 K electron or neutron irradiations are observed in SmS and SmSsub(1-x)Psub(x). It is shown that they are related to Sm defects. A possible mechanism is 4f electron delocalization around radiation defects. In SmB 6 , the low temperature resistivity increase desappears under 21 K irradiation. The thermal stability of the defects is also investigated up to room temperature
Effect on Prediction when Modeling Covariates in Bayesian Nonparametric Models.
Cruz-Marcelo, Alejandro; Rosner, Gary L; Müller, Peter; Stewart, Clinton F
2013-04-01
In biomedical research, it is often of interest to characterize biologic processes giving rise to observations and to make predictions of future observations. Bayesian nonparametric methods provide a means for carrying out Bayesian inference making as few assumptions about restrictive parametric models as possible. There are several proposals in the literature for extending Bayesian nonparametric models to include dependence on covariates. Limited attention, however, has been directed to the following two aspects. In this article, we examine the effect on fitting and predictive performance of incorporating covariates in a class of Bayesian nonparametric models by one of two primary ways: either in the weights or in the locations of a discrete random probability measure. We show that different strategies for incorporating continuous covariates in Bayesian nonparametric models can result in big differences when used for prediction, even though they lead to otherwise similar posterior inferences. When one needs the predictive density, as in optimal design, and this density is a mixture, it is better to make the weights depend on the covariates. We demonstrate these points via a simulated data example and in an application in which one wants to determine the optimal dose of an anticancer drug used in pediatric oncology.
The QCD/SM Working Group: Summary Report
International Nuclear Information System (INIS)
Dobbs, M.
2004-01-01
Among the many physics processes at TeV hadron colliders, we look most eagerly for those that display signs of the Higgs boson or of new physics. We do so however amid an abundance of processes that proceed via Standard Model (SM) and in particular Quantum Chromodynamics (QCD) interactions, and that are interesting in their own right. Good knowledge of these processes is required to help us distinguish the new from the known. Their theoretical and experimental study teaches us at the same time more about QCD/SM dynamics, and thereby enables us to further improve such distinctions. This is important because it is becoming increasingly clear that the success of finding and exploring Higgs boson physics or other New Physics at the Tevatron and LHC will depend significantly on precise understanding of QCD/SM effects for many observables. To improve predictions and deepen the study of QCD/SM signals and backgrounds was therefore the ambition for our QCD/SM working group at this Les Houches workshop. Members of the working group made significant progress towards this on a number of fronts. A variety of tools were further developed, from methods to perform higher order perturbative calculations or various types of resummation, to improvements in the modeling of underlying events and parton showers. Furthermore, various precise studies of important specific processes were conducted. A significant part of the activities in Les Houches revolved around Monte Carlo simulation of collision events. A number of contributions in this report reflect the progress made in this area. At present a large number of Monte Carlo programs exist, each written with a different purpose and employing different techniques. Discussions in Les Houches revealed the need for an accessible primer on Monte Carlo programs, featuring a listing of various codes, each with a short description, but also providing a low-level explanation of the underlying methods. This primer has now been compiled and a
The QCD/SM working group: Summary report
Energy Technology Data Exchange (ETDEWEB)
Dobbs, Matt; Frixione, S.; Laenen, E.; De Roeck, A.; Tollefson, K.; Andersen, J.; Balazs, C.; Banfi, A.; Bernreuther, W.; Binoth, T.; Brandenburg, A.; Buttar, C.; Cao, C-H.; Cruz, A.; Dawson, I.; DelDuca, V.; Drollinger, V.; Dudko, L.; Eynck, T.; Field, R.; Grazzini, M.; Guillet, J.P.; Heinrich, G.; Huston, J.; Kauer, N.; Kidonakis, N.; Kulesza, A.; Lassila-Perini, K.; Magnea, L.; Mahmoudi, F.; Maina, E.; Maltoni, F.; Nolten, M.; Moraes, A.; Moretti, S.; Mrenna, S.; Nagy, Z.; Olness, F.; Puljak, I.; Ross, D.A.; Sabio-Vera, A.; Salam, G.P.; Sherstnev, A.; Si, Z.G.; Sjostrand, T.; Skands, P.; Thome, E.; Trocsanyi, Z.; Uwer, P.; Weinzierl, S.; Yuan, C.P.; Zanderighi,G.; Zanderighi, G.
2004-04-09
Among the many physics processes at TeV hadron colliders, we look most eagerly for those that display signs of the Higgs boson or of new physics. We do so however amid an abundance of processes that proceed via Standard Model (SM) and in particular Quantum Chromodynamics (QCD) interactions, and that are interesting in their own right. Good knowledge of these processes is required to help us distinguish the new from the known. Their theoretical and experimental study teaches us at the same time more about QCD/SM dynamics, and thereby enables us to further improve such distinctions. This is important because it is becoming increasingly clear that the success of finding and exploring Higgs boson physics or other New Physics at the Tevatron and LHC will depend significantly on precise understanding of QCD/SM effects for many observables. To improve predictions and deepen the study of QCD/SM signals and backgrounds was therefore the ambition for our QCD/SM working group at this Les Houches workshop. Members of the working group made significant progress towards this on a number of fronts. A variety of tools were further developed, from methods to perform higher order perturbative calculations or various types of resummation, to improvements in the modeling of underlying events and parton showers. Furthermore, various precise studies of important specific processes were conducted. A significant part of the activities in Les Houches revolved around Monte Carlo simulation of collision events. A number of contributions in this report reflect the progress made in this area. At present a large number of Monte Carlo programs exist, each written with a different purpose and employing different techniques. Discussions in Les Houches revealed the need for an accessible primer on Monte Carlo programs, featuring a listing of various codes, each with a short description, but also providing a low-level explanation of the underlying methods. This primer has now been compiled and a
Combining GPS measurements and IRI model predictions
International Nuclear Information System (INIS)
Hernandez-Pajares, M.; Juan, J.M.; Sanz, J.; Bilitza, D.
2002-01-01
The free electrons distributed in the ionosphere (between one hundred and thousands of km in height) produce a frequency-dependent effect on Global Positioning System (GPS) signals: a delay in the pseudo-orange and an advance in the carrier phase. These effects are proportional to the columnar electron density between the satellite and receiver, i.e. the integrated electron density along the ray path. Global ionospheric TEC (total electron content) maps can be obtained with GPS data from a network of ground IGS (international GPS service) reference stations with an accuracy of few TEC units. The comparison with the TOPEX TEC, mainly measured over the oceans far from the IGS stations, shows a mean bias and standard deviation of about 2 and 5 TECUs respectively. The discrepancies between the STEC predictions and the observed values show an RMS typically below 5 TECUs (which also includes the alignment code noise). he existence of a growing database 2-hourly global TEC maps and with resolution of 5x2.5 degrees in longitude and latitude can be used to improve the IRI prediction capability of the TEC. When the IRI predictions and the GPS estimations are compared for a three month period around the Solar Maximum, they are in good agreement for middle latitudes. An over-determination of IRI TEC has been found at the extreme latitudes, the IRI predictions being, typically two times higher than the GPS estimations. Finally, local fits of the IRI model can be done by tuning the SSN from STEC GPS observations
Mathematical models for indoor radon prediction
International Nuclear Information System (INIS)
Malanca, A.; Pessina, V.; Dallara, G.
1995-01-01
It is known that the indoor radon (Rn) concentration can be predicted by means of mathematical models. The simplest model relies on two variables only: the Rn source strength and the air exchange rate. In the Lawrence Berkeley Laboratory (LBL) model several environmental parameters are combined into a complex equation; besides, a correlation between the ventilation rate and the Rn entry rate from the soil is admitted. The measurements were carried out using activated carbon canisters. Seventy-five measurements of Rn concentrations were made inside two rooms placed on the second floor of a building block. One of the rooms had a single-glazed window whereas the other room had a double pane window. During three different experimental protocols, the mean Rn concentration was always higher into the room with a double-glazed window. That behavior can be accounted for by the simplest model. A further set of 450 Rn measurements was collected inside a ground-floor room with a grounding well in it. This trend maybe accounted for by the LBL model
A Predictive Maintenance Model for Railway Tracks
DEFF Research Database (Denmark)
Li, Rui; Wen, Min; Salling, Kim Bang
2015-01-01
presents a mathematical model based on Mixed Integer Programming (MIP) which is designed to optimize the predictive railway tamping activities for ballasted track for the time horizon up to four years. The objective function is setup to minimize the actual costs for the tamping machine (measured by time......). Five technical and economic aspects are taken into account to schedule tamping: (1) track degradation of the standard deviation of the longitudinal level over time; (2) track geometrical alignment; (3) track quality thresholds based on the train speed limits; (4) the dependency of the track quality...... recovery on the track quality after tamping operation and (5) Tamping machine operation factors. A Danish railway track between Odense and Fredericia with 57.2 km of length is applied for a time period of two to four years in the proposed maintenance model. The total cost can be reduced with up to 50...
Selected Topics in the searches for the SM Higgs boson in ATLAS
Bernat, P
2009-01-01
One of the main goals of the Large Hadron Collider at CERN is to try understand the dynamics responsible for the EW symmetry breaking which, in the standard model (SM) is achieved by the Higgs mechanism. Although this model has in part been corroborated by indirect measurements, one of its most striking prediction, the existence of a scalar particle has eluded experiments. Its direct search is an outstanding part of the LHC research program. At tree level, the Higgs boson mass is essentially a free parameter of the theory. However, the direct search for the SM Higgs boson at the CERN LEP e+e-collider has constrained its mass to be greater than 114.4 GeV/c2 (95% CL) and the electroweak fit suggests that the Higgs boson mass be smaller than 154 GeV/c2 (95% CL). The ATLAS experiment at LHC will be able to discover the SM Higgs boson if it exists, its discovery potential has been recently reappraised and will be presented. The channels such as the diphoton and VBF tautau at relatively low masses (m<130-140...
An Operational Model for the Prediction of Jet Blast
2012-01-09
This paper presents an operational model for the prediction of jet blast. The model was : developed based upon three modules including a jet exhaust model, jet centerline decay : model and aircraft motion model. The final analysis was compared with d...
Indian Academy of Sciences (India)
We calculate the q q ¯ pair production probability in the colour-flux tube model by considering the effect of non-Abelian interactions in the theory. Non-Abelian interactions in the colour field are time-dependent and hence should oscillate with a characteristic frequency 0 , which depends on the amplitude of the field ...
Molehin, Adebayo J; Sennoune, Souad R; Zhang, Weidong; Rojo, Juan U; Siddiqui, Arif J; Herrera, Karlie A; Johnson, Laura; Sudduth, Justin; May, Jordan; Siddiqui, Afzal A
2017-11-01
Schistosomiasis remains a major global health problem. Despite large-scale schistosomiasis control efforts, clear limitations such as possible emergence of drug resistance and reinfection rates highlight the need for an effective schistosomiasis vaccine. Schistosoma mansoni large subunit of calpain (Sm-p80)-based vaccine formulations have shown remarkable efficacy in protecting against S. mansoni challenge infections in mice and baboons. In this study, we evaluated the cross-species protective efficacy of Sm-p80 vaccine against S. japonicum and S. haematobium challenge infections in rodent models. We also elucidated the expression of Sm-p80 and Sm-p80 ortholog proteins in different developmental stages of S. mansoni, S. haematobium, and S. japonicum. Immunization with Sm-p80 vaccine reduced worm burden by 46.75% against S. japonicum challenge infection in mice. DNA prime/protein boost (1 + 1 dose administered on a single day) resulted in 26.95% reduction in worm burden in S. haematobium-hamster infection/challenge model. A balanced Th1 (IFN-γ, TNF-α, IL-2, and IL-12) and Th2 (IL-4, IgG1) type of responses were observed following vaccination in both S. japonicum and S. haematobium challenge trials and these are associated with the prophylactic efficacy of Sm-p80 vaccine. Immunohistochemistry demonstrated that Sm-p80/Sm-p80 ortholog proteins are expressed in different life cycle stages of the three major human species of schistosomes studied. The data presented in this study reinforce the potential of Sm-p80-based vaccine for both hepatic/intestinal and urogenital schistosomiasis occurring in different geographical areas of the world. Differential expression of Sm-p80/Sm-p80 protein orthologs in different life cycle makes this vaccine potentially useful in targeting different levels of infection, disease, and transmission.
DEFF Research Database (Denmark)
Jørgensen, John Bagterp; Jørgensen, Sten Bay
2007-01-01
A Prediction-error-method tailored for model based predictive control is presented. The prediction-error method studied are based on predictions using the Kalman filter and Kalman predictors for a linear discrete-time stochastic state space model. The linear discrete-time stochastic state space...... model is realized from a continuous-discrete-time linear stochastic system specified using transfer functions with time-delays. It is argued that the prediction-error criterion should be selected such that it is compatible with the objective function of the predictive controller in which the model...
Discrimination of SM-Identified Individuals
Wright, Susan
2006-01-01
The misconception that sadomasochism (SM) is violence or abusive behavior has resulted in significant persecution against SM-identified individuals in the form of discrimination, harassment and physical attacks. Historically, women who practiced SM were targeted within the feminist movement due to official opposition against sadomasochistic practices led by the National Organization for Women (NOW). Current statistics of incidents of discrimination, harassment and physical attacks against SM-...
McCarthy, Tom; The ATLAS collaboration
2018-01-01
This overview talk highlights some of the latest results by the ATLAS, CMS & LHCb collaborations. A particular focus is placed on some of the rarer Standard Model processes which have recently become accessible with the large set of proton-proton collision data collected during the successful second run of the LHC at $\\sqrt{s}$ = 13 TeV. Searches and cross-section measurements involving top quark signatures are given prominence, as well as those targeting highly boosted objects such as SM W/Z and Higgs bosons, and which consequently feature large-R jets and substructure techniques.
The neutron EDM in the SM: a review
International Nuclear Information System (INIS)
Dar, Shahida
2000-08-01
We review the status of the electric dipole moment (EDM) of neutron in the Standard Model (SM). The contributions of the strong and electroweak interactions are discussed separately. In each case the structure of Lagrangian and the sources of CP violation are specified, and subsequently calculational details are given. These two contributions to the neutron EDM exist in any extension of the SM including supersymmetry, two-doublet models as well as models with more than three generations of fermions. We briefly discuss the status of the neutron EDM in such extensions and give the relevant literature. (author)
Latest SM results with the ATLAS detector
Sforza, Federico; The ATLAS collaboration
2018-01-01
The precise measurement of SM observables is a key element of the LHC programme, both as a probe of the fundamental properties of Nature and as a tool to improve future experimental and theoretical studies. This talk reviews recent highlights in the measurement of SM processes, including the SM-Higgs properties, performed by the ATLAS collaboration.
r-Sm14 - pRSETA efficacy in experimental animals
Directory of Open Access Journals (Sweden)
Ramos Celso Raul Romero
2001-01-01
Full Text Available Previous studies carried out with Sm14 in experimental vaccination against Schistosoma mansoni or Fasciola hepatica infections were performed with recombinant Sm14 (rSm14 produced in Escherichia coli by the pGEMEX system (Promega. The rSm14 was expressed as a 40 kDa fusion protein with the major bacteriophage T7 capsid protein. Vaccination experiments with this rSm14 in animal models resulted in consistent high protective activity against S. mansoni cercariae challenge and enabled rSm14 to be included among the vaccine antigens endorsed by the World Health Organization for phase I/II clinical trials. Since the preparation of pGEMEX based rSm14 is time consuming and results in low yield for large scale production, we have tested other E. coli expression systems which would be more suitable for scale up and downstream processing. We expressed two different 6XHis-tagged Sm14 fusion proteins in a T7 promoter based plasmids. The 6XHis-tag fusions allowed rapid purification of the recombinant proteins through a Ni+2-charged resin. The resulted recombinant 18 and 16 kDa proteins were recognized by anti-Sm14 antibodies and also by antiserum against adult S. mansoni soluble secreted/excreted proteins in Western-Blot. Both proteins were also protective against S. mansoni cercariae infection to the same extent as the rSm14 expressed by the pGEMEX system.
Predictive modeling: potential application in prevention services.
Wilson, Moira L; Tumen, Sarah; Ota, Rissa; Simmers, Anthony G
2015-05-01
In 2012, the New Zealand Government announced a proposal to introduce predictive risk models (PRMs) to help professionals identify and assess children at risk of abuse or neglect as part of a preventive early intervention strategy, subject to further feasibility study and trialing. The purpose of this study is to examine technical feasibility and predictive validity of the proposal, focusing on a PRM that would draw on population-wide linked administrative data to identify newborn children who are at high priority for intensive preventive services. Data analysis was conducted in 2013 based on data collected in 2000-2012. A PRM was developed using data for children born in 2010 and externally validated for children born in 2007, examining outcomes to age 5 years. Performance of the PRM in predicting administratively recorded substantiations of maltreatment was good compared to the performance of other tools reviewed in the literature, both overall, and for indigenous Māori children. Some, but not all, of the children who go on to have recorded substantiations of maltreatment could be identified early using PRMs. PRMs should be considered as a potential complement to, rather than a replacement for, professional judgment. Trials are needed to establish whether risks can be mitigated and PRMs can make a positive contribution to frontline practice, engagement in preventive services, and outcomes for children. Deciding whether to proceed to trial requires balancing a range of considerations, including ethical and privacy risks and the risk of compounding surveillance bias. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.
Extraction of Sm from Sm-Fe alloys by the glass slag method
International Nuclear Information System (INIS)
Saito, Tetsuji; Sato, Hironori; Motegi, Tetsuichi
2005-01-01
Application of the glass slag method to the extraction of samarium from Sm-Fe alloys was studied. The magnetic Sm 2 Fe 17 phase was decomposed into the Sm oxide phase and Fe phases by the glass slag method. The Sm oxide phase was extracted by the surrounding molten glass slag material in the glass slag method. The resultant alloys consisted of neither the Sm 2 Fe 17 phase nor the Sm oxide phase. The Sm-Fe alloys were therefore separated into Sm-containing glass slag material and an Fe-B alloy by the glass slag method. This method was found to be suitable for the extraction of Sm from Sm-Fe alloys, as was the case for the extraction of neodymium from Nd-Fe-B alloys
A revision of the Sm-rich region of the Sm-Co system
International Nuclear Information System (INIS)
Yuan, Y.; Delsante, S.; Yi, J.; Borzone, G.
2010-01-01
Research highlights: → The Sm-rich side of the Sm-Co system has been experimentally reinvestigated using Differential Scanning Calorimetry (DSC), microscope and X-Ray diffraction (XRD) analyses. → Results confirm the existence of the Sm 3 Co and Sm 5 Co 2 phases. → Five invariant reactions have been determined in the Sm-rich region. - Abstract: The Sm-rich side of the Sm-Co system has been experimentally reinvestigated using the differential scanning calorimetry (DSC) method with slow rates of 1 o C/min and 0.5 o C/min as well as microscope and X-ray diffraction (XRD) analyses. Combining the experimental results from DSC, XRD and EPMA (Electron Probe MicroAnalysis) analyses, five invariant reactions have been determined and the existence of the Sm 3 Co and Sm 5 Co 2 phases confirmed. The phase diagram of Sm-Co system has been accordingly modified.
Heuristic Modeling for TRMM Lifetime Predictions
Jordan, P. S.; Sharer, P. J.; DeFazio, R. L.
1996-01-01
Analysis time for computing the expected mission lifetimes of proposed frequently maneuvering, tightly altitude constrained, Earth orbiting spacecraft have been significantly reduced by means of a heuristic modeling method implemented in a commercial-off-the-shelf spreadsheet product (QuattroPro) running on a personal computer (PC). The method uses a look-up table to estimate the maneuver frequency per month as a function of the spacecraft ballistic coefficient and the solar flux index, then computes the associated fuel use by a simple engine model. Maneuver frequency data points are produced by means of a single 1-month run of traditional mission analysis software for each of the 12 to 25 data points required for the table. As the data point computations are required only a mission design start-up and on the occasion of significant mission redesigns, the dependence on time consuming traditional modeling methods is dramatically reduced. Results to date have agreed with traditional methods to within 1 to 1.5 percent. The spreadsheet approach is applicable to a wide variety of Earth orbiting spacecraft with tight altitude constraints. It will be particularly useful to such missions as the Tropical Rainfall Measurement Mission scheduled for launch in 1997, whose mission lifetime calculations are heavily dependent on frequently revised solar flux predictions.
A Computational Model for Predicting Gas Breakdown
Gill, Zachary
2017-10-01
Pulsed-inductive discharges are a common method of producing a plasma. They provide a mechanism for quickly and efficiently generating a large volume of plasma for rapid use and are seen in applications including propulsion, fusion power, and high-power lasers. However, some common designs see a delayed response time due to the plasma forming when the magnitude of the magnetic field in the thruster is at a minimum. New designs are difficult to evaluate due to the amount of time needed to construct a new geometry and the high monetary cost of changing the power generation circuit. To more quickly evaluate new designs and better understand the shortcomings of existing designs, a computational model is developed. This model uses a modified single-electron model as the basis for a Mathematica code to determine how the energy distribution in a system changes with regards to time and location. By analyzing this energy distribution, the approximate time and location of initial plasma breakdown can be predicted. The results from this code are then compared to existing data to show its validity and shortcomings. Missouri S&T APLab.
Distributed model predictive control made easy
Negenborn, Rudy
2014-01-01
The rapid evolution of computer science, communication, and information technology has enabled the application of control techniques to systems beyond the possibilities of control theory just a decade ago. Critical infrastructures such as electricity, water, traffic and intermodal transport networks are now in the scope of control engineers. The sheer size of such large-scale systems requires the adoption of advanced distributed control approaches. Distributed model predictive control (MPC) is one of the promising control methodologies for control of such systems. This book provides a state-of-the-art overview of distributed MPC approaches, while at the same time making clear directions of research that deserve more attention. The core and rationale of 35 approaches are carefully explained. Moreover, detailed step-by-step algorithmic descriptions of each approach are provided. These features make the book a comprehensive guide both for those seeking an introduction to distributed MPC as well as for those ...
Crinière, Antoine; Dumoulin, Jean; Mevel, Laurent; Andrade-Barosso, Guillermo; Simonin, Matthieu
2015-04-01
From the past decades the monitoring of civil engineering structure became a major field of research and development process in the domains of modelling and integrated instrumentation. This increasing of interest can be attributed in part to the need of controlling the aging of such structures and on the other hand to the need to optimize maintenance costs. From this standpoint the project Cloud2SM (Cloud architecture design for Structural Monitoring with in-line Sensors and Models tasking), has been launched to develop a robust information system able to assess the long term monitoring of civil engineering structures as well as interfacing various sensors and data. The specificity of such architecture is to be based on the notion of data processing through physical or statistical models. Thus the data processing, whether material or mathematical, can be seen here as a resource of the main architecture. The project can be divided in various items: -The sensors and their measurement process: Those items provide data to the main architecture and can embed storage or computational resources. Dependent of onboard capacity and the amount of data generated it can be distinguished heavy and light sensors. - The storage resources: Based on the cloud concept this resource can store at least two types of data, raw data and processed ones. - The computational resources: This item includes embedded "pseudo real time" resources as the dedicated computer cluster or computational resources. - The models: Used for the conversion of raw data to meaningful data. Those types of resources inform the system of their needs they can be seen as independents blocks of the system. - The user interface: This item can be divided in various HMI to assess maintaining operation on the sensors or pop-up some information to the user. - The demonstrators: The structures themselves. This project follows previous research works initiated in the European project ISTIMES [1]. It includes the infrared
Donato, Silvio
2013-01-01
A search for the Standard Model (SM) Higgs boson decaying into two b jets using associated production with a Z boson decaying into a pair of neutrinos is presented at LHCP. The CMS pp collisions data-samples of 4.7 $\\text{ /fb}$ of at the center-of-mass energy of 7 TeV and 19.0 $\\text{ /fb}$ at the energy of 8 TeV have been analyzed. The techniques employed to discriminate signal from background are explained. An upper limit of 2.3 times the SM Higgs cross section at $95\\%$ of confidence level has been observed. The signal strength for $m_H=125\\text{ GeV}$ is $1.0 \\pm 0.8$ times the SM prediction.
Directory of Open Access Journals (Sweden)
Donato Silvio
2013-11-01
Full Text Available A search for the Standard Model (SM Higgs boson decaying into two b jets using associated production with a Z boson decaying into a pair of neutrinos is presented at LHCP. The CMS pp collisions data-samples of 4.7/fb of at the center-of-mass energy of 7 TeV and 19.0 /fb at the energy of 8 TeV have been analyzed. The techniques employed to discriminate signal from background are explained. An upper limit of 2.3 times the SM Higgs cross section at 95% of confidence level has been observed. The signal strength for mH = 125 GeV is 1.0 ± 0.8 times the SM prediction.
The glomerular parietal epithelial cell's responses are influenced by SM22 alpha levels.
Naito, Shokichi; Pippin, Jeffrey W; Shankland, Stuart J
2014-11-06
Studies have shown in several diseases initially affecting podocytes, that the neighboring glomerular parietal epithelial cells (PECs) are secondarily involved. The PEC response might be reparative under certain circumstances, yet injurious under others. The factors governing these are not well understood. We have shown that SM22α, an actin-binding protein considered a marker of smooth muscle differentiation, is upregulated in podocytes and PECs in several models of podocyte disease. However, the impact of SM22α levels on PECs is not known. Experimental glomerular disease, characterized by primary podocyte injury, was induced in aged-matched SM22α+/+ and SM22α-/-mice by intraperitoneal injection of sheep anti-rabbit glomeruli antibody. Immunostaining methods were employed on days 7 and 14 of disease. The number of PEC transition cells, defined as cells co-expressing a PEC protein (PAX2) and podocyte protein (Synaptopodin) was higher in diseased SM22α-/-mice compared with SM22α+/+mice. WT1 staining along Bowman's capsule is higher in diseased SM22α-/-mice. This was accompanied by increased PEC proliferation (measured by ki-67 staining), and an increase in immunostaining for the progenitor marker NCAM, in a subpopulation of PECs in diseased SM22α-/-mice. In addition, immunostaining for vimentin and alpha smooth muscle actin, markers of epithelial-to-mesenchymal transition (EMT), was lower in diseased SM22α-/-mice compared to diseased SM22α+/+mice. SM22α levels may impact how PECs respond following a primary podocyte injury in experimental glomerular disease. Absent/lower levels favor an increase in PEC transition cells and PECs expressing a progenitor marker, and a lower EMT rate compared to SM22α+/+mice, where SM22 levels are markedly increased in PECs.
Tollenaar, N.; van der Heijden, P.G.M.
2012-01-01
Using criminal population conviction histories of recent offenders, prediction mod els are developed that predict three types of criminal recidivism: general recidivism, violent recidivism and sexual recidivism. The research question is whether prediction techniques from modern statistics, data mining and machine learning provide an improvement in predictive performance over classical statistical methods, namely logistic regression and linear discrim inant analysis. These models are compared ...
Fuzzy predictive filtering in nonlinear economic model predictive control for demand response
DEFF Research Database (Denmark)
Santos, Rui Mirra; Zong, Yi; Sousa, Joao M. C.
2016-01-01
The performance of a model predictive controller (MPC) is highly correlated with the model's accuracy. This paper introduces an economic model predictive control (EMPC) scheme based on a nonlinear model, which uses a branch-and-bound tree search for solving the inherent non-convex optimization...
Rainfall estimation from soil moisture data: crash test for SM2RAIN algorithm
Brocca, Luca; Albergel, Clement; Massari, Christian; Ciabatta, Luca; Moramarco, Tommaso; de Rosnay, Patricia
2015-04-01
Soil moisture governs the partitioning of mass and energy fluxes between the land surface and the atmosphere and, hence, it represents a key variable for many applications in hydrology and earth science. In recent years, it was demonstrated that soil moisture observations from ground and satellite sensors contain important information useful for improving rainfall estimation. Indeed, soil moisture data have been used for correcting rainfall estimates from state-of-the-art satellite sensors (e.g. Crow et al., 2011), and also for improving flood prediction through a dual data assimilation approach (e.g. Massari et al., 2014; Chen et al., 2014). Brocca et al. (2013; 2014) developed a simple algorithm, called SM2RAIN, which allows estimating rainfall directly from soil moisture data. SM2RAIN has been applied successfully to in situ and satellite observations. Specifically, by using three satellite soil moisture products from ASCAT (Advanced SCATterometer), AMSR-E (Advanced Microwave Scanning Radiometer for Earth Observation) and SMOS (Soil Moisture and Ocean Salinity); it was found that the SM2RAIN-derived rainfall products are as accurate as state-of-the-art products, e.g., the real-time version of the TRMM (Tropical Rainfall Measuring Mission) product. Notwithstanding these promising results, a detailed study investigating the physical basis of the SM2RAIN algorithm, its range of applicability and its limitations on a global scale has still to be carried out. In this study, we carried out a crash test for SM2RAIN algorithm on a global scale by performing a synthetic experiment. Specifically, modelled soil moisture data are obtained from HTESSEL model (Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land) forced by ERA-Interim near-surface meteorology. Afterwards, the modelled soil moisture data are used as input into SM2RAIN algorithm for testing weather or not the resulting rainfall estimates are able to reproduce ERA-Interim rainfall data. Correlation, root
QuickSiteSM, the Argonne expedited site characterization methodology
International Nuclear Information System (INIS)
Burton, J.C.; Meyer, W.T.
1997-09-01
Expedited site characterization (ESC), developed by Argonne National Laboratory, is an interactive, integrated process emphasizing the use of existing data of sufficient quality, multiple complementary characterization methods, and on-site decision making to optimize site investigations. The Argonne ESC is the basis for the provisional ESC standard of the ASTM (American Society for Testing and Materials). QuickSite SM is the implementation package developed by Argonne to facilitate ESC of sites contaminated with hazardous wastes. At various sites, Argonne has successfully implemented QuickSite SM and demonstrated the technical superiority of the ESC process over traditional methodologies guided by statistics and random-sampling approaches. For example, in a QuickSite SM characterization of a perched aquifer at the Pantex Plant in Texas, past data and geochemical analyses of existing wells were used to develop a model for recharge and contaminant movement. With the model as a guide, closure was achieved with minimal field work
Subcoulomb fusion of 16O in odd Sm isotopes
International Nuclear Information System (INIS)
Pacheco, A.J.
1989-01-01
Cross sections for the formation of evaporation residues were measured for the reaction of 16 O with the odd 147 Sm and 149 Sm nuclei at near barrier energies. The results are well described by statistical model calculations. Fusion cross sections as a function of energy do not show any unusual behaviour that could be attributed to the presence of unpaired nucleons. An analysis based on a one-dimensional penetration model that includes the effect of permanent quadrupolar deformations shows that the extracted values of the parameter β 2 follow the systematics established by the rest of the even samarium isotopes. The dependence of β 2 on the mass of the target nucleus indicates that the influence exerted by collective aspects upon the subbarrier fusion process increases rapidly as a function of the number of neutrons added to the spherical semimagic 144 Sm nucleus. (Author) [es
Model for predicting mountain wave field uncertainties
Damiens, Florentin; Lott, François; Millet, Christophe; Plougonven, Riwal
2017-04-01
Studying the propagation of acoustic waves throughout troposphere requires knowledge of wind speed and temperature gradients from the ground up to about 10-20 km. Typical planetary boundary layers flows are known to present vertical low level shears that can interact with mountain waves, thereby triggering small-scale disturbances. Resolving these fluctuations for long-range propagation problems is, however, not feasible because of computer memory/time restrictions and thus, they need to be parameterized. When the disturbances are small enough, these fluctuations can be described by linear equations. Previous works by co-authors have shown that the critical layer dynamics that occur near the ground produces large horizontal flows and buoyancy disturbances that result in intense downslope winds and gravity wave breaking. While these phenomena manifest almost systematically for high Richardson numbers and when the boundary layer depth is relatively small compare to the mountain height, the process by which static stability affects downslope winds remains unclear. In the present work, new linear mountain gravity wave solutions are tested against numerical predictions obtained with the Weather Research and Forecasting (WRF) model. For Richardson numbers typically larger than unity, the mesoscale model is used to quantify the effect of neglected nonlinear terms on downslope winds and mountain wave patterns. At these regimes, the large downslope winds transport warm air, a so called "Foehn" effect than can impact sound propagation properties. The sensitivity of small-scale disturbances to Richardson number is quantified using two-dimensional spectral analysis. It is shown through a pilot study of subgrid scale fluctuations of boundary layer flows over realistic mountains that the cross-spectrum of mountain wave field is made up of the same components found in WRF simulations. The impact of each individual component on acoustic wave propagation is discussed in terms of
Model Predictive Control for an Industrial SAG Mill
DEFF Research Database (Denmark)
Ohan, Valeriu; Steinke, Florian; Metzger, Michael
2012-01-01
We discuss Model Predictive Control (MPC) based on ARX models and a simple lower order disturbance model. The advantage of this MPC formulation is that it has few tuning parameters and is based on an ARX prediction model that can readily be identied using standard technologies from system identic...
Dark Matter and Color Octets Beyond the Standard Model
Energy Technology Data Exchange (ETDEWEB)
Krnjaic, Gordan Zdenko [Johns Hopkins Univ., Baltimore, MD (United States)
2012-07-01
Although the Standard Model (SM) of particles and interactions has survived forty years of experimental tests, it does not provide a complete description of nature. From cosmological and astrophysical observations, it is now clear that the majority of matter in the universe is not baryonic and interacts very weakly (if at all) via non-gravitational forces. The SM does not provide a dark matter candidate, so new particles must be introduced. Furthermore, recent Tevatron results suggest that SM predictions for benchmark collider observables are in tension with experimental observations. In this thesis, we will propose extensions to the SM that address each of these issues.
Uncertainties in spatially aggregated predictions from a logistic regression model
Horssen, P.W. van; Pebesma, E.J.; Schot, P.P.
2002-01-01
This paper presents a method to assess the uncertainty of an ecological spatial prediction model which is based on logistic regression models, using data from the interpolation of explanatory predictor variables. The spatial predictions are presented as approximate 95% prediction intervals. The
Dealing with missing predictor values when applying clinical prediction models.
Janssen, K.J.; Vergouwe, Y.; Donders, A.R.T.; Harrell Jr, F.E.; Chen, Q.; Grobbee, D.E.; Moons, K.G.
2009-01-01
BACKGROUND: Prediction models combine patient characteristics and test results to predict the presence of a disease or the occurrence of an event in the future. In the event that test results (predictor) are unavailable, a strategy is needed to help users applying a prediction model to deal with
Directory of Open Access Journals (Sweden)
Jing Lu
2014-11-01
Full Text Available We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM, and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the “fuzzy rule-based neural network”, which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the “neural fuzzy inference system”, which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro and NFIS-WPM (Ave are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the “accurate” prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy.
Foundation Settlement Prediction Based on a Novel NGM Model
Directory of Open Access Journals (Sweden)
Peng-Yu Chen
2014-01-01
Full Text Available Prediction of foundation or subgrade settlement is very important during engineering construction. According to the fact that there are lots of settlement-time sequences with a nonhomogeneous index trend, a novel grey forecasting model called NGM (1,1,k,c model is proposed in this paper. With an optimized whitenization differential equation, the proposed NGM (1,1,k,c model has the property of white exponential law coincidence and can predict a pure nonhomogeneous index sequence precisely. We used two case studies to verify the predictive effect of NGM (1,1,k,c model for settlement prediction. The results show that this model can achieve excellent prediction accuracy; thus, the model is quite suitable for simulation and prediction of approximate nonhomogeneous index sequence and has excellent application value in settlement prediction.
Predictive capabilities of various constitutive models for arterial tissue.
Schroeder, Florian; Polzer, Stanislav; Slažanský, Martin; Man, Vojtěch; Skácel, Pavel
2018-02-01
Aim of this study is to validate some constitutive models by assessing their capabilities in describing and predicting uniaxial and biaxial behavior of porcine aortic tissue. 14 samples from porcine aortas were used to perform 2 uniaxial and 5 biaxial tensile tests. Transversal strains were furthermore stored for uniaxial data. The experimental data were fitted by four constitutive models: Holzapfel-Gasser-Ogden model (HGO), model based on generalized structure tensor (GST), Four-Fiber-Family model (FFF) and Microfiber model. Fitting was performed to uniaxial and biaxial data sets separately and descriptive capabilities of the models were compared. Their predictive capabilities were assessed in two ways. Firstly each model was fitted to biaxial data and its accuracy (in term of R 2 and NRMSE) in prediction of both uniaxial responses was evaluated. Then this procedure was performed conversely: each model was fitted to both uniaxial tests and its accuracy in prediction of 5 biaxial responses was observed. Descriptive capabilities of all models were excellent. In predicting uniaxial response from biaxial data, microfiber model was the most accurate while the other models showed also reasonable accuracy. Microfiber and FFF models were capable to reasonably predict biaxial responses from uniaxial data while HGO and GST models failed completely in this task. HGO and GST models are not capable to predict biaxial arterial wall behavior while FFF model is the most robust of the investigated constitutive models. Knowledge of transversal strains in uniaxial tests improves robustness of constitutive models. Copyright © 2017 Elsevier Ltd. All rights reserved.
Comparing National Water Model Inundation Predictions with Hydrodynamic Modeling
Egbert, R. J.; Shastry, A.; Aristizabal, F.; Luo, C.
2017-12-01
The National Water Model (NWM) simulates the hydrologic cycle and produces streamflow forecasts, runoff, and other variables for 2.7 million reaches along the National Hydrography Dataset for the continental United States. NWM applies Muskingum-Cunge channel routing which is based on the continuity equation. However, the momentum equation also needs to be considered to obtain better estimates of streamflow and stage in rivers especially for applications such as flood inundation mapping. Simulation Program for River NeTworks (SPRNT) is a fully dynamic model for large scale river networks that solves the full nonlinear Saint-Venant equations for 1D flow and stage height in river channel networks with non-uniform bathymetry. For the current work, the steady-state version of the SPRNT model was leveraged. An evaluation on SPRNT's and NWM's abilities to predict inundation was conducted for the record flood of Hurricane Matthew in October 2016 along the Neuse River in North Carolina. This event was known to have been influenced by backwater effects from the Hurricane's storm surge. Retrospective NWM discharge predictions were converted to stage using synthetic rating curves. The stages from both models were utilized to produce flood inundation maps using the Height Above Nearest Drainage (HAND) method which uses the local relative heights to provide a spatial representation of inundation depths. In order to validate the inundation produced by the models, Sentinel-1A synthetic aperture radar data in the VV and VH polarizations along with auxiliary data was used to produce a reference inundation map. A preliminary, binary comparison of the inundation maps to the reference, limited to the five HUC-12 areas of Goldsboro, NC, yielded that the flood inundation accuracies for NWM and SPRNT were 74.68% and 78.37%, respectively. The differences for all the relevant test statistics including accuracy, true positive rate, true negative rate, and positive predictive value were found
Predictive models for moving contact line flows
Rame, Enrique; Garoff, Stephen
2003-01-01
Modeling flows with moving contact lines poses the formidable challenge that the usual assumptions of Newtonian fluid and no-slip condition give rise to a well-known singularity. This singularity prevents one from satisfying the contact angle condition to compute the shape of the fluid-fluid interface, a crucial calculation without which design parameters such as the pressure drop needed to move an immiscible 2-fluid system through a solid matrix cannot be evaluated. Some progress has been made for low Capillary number spreading flows. Combining experimental measurements of fluid-fluid interfaces very near the moving contact line with an analytical expression for the interface shape, we can determine a parameter that forms a boundary condition for the macroscopic interface shape when Ca much les than l. This parameter, which plays the role of an "apparent" or macroscopic dynamic contact angle, is shown by the theory to depend on the system geometry through the macroscopic length scale. This theoretically established dependence on geometry allows this parameter to be "transferable" from the geometry of the measurement to any other geometry involving the same material system. Unfortunately this prediction of the theory cannot be tested on Earth.
Developmental prediction model for early alcohol initiation in Dutch adolescents
Geels, L.M.; Vink, J.M.; Beijsterveldt, C.E.M. van; Bartels, M.; Boomsma, D.I.
2013-01-01
Objective: Multiple factors predict early alcohol initiation in teenagers. Among these are genetic risk factors, childhood behavioral problems, life events, lifestyle, and family environment. We constructed a developmental prediction model for alcohol initiation below the Dutch legal drinking age
Seasonal predictability of Kiremt rainfall in coupled general circulation models
Gleixner, Stephanie; Keenlyside, Noel S.; Demissie, Teferi D.; Counillon, François; Wang, Yiguo; Viste, Ellen
2017-11-01
The Ethiopian economy and population is strongly dependent on rainfall. Operational seasonal predictions for the main rainy season (Kiremt, June-September) are based on statistical approaches with Pacific sea surface temperatures (SST) as the main predictor. Here we analyse dynamical predictions from 11 coupled general circulation models for the Kiremt seasons from 1985-2005 with the forecasts starting from the beginning of May. We find skillful predictions from three of the 11 models, but no model beats a simple linear prediction model based on the predicted Niño3.4 indices. The skill of the individual models for dynamically predicting Kiremt rainfall depends on the strength of the teleconnection between Kiremt rainfall and concurrent Pacific SST in the models. Models that do not simulate this teleconnection fail to capture the observed relationship between Kiremt rainfall and the large-scale Walker circulation.
MODELLING OF DYNAMIC SPEED LIMITS USING THE MODEL PREDICTIVE CONTROL
Directory of Open Access Journals (Sweden)
Andrey Borisovich Nikolaev
2017-09-01
Full Text Available The article considers the issues of traffic management using intelligent system “Car-Road” (IVHS, which consist of interacting intelligent vehicles (IV and intelligent roadside controllers. Vehicles are organized in convoy with small distances between them. All vehicles are assumed to be fully automated (throttle control, braking, steering. Proposed approaches for determining speed limits for traffic cars on the motorway using a model predictive control (MPC. The article proposes an approach to dynamic speed limit to minimize the downtime of vehicles in traffic.
The QCD/SM working group: Summary report
International Nuclear Information System (INIS)
Dobbs, M.; Frixione, S.; Laenen, E.; De Roeck, A.; Tollefson, K.; Andersen, J.; Balazs, C.; Banfi, A.; Berge, S.; Bernreuther, W.; Binoth, T.; Brandenburg, A.; Buttar, C.; Cao, Q-H.; Corcella, G.; Cruz, A.; Dawson, I.; Del Duca, V.; De Roeck, A.; Drollinger, V.; Dudko, L.; Eynck, T.; Field, R.; Grazzini, M.; Guillet, J.P.; Heinrich, G.; Huston, J.; Kauer, N.; Kidonakis, N.; Kulesza, A.; Lassil-Perini, K.; Magnea, L.; Mahmoudi, F.; Maina, E.; Maltoni, F.; Nolten, M.; Moraes, A.; Moretti, S.; Mrenna, S.; Nadolsky, P.; Nagy, Z.; Olness, F.; Puljak, I.; Ross, D.A.; Sabio-Vera, A.; Salam, G.P.; Sherstnev, A.; Si, Z.G.; Sjostrand, T.; Skands, P.; Thome, E.; Trocsanyi, Z.; Uwer, P.; Weinzierl, S.; Yuan, C.P.; Zanderighi, G.
2004-01-01
Among the many physics processes at TeV hadron colliders, we look most eagerly for those that display signs of the Higgs boson or of new physics. We do so however amid an abundance of processes that proceed via Standard Model (SM) and in particular Quantum Chromodynamics (QCD) interactions, and that are interesting in their own right. Good knowledge of these processes is required to help us distinguish the new from the known. Their theoretical and experimental study teaches us at the same time more about QCD/SMdynamics, and thereby enables us to further improve such distinctions. This is important because it is becoming increasingly clear that the success of finding and exploring Higgs boson physics or other New Physics at the Tevatron and LHC will depend significantly on precise understanding of QCD/SM effects for many observables. To improve predictions and deepen the study of QCD/SM signals and backgrounds was therefore the ambition for our QCD/SM working group at this Les Houches workshop. Members of the working group made significant progress towards this on a number of fronts. A variety of tools were further developed, from methods to perform higher order perturbative calculations or various types of resummation, to improvements in the modeling of underlying events and parton showers. Furthermore, various precise studies of important specific processes were conducted. A significant part of the activities in Les Houches revolved around Monte Carlo simulation of collision events. A number of contributions in this report reflect the progress made in this area. At present a large number of Monte Carlo programs exist, each written with a different purpose and employing different techniques. Discussions in Les Houches revealed the need for an accessible primer on Monte Carlo programs, featuring a listing of various codes, each with a short description, but also providing a low-level explanation of the underlying methods. This primer has now been compiled and a
Energy Technology Data Exchange (ETDEWEB)
Crane, Nathan Karl
2013-07-01
Presented in this document are the theoretical aspects of capabilities contained in the Sierra/SM code. This manuscript serves as an ideal starting point for understanding the theoretical foundations of the code. For a comprehensive study of these capabilities, the reader is encouraged to explore the many references to scientific articles and textbooks contained in this manual. It is important to point out that some capabilities are still in development and may not be presented in this document. Further updates to this manuscript will be made as these capabilites come closer to production level.
Predictability in models of the atmospheric circulation
Houtekamer, P.L.
1992-01-01
It will be clear from the above discussions that skill forecasts are still in their infancy. Operational skill predictions do not exist. One is still struggling to prove that skill predictions, at any range, have any quality at all. It is not clear what the statistics of the analysis error
Required Collaborative Work in Online Courses: A Predictive Modeling Approach
Smith, Marlene A.; Kellogg, Deborah L.
2015-01-01
This article describes a predictive model that assesses whether a student will have greater perceived learning in group assignments or in individual work. The model produces correct classifications 87.5% of the time. The research is notable in that it is the first in the education literature to adopt a predictive modeling methodology using data…
Models for predicting compressive strength and water absorption of ...
African Journals Online (AJOL)
This work presents a mathematical model for predicting the compressive strength and water absorption of laterite-quarry dust cement block using augmented Scheffe's simplex lattice design. The statistical models developed can predict the mix proportion that will yield the desired property. The models were tested for lack of ...
African Journals Online (AJOL)
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VCl3, SnCl2⋅2H2O, Sm(NO3)2⋅6H2O and SmCl3⋅6H2O) with slightly excess amounts of nitrilotriacetic acid in DMF resulted in the ... and Sm(III) ions through its three oxygen atoms and the nitrogen atom. The same tetra-dentate coordination ...
Alligné, S.; Nicolet, C.; Béguin, A.; Landry, C.; Gomes, J.; Avellan, F.
2017-04-01
The prediction of pressure and output power fluctuations amplitudes on Francis turbine prototype is a challenge for hydro-equipment industry since it is subjected to guarantees to ensure smooth and reliable operation of the hydro units. The European FP7 research project Hyperbole aims to setup a methodology to transpose the pressure fluctuations induced by the cavitation vortex rope from the reduced scale model to the prototype generating units. A Francis turbine unit of 444MW with a specific speed value of ν = 0.29, is considered as case study. A SIMSEN model of the power station including electrical system, controllers, rotating train and hydraulic system with transposed draft tube excitation sources is setup. Based on this model, a frequency analysis of the hydroelectric system is performed for all technologies to analyse potential interactions between hydraulic excitation sources and electrical components. Three technologies have been compared: the classical fixed speed configuration with Synchronous Machine (SM) and the two variable speed technologies which are Doubly Fed Induction Machine (DFIM) and Full Size Frequency Converter (FSFC).
Regression models for predicting anthropometric measurements of ...
African Journals Online (AJOL)
measure anthropometric dimensions to predict difficult-to-measure dimensions required for ergonomic design of school furniture. A total of 143 students aged between 16 and 18 years from eight public secondary schools in Ogbomoso, Nigeria ...
FINITE ELEMENT MODEL FOR PREDICTING RESIDUAL ...
African Journals Online (AJOL)
direction (σx) had a maximum value of 375MPa (tensile) and minimum value of ... These results shows that the residual stresses obtained by prediction from the finite element method are in fair agreement with the experimental results.
Probabilistic Modeling and Visualization for Bankruptcy Prediction
DEFF Research Database (Denmark)
Antunes, Francisco; Ribeiro, Bernardete; Pereira, Francisco Camara
2017-01-01
In accounting and finance domains, bankruptcy prediction is of great utility for all of the economic stakeholders. The challenge of accurate assessment of business failure prediction, specially under scenarios of financial crisis, is known to be complicated. Although there have been many successful......). Using real-world bankruptcy data, an in-depth analysis is conducted showing that, in addition to a probabilistic interpretation, the GP can effectively improve the bankruptcy prediction performance with high accuracy when compared to the other approaches. We additionally generate a complete graphical...... visualization to improve our understanding of the different attained performances, effectively compiling all the conducted experiments in a meaningful way. We complete our study with an entropy-based analysis that highlights the uncertainty handling properties provided by the GP, crucial for prediction tasks...
Prediction for Major Adverse Outcomes in Cardiac Surgery: Comparison of Three Prediction Models
Directory of Open Access Journals (Sweden)
Cheng-Hung Hsieh
2007-09-01
Conclusion: The Parsonnet score performed as well as the logistic regression models in predicting major adverse outcomes. The Parsonnet score appears to be a very suitable model for clinicians to use in risk stratification of cardiac surgery.
From Predictive Models to Instructional Policies
Rollinson, Joseph; Brunskill, Emma
2015-01-01
At their core, Intelligent Tutoring Systems consist of a student model and a policy. The student model captures the state of the student and the policy uses the student model to individualize instruction. Policies require different properties from the student model. For example, a mastery threshold policy requires the student model to have a way…
Comparisons of Faulting-Based Pavement Performance Prediction Models
Directory of Open Access Journals (Sweden)
Weina Wang
2017-01-01
Full Text Available Faulting prediction is the core of concrete pavement maintenance and design. Highway agencies are always faced with the problem of lower accuracy for the prediction which causes costly maintenance. Although many researchers have developed some performance prediction models, the accuracy of prediction has remained a challenge. This paper reviews performance prediction models and JPCP faulting models that have been used in past research. Then three models including multivariate nonlinear regression (MNLR model, artificial neural network (ANN model, and Markov Chain (MC model are tested and compared using a set of actual pavement survey data taken on interstate highway with varying design features, traffic, and climate data. It is found that MNLR model needs further recalibration, while the ANN model needs more data for training the network. MC model seems a good tool for pavement performance prediction when the data is limited, but it is based on visual inspections and not explicitly related to quantitative physical parameters. This paper then suggests that the further direction for developing the performance prediction model is incorporating the advantages and disadvantages of different models to obtain better accuracy.
Probing metastable Sm2+ and optically stimulated tunnelling emission in YPO4: Ce, Sm
DEFF Research Database (Denmark)
Prasad, Amit Kumar; Kook, Myung Ho; Jain, Mayank
2017-01-01
optical stimulation. The work presented here adds further details to the energy levels of the metastable Sm2+ defect and the electron transfer processes by undertaking measurements of a) Sm2+ excitation spectrum through the internal 5D0 → 7F2 emission at 7 K, b) relaxation lifetime of Sm2+ (5D0 state...
Distinguishing a SM-like MSSM Higgs boson from SM Higgs boson ...
Indian Academy of Sciences (India)
We explore the possibility of distinguishing the SM-like MSSM Higgs boson from the SM Higgs boson via Higgs boson pair production at future muon collider. We study the behavior of the production cross-section in SM and MSSM with Higgs boson mass for various MSSM parameters tan and A. We observe that at fixed ...
Hahn, T; Heinemeyer, S; Hollik, W; Rzehak, H; Weiglein, G
2014-04-11
For the interpretation of the signal discovered in the Higgs searches at the LHC it will be crucial in particular to discriminate between the minimal Higgs sector realized in the standard model (SM) and its most commonly studied extension, the minimal supersymmetric standard model (MSSM). The measured mass value, having already reached the level of a precision observable with an experimental accuracy of about 500 MeV, plays an important role in this context. In the MSSM the mass of the light CP-even Higgs boson, Mh, can directly be predicted from the other parameters of the model. The accuracy of this prediction should at least match the one of the experimental result. The relatively high mass value of about 126 GeV has led to many investigations where the scalar top quarks are in the multi-TeV range. We improve the prediction for Mh in the MSSM by combining the existing fixed-order result, comprising the full one-loop and leading and subleading two-loop corrections, with a resummation of the leading and subleading logarithmic contributions from the scalar top sector to all orders. In this way for the first time a high-precision prediction for the mass of the light CP-even Higgs boson in the MSSM is possible all the way up to the multi-TeV region of the relevant supersymmetric particles. The results are included in the code FEYNHIGGS.
A model to predict the beginning of the pollen season
DEFF Research Database (Denmark)
Toldam-Andersen, Torben Bo
1991-01-01
In order to predict the beginning of the pollen season, a model comprising the Utah phenoclirnatography Chill Unit (CU) and ASYMCUR-Growing Degree Hour (GDH) submodels were used to predict the first bloom in Alms, Ulttirrs and Berirln. The model relates environmental temperatures to rest completion...... and bud development. As phenologic parameter 14 years of pollen counts were used. The observed datcs for the beginning of the pollen seasons were defined from the pollen counts and compared with the model prediction. The CU and GDH submodels were used as: 1. A fixed day model, using only the GDH model...... for fruit trees are generally applicable, and give a reasonable description of the growth processes of other trees. This type of model can therefore be of value in predicting the start of the pollen season. The predicted dates were generally within 3-5 days of the observed. Finally the possibility of frost...
Risk prediction model: Statistical and artificial neural network approach
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.
Evaluation of the US Army fallout prediction model
International Nuclear Information System (INIS)
Pernick, A.; Levanon, I.
1987-01-01
The US Army fallout prediction method was evaluated against an advanced fallout prediction model--SIMFIC (Simplified Fallout Interpretive Code). The danger zone areas of the US Army method were found to be significantly greater (up to a factor of 8) than the areas of corresponding radiation hazard as predicted by SIMFIC. Nonetheless, because the US Army's method predicts danger zone lengths that are commonly shorter than the corresponding hot line distances of SIMFIC, the US Army's method is not reliably conservative
Comparative Evaluation of Some Crop Yield Prediction Models ...
African Journals Online (AJOL)
A computer program was adopted from the work of Hill et al. (1982) to calibrate and test three of the existing yield prediction models using tropical cowpea yieldÐweather data. The models tested were Hanks Model (first and second versions). Stewart Model (first and second versions) and HallÐButcher Model. Three sets of ...
Comparative Evaluation of Some Crop Yield Prediction Models ...
African Journals Online (AJOL)
(1982) to calibrate and test three of the existing yield prediction models using tropical cowpea yieldÐweather data. The models tested were Hanks Model (first and second versions). Stewart Model (first and second versions) and HallÐButcher Model. Three sets of cowpea yield-water use and weather data were collected.
Prediction of speech intelligibility based on an auditory preprocessing model
DEFF Research Database (Denmark)
Christiansen, Claus Forup Corlin; Pedersen, Michael Syskind; Dau, Torsten
2010-01-01
Classical speech intelligibility models, such as the speech transmission index (STI) and the speech intelligibility index (SII) are based on calculations on the physical acoustic signals. The present study predicts speech intelligibility by combining a psychoacoustically validated model of auditory...
Modelling microbial interactions and food structure in predictive microbiology
Malakar, P.K.
2002-01-01
Keywords: modelling, dynamic models, microbial interactions, diffusion, microgradients, colony growth, predictive microbiology.
Growth response of microorganisms in foods is a complex process. Innovations in food production and preservation techniques have resulted in adoption of
Ocean wave prediction using numerical and neural network models
Digital Repository Service at National Institute of Oceanography (India)
Mandal, S.; Prabaharan, N.
This paper presents an overview of the development of the numerical wave prediction models and recently used neural networks for ocean wave hindcasting and forecasting. The numerical wave models express the physical concepts of the phenomena...
A Prediction Model of the Capillary Pressure J-Function.
Directory of Open Access Journals (Sweden)
W S Xu
Full Text Available The capillary pressure J-function is a dimensionless measure of the capillary pressure of a fluid in a porous medium. The function was derived based on a capillary bundle model. However, the dependence of the J-function on the saturation Sw is not well understood. A prediction model for it is presented based on capillary pressure model, and the J-function prediction model is a power function instead of an exponential or polynomial function. Relative permeability is calculated with the J-function prediction model, resulting in an easier calculation and results that are more representative.
Liquidus surface of Sm-Ga-S system
International Nuclear Information System (INIS)
Alieva, O.A.; Aliev, O.M.
1989-01-01
Using methods of physicochemic alanalysis for the first time phase diagrams of SmS-Ga 2 S 3 , Sm 2 S 3 -Ga 2 S 3 , SmS-GaS, Sm-GaS, SmGa 2 -SmS, SmGa 2 -GaS, GaS-SmGa 2 S 4 , Sm 3 S 4 -Ga 2 S 3 , Sm 2 S 3 -GaS, Sm 3 S 4 -SmGa 2 S 4 cross sections are studied and the projection of the liquidus surface of the Sm-Ga-S ternary system is plotted. It is found that there exist 6 ternary compounds in the system (SmGa 2 S 4 , SmGaS 3 , SmGa 4 S 7 , Sm 4 GaS 5 , Sm 3 Ga 2 S 5 , Sm 6 Ga 10/3 S 14 ) and, besides, found solid solutions based on GaS, Ga 2 S 3 and SmGa 2 S 4
Statistical model based gender prediction for targeted NGS clinical panels
Directory of Open Access Journals (Sweden)
Palani Kannan Kandavel
2017-12-01
The reference test dataset are being used to test the model. The sensitivity on predicting the gender has been increased from the current “genotype composition in ChrX” based approach. In addition, the prediction score given by the model can be used to evaluate the quality of clinical dataset. The higher prediction score towards its respective gender indicates the higher quality of sequenced data.
comparative analysis of two mathematical models for prediction
African Journals Online (AJOL)
Abstract. A mathematical modeling for prediction of compressive strength of sandcrete blocks was performed using statistical analysis for the sandcrete block data ob- tained from experimental work done in this study. The models used are Scheffes and Osadebes optimization theories to predict the compressive strength of ...
Comparison of predictive models for the early diagnosis of diabetes
M. Jahani (Meysam); M. Mahdavi (Mahdi)
2016-01-01
textabstractObjectives: This study develops neural network models to improve the prediction of diabetes using clinical and lifestyle characteristics. Prediction models were developed using a combination of approaches and concepts. Methods: We used memetic algorithms to update weights and to improve
Testing and analysis of internal hardwood log defect prediction models
R. Edward. Thomas
2011-01-01
The severity and location of internal defects determine the quality and value of lumber sawn from hardwood logs. Models have been developed to predict the size and position of internal defects based on external defect indicator measurements. These models were shown to predict approximately 80% of all internal knots based on external knot indicators. However, the size...
Hidden Markov Model for quantitative prediction of snowfall
Indian Academy of Sciences (India)
A Hidden Markov Model (HMM) has been developed for prediction of quantitative snowfall in Pir-Panjal and Great Himalayan mountain ranges of Indian Himalaya. The model predicts snowfall for two days in advance using daily recorded nine meteorological variables of past 20 winters from 1992–2012. There are six ...
Bayesian variable order Markov models: Towards Bayesian predictive state representations
Dimitrakakis, C.
2009-01-01
We present a Bayesian variable order Markov model that shares many similarities with predictive state representations. The resulting models are compact and much easier to specify and learn than classical predictive state representations. Moreover, we show that they significantly outperform a more
Demonstrating the improvement of predictive maturity of a computational model
Energy Technology Data Exchange (ETDEWEB)
Hemez, Francois M [Los Alamos National Laboratory; Unal, Cetin [Los Alamos National Laboratory; Atamturktur, Huriye S [CLEMSON UNIV.
2010-01-01
We demonstrate an improvement of predictive capability brought to a non-linear material model using a combination of test data, sensitivity analysis, uncertainty quantification, and calibration. A model that captures increasingly complicated phenomena, such as plasticity, temperature and strain rate effects, is analyzed. Predictive maturity is defined, here, as the accuracy of the model to predict multiple Hopkinson bar experiments. A statistical discrepancy quantifies the systematic disagreement (bias) between measurements and predictions. Our hypothesis is that improving the predictive capability of a model should translate into better agreement between measurements and predictions. This agreement, in turn, should lead to a smaller discrepancy. We have recently proposed to use discrepancy and coverage, that is, the extent to which the physical experiments used for calibration populate the regime of applicability of the model, as basis to define a Predictive Maturity Index (PMI). It was shown that predictive maturity could be improved when additional physical tests are made available to increase coverage of the regime of applicability. This contribution illustrates how the PMI changes as 'better' physics are implemented in the model. The application is the non-linear Preston-Tonks-Wallace (PTW) strength model applied to Beryllium metal. We demonstrate that our framework tracks the evolution of maturity of the PTW model. Robustness of the PMI with respect to the selection of coefficients needed in its definition is also studied.
Refining the Committee Approach and Uncertainty Prediction in Hydrological Modelling
Kayastha, N.
2014-01-01
Due to the complexity of hydrological systems a single model may be unable to capture the full range of a catchment response and accurately predict the streamflows. The multi modelling approach opens up possibilities for handling such difficulties and allows improve the predictive capability of
Refining the committee approach and uncertainty prediction in hydrological modelling
Kayastha, N.
2014-01-01
Due to the complexity of hydrological systems a single model may be unable to capture the full range of a catchment response and accurately predict the streamflows. The multi modelling approach opens up possibilities for handling such difficulties and allows improve the predictive capability of
Wind turbine control and model predictive control for uncertain systems
DEFF Research Database (Denmark)
Thomsen, Sven Creutz
as disturbance models for controller design. The theoretical study deals with Model Predictive Control (MPC). MPC is an optimal control method which is characterized by the use of a receding prediction horizon. MPC has risen in popularity due to its inherent ability to systematically account for time...
Hidden Markov Model for quantitative prediction of snowfall and ...
Indian Academy of Sciences (India)
A Hidden Markov Model (HMM) has been developed for prediction of quantitative snowfall in Pir-Panjal and Great Himalayan mountain ranges of Indian Himalaya. The model predicts snowfall for two days in advance using daily recorded nine meteorological variables of past 20 winters from 1992–2012. There are six ...
Model predictive control of a 3-DOF helicopter system using ...
African Journals Online (AJOL)
... by simulation, and its performance is compared with that achieved by linear model predictive control (LMPC). Keywords: nonlinear systems, helicopter dynamics, MIMO systems, model predictive control, successive linearization. International Journal of Engineering, Science and Technology, Vol. 2, No. 10, 2010, pp. 9-19 ...
Models for predicting fuel consumption in sagebrush-dominated ecosystems
Clinton S. Wright
2013-01-01
Fuel consumption predictions are necessary to accurately estimate or model fire effects, including pollutant emissions during wildland fires. Fuel and environmental measurements on a series of operational prescribed fires were used to develop empirical models for predicting fuel consumption in big sagebrush (Artemisia tridentate Nutt.) ecosystems....
Comparative Analysis of Two Mathematical Models for Prediction of ...
African Journals Online (AJOL)
A mathematical modeling for prediction of compressive strength of sandcrete blocks was performed using statistical analysis for the sandcrete block data obtained from experimental work done in this study. The models used are Scheffe's and Osadebe's optimization theories to predict the compressive strength of sandcrete ...
Anti-skin-aging benefits of exopolymers from Aureobasidium pullulans SM2001.
Kim, Kyung Hu; Park, Soo Jin; Lee, Ji Eun; Lee, Young Joon; Song, Chang Hyun; Choi, Seong Hun; Ku, Sae Kwang; Kang, Su Jin
2014-01-01
There have been many attempts to search for affordable and effective functional cosmetic ingredients, especially from natural sources. As research into developing a functional cosmetic ingredient, we investigated whether exopolymers from Aureobasidium pullulans SM2001 (E-AP-SM2001) exert antioxidant, antiwrinkle, whitening, and skin moisturizing effects. Antioxidant effects of E-AP-SM2001 were determined by measuring free radical scavenging capacity and superoxide dismutase (SOD)-like activity. Antiwrinkle effects were assessed through the inhibition of hyaluronidase, elastase, collagenase, and matrix metalloproteinase (MMP)-1. Whitening effects were measured by tyrosinase inhibition assay, and by melanin formation test in B16/F10 melanoma cells. Skin moisturizing effects were detected by mouse skin water content test. E-AP-SM2001 showed potent DPPH radical scavenging activity and SOD-like effects. Additionally, hyaluronidase, elastase, collagenase, and MMP-1 activities were significantly inhibited by E-AP-SM2001. We also observed that E-AP-SM2001 effectively reduced melanin production by B16/F10 melanoma cells and mushroom tyrosinase activities. Furthermore, significant increases in skin water content were detected in E-AP-SM2001- treated mouse skin, as compared with vehicle-treated control skin. Notably, a mask pack containing E-AP-SM2001 showed a >twofold more extensive moisturizing effect compared with one containing Saccharomycopsis ferment filtrate. Our results suggest that E-AP-SM2001 has adequate antiaging, antiwrinkle, and whitening benefits and skin moisturizing effect. These effects involve reducing hyaluronidase, elastase, collagenase, and MMP-1 activities, as well as inhibition of melanin production and tyrosinase activities. Therefore, the antioxidant E-AP-SM2001 may serve as a predictable functional ingredient.
A mathematical model for predicting earthquake occurrence ...
African Journals Online (AJOL)
We consider the continental crust under damage. We use the observed results of microseism in many seismic stations of the world which was established to study the time series of the activities of the continental crust with a view to predicting possible time of occurrence of earthquake. We consider microseism time series ...
Model for predicting the injury severity score.
Hagiwara, Shuichi; Oshima, Kiyohiro; Murata, Masato; Kaneko, Minoru; Aoki, Makoto; Kanbe, Masahiko; Nakamura, Takuro; Ohyama, Yoshio; Tamura, Jun'ichi
2015-07-01
To determine the formula that predicts the injury severity score from parameters that are obtained in the emergency department at arrival. We reviewed the medical records of trauma patients who were transferred to the emergency department of Gunma University Hospital between January 2010 and December 2010. The injury severity score, age, mean blood pressure, heart rate, Glasgow coma scale, hemoglobin, hematocrit, red blood cell count, platelet count, fibrinogen, international normalized ratio of prothrombin time, activated partial thromboplastin time, and fibrin degradation products, were examined in those patients on arrival. To determine the formula that predicts the injury severity score, multiple linear regression analysis was carried out. The injury severity score was set as the dependent variable, and the other parameters were set as candidate objective variables. IBM spss Statistics 20 was used for the statistical analysis. Statistical significance was set at P Watson ratio was 2.200. A formula for predicting the injury severity score in trauma patients was developed with ordinary parameters such as fibrin degradation products and mean blood pressure. This formula is useful because we can predict the injury severity score easily in the emergency department.
Econometric models for predicting confusion crop ratios
Umberger, D. E.; Proctor, M. H.; Clark, J. E.; Eisgruber, L. M.; Braschler, C. B. (Principal Investigator)
1979-01-01
Results for both the United States and Canada show that econometric models can provide estimates of confusion crop ratios that are more accurate than historical ratios. Whether these models can support the LACIE 90/90 accuracy criterion is uncertain. In the United States, experimenting with additional model formulations could provide improved methods models in some CRD's, particularly in winter wheat. Improved models may also be possible for the Canadian CD's. The more aggressive province/state models outperformed individual CD/CRD models. This result was expected partly because acreage statistics are based on sampling procedures, and the sampling precision declines from the province/state to the CD/CRD level. Declining sampling precision and the need to substitute province/state data for the CD/CRD data introduced measurement error into the CD/CRD models.
Rubinstein, Justin L.; Ellsworth, William L.; Chen, Kate Huihsuan; Uchida, Naoki
2012-01-01
The behavior of individual events in repeating earthquake sequences in California, Taiwan and Japan is better predicted by a model with fixed inter-event time or fixed slip than it is by the time- and slip-predictable models for earthquake occurrence. Given that repeating earthquakes are highly regular in both inter-event time and seismic moment, the time- and slip-predictable models seem ideally suited to explain their behavior. Taken together with evidence from the companion manuscript that shows similar results for laboratory experiments we conclude that the short-term predictions of the time- and slip-predictable models should be rejected in favor of earthquake models that assume either fixed slip or fixed recurrence interval. This implies that the elastic rebound model underlying the time- and slip-predictable models offers no additional value in describing earthquake behavior in an event-to-event sense, but its value in a long-term sense cannot be determined. These models likely fail because they rely on assumptions that oversimplify the earthquake cycle. We note that the time and slip of these events is predicted quite well by fixed slip and fixed recurrence models, so in some sense they are time- and slip-predictable. While fixed recurrence and slip models better predict repeating earthquake behavior than the time- and slip-predictable models, we observe a correlation between slip and the preceding recurrence time for many repeating earthquake sequences in Parkfield, California. This correlation is not found in other regions, and the sequences with the correlative slip-predictable behavior are not distinguishable from nearby earthquake sequences that do not exhibit this behavior.
Adding propensity scores to pure prediction models fails to improve predictive performance
Directory of Open Access Journals (Sweden)
Amy S. Nowacki
2013-08-01
Full Text Available Background. Propensity score usage seems to be growing in popularity leading researchers to question the possible role of propensity scores in prediction modeling, despite the lack of a theoretical rationale. It is suspected that such requests are due to the lack of differentiation regarding the goals of predictive modeling versus causal inference modeling. Therefore, the purpose of this study is to formally examine the effect of propensity scores on predictive performance. Our hypothesis is that a multivariable regression model that adjusts for all covariates will perform as well as or better than those models utilizing propensity scores with respect to model discrimination and calibration.Methods. The most commonly encountered statistical scenarios for medical prediction (logistic and proportional hazards regression were used to investigate this research question. Random cross-validation was performed 500 times to correct for optimism. The multivariable regression models adjusting for all covariates were compared with models that included adjustment for or weighting with the propensity scores. The methods were compared based on three predictive performance measures: (1 concordance indices; (2 Brier scores; and (3 calibration curves.Results. Multivariable models adjusting for all covariates had the highest average concordance index, the lowest average Brier score, and the best calibration. Propensity score adjustment and inverse probability weighting models without adjustment for all covariates performed worse than full models and failed to improve predictive performance with full covariate adjustment.Conclusion. Propensity score techniques did not improve prediction performance measures beyond multivariable adjustment. Propensity scores are not recommended if the analytical goal is pure prediction modeling.
PEEX Modelling Platform for Seamless Environmental Prediction
Baklanov, Alexander; Mahura, Alexander; Arnold, Stephen; Makkonen, Risto; Petäjä, Tuukka; Kerminen, Veli-Matti; Lappalainen, Hanna K.; Ezau, Igor; Nuterman, Roman; Zhang, Wen; Penenko, Alexey; Gordov, Evgeny; Zilitinkevich, Sergej; Kulmala, Markku
2017-04-01
The Pan-Eurasian EXperiment (PEEX) is a multidisciplinary, multi-scale research programme stared in 2012 and aimed at resolving the major uncertainties in Earth System Science and global sustainability issues concerning the Arctic and boreal Northern Eurasian regions and in China. Such challenges include climate change, air quality, biodiversity loss, chemicalization, food supply, and the use of natural resources by mining, industry, energy production and transport. The research infrastructure introduces the current state of the art modeling platform and observation systems in the Pan-Eurasian region and presents the future baselines for the coherent and coordinated research infrastructures in the PEEX domain. The PEEX modeling Platform is characterized by a complex seamless integrated Earth System Modeling (ESM) approach, in combination with specific models of different processes and elements of the system, acting on different temporal and spatial scales. The ensemble approach is taken to the integration of modeling results from different models, participants and countries. PEEX utilizes the full potential of a hierarchy of models: scenario analysis, inverse modeling, and modeling based on measurement needs and processes. The models are validated and constrained by available in-situ and remote sensing data of various spatial and temporal scales using data assimilation and top-down modeling. The analyses of the anticipated large volumes of data produced by available models and sensors will be supported by a dedicated virtual research environment developed for these purposes.
Models Predicting Success of Infertility Treatment: A Systematic Review
Zarinara, Alireza; Zeraati, Hojjat; Kamali, Koorosh; Mohammad, Kazem; Shahnazari, Parisa; Akhondi, Mohammad Mehdi
2016-01-01
Background: Infertile couples are faced with problems that affect their marital life. Infertility treatment is expensive and time consuming and occasionally isn’t simply possible. Prediction models for infertility treatment have been proposed and prediction of treatment success is a new field in infertility treatment. Because prediction of treatment success is a new need for infertile couples, this paper reviewed previous studies for catching a general concept in applicability of the models. Methods: This study was conducted as a systematic review at Avicenna Research Institute in 2015. Six data bases were searched based on WHO definitions and MESH key words. Papers about prediction models in infertility were evaluated. Results: Eighty one papers were eligible for the study. Papers covered years after 1986 and studies were designed retrospectively and prospectively. IVF prediction models have more shares in papers. Most common predictors were age, duration of infertility, ovarian and tubal problems. Conclusion: Prediction model can be clinically applied if the model can be statistically evaluated and has a good validation for treatment success. To achieve better results, the physician and the couples’ needs estimation for treatment success rate were based on history, the examination and clinical tests. Models must be checked for theoretical approach and appropriate validation. The privileges for applying the prediction models are the decrease in the cost and time, avoiding painful treatment of patients, assessment of treatment approach for physicians and decision making for health managers. The selection of the approach for designing and using these models is inevitable. PMID:27141461
Towards a generalized energy prediction model for machine tools.
Bhinge, Raunak; Park, Jinkyoo; Law, Kincho H; Dornfeld, David A; Helu, Moneer; Rachuri, Sudarsan
2017-04-01
Energy prediction of machine tools can deliver many advantages to a manufacturing enterprise, ranging from energy-efficient process planning to machine tool monitoring. Physics-based, energy prediction models have been proposed in the past to understand the energy usage pattern of a machine tool. However, uncertainties in both the machine and the operating environment make it difficult to predict the energy consumption of the target machine reliably. Taking advantage of the opportunity to collect extensive, contextual, energy-consumption data, we discuss a data-driven approach to develop an energy prediction model of a machine tool in this paper. First, we present a methodology that can efficiently and effectively collect and process data extracted from a machine tool and its sensors. We then present a data-driven model that can be used to predict the energy consumption of the machine tool for machining a generic part. Specifically, we use Gaussian Process (GP) Regression, a non-parametric machine-learning technique, to develop the prediction model. The energy prediction model is then generalized over multiple process parameters and operations. Finally, we apply this generalized model with a method to assess uncertainty intervals to predict the energy consumed to machine any part using a Mori Seiki NVD1500 machine tool. Furthermore, the same model can be used during process planning to optimize the energy-efficiency of a machining process.
Poisson Mixture Regression Models for Heart Disease Prediction
Erol, Hamza
2016-01-01
Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611
Comparison of Predictive Models for the Early Diagnosis of Diabetes.
Jahani, Meysam; Mahdavi, Mahdi
2016-04-01
This study develops neural network models to improve the prediction of diabetes using clinical and lifestyle characteristics. Prediction models were developed using a combination of approaches and concepts. We used memetic algorithms to update weights and to improve prediction accuracy of models. In the first step, the optimum amount for neural network parameters such as momentum rate, transfer function, and error function were obtained through trial and error and based on the results of previous studies. In the second step, optimum parameters were applied to memetic algorithms in order to improve the accuracy of prediction. This preliminary analysis showed that the accuracy of neural networks is 88%. In the third step, the accuracy of neural network models was improved using a memetic algorithm and resulted model was compared with a logistic regression model using a confusion matrix and receiver operating characteristic curve (ROC). The memetic algorithm improved the accuracy from 88.0% to 93.2%. We also found that memetic algorithm had a higher accuracy than the model from the genetic algorithm and a regression model. Among models, the regression model has the least accuracy. For the memetic algorithm model the amount of sensitivity, specificity, positive predictive value, negative predictive value, and ROC are 96.2, 95.3, 93.8, 92.4, and 0.958 respectively. The results of this study provide a basis to design a Decision Support System for risk management and planning of care for individuals at risk of diabetes.
Applications of modeling in polymer-property prediction
Case, F. H.
1996-08-01
A number of molecular modeling techniques have been applied for the prediction of polymer properties and behavior. Five examples illustrate the range of methodologies used. A simple atomistic simulation of small polymer fragments is used to estimate drug compatibility with a polymer matrix. The analysis of molecular dynamics results from a more complex model of a swollen hydrogel system is used to study gas diffusion in contact lenses. Statistical mechanics are used to predict conformation dependent properties — an example is the prediction of liquid-crystal formation. The effect of the molecular weight distribution on phase separation in polyalkanes is predicted using thermodynamic models. In some cases, the properties of interest cannot be directly predicted using simulation methods or polymer theory. Correlation methods may be used to bridge the gap between molecular structure and macroscopic properties. The final example shows how connectivity-indices-based quantitative structure-property relationships were used to predict properties for candidate polyimids in an electronics application.
Artificial Neural Network Model for Predicting Compressive
Salim T. Yousif; Salwa M. Abdullah
2013-01-01
Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum...
Prediction of hourly solar radiation with multi-model framework
International Nuclear Information System (INIS)
Wu, Ji; Chan, Chee Keong
2013-01-01
Highlights: • A novel approach to predict solar radiation through the use of clustering paradigms. • Development of prediction models based on the intrinsic pattern observed in each cluster. • Prediction based on proper clustering and selection of model on current time provides better results than other methods. • Experiments were conducted on actual solar radiation data obtained from a weather station in Singapore. - Abstract: In this paper, a novel multi-model prediction framework for prediction of solar radiation is proposed. The framework started with the assumption that there are several patterns embedded in the solar radiation series. To extract the underlying pattern, the solar radiation series is first segmented into smaller subsequences, and the subsequences are further grouped into different clusters. For each cluster, an appropriate prediction model is trained. Hence a procedure for pattern identification is developed to identify the proper pattern that fits the current period. Based on this pattern, the corresponding prediction model is applied to obtain the prediction value. The prediction result of the proposed framework is then compared to other techniques. It is shown that the proposed framework provides superior performance as compared to others
New neutron-rich isotope production in 154Sm+160Gd
Directory of Open Access Journals (Sweden)
Ning Wang
2016-09-01
Full Text Available Deep inelastic scattering in 154Sm+160Gd at energies above the Bass barrier is for the first time investigated with two different microscopic dynamics approaches: improved quantum molecular dynamics (ImQMD model and time dependent Hartree–Fock (TDHF theory. No fusion is observed from both models. The capture pocket disappears for this reaction due to strong Coulomb repulsion and the contact time of the di-nuclear system formed in head-on collisions is about 700 fm/c at an incident energy of 440 MeV. The isotope distribution of fragments in the deep inelastic scattering process is predicted with the simulations of the latest ImQMD-v2.2 model together with a statistical code (GEMINI for describing the secondary decay of fragments. More than 40 extremely neutron-rich unmeasured nuclei with 58≤Z≤76 are observed and the production cross sections are at the order of μb to mb. The multi-nucleon transfer reaction of Sm+Gd could be an alternative way to synthesize new neutron-rich lanthanides which are difficult to be produced with traditional fusion reactions or fission of actinides.
Posterior Predictive Model Checking for Multidimensionality in Item Response Theory
Levy, Roy; Mislevy, Robert J.; Sinharay, Sandip
2009-01-01
If data exhibit multidimensionality, key conditional independence assumptions of unidimensional models do not hold. The current work pursues posterior predictive model checking, a flexible family of model-checking procedures, as a tool for criticizing models due to unaccounted for dimensions in the context of item response theory. Factors…
Model predictive control of a crude oil distillation column
Directory of Open Access Journals (Sweden)
Morten Hovd
1999-04-01
Full Text Available The project of designing and implementing model based predictive control on the vacuum distillation column at the Nynäshamn Refinery of Nynäs AB is described in this paper. The paper describes in detail the modeling for the model based control, covers the controller implementation, and documents the benefits gained from the model based controller.
Enhancing Flood Prediction Reliability Using Bayesian Model Averaging
Liu, Z.; Merwade, V.
2017-12-01
Uncertainty analysis is an indispensable part of modeling the hydrology and hydrodynamics of non-idealized environmental systems. Compared to reliance on prediction from one model simulation, using on ensemble of predictions that consider uncertainty from different sources is more reliable. In this study, Bayesian model averaging (BMA) is applied to Black River watershed in Arkansas and Missouri by combining multi-model simulations to get reliable deterministic water stage and probabilistic inundation extent predictions. The simulation ensemble is generated from 81 LISFLOOD-FP subgrid model configurations that include uncertainty from channel shape, channel width, channel roughness and discharge. Model simulation outputs are trained with observed water stage data during one flood event, and BMA prediction ability is validated for another flood event. Results from this study indicate that BMA does not always outperform all members in the ensemble, but it provides relatively robust deterministic flood stage predictions across the basin. Station based BMA (BMA_S) water stage prediction has better performance than global based BMA (BMA_G) prediction which is superior to the ensemble mean prediction. Additionally, high-frequency flood inundation extent (probability greater than 60%) in BMA_G probabilistic map is more accurate than the probabilistic flood inundation extent based on equal weights.
Spiliopoulou, Athina; Nagy, Reka; Bermingham, Mairead L.; Huffman, Jennifer E.; Hayward, Caroline; Vitart, Veronique; Rudan, Igor; Campbell, Harry; Wright, Alan F.; Wilson, James F.; Pong-Wong, Ricardo; Agakov, Felix; Navarro, Pau; Haley, Chris S.
2015-01-01
We explore the prediction of individuals' phenotypes for complex traits using genomic data. We compare several widely used prediction models, including Ridge Regression, LASSO and Elastic Nets estimated from cohort data, and polygenic risk scores constructed using published summary statistics from genome-wide association meta-analyses (GWAMA). We evaluate the interplay between relatedness, trait architecture and optimal marker density, by predicting height, body mass index (BMI) and high-density lipoprotein level (HDL) in two data cohorts, originating from Croatia and Scotland. We empirically demonstrate that dense models are better when all genetic effects are small (height and BMI) and target individuals are related to the training samples, while sparse models predict better in unrelated individuals and when some effects have moderate size (HDL). For HDL sparse models achieved good across-cohort prediction, performing similarly to the GWAMA risk score and to models trained within the same cohort, which indicates that, for predicting traits with moderately sized effects, large sample sizes and familial structure become less important, though still potentially useful. Finally, we propose a novel ensemble of whole-genome predictors with GWAMA risk scores and demonstrate that the resulting meta-model achieves higher prediction accuracy than either model on its own. We conclude that although current genomic predictors are not accurate enough for diagnostic purposes, performance can be improved without requiring access to large-scale individual-level data. Our methodologically simple meta-model is a means of performing predictive meta-analysis for optimizing genomic predictions and can be easily extended to incorporate multiple population-level summary statistics or other domain knowledge. PMID:25918167
Structure of smAKAP and its regulation by PKA-mediated phosphorylation
Burgers, Pepijn P.; Bruystens, Jessica; Burnley, Rebecca J.; Nikolaev, Viacheslav O.; Keshwani, Malik; Wu, Jian; Janssen, Bert J. C.; Taylor, Susan S.; Heck, Albert J. R.; Scholten, Arjen
2016-01-01
The A-kinase anchoring protein (AKAP) smAKAP has three extraordinary features; it is very small, it is anchored directly to membranes by acyl motifs, and it interacts almost exclusively with the type I regulatory subunits (RI) of cAMP-dependent kinase (PKA). Here, we determined the crystal structure of smAKAP’s A-kinase binding domain (smAKAP-AKB) in complex with the dimerization/docking (D/D) domain of RIα which reveals an extended hydrophobic interface with unique interaction pockets that drive smAKAP’s high specificity for RI subunits. We also identify a conserved PKA phosphorylation site at Ser66 in the AKB domain which we predict would cause steric clashes and disrupt binding. This correlates with in vivo colocalization and fluorescence polarization studies, where Ser66 AKB phosphorylation ablates RI binding. Hydrogen/deuterium exchange studies confirm that the AKB helix is accessible and dynamic. Furthermore, full-length smAKAP as well as the unbound AKB is predicted to contain a break at the phosphorylation site, and circular dichroism measurements confirm that the AKB domain loses its helicity following phosphorylation. As the active site of PKA’s catalytic subunit does not accommodate α-helices, we predict that the inherent flexibility of the AKB domain enables its phosphorylation by PKA. This represents a novel mechanism, whereby activation of anchored PKA can terminate its binding to smAKAP affecting the regulation of localized cAMP signaling events. PMID:27028580
Predictive models for acute kidney injury following cardiac surgery.
Demirjian, Sevag; Schold, Jesse D; Navia, Jose; Mastracci, Tara M; Paganini, Emil P; Yared, Jean-Pierre; Bashour, Charles A
2012-03-01
Accurate prediction of cardiac surgery-associated acute kidney injury (AKI) would improve clinical decision making and facilitate timely diagnosis and treatment. The aim of the study was to develop predictive models for cardiac surgery-associated AKI using presurgical and combined pre- and intrasurgical variables. Prospective observational cohort. 25,898 patients who underwent cardiac surgery at Cleveland Clinic in 2000-2008. Presurgical and combined pre- and intrasurgical variables were used to develop predictive models. Dialysis therapy and a composite of doubling of serum creatinine level or dialysis therapy within 2 weeks (or discharge if sooner) after cardiac surgery. Incidences of dialysis therapy and the composite of doubling of serum creatinine level or dialysis therapy were 1.7% and 4.3%, respectively. Kidney function parameters were strong independent predictors in all 4 models. Surgical complexity reflected by type and history of previous cardiac surgery were robust predictors in models based on presurgical variables. However, the inclusion of intrasurgical variables accounted for all explained variance by procedure-related information. Models predictive of dialysis therapy showed good calibration and superb discrimination; a combined (pre- and intrasurgical) model performed better than the presurgical model alone (C statistics, 0.910 and 0.875, respectively). Models predictive of the composite end point also had excellent discrimination with both presurgical and combined (pre- and intrasurgical) variables (C statistics, 0.797 and 0.825, respectively). However, the presurgical model predictive of the composite end point showed suboptimal calibration (P predictive models in other cohorts is required before wide-scale application. We developed and internally validated 4 new models that accurately predict cardiac surgery-associated AKI. These models are based on readily available clinical information and can be used for patient counseling, clinical
Modeling number of claims and prediction of total claim amount
Acar, Aslıhan Şentürk; Karabey, Uǧur
2017-07-01
In this study we focus on annual number of claims of a private health insurance data set which belongs to a local insurance company in Turkey. In addition to Poisson model and negative binomial model, zero-inflated Poisson model and zero-inflated negative binomial model are used to model the number of claims in order to take into account excess zeros. To investigate the impact of different distributional assumptions for the number of claims on the prediction of total claim amount, predictive performances of candidate models are compared by using root mean square error (RMSE) and mean absolute error (MAE) criteria.
Assessment of performance of survival prediction models for cancer prognosis
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Chen Hung-Chia
2012-07-01
Full Text Available Abstract Background Cancer survival studies are commonly analyzed using survival-time prediction models for cancer prognosis. A number of different performance metrics are used to ascertain the concordance between the predicted risk score of each patient and the actual survival time, but these metrics can sometimes conflict. Alternatively, patients are sometimes divided into two classes according to a survival-time threshold, and binary classifiers are applied to predict each patient’s class. Although this approach has several drawbacks, it does provide natural performance metrics such as positive and negative predictive values to enable unambiguous assessments. Methods We compare the survival-time prediction and survival-time threshold approaches to analyzing cancer survival studies. We review and compare common performance metrics for the two approaches. We present new randomization tests and cross-validation methods to enable unambiguous statistical inferences for several performance metrics used with the survival-time prediction approach. We consider five survival prediction models consisting of one clinical model, two gene expression models, and two models from combinations of clinical and gene expression models. Results A public breast cancer dataset was used to compare several performance metrics using five prediction models. 1 For some prediction models, the hazard ratio from fitting a Cox proportional hazards model was significant, but the two-group comparison was insignificant, and vice versa. 2 The randomization test and cross-validation were generally consistent with the p-values obtained from the standard performance metrics. 3 Binary classifiers highly depended on how the risk groups were defined; a slight change of the survival threshold for assignment of classes led to very different prediction results. Conclusions 1 Different performance metrics for evaluation of a survival prediction model may give different conclusions in
International Nuclear Information System (INIS)
Sumida, Masaki; Shiohara, Yuh; Umeda, Takateru
2000-01-01
The interface stability of the Sm 123 superconductor was analyzed in accordance with the constitutional undercooling criterion. As the single-crystal growth of the 123 phase is largely dependent on the growth-interface stability, a quantitative analysis was very much required. From this analysis, it was clarified that the constitutional undercooling must exist in the liquid when the 123 growth interface comes close to a 211 particle during the peritectic solidification. It was also predicted that the larger 211 particle radius, smaller volume fraction of the 211 particles, larger growth rate, or smaller imposed temperature gradient would cause easy occurrence of the constitutional undercooling ahead of the 123 growth interface. Taking into account the nucleation at the L/211 interface just ahead of the 123 growth front due to the constitutional undercooling, the transition of 123 growth from a planar-interface morphology to an equiaxed blocky morphology was investigated quantitatively and qualitatively
Model-based uncertainty in species range prediction
DEFF Research Database (Denmark)
Pearson, R. G.; Thuiller, Wilfried; Bastos Araujo, Miguel
2006-01-01
algorithm when extrapolating beyond the range of data used to build the model. The effects of these factors should be carefully considered when using this modelling approach to predict species ranges. Main conclusions We highlight an important source of uncertainty in assessments of the impacts of climate......Aim Many attempts to predict the potential range of species rely on environmental niche (or 'bioclimate envelope') modelling, yet the effects of using different niche-based methodologies require further investigation. Here we investigate the impact that the choice of model can have on predictions......, identify key reasons why model output may differ and discuss the implications that model uncertainty has for policy-guiding applications. Location The Western Cape of South Africa. Methods We applied nine of the most widely used modelling techniques to model potential distributions under current...
Prediction Model for Gastric Cancer Incidence in Korean Population.
Eom, Bang Wool; Joo, Jungnam; Kim, Sohee; Shin, Aesun; Yang, Hye-Ryung; Park, Junghyun; Choi, Il Ju; Kim, Young-Woo; Kim, Jeongseon; Nam, Byung-Ho
2015-01-01
Predicting high risk groups for gastric cancer and motivating these groups to receive regular checkups is required for the early detection of gastric cancer. The aim of this study is was to develop a prediction model for gastric cancer incidence based on a large population-based cohort in Korea. Based on the National Health Insurance Corporation data, we analyzed 10 major risk factors for gastric cancer. The Cox proportional hazards model was used to develop gender specific prediction models for gastric cancer development, and the performance of the developed model in terms of discrimination and calibration was also validated using an independent cohort. Discrimination ability was evaluated using Harrell's C-statistics, and the calibration was evaluated using a calibration plot and slope. During a median of 11.4 years of follow-up, 19,465 (1.4%) and 5,579 (0.7%) newly developed gastric cancer cases were observed among 1,372,424 men and 804,077 women, respectively. The prediction models included age, BMI, family history, meal regularity, salt preference, alcohol consumption, smoking and physical activity for men, and age, BMI, family history, salt preference, alcohol consumption, and smoking for women. This prediction model showed good accuracy and predictability in both the developing and validation cohorts (C-statistics: 0.764 for men, 0.706 for women). In this study, a prediction model for gastric cancer incidence was developed that displayed a good performance.
AN EFFICIENT PATIENT INFLOW PREDICTION MODEL FOR HOSPITAL RESOURCE MANAGEMENT
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Kottalanka Srikanth
2017-07-01
Full Text Available There has been increasing demand in improving service provisioning in hospital resources management. Hospital industries work with strict budget constraint at the same time assures quality care. To achieve quality care with budget constraint an efficient prediction model is required. Recently there has been various time series based prediction model has been proposed to manage hospital resources such ambulance monitoring, emergency care and so on. These models are not efficient as they do not consider the nature of scenario such climate condition etc. To address this artificial intelligence is adopted. The issues with existing prediction are that the training suffers from local optima error. This induces overhead and affects the accuracy in prediction. To overcome the local minima error, this work presents a patient inflow prediction model by adopting resilient backpropagation neural network. Experiment are conducted to evaluate the performance of proposed model inter of RMSE and MAPE. The outcome shows the proposed model reduces RMSE and MAPE over existing back propagation based artificial neural network. The overall outcomes show the proposed prediction model improves the accuracy of prediction which aid in improving the quality of health care management.
Risk Prediction Model for Severe Postoperative Complication in Bariatric Surgery.
Stenberg, Erik; Cao, Yang; Szabo, Eva; Näslund, Erik; Näslund, Ingmar; Ottosson, Johan
2018-01-12
Factors associated with risk for adverse outcome are important considerations in the preoperative assessment of patients for bariatric surgery. As yet, prediction models based on preoperative risk factors have not been able to predict adverse outcome sufficiently. This study aimed to identify preoperative risk factors and to construct a risk prediction model based on these. Patients who underwent a bariatric surgical procedure in Sweden between 2010 and 2014 were identified from the Scandinavian Obesity Surgery Registry (SOReg). Associations between preoperative potential risk factors and severe postoperative complications were analysed using a logistic regression model. A multivariate model for risk prediction was created and validated in the SOReg for patients who underwent bariatric surgery in Sweden, 2015. Revision surgery (standardized OR 1.19, 95% confidence interval (CI) 1.14-0.24, p prediction model. Despite high specificity, the sensitivity of the model was low. Revision surgery, high age, low BMI, large waist circumference, and dyspepsia/GERD were associated with an increased risk for severe postoperative complication. The prediction model based on these factors, however, had a sensitivity that was too low to predict risk in the individual patient case.
Prediction Model for Gastric Cancer Incidence in Korean Population.
Directory of Open Access Journals (Sweden)
Bang Wool Eom
Full Text Available Predicting high risk groups for gastric cancer and motivating these groups to receive regular checkups is required for the early detection of gastric cancer. The aim of this study is was to develop a prediction model for gastric cancer incidence based on a large population-based cohort in Korea.Based on the National Health Insurance Corporation data, we analyzed 10 major risk factors for gastric cancer. The Cox proportional hazards model was used to develop gender specific prediction models for gastric cancer development, and the performance of the developed model in terms of discrimination and calibration was also validated using an independent cohort. Discrimination ability was evaluated using Harrell's C-statistics, and the calibration was evaluated using a calibration plot and slope.During a median of 11.4 years of follow-up, 19,465 (1.4% and 5,579 (0.7% newly developed gastric cancer cases were observed among 1,372,424 men and 804,077 women, respectively. The prediction models included age, BMI, family history, meal regularity, salt preference, alcohol consumption, smoking and physical activity for men, and age, BMI, family history, salt preference, alcohol consumption, and smoking for women. This prediction model showed good accuracy and predictability in both the developing and validation cohorts (C-statistics: 0.764 for men, 0.706 for women.In this study, a prediction model for gastric cancer incidence was developed that displayed a good performance.
Stage-specific predictive models for breast cancer survivability.
Kate, Rohit J; Nadig, Ramya
2017-01-01
Survivability rates vary widely among various stages of breast cancer. Although machine learning models built in past to predict breast cancer survivability were given stage as one of the features, they were not trained or evaluated separately for each stage. To investigate whether there are differences in performance of machine learning models trained and evaluated across different stages for predicting breast cancer survivability. Using three different machine learning methods we built models to predict breast cancer survivability separately for each stage and compared them with the traditional joint models built for all the stages. We also evaluated the models separately for each stage and together for all the stages. Our results show that the most suitable model to predict survivability for a specific stage is the model trained for that particular stage. In our experiments, using additional examples of other stages during training did not help, in fact, it made it worse in some cases. The most important features for predicting survivability were also found to be different for different stages. By evaluating the models separately on different stages we found that the performance widely varied across them. We also demonstrate that evaluating predictive models for survivability on all the stages together, as was done in the past, is misleading because it overestimates performance. Copyright Â© 2016 Elsevier Ireland Ltd. All rights reserved.
The QCD/SM working group: Summary report
Energy Technology Data Exchange (ETDEWEB)
W. Giele et al.
2004-01-12
Quantum Chromo-Dynamics (QCD), and more generally the physics of the Standard Model (SM), enter in many ways in high energy processes at TeV Colliders, and especially in hadron colliders (the Tevatron at Fermilab and the forthcoming LHC at CERN), First of all, at hadron colliders, QCD controls the parton luminosity, which rules the production rates of any particle or system with large invariant mass and/or large transverse momentum. Accurate predictions for any signal of possible ''New Physics'' sought at hadron colliders, as well as the corresponding backgrounds, require an improvement in the control of uncertainties on the determination of PDF and of the propagation of these uncertainties in the predictions. Furthermore, to fully exploit these new types of PDF with uncertainties, uniform tools (computer interfaces, standardization of the PDF evolution codes used by the various groups fitting PDF's) need to be proposed and developed. The dynamics of colour also affects, both in normalization and shape, various observables of the signals of any possible ''New Physics'' sought at the TeV scale, such as, e.g. the production rate, or the distributions in transverse momentum of the Higgs boson. Last, but not least, QCD governs many backgrounds to the searches for this ''New Physics''. Large and important QCD corrections may come from extra hard parton emission (and the corresponding virtual corrections), involving multi-leg and/or multi-loop amplitudes. This requires complex higher order calculations, and new methods have to be designed to compute the required multi-legs and/or multi-loop corrections in a tractable form. In the case of semi-inclusive observables, logarithmically enhanced contributions coming from multiple soft and collinear gluon emission require sophisticated QCD resummation techniques. Resummation is a catch-all name for efforts to extend the predictive power of QCD by summing the large
Evaluation of wave runup predictions from numerical and parametric models
Stockdon, Hilary F.; Thompson, David M.; Plant, Nathaniel G.; Long, Joseph W.
2014-01-01
Wave runup during storms is a primary driver of coastal evolution, including shoreline and dune erosion and barrier island overwash. Runup and its components, setup and swash, can be predicted from a parameterized model that was developed by comparing runup observations to offshore wave height, wave period, and local beach slope. Because observations during extreme storms are often unavailable, a numerical model is used to simulate the storm-driven runup to compare to the parameterized model and then develop an approach to improve the accuracy of the parameterization. Numerically simulated and parameterized runup were compared to observations to evaluate model accuracies. The analysis demonstrated that setup was accurately predicted by both the parameterized model and numerical simulations. Infragravity swash heights were most accurately predicted by the parameterized model. The numerical model suffered from bias and gain errors that depended on whether a one-dimensional or two-dimensional spatial domain was used. Nonetheless, all of the predictions were significantly correlated to the observations, implying that the systematic errors can be corrected. The numerical simulations did not resolve the incident-band swash motions, as expected, and the parameterized model performed best at predicting incident-band swash heights. An assimilated prediction using a weighted average of the parameterized model and the numerical simulations resulted in a reduction in prediction error variance. Finally, the numerical simulations were extended to include storm conditions that have not been previously observed. These results indicated that the parameterized predictions of setup may need modification for extreme conditions; numerical simulations can be used to extend the validity of the parameterized predictions of infragravity swash; and numerical simulations systematically underpredict incident swash, which is relatively unimportant under extreme conditions.
Femtocells Sharing Management using mobility prediction model
Barth, Dominique; Choutri, Amira; Kloul, Leila; Marcé, Olivier
2013-01-01
Bandwidth sharing paradigm constitutes an incentive solution for the serious capacity management problem faced by operators as femtocells owners are able to offer a QoS guaranteed network access to mobile users in their femtocell coverage. In this paper, we consider a technico-economic bandwidth sharing model based on a reinforcement learning algorithm. Because such a model does not allow the convergence of the learning algorithm, due to the small size of the femtocells, the mobile users velo...
Validating predictions from climate envelope models
Watling, J.; Bucklin, D.; Speroterra, C.; Brandt, L.; Cabal, C.; Romañach, Stephanie S.; Mazzotti, Frank J.
2013-01-01
Climate envelope models are a potentially important conservation tool, but their ability to accurately forecast species’ distributional shifts using independent survey data has not been fully evaluated. We created climate envelope models for 12 species of North American breeding birds previously shown to have experienced poleward range shifts. For each species, we evaluated three different approaches to climate envelope modeling that differed in the way they treated climate-induced range expansion and contraction, using random forests and maximum entropy modeling algorithms. All models were calibrated using occurrence data from 1967–1971 (t1) and evaluated using occurrence data from 1998–2002 (t2). Model sensitivity (the ability to correctly classify species presences) was greater using the maximum entropy algorithm than the random forest algorithm. Although sensitivity did not differ significantly among approaches, for many species, sensitivity was maximized using a hybrid approach that assumed range expansion, but not contraction, in t2. Species for which the hybrid approach resulted in the greatest improvement in sensitivity have been reported from more land cover types than species for which there was little difference in sensitivity between hybrid and dynamic approaches, suggesting that habitat generalists may be buffered somewhat against climate-induced range contractions. Specificity (the ability to correctly classify species absences) was maximized using the random forest algorithm and was lowest using the hybrid approach. Overall, our results suggest cautious optimism for the use of climate envelope models to forecast range shifts, but also underscore the importance of considering non-climate drivers of species range limits. The use of alternative climate envelope models that make different assumptions about range expansion and contraction is a new and potentially useful way to help inform our understanding of climate change effects on species.
Validating predictions from climate envelope models.
Directory of Open Access Journals (Sweden)
James I Watling
Full Text Available Climate envelope models are a potentially important conservation tool, but their ability to accurately forecast species' distributional shifts using independent survey data has not been fully evaluated. We created climate envelope models for 12 species of North American breeding birds previously shown to have experienced poleward range shifts. For each species, we evaluated three different approaches to climate envelope modeling that differed in the way they treated climate-induced range expansion and contraction, using random forests and maximum entropy modeling algorithms. All models were calibrated using occurrence data from 1967-1971 (t1 and evaluated using occurrence data from 1998-2002 (t2. Model sensitivity (the ability to correctly classify species presences was greater using the maximum entropy algorithm than the random forest algorithm. Although sensitivity did not differ significantly among approaches, for many species, sensitivity was maximized using a hybrid approach that assumed range expansion, but not contraction, in t2. Species for which the hybrid approach resulted in the greatest improvement in sensitivity have been reported from more land cover types than species for which there was little difference in sensitivity between hybrid and dynamic approaches, suggesting that habitat generalists may be buffered somewhat against climate-induced range contractions. Specificity (the ability to correctly classify species absences was maximized using the random forest algorithm and was lowest using the hybrid approach. Overall, our results suggest cautious optimism for the use of climate envelope models to forecast range shifts, but also underscore the importance of considering non-climate drivers of species range limits. The use of alternative climate envelope models that make different assumptions about range expansion and contraction is a new and potentially useful way to help inform our understanding of climate change effects on
North Atlantic climate model bias influence on multiyear predictability
Wu, Y.; Park, T.; Park, W.; Latif, M.
2018-01-01
The influences of North Atlantic biases on multiyear predictability of unforced surface air temperature (SAT) variability are examined in the Kiel Climate Model (KCM). By employing a freshwater flux correction over the North Atlantic to the model, which strongly alleviates both North Atlantic sea surface salinity (SSS) and sea surface temperature (SST) biases, the freshwater flux-corrected integration depicts significantly enhanced multiyear SAT predictability in the North Atlantic sector in comparison to the uncorrected one. The enhanced SAT predictability in the corrected integration is due to a stronger and more variable Atlantic Meridional Overturning Circulation (AMOC) and its enhanced influence on North Atlantic SST. Results obtained from preindustrial control integrations of models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) support the findings obtained from the KCM: models with large North Atlantic biases tend to have a weak AMOC influence on SAT and exhibit a smaller SAT predictability over the North Atlantic sector.
Osman, Marisol; Vera, C. S.
2017-10-01
This work presents an assessment of the predictability and skill of climate anomalies over South America. The study was made considering a multi-model ensemble of seasonal forecasts for surface air temperature, precipitation and regional circulation, from coupled global circulation models included in the Climate Historical Forecast Project. Predictability was evaluated through the estimation of the signal-to-total variance ratio while prediction skill was assessed computing anomaly correlation coefficients. Both indicators present over the continent higher values at the tropics than at the extratropics for both, surface air temperature and precipitation. Moreover, predictability and prediction skill for temperature are slightly higher in DJF than in JJA while for precipitation they exhibit similar levels in both seasons. The largest values of predictability and skill for both variables and seasons are found over northwestern South America while modest but still significant values for extratropical precipitation at southeastern South America and the extratropical Andes. The predictability levels in ENSO years of both variables are slightly higher, although with the same spatial distribution, than that obtained considering all years. Nevertheless, predictability at the tropics for both variables and seasons diminishes in both warm and cold ENSO years respect to that in all years. The latter can be attributed to changes in signal rather than in the noise. Predictability and prediction skill for low-level winds and upper-level zonal winds over South America was also assessed. Maximum levels of predictability for low-level winds were found were maximum mean values are observed, i.e. the regions associated with the equatorial trade winds, the midlatitudes westerlies and the South American Low-Level Jet. Predictability maxima for upper-level zonal winds locate where the subtropical jet peaks. Seasonal changes in wind predictability are observed that seem to be related to
Prediction skill of rainstorm events over India in the TIGGE weather prediction models
Karuna Sagar, S.; Rajeevan, M.; Vijaya Bhaskara Rao, S.; Mitra, A. K.
2017-12-01
Extreme rainfall events pose a serious threat of leading to severe floods in many countries worldwide. Therefore, advance prediction of its occurrence and spatial distribution is very essential. In this paper, an analysis has been made to assess the skill of numerical weather prediction models in predicting rainstorms over India. Using gridded daily rainfall data set and objective criteria, 15 rainstorms were identified during the monsoon season (June to September). The analysis was made using three TIGGE (THe Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble) models. The models considered are the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centre for Environmental Prediction (NCEP) and the UK Met Office (UKMO). Verification of the TIGGE models for 43 observed rainstorm days from 15 rainstorm events has been made for the period 2007-2015. The comparison reveals that rainstorm events are predictable up to 5 days in advance, however with a bias in spatial distribution and intensity. The statistical parameters like mean error (ME) or Bias, root mean square error (RMSE) and correlation coefficient (CC) have been computed over the rainstorm region using the multi-model ensemble (MME) mean. The study reveals that the spread is large in ECMWF and UKMO followed by the NCEP model. Though the ensemble spread is quite small in NCEP, the ensemble member averages are not well predicted. The rank histograms suggest that the forecasts are under prediction. The modified Contiguous Rain Area (CRA) technique was used to verify the spatial as well as the quantitative skill of the TIGGE models. Overall, the contribution from the displacement and pattern errors to the total RMSE is found to be more in magnitude. The volume error increases from 24 hr forecast to 48 hr forecast in all the three models.
Micro-mechanical studies on graphite strength prediction models
Kanse, Deepak; Khan, I. A.; Bhasin, V.; Vaze, K. K.
2013-06-01
The influence of type of loading and size-effects on the failure strength of graphite were studied using Weibull model. It was observed that this model over-predicts size effect in tension. However, incorporation of grain size effect in Weibull model, allows a more realistic simulation of size effects. Numerical prediction of strength of four-point bend specimen was made using the Weibull parameters obtained from tensile test data. Effective volume calculations were carried out and subsequently predicted strength was compared with experimental data. It was found that Weibull model can predict mean flexural strength with reasonable accuracy even when grain size effect was not incorporated. In addition, the effects of microstructural parameters on failure strength were analyzed using Rose and Tucker model. Uni-axial tensile, three-point bend and four-point bend strengths were predicted using this model and compared with the experimental data. It was found that this model predicts flexural strength within 10%. For uni-axial tensile strength, difference was 22% which can be attributed to less number of tests on tensile specimens. In order to develop failure surface of graphite under multi-axial state of stress, an open ended hollow tube of graphite was subjected to internal pressure and axial load and Batdorf model was employed to calculate failure probability of the tube. Bi-axial failure surface was generated in the first and fourth quadrant for 50% failure probability by varying both internal pressure and axial load.
New Approaches for Channel Prediction Based on Sinusoidal Modeling
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Ekman Torbjörn
2007-01-01
Full Text Available Long-range channel prediction is considered to be one of the most important enabling technologies to future wireless communication systems. The prediction of Rayleigh fading channels is studied in the frame of sinusoidal modeling in this paper. A stochastic sinusoidal model to represent a Rayleigh fading channel is proposed. Three different predictors based on the statistical sinusoidal model are proposed. These methods outperform the standard linear predictor (LP in Monte Carlo simulations, but underperform with real measurement data, probably due to nonstationary model parameters. To mitigate these modeling errors, a joint moving average and sinusoidal (JMAS prediction model and the associated joint least-squares (LS predictor are proposed. It combines the sinusoidal model with an LP to handle unmodeled dynamics in the signal. The joint LS predictor outperforms all the other sinusoidal LMMSE predictors in suburban environments, but still performs slightly worse than the standard LP in urban environments.
International Nuclear Information System (INIS)
Huang Zhenjun; Wang Jianzhen
1987-01-01
The working principle and characteristics of SM-1 Negative Ion Source is mainly introduced. In the instrument, there is a device to remove O 3 . This instrument can keep high density of negative ions which is generated by the electrical coronas setting out electricity at negative high voltage and can remove the O 3 component which is harmful to the human body. The density of negative ions is higher than 2.5 x 10 6 p./cm 3 while that of O 3 components is less than 1 ppb at the distance of 50 cm from the panel of the instrument. The instrument sprays negative ions automatically without the help of electric fan, so it works noiselessly. It is widely used in national defence, industry, agriculture, forestry, stock raising, sidelines and in the places with an equipment of low density of negative ion or high concentration of O 3 components. Besides, the instrument may also be used to treat diseases, to prevent against rot, to arrest bacteria, to purify air and so on
checkCIF/PLATON report Datablock: sm
Indian Academy of Sciences (India)
Sm (C12 H12 N2) (C15 H11. O2)3. Sum formula. C57 H45 N2 O6 Sm. C57 H45 N2 O6 Sm. Mr. 1004.31. 1004.31. Dx,g cm-3. 1.430. 1.430. Z. 8. 8. Mu (mm-1). 1.313. 1.313. F000. 4088.0. 4088.0. F000'. 4088.21 h,k,lmax. 24,26,31. 24,26,31. Nref. 10796. 10767. Tmin,Tmax. 0.538,0.674. 0.572,0.747. Tmin'. 0.514. Correction ...
Bayesian Age-Period-Cohort Modeling and Prediction - BAMP
Directory of Open Access Journals (Sweden)
Volker J. Schmid
2007-10-01
Full Text Available The software package BAMP provides a method of analyzing incidence or mortality data on the Lexis diagram, using a Bayesian version of an age-period-cohort model. A hierarchical model is assumed with a binomial model in the first-stage. As smoothing priors for the age, period and cohort parameters random walks of first and second order, with and without an additional unstructured component are available. Unstructured heterogeneity can also be included in the model. In order to evaluate the model fit, posterior deviance, DIC and predictive deviances are computed. By projecting the random walk prior into the future, future death rates can be predicted.
Modeling for prediction of restrained shrinkage effect in concrete repair
International Nuclear Information System (INIS)
Yuan Yingshu; Li Guo; Cai Yue
2003-01-01
A general model of autogenous shrinkage caused by chemical reaction (chemical shrinkage) is developed by means of Arrhenius' law and a degree of chemical reaction. Models of tensile creep and relaxation modulus are built based on a viscoelastic, three-element model. Tests of free shrinkage and tensile creep were carried out to determine some coefficients in the models. Two-dimensional FEM analysis based on the models and other constitutions can predict the development of tensile strength and cracking. Three groups of patch-repaired beams were designed for analysis and testing. The prediction from the analysis shows agreement with the test results. The cracking mechanism after repair is discussed
Predicting Footbridge Response using Stochastic Load Models
DEFF Research Database (Denmark)
Pedersen, Lars; Frier, Christian
2013-01-01
Walking parameters such as step frequency, pedestrian mass, dynamic load factor, etc. are basically stochastic, although it is quite common to adapt deterministic models for these parameters. The present paper considers a stochastic approach to modeling the action of pedestrians, but when doing so...... decisions need to be made in terms of statistical distributions of walking parameters and in terms of the parameters describing the statistical distributions. The paper explores how sensitive computations of bridge response are to some of the decisions to be made in this respect. This is useful...
Uncertainties in model-based outcome predictions for treatment planning
International Nuclear Information System (INIS)
Deasy, Joseph O.; Chao, K.S. Clifford; Markman, Jerry
2001-01-01
Purpose: Model-based treatment-plan-specific outcome predictions (such as normal tissue complication probability [NTCP] or the relative reduction in salivary function) are typically presented without reference to underlying uncertainties. We provide a method to assess the reliability of treatment-plan-specific dose-volume outcome model predictions. Methods and Materials: A practical method is proposed for evaluating model prediction based on the original input data together with bootstrap-based estimates of parameter uncertainties. The general framework is applicable to continuous variable predictions (e.g., prediction of long-term salivary function) and dichotomous variable predictions (e.g., tumor control probability [TCP] or NTCP). Using bootstrap resampling, a histogram of the likelihood of alternative parameter values is generated. For a given patient and treatment plan we generate a histogram of alternative model results by computing the model predicted outcome for each parameter set in the bootstrap list. Residual uncertainty ('noise') is accounted for by adding a random component to the computed outcome values. The residual noise distribution is estimated from the original fit between model predictions and patient data. Results: The method is demonstrated using a continuous-endpoint model to predict long-term salivary function for head-and-neck cancer patients. Histograms represent the probabilities for the level of posttreatment salivary function based on the input clinical data, the salivary function model, and the three-dimensional dose distribution. For some patients there is significant uncertainty in the prediction of xerostomia, whereas for other patients the predictions are expected to be more reliable. In contrast, TCP and NTCP endpoints are dichotomous, and parameter uncertainties should be folded directly into the estimated probabilities, thereby improving the accuracy of the estimates. Using bootstrap parameter estimates, competing treatment
Validation of a tuber blight (Phytophthora infestans) prediction model
Potato tuber blight caused by Phytophthora infestans accounts for significant losses in storage. There is limited published quantitative data on predicting tuber blight. We validated a tuber blight prediction model developed in New York with cultivars Allegany, NY 101, and Katahdin using independent...
Geospatial application of the Water Erosion Prediction Project (WEPP) Model
D. C. Flanagan; J. R. Frankenberger; T. A. Cochrane; C. S. Renschler; W. J. Elliot
2011-01-01
The Water Erosion Prediction Project (WEPP) model is a process-based technology for prediction of soil erosion by water at hillslope profile, field, and small watershed scales. In particular, WEPP utilizes observed or generated daily climate inputs to drive the surface hydrology processes (infiltration, runoff, ET) component, which subsequently impacts the rest of the...
Reduced order modelling and predictive control of multivariable ...
Indian Academy of Sciences (India)
Anuj Abraham
2018-03-16
Mar 16, 2018 ... The performance of constraint generalized predictive control scheme is found to be superior to that of the conventional PID controller in terms of overshoot, settling time and performance indices, mainly ISE, IAE and MSE. Keywords. Predictive control; distillation column; reduced order model; dominant pole; ...
Mixed models for predictive modeling in actuarial science
Antonio, K.; Zhang, Y.
2012-01-01
We start with a general discussion of mixed (also called multilevel) models and continue with illustrating specific (actuarial) applications of this type of models. Technical details on (linear, generalized, non-linear) mixed models follow: model assumptions, specifications, estimation techniques
Consensus models to predict endocrine disruption for all ...
Humans are potentially exposed to tens of thousands of man-made chemicals in the environment. It is well known that some environmental chemicals mimic natural hormones and thus have the potential to be endocrine disruptors. Most of these environmental chemicals have never been tested for their ability to disrupt the endocrine system, in particular, their ability to interact with the estrogen receptor. EPA needs tools to prioritize thousands of chemicals, for instance in the Endocrine Disruptor Screening Program (EDSP). Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) was intended to be a demonstration of the use of predictive computational models on HTS data including ToxCast and Tox21 assays to prioritize a large chemical universe of 32464 unique structures for one specific molecular target – the estrogen receptor. CERAPP combined multiple computational models for prediction of estrogen receptor activity, and used the predicted results to build a unique consensus model. Models were developed in collaboration between 17 groups in the U.S. and Europe and applied to predict the common set of chemicals. Structure-based techniques such as docking and several QSAR modeling approaches were employed, mostly using a common training set of 1677 compounds provided by U.S. EPA, to build a total of 42 classification models and 8 regression models for binding, agonist and antagonist activity. All predictions were evaluated on ToxCast data and on an exte
Dietary information improves cardiovascular disease risk prediction models.
Baik, I; Cho, N H; Kim, S H; Shin, C
2013-01-01
Data are limited on cardiovascular disease (CVD) risk prediction models that include dietary predictors. Using known risk factors and dietary information, we constructed and evaluated CVD risk prediction models. Data for modeling were from population-based prospective cohort studies comprised of 9026 men and women aged 40-69 years. At baseline, all were free of known CVD and cancer, and were followed up for CVD incidence during an 8-year period. We used Cox proportional hazard regression analysis to construct a traditional risk factor model, an office-based model, and two diet-containing models and evaluated these models by calculating Akaike information criterion (AIC), C-statistics, integrated discrimination improvement (IDI), net reclassification improvement (NRI) and calibration statistic. We constructed diet-containing models with significant dietary predictors such as poultry, legumes, carbonated soft drinks or green tea consumption. Adding dietary predictors to the traditional model yielded a decrease in AIC (delta AIC=15), a 53% increase in relative IDI (P-value for IDI NRI (category-free NRI=0.14, P NRI (category-free NRI=0.08, P<0.01) compared with the office-based model. The calibration plots for risk prediction demonstrated that the inclusion of dietary predictors contributes to better agreement in persons at high risk for CVD. C-statistics for the four models were acceptable and comparable. We suggest that dietary information may be useful in constructing CVD risk prediction models.
Scanpath Based N-Gram Models for Predicting Reading Behavior
DEFF Research Database (Denmark)
Mishra, Abhijit; Bhattacharyya, Pushpak; Carl, Michael
2013-01-01
Predicting reading behavior is a difficult task. Reading behavior depends on various linguistic factors (e.g. sentence length, structural complexity etc.) and other factors (e.g individual's reading style, age etc.). Ideally, a reading model should be similar to a language model where the model i...
Unsupervised ship trajectory modeling and prediction using compression and clustering
de Vries, G.; van Someren, M.; van Erp, M.; Stehouwer, H.; van Zaanen, M.
2009-01-01
In this paper we show how to build a model of ship trajectories in a certain maritime region and use this model to predict future ship movements. The presented method is unsupervised and based on existing compression (line-simplification) and clustering techniques. We evaluate the model with a
Prediction of annual rainfall pattern using Hidden Markov Model ...
African Journals Online (AJOL)
A hidden Markov model to predict annual rainfall pattern has been presented in this paper. The model is developed to provide necessary information for the farmers, agronomists, water resource management scientists and policy makers to enable them plan for the uncertainty of annual rainfall. The model classified annual ...
The Selection of Turbulence Models for Prediction of Room Airflow
DEFF Research Database (Denmark)
Nielsen, Peter V.
This paper discusses the use of different turbulence models and their advantages in given situations. As an example, it is shown that a simple zero-equation model can be used for the prediction of special situations as flow with a low level of turbulence. A zero-equation model with compensation...
Model Predictive Control of Wind Turbines using Uncertain LIDAR Measurements
DEFF Research Database (Denmark)
Mirzaei, Mahmood; Soltani, Mohsen; Poulsen, Niels Kjølstad
2013-01-01
The problem of Model predictive control (MPC) of wind turbines using uncertain LIDAR (LIght Detection And Ranging) measurements is considered. A nonlinear dynamical model of the wind turbine is obtained. We linearize the obtained nonlinear model for different operating points, which are determined...
Using Pareto points for model identification in predictive toxicology
2013-01-01
Predictive toxicology is concerned with the development of models that are able to predict the toxicity of chemicals. A reliable prediction of toxic effects of chemicals in living systems is highly desirable in cosmetics, drug design or food protection to speed up the process of chemical compound discovery while reducing the need for lab tests. There is an extensive literature associated with the best practice of model generation and data integration but management and automated identification of relevant models from available collections of models is still an open problem. Currently, the decision on which model should be used for a new chemical compound is left to users. This paper intends to initiate the discussion on automated model identification. We present an algorithm, based on Pareto optimality, which mines model collections and identifies a model that offers a reliable prediction for a new chemical compound. The performance of this new approach is verified for two endpoints: IGC50 and LogP. The results show a great potential for automated model identification methods in predictive toxicology. PMID:23517649
DEFF Research Database (Denmark)
Christensen, Nikolaj Kruse; Christensen, Steen; Ferre, Ty
constructed from geological and hydrological data. However, geophysical data are increasingly used to inform hydrogeologic models because they are collected at lower cost and much higher density than geological and hydrological data. Despite increased use of geophysics, it is still unclear whether...... the integration of geophysical data in the construction of a groundwater model increases the prediction performance. We suggest that modelers should perform a hydrogeophysical “test-bench” analysis of the likely value of geophysics data for improving groundwater model prediction performance before actually...... collecting geophysical data. At a minimum, an analysis should be conducted assuming settings that are favorable for the chosen geophysical method. If the analysis suggests that data collected by the geophysical method is unlikely to improve model prediction performance under these favorable settings...
Hybrid Corporate Performance Prediction Model Considering Technical Capability
Directory of Open Access Journals (Sweden)
Joonhyuck Lee
2016-07-01
Full Text Available Many studies have tried to predict corporate performance and stock prices to enhance investment profitability using qualitative approaches such as the Delphi method. However, developments in data processing technology and machine-learning algorithms have resulted in efforts to develop quantitative prediction models in various managerial subject areas. We propose a quantitative corporate performance prediction model that applies the support vector regression (SVR algorithm to solve the problem of the overfitting of training data and can be applied to regression problems. The proposed model optimizes the SVR training parameters based on the training data, using the genetic algorithm to achieve sustainable predictability in changeable markets and managerial environments. Technology-intensive companies represent an increasing share of the total economy. The performance and stock prices of these companies are affected by their financial standing and their technological capabilities. Therefore, we apply both financial indicators and technical indicators to establish the proposed prediction model. Here, we use time series data, including financial, patent, and corporate performance information of 44 electronic and IT companies. Then, we predict the performance of these companies as an empirical verification of the prediction performance of the proposed model.
Preoperative prediction model of outcome after cholecystectomy for symptomatic gallstones
DEFF Research Database (Denmark)
Borly, L; Anderson, I B; Bardram, Linda
1999-01-01
and sonography evaluated gallbladder motility, gallstones, and gallbladder volume. Preoperative variables in patients with or without postcholecystectomy pain were compared statistically, and significant variables were combined in a logistic regression model to predict the postoperative outcome. RESULTS: Eighty...... and by the absence of 'agonizing' pain and of symptoms coinciding with pain (P model 15 of 18 predicted patients had postoperative pain (PVpos = 0.83). Of 62 patients predicted as having no pain postoperatively, 56 were pain-free (PVneg = 0.90). Overall accuracy...... was 89%. CONCLUSION: From this prospective study a model based on preoperative symptoms was developed to predict postcholecystectomy pain. Since intrastudy reclassification may give too optimistic results, the model should be validated in future studies....
Prediction of Chemical Function: Model Development and Application
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 (...
Linear regression crash prediction models : issues and proposed solutions.
2010-05-01
The paper develops a linear regression model approach that can be applied to : crash data to predict vehicle crashes. The proposed approach involves novice data aggregation : to satisfy linear regression assumptions; namely error structure normality ...
FPGA implementation of predictive degradation model for engine oil lifetime
Idros, M. F. M.; Razak, A. H. A.; Junid, S. A. M. Al; Suliman, S. I.; Halim, A. K.
2018-03-01
This paper presents the implementation of linear regression model for degradation prediction on Register Transfer Logic (RTL) using QuartusII. A stationary model had been identified in the degradation trend for the engine oil in a vehicle in time series method. As for RTL implementation, the degradation model is written in Verilog HDL and the data input are taken at a certain time. Clock divider had been designed to support the timing sequence of input data. At every five data, a regression analysis is adapted for slope variation determination and prediction calculation. Here, only the negative value are taken as the consideration for the prediction purposes for less number of logic gate. Least Square Method is adapted to get the best linear model based on the mean values of time series data. The coded algorithm has been implemented on FPGA for validation purposes. The result shows the prediction time to change the engine oil.
Predictive Modeling: A New Paradigm for Managing Endometrial Cancer.
Bendifallah, Sofiane; Daraï, Emile; Ballester, Marcos
2016-03-01
With the abundance of new options in diagnostic and treatment modalities, a shift in the medical decision process for endometrial cancer (EC) has been observed. The emergence of individualized medicine and the increasing complexity of available medical data has lead to the development of several prediction models. In EC, those clinical models (algorithms, nomograms, and risk scoring systems) have been reported, especially for stratifying and subgrouping patients, with various unanswered questions regarding such things as the optimal surgical staging for lymph node metastasis as well as the assessment of recurrence and survival outcomes. In this review, we highlight existing prognostic and predictive models in EC, with a specific focus on their clinical applicability. We also discuss the methodologic aspects of the development of such predictive models and the steps that are required to integrate these tools into clinical decision making. In the future, the emerging field of molecular or biochemical markers research may substantially improve predictive and treatment approaches.
Predictive modeling in catalysis - from dream to reality
Maldonado, A.G.; Rothenberg, G.
2009-01-01
In silico catalyst optimization is the ultimate application of computers in catalysis. This article provides an overview of the basic concepts of predictive modeling and describes how this technique can be used in catalyst and reaction design.
Fuzzy model predictive control algorithm applied in nuclear power plant
International Nuclear Information System (INIS)
Zuheir, Ahmad
2006-01-01
The aim of this paper is to design a predictive controller based on a fuzzy model. The Takagi-Sugeno fuzzy model with an Adaptive B-splines neuro-fuzzy implementation is used and incorporated as a predictor in a predictive controller. An optimization approach with a simplified gradient technique is used to calculate predictions of the future control actions. In this approach, adaptation of the fuzzy model using dynamic process information is carried out to build the predictive controller. The easy description of the fuzzy model and the easy computation of the gradient sector during the optimization procedure are the main advantages of the computation algorithm. The algorithm is applied to the control of a U-tube steam generation unit (UTSG) used for electricity generation. (author)
Compensatory versus noncompensatory models for predicting consumer preferences
Directory of Open Access Journals (Sweden)
Anja Dieckmann
2009-04-01
Full Text Available Standard preference models in consumer research assume that people weigh and add all attributes of the available options to derive a decision, while there is growing evidence for the use of simplifying heuristics. Recently, a greedoid algorithm has been developed (Yee, Dahan, Hauser and Orlin, 2007; Kohli and Jedidi, 2007 to model lexicographic heuristics from preference data. We compare predictive accuracies of the greedoid approach and standard conjoint analysis in an online study with a rating and a ranking task. The lexicographic model derived from the greedoid algorithm was better at predicting ranking compared to rating data, but overall, it achieved lower predictive accuracy for hold-out data than the compensatory model estimated by conjoint analysis. However, a considerable minority of participants was better predicted by lexicographic strategies. We conclude that the new algorithm will not replace standard tools for analyzing preferences, but can boost the study of situational and individual differences in preferential choice processes.
Predictive Modeling of Partitioned Systems: Implementation and Applications
Latten, Christine
2014-01-01
A general mathematical methodology for predictive modeling of coupled multi-physics systems is implemented and has been applied without change to an illustrative heat conduction example and reactor physics benchmarks.
A new, accurate predictive model for incident hypertension
DEFF Research Database (Denmark)
Völzke, Henry; Fung, Glenn; Ittermann, Till
2013-01-01
Data mining represents an alternative approach to identify new predictors of multifactorial diseases. This work aimed at building an accurate predictive model for incident hypertension using data mining procedures....
Fanti, Marcello; The ATLAS collaboration
2017-01-01
One of the fundamental properties of the Higgs boson, its mass, is measured by studying the invariant mass of the 4-lepton and diphoton decay channels with about 36 fb-1 of data collected at 13 TeV collision energy by the ATLAS experiment. Since in this channel the final state can be reconstructed as invariant mass peak with a good experimental resolution, this measurement can be done in a model-independent way and used as an input to compare other measurement properties with the SM predictions.
Model Predictive Control for Ethanol Steam Reformers
Li, Mingming
2014-01-01
This thesis firstly proposes a new approach of modelling an ethanol steam reformer (ESR) for producing pure hydrogen. Hydrogen has obvious benefits as an alternative for feeding the proton exchange membrane fuel cells (PEMFCs) to produce electricity. However, an important drawback is that the hydrogen distribution and storage have high cost. So the ESR is regarded as a way to overcome these difficulties. Ethanol is currently considered as a promising energy source under the res...
Haskell financial data modeling and predictive analytics
Ryzhov, Pavel
2013-01-01
This book is a hands-on guide that teaches readers how to use Haskell's tools and libraries to analyze data from real-world sources in an easy-to-understand manner.This book is great for developers who are new to financial data modeling using Haskell. A basic knowledge of functional programming is not required but will be useful. An interest in high frequency finance is essential.
A highly predictive A 4 flavor 3-3-1 model with radiative inverse seesaw mechanism
Cárcamo Hernández, A. E.; Long, H. N.
2018-04-01
We build a highly predictive 3-3-1 model, where the field content is extended by including several SU(3) L scalar singlets and six right handed Majorana neutrinos. In our model the {SU}{(3)}C× {SU}{(3)}L× U{(1)}X gauge symmetry is supplemented by the {A}4× {Z}4× {Z}6× {Z}16× {Z}16{\\prime } discrete group, which allows to get a very good description of the low energy fermion flavor data. In the model under consideration, the {A}4× {Z}4× {Z}6× {Z}16× {Z}16{\\prime } discrete group is broken at very high energy scale down to the preserved Z 2 discrete symmetry, thus generating the observed pattern of SM fermion masses and mixing angles and allowing the implementation of the loop level inverse seesaw mechanism for the generation of the light active neutrino masses, respectively. The obtained values for the physical observables in the quark sector agree with the experimental data, whereas those ones for the lepton sector also do, only for the case of inverted neutrino mass spectrum. The normal neutrino mass hierarchy scenario of the model is ruled out by the neutrino oscillation experimental data. We find an effective Majorana neutrino mass parameter of neutrinoless double beta decay of m ee = 46.9 meV, a leptonic Dirac CP violating phase of -81.37° and a Jarlskog invariant of about 10-2 for the inverted neutrino mass hierarchy. The preserved Z 2 symmetry allows for a stable scalar dark matter candidate.
Wireless model predictive control: Application to water-level system
Directory of Open Access Journals (Sweden)
Ramdane Hedjar
2016-04-01
Full Text Available This article deals with wireless model predictive control of a water-level control system. The objective of the model predictive control algorithm is to constrain the control signal inside saturation limits and maintain the water level around the desired level. Linear modeling of any nonlinear plant leads to parameter uncertainties and non-modeled dynamics in the linearized mathematical model. These uncertainties induce a steady-state error in the output response of the water level. To eliminate this steady-state error and increase the robustness of the control algorithm, an integral action is included in the closed loop. To control the water-level system remotely, the communication between the controller and the process is performed using radio channel. To validate the proposed scheme, simulation and real-time implementation of the algorithm have been conducted, and the results show the effectiveness of wireless model predictive control with integral action.
Isomer decay spectroscopy of 164Sm and 166Gd: midshell collectivity around N=100.
Patel, Z; Söderström, P-A; Podolyák, Zs; Regan, P H; Walker, P M; Watanabe, H; Ideguchi, E; Simpson, G S; Liu, H L; Nishimura, S; Wu, Q; Xu, F R; Browne, F; Doornenbal, P; Lorusso, G; Rice, S; Sinclair, L; Sumikama, T; Wu, J; Xu, Z Y; Aoi, N; Baba, H; Bello Garrote, F L; Benzoni, G; Daido, R; Fang, Y; Fukuda, N; Gey, G; Go, S; Gottardo, A; Inabe, N; Isobe, T; Kameda, D; Kobayashi, K; Kobayashi, M; Komatsubara, T; Kojouharov, I; Kubo, T; Kurz, N; Kuti, I; Li, Z; Matsushita, M; Michimasa, S; Moon, C-B; Nishibata, H; Nishizuka, I; Odahara, A; Şahin, E; Sakurai, H; Schaffner, H; Suzuki, H; Takeda, H; Tanaka, M; Taprogge, J; Vajta, Zs; Yagi, A; Yokoyama, R
2014-12-31
Excited states in the N=102 isotones 166Gd and 164Sm have been observed following isomeric decay for the first time at RIBF, RIKEN. The half-lives of the isomeric states have been measured to be 950(60) and 600(140) ns for 166Gd and 164Sm, respectively. Based on the decay patterns and potential energy surface calculations, including β6 deformation, a spin and parity of 6- has been assigned to the isomeric states in both nuclei. Collective observables are discussed in light of the systematics of the region, giving insight into nuclear shape evolution. The decrease in the ground-band energies of 166Gd and 164Sm (N=102) compared to 164Gd and 162Sm (N=100), respectively, presents evidence for the predicted deformed shell closure at N=100.
Isomer Decay Spectroscopy of Sm 164 and Gd 166 : Midshell Collectivity Around N =100
Patel, Z.; Söderström, P.-A.; Podolyák, Zs.; Regan, P. H.; Walker, P. M.; Watanabe, H.; Ideguchi, E.; Simpson, G. S.; Liu, H. L.; Nishimura, S.; Wu, Q.; Xu, F. R.; Browne, F.; Doornenbal, P.; Lorusso, G.; Rice, S.; Sinclair, L.; Sumikama, T.; Wu, J.; Xu, Z. Y.; Aoi, N.; Baba, H.; Bello Garrote, F. L.; Benzoni, G.; Daido, R.; Fang, Y.; Fukuda, N.; Gey, G.; Go, S.; Gottardo, A.; Inabe, N.; Isobe, T.; Kameda, D.; Kobayashi, K.; Kobayashi, M.; Komatsubara, T.; Kojouharov, I.; Kubo, T.; Kurz, N.; Kuti, I.; Li, Z.; Matsushita, M.; Michimasa, S.; Moon, C.-B.; Nishibata, H.; Nishizuka, I.; Odahara, A.; Şahin, E.; Sakurai, H.; Schaffner, H.; Suzuki, H.; Takeda, H.; Tanaka, M.; Taprogge, J.; Vajta, Zs.; Yagi, A.; Yokoyama, R.
2014-12-01
Excited states in the N =102 isotones Gd 166 and Sm 164 have been observed following isomeric decay for the first time at RIBF, RIKEN. The half-lives of the isomeric states have been measured to be 950(60) and 600(140) ns for Gd 166 and Sm 164 , respectively. Based on the decay patterns and potential energy surface calculations, including β6 deformation, a spin and parity of 6- has been assigned to the isomeric states in both nuclei. Collective observables are discussed in light of the systematics of the region, giving insight into nuclear shape evolution. The decrease in the ground-band energies of Gd 166 and Sm 164 (N =102 ) compared to Gd 164 and Sm 162 (N =100 ), respectively, presents evidence for the predicted deformed shell closure at N =100 .
Aqua/Aura Updated Inclination Adjust Maneuver Performance Prediction Model
Boone, Spencer
2017-01-01
This presentation will discuss the updated Inclination Adjust Maneuver (IAM) performance prediction model that was developed for Aqua and Aura following the 2017 IAM series. This updated model uses statistical regression methods to identify potential long-term trends in maneuver parameters, yielding improved predictions when re-planning past maneuvers. The presentation has been reviewed and approved by Eric Moyer, ESMO Deputy Project Manager.
Approximating prediction uncertainty for random forest regression models
John W. Coulston; Christine E. Blinn; Valerie A. Thomas; Randolph H. Wynne
2016-01-01
Machine learning approaches such as random forest haveÂ increased for the spatial modeling and mapping of continuousÂ variables. Random forest is a non-parametric ensembleÂ approach, and unlike traditional regression approaches thereÂ is no direct quantification of prediction error. UnderstandingÂ prediction uncertainty is important when using model-basedÂ continuous maps as...
Prediction of cloud droplet number in a general circulation model
Energy Technology Data Exchange (ETDEWEB)
Ghan, S.J.; Leung, L.R. [Pacific Northwest National Lab., Richland, WA (United States)
1996-04-01
We have applied the Colorado State University Regional Atmospheric Modeling System (RAMS) bulk cloud microphysics parameterization to the treatment of stratiform clouds in the National Center for Atmospheric Research Community Climate Model (CCM2). The RAMS predicts mass concentrations of cloud water, cloud ice, rain and snow, and number concnetration of ice. We have introduced the droplet number conservation equation to predict droplet number and it`s dependence on aerosols.
The Next Page Access Prediction Using Makov Model
Deepti Razdan
2011-01-01
Predicting the next page to be accessed by the Webusers has attracted a large amount of research. In this paper, anew web usage mining approach is proposed to predict next pageaccess. It is proposed to identify similar access patterns from weblog using K-mean clustering and then Markov model is used forprediction for next page accesses. The tightness of clusters isimproved by setting similarity threshold while forming clusters.In traditional recommendation models, clustering by nonsequentiald...
Energy Technology Data Exchange (ETDEWEB)
Bu, Shao-Jing; Duan, Xiu-Li; Han, Xu-Hao; Sun, Ji-Bing, E-mail: hbgdsjb@126.com; Chi, Xiang; Cui, Chun-Xiang
2017-02-01
Hard/soft Sm-Co/Co nanocomposite particles were prepared by reducing CoCl{sub 2}·6H{sub 2}O in the solution containing ball-milled Sm(Co, Hf){sub 7} particles by a simple polyol method with ethylene glycol as the solvent. Phase composition, microstructure and magnetic properties of the particles were analyzed by XRD, TEM (HRTEM) and VSM, respectively. It has been found that Sm-Co/Co core/shell structure is formed in which the Co shell is 3–5 nm in thickness and mainly exists in hcp-Co phase. At the same time, fcc-Co tends to nucleate and grow independently between Sm-Co particles. The formation mechanism of Sm-Co/Co composite particles is discussed and corresponding model is established. Sm-Co/Co composite particles perform obvious remanence enhancement effects especially after being heated at 450 °C for 15 min.
Bu, Shao-Jing; Duan, Xiu-Li; Han, Xu-Hao; Sun, Ji-Bing; Chi, Xiang; Cui, Chun-Xiang
2017-02-01
Hard/soft Sm-Co/Co nanocomposite particles were prepared by reducing CoCl2·6H2O in the solution containing ball-milled Sm(Co, Hf)7 particles by a simple polyol method with ethylene glycol as the solvent. Phase composition, microstructure and magnetic properties of the particles were analyzed by XRD, TEM (HRTEM) and VSM, respectively. It has been found that Sm-Co/Co core/shell structure is formed in which the Co shell is 3-5 nm in thickness and mainly exists in hcp-Co phase. At the same time, fcc-Co tends to nucleate and grow independently between Sm-Co particles. The formation mechanism of Sm-Co/Co composite particles is discussed and corresponding model is established. Sm-Co/Co composite particles perform obvious remanence enhancement effects especially after being heated at 450 °C for 15 min
Tang, J. L.; Cai, C. Z.; Xiao, T. T.; Huang, S. J.
2012-07-01
The electrical conductivity of solid oxide fuel cell (SOFC) cathode is one of the most important indices affecting the efficiency of SOFC. In order to improve the performance of fuel cell system, it is advantageous to have accurate model with which one can predict the electrical conductivity. In this paper, a model utilizing support vector regression (SVR) approach combined with particle swarm optimization (PSO) algorithm for its parameter optimization was established to modeling and predicting the electrical conductivity of Ba0.5Sr0.5Co0.8Fe0.2 O3-δ-xSm0.5Sr0.5CoO3-δ (BSCF-xSSC) composite cathode under two influence factors, including operating temperature (T) and SSC content (x) in BSCF-xSSC composite cathode. The leave-one-out cross validation (LOOCV) test result by SVR strongly supports that the generalization ability of SVR model is high enough. The absolute percentage error (APE) of 27 samples does not exceed 0.05%. The mean absolute percentage error (MAPE) of all 30 samples is only 0.09% and the correlation coefficient (R2) as high as 0.999. This investigation suggests that the hybrid PSO-SVR approach may be not only a promising and practical methodology to simulate the properties of fuel cell system, but also a powerful tool to be used for optimal designing or controlling the operating process of a SOFC system.
International Nuclear Information System (INIS)
Arzumanyan, G.A.
1982-01-01
Vast regions (up to 25 mol%) of the existence of limited solid solutions with Dalton compositions Cu 3 Sm(WO 4 ) 3 , Cu 13 Sm 7 x(WO 4 ) 17 , Cu 9 Sm 11 (WO 4 ) 21 and Cu 3 Sm 17 (WO 4 ) 27 possessing the structures related to scheelite distorted monoclinic structure of Eu 2 (WO 4 ) 3 A-phase were revealed in the Cu 3 Sm(WO 4 ) 3 -Sm 2 (WO 4 ) 3 system at 500-1100 deg C temperatures and oxigen partial pressure of 0.9-1.3 Pa (total pressure of the noble gas - 102.1+-0.6 kPa)
Working Towards a Risk Prediction Model for Neural Tube Defects
Agopian, A.J.; Lupo, Philip J.; Tinker, Sarah C.; Canfield, Mark A.; Mitchell, Laura E.
2015-01-01
BACKGROUND Several risk factors have been consistently associated with neural tube defects (NTDs). However, the predictive ability of these risk factors in combination has not been evaluated. METHODS To assess the predictive ability of established risk factors for NTDs, we built predictive models using data from the National Birth Defects Prevention Study, which is a large, population-based study of nonsyndromic birth defects. Cases with spina bifida or anencephaly, or both (n = 1239), and controls (n = 8494) were randomly divided into separate training (75% of cases and controls) and validation (remaining 25%) samples. Multivariable logistic regression models were constructed with the training samples. The predictive ability of these models was evaluated in the validation samples by assessing the area under the receiver operator characteristic curves. An ordinal predictive risk index was also constructed and evaluated. In addition, the ability of classification and regression tree (CART) analysis to identify subgroups of women at increased risk for NTDs in offspring was evaluated. RESULTS The predictive ability of the multivariable models was poor (area under the receiver operating curve: 0.55 for spina bifida only, 0.59 for anencephaly only, and 0.56 for anencephaly and spina bifida combined). The predictive abilities of the ordinal risk indexes and CART models were also low. CONCLUSION Current established risk factors for NTDs are insufficient for population-level prediction of a women’s risk for having affected offspring. Identification of genetic risk factors and novel nongenetic risk factors will be critical to establishing models, with good predictive ability, for NTDs. PMID:22253139
Assessing the Reliability of Quantitative Imaging of Sm-153
Poh, Zijie; Dagan, Maáyan; Veldman, Jeanette; Trees, Brad
2013-03-01
Samarium-153 is used for palliation of and recently has been investigated for therapy for bone metastases. Patient specific dosing of Sm-153 is based on quantitative single-photon emission computed tomography (SPECT) and knowing the accuracy and precision of image-based estimates of the in vivo activity distribution. Physical phantom studies are useful for estimating these in simple objects, but do not model realistic activity distributions. We are using realistic Monte Carlo simulations combined with a realistic digital phantom modeling human anatomy to assess the accuracy and precision of Sm-153 SPECT. Preliminary data indicates that we can simulate projection images and reconstruct them with compensation for various physical image degrading factors, such as attenuation and scatter in the body as well as non-idealities in the imaging system, to provide realistic SPECT images.
Predictive QSAR Models for the Toxicity of Disinfection Byproducts.
Qin, Litang; Zhang, Xin; Chen, Yuhan; Mo, Lingyun; Zeng, Honghu; Liang, Yanpeng
2017-10-09
Several hundred disinfection byproducts (DBPs) in drinking water have been identified, and are known to have potentially adverse health effects. There are toxicological data gaps for most DBPs, and the predictive method may provide an effective way to address this. The development of an in-silico model of toxicology endpoints of DBPs is rarely studied. The main aim of the present study is to develop predictive quantitative structure-activity relationship (QSAR) models for the reactive toxicities of 50 DBPs in the five bioassays of X-Microtox, GSH+, GSH-, DNA+ and DNA-. All-subset regression was used to select the optimal descriptors, and multiple linear-regression models were built. The developed QSAR models for five endpoints satisfied the internal and external validation criteria: coefficient of determination ( R ²) > 0.7, explained variance in leave-one-out prediction ( Q ² LOO ) and in leave-many-out prediction ( Q ² LMO ) > 0.6, variance explained in external prediction ( Q ² F1 , Q ² F2 , and Q ² F3 ) > 0.7, and concordance correlation coefficient ( CCC ) > 0.85. The application domains and the meaning of the selective descriptors for the QSAR models were discussed. The obtained QSAR models can be used in predicting the toxicities of the 50 DBPs.
Predictive QSAR Models for the Toxicity of Disinfection Byproducts
Directory of Open Access Journals (Sweden)
Litang Qin
2017-10-01
Full Text Available Several hundred disinfection byproducts (DBPs in drinking water have been identified, and are known to have potentially adverse health effects. There are toxicological data gaps for most DBPs, and the predictive method may provide an effective way to address this. The development of an in-silico model of toxicology endpoints of DBPs is rarely studied. The main aim of the present study is to develop predictive quantitative structure–activity relationship (QSAR models for the reactive toxicities of 50 DBPs in the five bioassays of X-Microtox, GSH+, GSH−, DNA+ and DNA−. All-subset regression was used to select the optimal descriptors, and multiple linear-regression models were built. The developed QSAR models for five endpoints satisfied the internal and external validation criteria: coefficient of determination (R2 > 0.7, explained variance in leave-one-out prediction (Q2LOO and in leave-many-out prediction (Q2LMO > 0.6, variance explained in external prediction (Q2F1, Q2F2, and Q2F3 > 0.7, and concordance correlation coefficient (CCC > 0.85. The application domains and the meaning of the selective descriptors for the QSAR models were discussed. The obtained QSAR models can be used in predicting the toxicities of the 50 DBPs.
Nonconvex Model Predictive Control for Commercial Refrigeration
DEFF Research Database (Denmark)
Hovgaard, Tobias Gybel; Larsen, Lars F.S.; Jørgensen, John Bagterp
2013-01-01
function, however, is nonconvex due to the temperature dependence of thermodynamic efficiency. To handle this nonconvexity we propose a sequential convex optimization method, which typically converges in fewer than 5 or so iterations. We employ a fast convex quadratic programming solver to carry out...... the iterations, which is more than fast enough to run in real-time. We demonstrate our method on a realistic model, with a full year simulation and 15 minute time periods, using historical electricity prices and weather data, as well as random variations in thermal load. These simulations show substantial cost...... capacity associated with large penetration of intermittent renewable energy sources in a future smart grid....
Maxent modelling for predicting the potential distribution of Thai Palms
DEFF Research Database (Denmark)
Tovaranonte, Jantrararuk; Barfod, Anders S.; Overgaard, Anne Blach
2011-01-01
Increasingly species distribution models are being used to address questions related to ecology, biogeography and species conservation on global and regional scales. We used the maximum entropy approach implemented in the MAXENT programme to build a habitat suitability model for Thai palms based...... overprediction of species distribution ranges. The models with the best predictive power were found by calculating the area under the curve (AUC) of receiver-operating characteristic (ROC). Here, we provide examples of contrasting predicted species distribution ranges as well as a map of modeled palm diversity...
Validation of Fatigue Modeling Predictions in Aviation Operations
Gregory, Kevin; Martinez, Siera; Flynn-Evans, Erin
2017-01-01
Bio-mathematical fatigue models that predict levels of alertness and performance are one potential tool for use within integrated fatigue risk management approaches. A number of models have been developed that provide predictions based on acute and chronic sleep loss, circadian desynchronization, and sleep inertia. Some are publicly available and gaining traction in settings such as commercial aviation as a means of evaluating flight crew schedules for potential fatigue-related risks. Yet, most models have not been rigorously evaluated and independently validated for the operations to which they are being applied and many users are not fully aware of the limitations in which model results should be interpreted and applied.
Aero-acoustic noise of wind turbines. Noise prediction models
Energy Technology Data Exchange (ETDEWEB)
Maribo Pedersen, B. [ed.
1997-12-31
Semi-empirical and CAA (Computational AeroAcoustics) noise prediction techniques are the subject of this expert meeting. The meeting presents and discusses models and methods. The meeting may provide answers to the following questions: What Noise sources are the most important? How are the sources best modeled? What needs to be done to do better predictions? Does it boil down to correct prediction of the unsteady aerodynamics around the rotor? Or is the difficult part to convert the aerodynamics into acoustics? (LN)
Using a Prediction Model to Manage Cyber Security Threats.
Jaganathan, Venkatesh; Cherurveettil, Priyesh; Muthu Sivashanmugam, Premapriya
2015-01-01
Cyber-attacks are an important issue faced by all organizations. Securing information systems is critical. Organizations should be able to understand the ecosystem and predict attacks. Predicting attacks quantitatively should be part of risk management. The cost impact due to worms, viruses, or other malicious software is significant. This paper proposes a mathematical model to predict the impact of an attack based on significant factors that influence cyber security. This model also considers the environmental information required. It is generalized and can be customized to the needs of the individual organization.
Using a Prediction Model to Manage Cyber Security Threats
Directory of Open Access Journals (Sweden)
Venkatesh Jaganathan
2015-01-01
Full Text Available Cyber-attacks are an important issue faced by all organizations. Securing information systems is critical. Organizations should be able to understand the ecosystem and predict attacks. Predicting attacks quantitatively should be part of risk management. The cost impact due to worms, viruses, or other malicious software is significant. This paper proposes a mathematical model to predict the impact of an attack based on significant factors that influence cyber security. This model also considers the environmental information required. It is generalized and can be customized to the needs of the individual organization.
Predictions for mt and MW in minimal supersymmetric models
International Nuclear Information System (INIS)
Buchmueller, O.; Ellis, J.R.; Flaecher, H.; Isidori, G.
2009-12-01
Using a frequentist analysis of experimental constraints within two versions of the minimal supersymmetric extension of the Standard Model, we derive the predictions for the top quark mass, m t , and the W boson mass, m W . We find that the supersymmetric predictions for both m t and m W , obtained by incorporating all the relevant experimental information and state-of-the-art theoretical predictions, are highly compatible with the experimental values with small remaining uncertainties, yielding an improvement compared to the case of the Standard Model. (orig.)
Tollenaar, N.; Van der Heijden, P.G.M.
2013-01-01
Using criminal population criminal conviction history information, prediction models are developed that predict three types of criminal recidivism: general recidivism, violent recidivism and sexual recidivism. The research question is whether prediction techniques from modern statistics, data mining
Preprocedural Prediction Model for Contrast-Induced Nephropathy Patients.
Yin, Wen-Jun; Yi, Yi-Hu; Guan, Xiao-Feng; Zhou, Ling-Yun; Wang, Jiang-Lin; Li, Dai-Yang; Zuo, Xiao-Cong
2017-02-03
Several models have been developed for prediction of contrast-induced nephropathy (CIN); however, they only contain patients receiving intra-arterial contrast media for coronary angiographic procedures, which represent a small proportion of all contrast procedures. In addition, most of them evaluate radiological interventional procedure-related variables. So it is necessary for us to develop a model for prediction of CIN before radiological procedures among patients administered contrast media. A total of 8800 patients undergoing contrast administration were randomly assigned in a 4:1 ratio to development and validation data sets. CIN was defined as an increase of 25% and/or 0.5 mg/dL in serum creatinine within 72 hours above the baseline value. Preprocedural clinical variables were used to develop the prediction model from the training data set by the machine learning method of random forest, and 5-fold cross-validation was used to evaluate the prediction accuracies of the model. Finally we tested this model in the validation data set. The incidence of CIN was 13.38%. We built a prediction model with 13 preprocedural variables selected from 83 variables. The model obtained an area under the receiver-operating characteristic (ROC) curve (AUC) of 0.907 and gave prediction accuracy of 80.8%, sensitivity of 82.7%, specificity of 78.8%, and Matthews correlation coefficient of 61.5%. For the first time, 3 new factors are included in the model: the decreased sodium concentration, the INR value, and the preprocedural glucose level. The newly established model shows excellent predictive ability of CIN development and thereby provides preventative measures for CIN. © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.
Risk Prediction Models for Oral Clefts Allowing for Phenotypic Heterogeneity
Directory of Open Access Journals (Sweden)
Yalu eWen
2015-08-01
Full Text Available Oral clefts are common birth defects that have a major impact on the affected individual, their family and society. World-wide, the incidence of oral clefts is 1/700 live births, making them the most common craniofacial birth defects. The successful prediction of oral clefts may help identify sub-population at high risk, and promote new diagnostic and therapeutic strategies. Nevertheless, developing a clinically useful oral clefts risk prediction model remains a great challenge. Compelling evidences suggest the etiologies of oral clefts are highly heterogeneous, and the development of a risk prediction model with consideration of phenotypic heterogeneity may potentially improve the accuracy of a risk prediction model. In this study, we applied a previously developed statistical method to investigate the risk prediction on sub-phenotypes of oral clefts. Our results suggested subtypes of cleft lip and palate have similar genetic etiologies (AUC=0.572 with subtypes of cleft lip only (AUC=0.589, while the subtypes of cleft palate only (CPO have heterogeneous underlying mechanisms (AUCs for soft CPO and hard CPO are 0.617 and 0.623, respectively. This highlighted the potential that the hard and soft forms of CPO have their own mechanisms despite sharing some of the genetic risk factors. Comparing with conventional methods for risk prediction modeling, our method considers phenotypic heterogeneity of a disease, which potentially improves the accuracy for predicting each sub-phenotype of oral clefts.
Model output statistics applied to wind power prediction
Energy Technology Data Exchange (ETDEWEB)
Joensen, A.; Giebel, G.; Landberg, L. [Risoe National Lab., Roskilde (Denmark); Madsen, H.; Nielsen, H.A. [The Technical Univ. of Denmark, Dept. of Mathematical Modelling, Lyngby (Denmark)
1999-03-01
Being able to predict the output of a wind farm online for a day or two in advance has significant advantages for utilities, such as better possibility to schedule fossil fuelled power plants and a better position on electricity spot markets. In this paper prediction methods based on Numerical Weather Prediction (NWP) models are considered. The spatial resolution used in NWP models implies that these predictions are not valid locally at a specific wind farm. Furthermore, due to the non-stationary nature and complexity of the processes in the atmosphere, and occasional changes of NWP models, the deviation between the predicted and the measured wind will be time dependent. If observational data is available, and if the deviation between the predictions and the observations exhibits systematic behavior, this should be corrected for; if statistical methods are used, this approaches is usually referred to as MOS (Model Output Statistics). The influence of atmospheric turbulence intensity, topography, prediction horizon length and auto-correlation of wind speed and power is considered, and to take the time-variations into account, adaptive estimation methods are applied. Three estimation techniques are considered and compared, Extended Kalman Filtering, recursive least squares and a new modified recursive least squares algorithm. (au) EU-JOULE-3. 11 refs.
Survival prediction model for postoperative hepatocellular carcinoma patients.
Ren, Zhihui; He, Shasha; Fan, Xiaotang; He, Fangping; Sang, Wei; Bao, Yongxing; Ren, Weixin; Zhao, Jinming; Ji, Xuewen; Wen, Hao
2017-09-01
This study is to establish a predictive index (PI) model of 5-year survival rate for patients with hepatocellular carcinoma (HCC) after radical resection and to evaluate its prediction sensitivity, specificity, and accuracy.Patients underwent HCC surgical resection were enrolled and randomly divided into prediction model group (101 patients) and model evaluation group (100 patients). Cox regression model was used for univariate and multivariate survival analysis. A PI model was established based on multivariate analysis and receiver operating characteristic (ROC) curve was drawn accordingly. The area under ROC (AUROC) and PI cutoff value was identified.Multiple Cox regression analysis of prediction model group showed that neutrophil to lymphocyte ratio, histological grade, microvascular invasion, positive resection margin, number of tumor, and postoperative transcatheter arterial chemoembolization treatment were the independent predictors for the 5-year survival rate for HCC patients. The model was PI = 0.377 × NLR + 0.554 × HG + 0.927 × PRM + 0.778 × MVI + 0.740 × NT - 0.831 × transcatheter arterial chemoembolization (TACE). In the prediction model group, AUROC was 0.832 and the PI cutoff value was 3.38. The sensitivity, specificity, and accuracy were 78.0%, 80%, and 79.2%, respectively. In model evaluation group, AUROC was 0.822, and the PI cutoff value was well corresponded to the prediction model group with sensitivity, specificity, and accuracy of 85.0%, 83.3%, and 84.0%, respectively.The PI model can quantify the mortality risk of hepatitis B related HCC with high sensitivity, specificity, and accuracy.
A prediction model for assessing residential radon concentration in Switzerland
International Nuclear Information System (INIS)
Hauri, Dimitri D.; Huss, Anke; Zimmermann, Frank; Kuehni, Claudia E.; Röösli, Martin
2012-01-01
Indoor radon is regularly measured in Switzerland. However, a nationwide model to predict residential radon levels has not been developed. The aim of this study was to develop a prediction model to assess indoor radon concentrations in Switzerland. The model was based on 44,631 measurements from the nationwide Swiss radon database collected between 1994 and 2004. Of these, 80% randomly selected measurements were used for model development and the remaining 20% for an independent model validation. A multivariable log-linear regression model was fitted and relevant predictors selected according to evidence from the literature, the adjusted R², the Akaike's information criterion (AIC), and the Bayesian information criterion (BIC). The prediction model was evaluated by calculating Spearman rank correlation between measured and predicted values. Additionally, the predicted values were categorised into three categories (50th, 50th–90th and 90th percentile) and compared with measured categories using a weighted Kappa statistic. The most relevant predictors for indoor radon levels were tectonic units and year of construction of the building, followed by soil texture, degree of urbanisation, floor of the building where the measurement was taken and housing type (P-values <0.001 for all). Mean predicted radon values (geometric mean) were 66 Bq/m³ (interquartile range 40–111 Bq/m³) in the lowest exposure category, 126 Bq/m³ (69–215 Bq/m³) in the medium category, and 219 Bq/m³ (108–427 Bq/m³) in the highest category. Spearman correlation between predictions and measurements was 0.45 (95%-CI: 0.44; 0.46) for the development dataset and 0.44 (95%-CI: 0.42; 0.46) for the validation dataset. Kappa coefficients were 0.31 for the development and 0.30 for the validation dataset, respectively. The model explained 20% overall variability (adjusted R²). In conclusion, this residential radon prediction model, based on a large number of measurements, was demonstrated to be
Numerical modeling capabilities to predict repository performance
International Nuclear Information System (INIS)
1979-09-01
This report presents a summary of current numerical modeling capabilities that are applicable to the design and performance evaluation of underground repositories for the storage of nuclear waste. The report includes codes that are available in-house, within Golder Associates and Lawrence Livermore Laboratories; as well as those that are generally available within the industry and universities. The first listing of programs are in-house codes in the subject areas of hydrology, solute transport, thermal and mechanical stress analysis, and structural geology. The second listing of programs are divided by subject into the following categories: site selection, structural geology, mine structural design, mine ventilation, hydrology, and mine design/construction/operation. These programs are not specifically designed for use in the design and evaluation of an underground repository for nuclear waste; but several or most of them may be so used
Model Predictive Control for Smart Energy Systems
DEFF Research Database (Denmark)
Halvgaard, Rasmus
In this thesis, we consider control strategies for flexible distributed energy resources in the future intelligent energy system – the Smart Grid. The energy system is a large-scale complex network with many actors and objectives in different hierarchical layers. Specifically the power system must...... significantly. A Smart Grid calls for flexible consumers that can adjust their consumption based on the amount of green energy in the grid. This requires coordination through new large-scale control and optimization algorithms. Trading of flexibility is key to drive power consumption in a sustainable direction....... In Denmark, we expect that distributed energy resources such as heat pumps, and batteries in electric vehicles will mobilize part of the needed flexibility. Our primary objectives in the thesis were threefold: 1.Simulate the components in the power system based on simple models from literature (e.g. heat...
Model Predictive Control of Wind Turbines
DEFF Research Database (Denmark)
Henriksen, Lars Christian
Wind turbines play a major role in the transformation from a fossil fuel based energy production to a more sustainable production of energy. Total-cost-of-ownership is an important parameter when investors decide in which energy technology they should place their capital. Modern wind turbines...... are controlled by pitching the blades and by controlling the electro-magnetic torque of the generator, thus slowing the rotation of the blades. Improved control of wind turbines, leading to reduced fatigue loads, can be exploited by using less materials in the construction of the wind turbine or by reducing...... the need for maintenance of the wind turbine. Either way, better total-cost-of-ownership for wind turbine operators can be achieved by improved control of the wind turbines. Wind turbine control can be improved in two ways, by improving the model on which the controller bases its design or by improving...
Comparison of Linear Prediction Models for Audio Signals
Directory of Open Access Journals (Sweden)
2009-03-01
Full Text Available While linear prediction (LP has become immensely popular in speech modeling, it does not seem to provide a good approach for modeling audio signals. This is somewhat surprising, since a tonal signal consisting of a number of sinusoids can be perfectly predicted based on an (all-pole LP model with a model order that is twice the number of sinusoids. We provide an explanation why this result cannot simply be extrapolated to LP of audio signals. If noise is taken into account in the tonal signal model, a low-order all-pole model appears to be only appropriate when the tonal components are uniformly distributed in the Nyquist interval. Based on this observation, different alternatives to the conventional LP model can be suggested. Either the model should be changed to a pole-zero, a high-order all-pole, or a pitch prediction model, or the conventional LP model should be preceded by an appropriate frequency transform, such as a frequency warping or downsampling. By comparing these alternative LP models to the conventional LP model in terms of frequency estimation accuracy, residual spectral flatness, and perceptual frequency resolution, we obtain several new and promising approaches to LP-based audio modeling.
Model Predictive Control of a Wave Energy Converter
DEFF Research Database (Denmark)
Andersen, Palle; Pedersen, Tom Søndergård; Nielsen, Kirsten Mølgaard
2015-01-01
In this paper reactive control and Model Predictive Control (MPC) for a Wave Energy Converter (WEC) are compared. The analysis is based on a WEC from Wave Star A/S designed as a point absorber. The model predictive controller uses wave models based on the dominating sea states combined with a model......'s are designed for each sea state using a model assuming a linear loss torque. The mean power results from two controllers are compared using both loss models. Simulation results show that MPC can outperform a reactive controller if a good model of the conversion losses is available....... connecting undisturbed wave sequences to sequences of torque. Losses in the conversion from mechanical to electrical power are taken into account in two ways. Conventional reactive controllers are tuned for each sea state with the assumption that the converter has the same efficiency back and forth. MPC...
Review of Model Predictions for Extensive Air Showers
Pierog, Tanguy
In detailed air shower simulations, the uncertainty in the prediction of shower observable for different primary particles and energies is currently dominated by differences between hadronic interaction models. With the results of the first run of the LHC, the difference between post-LHC model predictions has been reduced at the same level as experimental uncertainties of cosmic ray experiments. At the same time new types of air shower observables, like the muon production depth, have been measured, adding new constraints on hadronic models. Currently no model is able to reproduce consistently all mass composition measurements possible with the Pierre Auger Observatory for instance. We review the current model predictions for various particle production observables and their link with air shower observables and discuss the future possible improvements.
Integrating predictive frameworks and cognitive models of face perception.
Trapp, Sabrina; Schweinberger, Stefan R; Hayward, William G; Kovács, Gyula
2018-02-08
The idea of a "predictive brain"-that is, the interpretation of internal and external information based on prior expectations-has been elaborated intensely over the past decade. Several domains in cognitive neuroscience have embraced this idea, including studies in perception, motor control, language, and affective, social, and clinical neuroscience. Despite the various studies that have used face stimuli to address questions related to predictive processing, there has been surprisingly little connection between this work and established cognitive models of face recognition. Here we suggest that the predictive framework can serve as an important complement of established cognitive face models. Conversely, the link to cognitive face models has the potential to shed light on issues that remain open in predictive frameworks.
A model for predicting lung cancer response to therapy
International Nuclear Information System (INIS)
Seibert, Rebecca M.; Ramsey, Chester R.; Hines, J. Wesley; Kupelian, Patrick A.; Langen, Katja M.; Meeks, Sanford L.; Scaperoth, Daniel D.
2007-01-01
Purpose: Volumetric computed tomography (CT) images acquired by image-guided radiation therapy (IGRT) systems can be used to measure tumor response over the course of treatment. Predictive adaptive therapy is a novel treatment technique that uses volumetric IGRT data to actively predict the future tumor response to therapy during the first few weeks of IGRT treatment. The goal of this study was to develop and test a model for predicting lung tumor response during IGRT treatment using serial megavoltage CT (MVCT). Methods and Materials: Tumor responses were measured for 20 lung cancer lesions in 17 patients that were imaged and treated with helical tomotherapy with doses ranging from 2.0 to 2.5 Gy per fraction. Five patients were treated with concurrent chemotherapy, and 1 patient was treated with neoadjuvant chemotherapy. Tumor response to treatment was retrospectively measured by contouring 480 serial MVCT images acquired before treatment. A nonparametric, memory-based locally weight regression (LWR) model was developed for predicting tumor response using the retrospective tumor response data. This model predicts future tumor volumes and the associated confidence intervals based on limited observations during the first 2 weeks of treatment. The predictive accuracy of the model was tested using a leave-one-out cross-validation technique with the measured tumor responses. Results: The predictive algorithm was used to compare predicted verse-measured tumor volume response for all 20 lesions. The average error for the predictions of the final tumor volume was 12%, with the true volumes always bounded by the 95% confidence interval. The greatest model uncertainty occurred near the middle of the course of treatment, in which the tumor response relationships were more complex, the model has less information, and the predictors were more varied. The optimal days for measuring the tumor response on the MVCT images were on elapsed Days 1, 2, 5, 9, 11, 12, 17, and 18 during
Predictive modeling of coupled multi-physics systems: I. Theory
International Nuclear Information System (INIS)
Cacuci, Dan Gabriel
2014-01-01
Highlights: • We developed “predictive modeling of coupled multi-physics systems (PMCMPS)”. • PMCMPS reduces predicted uncertainties in predicted model responses and parameters. • PMCMPS treats efficiently very large coupled systems. - Abstract: This work presents an innovative mathematical methodology for “predictive modeling of coupled multi-physics systems (PMCMPS).” This methodology takes into account fully the coupling terms between the systems but requires only the computational resources that would be needed to perform predictive modeling on each system separately. The PMCMPS methodology uses the maximum entropy principle to construct an optimal approximation of the unknown a priori distribution based on a priori known mean values and uncertainties characterizing the parameters and responses for both multi-physics models. This “maximum entropy”-approximate a priori distribution is combined, using Bayes’ theorem, with the “likelihood” provided by the multi-physics simulation models. Subsequently, the posterior distribution thus obtained is evaluated using the saddle-point method to obtain analytical expressions for the optimally predicted values for the multi-physics models parameters and responses along with corresponding reduced uncertainties. Noteworthy, the predictive modeling methodology for the coupled systems is constructed such that the systems can be considered sequentially rather than simultaneously, while preserving exactly the same results as if the systems were treated simultaneously. Consequently, very large coupled systems, which could perhaps exceed available computational resources if treated simultaneously, can be treated with the PMCMPS methodology presented in this work sequentially and without any loss of generality or information, requiring just the resources that would be needed if the systems were treated sequentially
Embryo quality predictive models based on cumulus cells gene expression
Directory of Open Access Journals (Sweden)
Devjak R
2016-06-01
Full Text Available Since the introduction of in vitro fertilization (IVF in clinical practice of infertility treatment, the indicators for high quality embryos were investigated. Cumulus cells (CC have a specific gene expression profile according to the developmental potential of the oocyte they are surrounding, and therefore, specific gene expression could be used as a biomarker. The aim of our study was to combine more than one biomarker to observe improvement in prediction value of embryo development. In this study, 58 CC samples from 17 IVF patients were analyzed. This study was approved by the Republic of Slovenia National Medical Ethics Committee. Gene expression analysis [quantitative real time polymerase chain reaction (qPCR] for five genes, analyzed according to embryo quality level, was performed. Two prediction models were tested for embryo quality prediction: a binary logistic and a decision tree model. As the main outcome, gene expression levels for five genes were taken and the area under the curve (AUC for two prediction models were calculated. Among tested genes, AMHR2 and LIF showed significant expression difference between high quality and low quality embryos. These two genes were used for the construction of two prediction models: the binary logistic model yielded an AUC of 0.72 ± 0.08 and the decision tree model yielded an AUC of 0.73 ± 0.03. Two different prediction models yielded similar predictive power to differentiate high and low quality embryos. In terms of eventual clinical decision making, the decision tree model resulted in easy-to-interpret rules that are highly applicable in clinical practice.
Comparison of Predictive Modeling Methods of Aircraft Landing Speed
Diallo, Ousmane H.
2012-01-01
Expected increases in air traffic demand have stimulated the development of air traffic control tools intended to assist the air traffic controller in accurately and precisely spacing aircraft landing at congested airports. Such tools will require an accurate landing-speed prediction to increase throughput while decreasing necessary controller interventions for avoiding separation violations. There are many practical challenges to developing an accurate landing-speed model that has acceptable prediction errors. This paper discusses the development of a near-term implementation, using readily available information, to estimate/model final approach speed from the top of the descent phase of flight to the landing runway. As a first approach, all variables found to contribute directly to the landing-speed prediction model are used to build a multi-regression technique of the response surface equation (RSE). Data obtained from operations of a major airlines for a passenger transport aircraft type to the Dallas/Fort Worth International Airport are used to predict the landing speed. The approach was promising because it decreased the standard deviation of the landing-speed error prediction by at least 18% from the standard deviation of the baseline error, depending on the gust condition at the airport. However, when the number of variables is reduced to the most likely obtainable at other major airports, the RSE model shows little improvement over the existing methods. Consequently, a neural network that relies on a nonlinear regression technique is utilized as an alternative modeling approach. For the reduced number of variables cases, the standard deviation of the neural network models errors represent over 5% reduction compared to the RSE model errors, and at least 10% reduction over the baseline predicted landing-speed error standard deviation. Overall, the constructed models predict the landing-speed more accurately and precisely than the current state-of-the-art.
Model Predictive Control of Three Phase Inverter for PV Systems
Irtaza M. Syed; Kaamran Raahemifar
2015-01-01
This paper presents a model predictive control (MPC) of a utility interactive three phase inverter (TPI) for a photovoltaic (PV) system at commercial level. The proposed model uses phase locked loop (PLL) to synchronize the TPI with the power electric grid (PEG) and performs MPC control in a dq reference frame. TPI model consists of a boost converter (BC), maximum power point tracking (MPPT) control, and a three-leg voltage source inverter (VSI). The operational model of ...
Prediction error, ketamine and psychosis: An updated model.
Corlett, Philip R; Honey, Garry D; Fletcher, Paul C
2016-11-01
In 2007, we proposed an explanation of delusion formation as aberrant prediction error-driven associative learning. Further, we argued that the NMDA receptor antagonist ketamine provided a good model for this process. Subsequently, we validated the model in patients with psychosis, relating aberrant prediction error signals to delusion severity. During the ensuing period, we have developed these ideas, drawing on the simple principle that brains build a model of the world and refine it by minimising prediction errors, as well as using it to guide perceptual inferences. While previously we focused on the prediction error signal per se, an updated view takes into account its precision, as well as the precision of prior expectations. With this expanded perspective, we see several possible routes to psychotic symptoms - which may explain the heterogeneity of psychotic illness, as well as the fact that other drugs, with different pharmacological actions, can produce psychotomimetic effects. In this article, we review the basic principles of this model and highlight specific ways in which prediction errors can be perturbed, in particular considering the reliability and uncertainty of predictions. The expanded model explains hallucinations as perturbations of the uncertainty mediated balance between expectation and prediction error. Here, expectations dominate and create perceptions by suppressing or ignoring actual inputs. Negative symptoms may arise due to poor reliability of predictions in service of action. By mapping from biology to belief and perception, the account proffers new explanations of psychosis. However, challenges remain. We attempt to address some of these concerns and suggest future directions, incorporating other symptoms into the model, building towards better understanding of psychosis. © The Author(s) 2016.
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.
Fournier's gangrene: a model for early prediction.
Palvolgyi, Roland; Kaji, Amy H; Valeriano, Javier; Plurad, David; Rajfer, Jacob; de Virgilio, Christian
2014-10-01
Early diagnosis remains the cornerstone of management of Fournier's gangrene. As a result of variable progression of disease, identifying early predictors of necrosis becomes a diagnostic challenge. We present a scoring system based on objective admission criteria, which can help distinguish Fournier's gangrene from nonnecrotizing scrotal infections. Ninety-six patients were identified, 38 diagnosed with Fournier's gangrene and 58 diagnosed with scrotal cellulitis or abscess. Statistical analyses comparing admission vital signs, laboratory values, and imaging studies were performed and Classification and Regression Tree analysis was used to construct a scoring system. Admission heart rate greater than 110 beats/minute, serum sodium less than 135 mmol/L, blood urea nitrogen greater than 15 mg/dL, and white blood cell count greater than 15 × 10(3)/μL were significant predictors of Fournier's gangrene. Using a threshold score of two or greater, our model differentiates patients with Fournier's gangrene from those with nonnecrotizing infections with a sensitivity of 84.2 per cent. Only 34.2 per cent of patients with Fournier's gangrene had hard signs of necrotizing infection on admission, which were not observed in patients with nonnecrotizing infections. Objective admission criteria assist in distinguishing Fournier's gangrene from scrotal cellulitis or abscess. In situations in which results of the physical examination are ambiguous, this scoring system can heighten the index of suspicion for Fournier's gangrene and prompt rapid surgical intervention.
SM Higgs search in double hadronic tau final state with the ATLAS experiment
Energy Technology Data Exchange (ETDEWEB)
Zanzi, Daniele; Bronner, Johanna; Kortner, Sandra; Manfredini, Alessandro; Sandstroem, Rikard; Stern, Sebastian [MPI, Muenchen (Germany); Glatzer, Julian [Albert-Ludwigs-Universitaet, Freiburg (Germany); Tsuno, Soshi [KEK (Japan); Xella, Stefania [NBI, Copenhagen (Denmark)
2012-07-01
The discovery or the exclusion of the SM Higgs boson is the ultimate step to either confirm the prediction of the Standard Model of fundamental particles or to open the path towards new physics. Thanks to outstanding performances, the experiments running at LHC, ATLAS and CMS, reached the sensitivity to exclude this particle in most of the allowed mass range just after two years of data taking. There is still a small window left around 115-130 GeV, where the Higgs boson is most likely to be and where searches are now focusing. One of the most important final states where such a light Higgs boson can be discovered is a pair of tau leptons. In this talk the SM Higgs boson search in events with two taus both decaying hadronically is presented. This study is based on all data collected in 2011 by the ATLAS Experiment in proton-proton collision at the center-of-mass energy of 7 TeV.
Alvarez-Garreton, C.; Ryu, D.; Western, A. W.; Su, C.-H.; Crow, W. T.; Robertson, D. E.; Leahy, C.
2015-04-01
Assimilation of remotely sensed soil moisture data (SM-DA) to correct soil water stores of rainfall-runoff models has shown skill in improving streamflow prediction. In the case of large and sparsely monitored catchments, SM-DA is a particularly attractive tool. Within this context, we assimilate satellite soil moisture (SM) retrievals from the Advanced Microwave Scanning Radiometer (AMSR-E), the Advanced Scatterometer (ASCAT) and the Soil Moisture and Ocean Salinity (SMOS) instrument, using an Ensemble Kalman filter to improve operational flood prediction within a large (> 40 000 km2) semi-arid catchment in Australia. We assess the importance of accounting for channel routing and the spatial distribution of forcing data by applying SM-DA to a lumped and a semi-distributed scheme of the probability distributed model (PDM). Our scheme also accounts for model error representation by explicitly correcting bias in soil moisture and streamflow in the ensemble generation process, and for seasonal biases and errors in the satellite data. Before assimilation, the semi-distributed model provided a more accurate streamflow prediction (Nash-Sutcliffe efficiency, NSE = 0.77) than the lumped model (NSE = 0.67) at the catchment outlet. However, this did not ensure good performance at the "ungauged" inner catchments (two of them with NSE below 0.3). After SM-DA, the streamflow ensemble prediction at the outlet was improved in both the lumped and the semi-distributed schemes: the root mean square error of the ensemble was reduced by 22 and 24%, respectively; the false alarm ratio was reduced by 9% in both cases; the peak volume error was reduced by 58 and 1%, respectively; the ensemble skill was improved (evidenced by 12 and 13% reductions in the continuous ranked probability scores, respectively); and the ensemble reliability was increased in both cases (expressed by flatter rank histograms). SM-DA did not improve NSE. Our findings imply that even when rainfall is the main driver
A deep auto-encoder model for gene expression prediction.
Xie, Rui; Wen, Jia; Quitadamo, Andrew; Cheng, Jianlin; Shi, Xinghua
2017-11-17
Gene expression is a key intermediate level that genotypes lead to a particular trait. Gene expression is affected by various factors including genotypes of genetic variants. With an aim of delineating the genetic impact on gene expression, we build a deep auto-encoder model to assess how good genetic variants will contribute to gene expression changes. This new deep learning model is a regression-based predictive model based on the MultiLayer Perceptron and Stacked Denoising Auto-encoder (MLP-SAE). The model is trained using a stacked denoising auto-encoder for feature selection and a multilayer perceptron framework for backpropagation. We further improve the model by introducing dropout to prevent overfitting and improve performance. To demonstrate the usage of this model, we apply MLP-SAE to a real genomic datasets with genotypes and gene expression profiles measured in yeast. Our results show that the MLP-SAE model with dropout outperforms other models including Lasso, Random Forests and the MLP-SAE model without dropout. Using the MLP-SAE model with dropout, we show that gene expression quantifications predicted by the model solely based on genotypes, align well with true gene expression patterns. We provide a deep auto-encoder model for predicting gene expression from SNP genotypes. This study demonstrates that deep learning is appropriate for tackling another genomic problem, i.e., building predictive models to understand genotypes' contribution to gene expression. With the emerging availability of richer genomic data, we anticipate that deep learning models play a bigger role in modeling and interpreting genomics.
Predicting the Yield Stress of SCC using Materials Modelling
DEFF Research Database (Denmark)
Thrane, Lars Nyholm; Hasholt, Marianne Tange; Pade, Claus
2005-01-01
A conceptual model for predicting the Bingham rheological parameter yield stress of SCC has been established. The model used here is inspired by previous work of Oh et al. (1), predicting that the yield stress of concrete relative to the yield stress of paste is a function of the relative thickne...... and distribution were varied between SCC types. The results indicate that yield stress of SCC may be predicted using the model.......A conceptual model for predicting the Bingham rheological parameter yield stress of SCC has been established. The model used here is inspired by previous work of Oh et al. (1), predicting that the yield stress of concrete relative to the yield stress of paste is a function of the relative thickness...... of excess paste around the aggregate. The thickness of excess paste is itself a function of particle shape, particle size distribution, and particle packing. Seven types of SCC were tested at four different excess paste contents in order to verify the conceptual model. Paste composition and aggregate shape...
Predictive models of prolonged mechanical ventilation yield moderate accuracy.
Figueroa-Casas, Juan B; Dwivedi, Alok K; Connery, Sean M; Quansah, Raphael; Ellerbrook, Lowell; Galvis, Juan
2015-06-01
To develop a model to predict prolonged mechanical ventilation within 48 hours of its initiation. In 282 general intensive care unit patients, multiple variables from the first 2 days on mechanical ventilation and their total ventilation duration were prospectively collected. Three models accounting for early deaths were developed using different analyses: (a) multinomial logistic regression to predict duration > 7 days vs duration ≤ 7 days alive vs duration ≤ 7 days death; (b) binary logistic regression to predict duration > 7 days for the entire cohort and for survivors only, separately; and (c) Cox regression to predict time to being free of mechanical ventilation alive. Positive end-expiratory pressure, postoperative state (negatively), and Sequential Organ Failure Assessment score were independently associated with prolonged mechanical ventilation. The multinomial regression model yielded an accuracy (95% confidence interval) of 60% (53%-64%). The binary regression models yielded accuracies of 67% (61%-72%) and 69% (63%-75%) for the entire cohort and for survivors, respectively. The Cox regression model showed an equivalent to area under the curve of 0.67 (0.62-0.71). Different predictive models of prolonged mechanical ventilation in general intensive care unit patients achieve a moderate level of overall accuracy, likely insufficient to assist in clinical decisions. Copyright © 2015 Elsevier Inc. All rights reserved.
Comparison of the models of financial distress prediction
Directory of Open Access Journals (Sweden)
Jiří Omelka
2013-01-01
Full Text Available Prediction of the financial distress is generally supposed as approximation if a business entity is closed on bankruptcy or at least on serious financial problems. Financial distress is defined as such a situation when a company is not able to satisfy its liabilities in any forms, or when its liabilities are higher than its assets. Classification of financial situation of business entities represents a multidisciplinary scientific issue that uses not only the economic theoretical bases but interacts to the statistical, respectively to econometric approaches as well.The first models of financial distress prediction have originated in the sixties of the 20th century. One of the most known is the Altman’s model followed by a range of others which are constructed on more or less conformable bases. In many existing models it is possible to find common elements which could be marked as elementary indicators of potential financial distress of a company. The objective of this article is, based on the comparison of existing models of prediction of financial distress, to define the set of basic indicators of company’s financial distress at conjoined identification of their critical aspects. The sample defined this way will be a background for future research focused on determination of one-dimensional model of financial distress prediction which would subsequently become a basis for construction of multi-dimensional prediction model.
Plant water potential improves prediction of empirical stomatal models.
Directory of Open Access Journals (Sweden)
William R L Anderegg
Full Text Available Climate change is expected to lead to increases in drought frequency and severity, with deleterious effects on many ecosystems. Stomatal responses to changing environmental conditions form the backbone of all ecosystem models, but are based on empirical relationships and are not well-tested during drought conditions. Here, we use a dataset of 34 woody plant species spanning global forest biomes to examine the effect of leaf water potential on stomatal conductance and test the predictive accuracy of three major stomatal models and a recently proposed model. We find that current leaf-level empirical models have consistent biases of over-prediction of stomatal conductance during dry conditions, particularly at low soil water potentials. Furthermore, the recently proposed stomatal conductance model yields increases in predictive capability compared to current models, and with particular improvement during drought conditions. Our results reveal that including stomatal sensitivity to declining water potential and consequent impairment of plant water transport will improve predictions during drought conditions and show that many biomes contain a diversity of plant stomatal strategies that range from risky to conservative stomatal regulation during water stress. Such improvements in stomatal simulation are greatly needed to help unravel and predict the response of ecosystems to future climate extremes.
Predicting the ungauged basin: model validation and realism assessment
van Emmerik, Tim; Mulder, Gert; Eilander, Dirk; Piet, Marijn; Savenije, Hubert
2015-01-01
The hydrological decade on Predictions in Ungauged Basins (PUB) led to many new insights in model development, calibration strategies, data acquisition and uncertainty analysis. Due to a limited amount of published studies on genuinely ungauged basins, model validation and realism assessment of
Predicting the ungauged basin : Model validation and realism assessment
van Emmerik, Tim; Mulder, Gert; Eilander, Dirk; Piet, Marijn; Savenije, Hubert
2015-01-01
The hydrological decade on Predictions in Ungauged Basins (PUB) led to many new insights in model development, calibration strategies, data acquisition and uncertainty analysis. Due to a limited amount of published studies on genuinely ungauged basins, model validation and realism assessment of
A Mathematical Model for the Prediction of Injectivity Decline | Odeh ...
African Journals Online (AJOL)
Injectivity impairment due to invasion of solid suspensions has been studied by several investigators and some modelling approaches have also been reported. Worthy of note is the development of analytical models for internal and external filtration coupled with transition time concept for predicting the overall decline in ...
Mathematical Model for Prediction of Flexural Strength of Mound ...
African Journals Online (AJOL)
The mound soil-cement blended proportions were mathematically optimized by using scheffe's approach and the optimization model developed. A computer program predicting the mix proportion for the model was written. The optimal proportion by the program was used prepare beam samples measuring 150mm x 150mm ...
Katz model prediction of Caenorhabditis elegans mutagenesis on STS-42
Cucinotta, Francis A.; Wilson, John W.; Katz, Robert; Badhwar, Gautam D.
1992-01-01
Response parameters that describe the production of recessive lethal mutations in C. elegans from ionizing radiation are obtained with the Katz track structure model. The authors used models of the space radiation environment and radiation transport to predict and discuss mutation rates for C. elegans on the IML-1 experiment aboard STS-42.
Accident Prediction Models for Akure – Ondo Carriageway, Ondo ...
African Journals Online (AJOL)
FIRST LADY
traffic exposure and intersection effects as independent variables. They suggested that the Poisson distribution allows for the relationship between exposure and crashes to be more accurately modeled as opposed to. Accident Prediction Models for Akure-Ondo Carriageway…Using Multiple Linear Regression ...
Multi-model prediction of downward short-wave radiation
Czech Academy of Sciences Publication Activity Database
Eben, Kryštof; Resler, Jaroslav; Krč, Pavel; Juruš, Pavel; Pelikán, Emil
2012-01-01
Roč. 9, - (2012), EMS2012-384 [EMS Annual Meeting /12./ and European Conference on Applied Climatology /9./. 10.09.2012-14.09.2012, Lodz] Institutional support: RVO:67985807 Keywords : multi-model prediction * NWP * model postprocessing Subject RIV: DG - Athmosphere Sciences, Meteorology
Atmospheric modelling for seasonal prediction at the CSIR
CSIR Research Space (South Africa)
Landman, WA
2014-10-01
Full Text Available by observed monthly sea-surface temperature (SST) and sea-ice fields. The AGCM is the conformal-cubic atmospheric model (CCAM) administered by the Council for Scientific and Industrial Research. Since the model is forced with observed rather than predicted...
Prediction Models and Decision Support: Chances and Challenges
Kappen, T.H.
2015-01-01
A clinical prediction model can assist doctors in arriving at the most likely diagnosis or estimating the prognosis. By utilizing various patient- and disease-related properties, such models can yield objective estimations of the risk of a disease or the probability of a certain disease course for
Validation of a multi-objective, predictive urban traffic model
Wilmink, I.R.; Haak, P. van den; Woldeab, Z.; Vreeswijk, J.
2013-01-01
This paper describes the results of the verification and validation of the ecoStrategic Model, which was developed, implemented and tested in the eCoMove project. The model uses real-time and historical traffic information to determine the current, predicted and desired state of traffic in a
Predictive ability of boiler production models | Ogundu | Animal ...
African Journals Online (AJOL)
The weekly body weight measurements of a growing strain of Ross broiler were used to compare the of ability of three mathematical models (the multi, linear, quadratic and Exponential) to predict 8 week body weight from early body measurements at weeks I, II, III, IV, V, VI and VII. The results suggest that the three models ...
Predictive modelling of noise level generated during sawing of rocks ...
Indian Academy of Sciences (India)
2016-08-26
Aug 26, 2016 ... Inﬂuence of the operating variables and rock properties on the noise level are investigated and analysed. Statistical analyses are then employed and models are built for the prediction of noise levels depending on the operating variables and the rock properties. The derived models are validated through ...
Modelling and prediction of non-stationary optical turbulence behaviour
Doelman, N.J.; Osborn, J.
2016-01-01
There is a strong need to model the temporal fluctuations in turbulence parameters, for instance for scheduling, simulation and prediction purposes. This paper aims at modelling the dynamic behaviour of the turbulence coherence length r0, utilising measurement data from the Stereo-SCIDAR instrument
Inferential ecosystem models, from network data to prediction
James S. Clark; Pankaj Agarwal; David M. Bell; Paul G. Flikkema; Alan Gelfand; Xuanlong Nguyen; Eric Ward; Jun. Yang
2011-01-01
Recent developments suggest that predictive modeling could begin to play a larger role not only for data analysis, but also for data collection. We address the example of efficient wireless sensor networks, where inferential ecosystem models can be used to weigh the value of an observation against the cost of data collection. Transmission costs make observations ââ...
Model prediction of maize yield responses to climate change in ...
African Journals Online (AJOL)
Observed data of the last three decades (1971 to 2000) from several climatological stations in north-eastern Zimbabwe and outputs from several global climate models were used. The downscaled model simulations consistently predicted a warming of between 1 and 2 ºC above the baseline period (1971-2000) at most of ...
A theoretical model for predicting neutron fluxes for cyclic Neutron ...
African Journals Online (AJOL)
A theoretical model has been developed for prediction of thermal neutron fluxes required for cyclic irradiations of a sample to obtain the same activity previously used for the detection of any radionuclide of interest. The model is suitable for radiotracer production or for long-lived neutron activation products where the ...
A model to predict the sound reflection from forests
Wunderli, J.M.; Salomons, E.M.
2009-01-01
A model is presented to predict the reflection of sound at forest edges. A single tree is modelled as a vertical cylinder. For the reflection at a cylinder an analytical solution is given based on the theory of scattering of spherical waves. The entire forest is represented by a line of cylinders
Model Predictive Control for Offset-Free Reference Tracking
Czech Academy of Sciences Publication Activity Database
Belda, Květoslav
2016-01-01
Roč. 5, č. 1 (2016), s. 8-13 ISSN 1805-3386 Institutional support: RVO:67985556 Keywords : offset-free reference tracking * predictive control * ARX model * state-space model * multi-input multi-output system * robotic system * mechatronic system Subject RIV: BC - Control Systems Theory http://library.utia.cas.cz/separaty/2016/AS/belda-0458355.pdf
Multi-model ensemble schemes for predicting northeast monsoon ...
Indian Academy of Sciences (India)
An attempt has been made to improve the accuracy of predicted rainfall using three different multi-model ensemble (MME) schemes, viz., simple arithmetic mean of models (EM), principal component regression (PCR) and singular value decomposition based multiple linear regressions (SVD). It is found out that among ...
Supervisory Model Predictive Control of the Heat Integrated Distillation Column
DEFF Research Database (Denmark)
Meyer, Kristian; Bisgaard, Thomas; Huusom, Jakob Kjøbsted
2017-01-01
This paper benchmarks a centralized control system based on model predictive control for the operation of the heat integrated distillation column (HIDiC) against a fully decentralized control system using the most complete column model currently available in the literature. The centralized contro...
Evaluation of preformance of Predictive Models for Deoxynivalenol in Wheat
Fels, van der H.J.
2014-01-01
The aim of this study was to evaluate the performance of two predictive models for deoxynivalenol contamination of wheat at harvest in the Netherlands, including the use of weather forecast data and external model validation. Data were collected in a different year and from different wheat fields
Three-model ensemble wind prediction in southern Italy
Directory of Open Access Journals (Sweden)
R. C. Torcasio
2016-03-01
Full Text Available Quality of wind prediction is of great importance since a good wind forecast allows the prediction of available wind power, improving the penetration of renewable energies into the energy market. Here, a 1-year (1 December 2012 to 30 November 2013 three-model ensemble (TME experiment for wind prediction is considered. The models employed, run operationally at National Research Council – Institute of Atmospheric Sciences and Climate (CNR-ISAC, are RAMS (Regional Atmospheric Modelling System, BOLAM (BOlogna Limited Area Model, and MOLOCH (MOdello LOCale in H coordinates. The area considered for the study is southern Italy and the measurements used for the forecast verification are those of the GTS (Global Telecommunication System. Comparison with observations is made every 3 h up to 48 h of forecast lead time. Results show that the three-model ensemble outperforms the forecast of each individual model. The RMSE improvement compared to the best model is between 22 and 30 %, depending on the season. It is also shown that the three-model ensemble outperforms the IFS (Integrated Forecasting System of the ECMWF (European Centre for Medium-Range Weather Forecast for the surface wind forecasts. Notably, the three-model ensemble forecast performs better than each unbiased model, showing the added value of the ensemble technique. Finally, the sensitivity of the three-model ensemble RMSE to the length of the training period is analysed.
Three-model ensemble wind prediction in southern Italy
Torcasio, Rosa Claudia; Federico, Stefano; Calidonna, Claudia Roberta; Avolio, Elenio; Drofa, Oxana; Landi, Tony Christian; Malguzzi, Piero; Buzzi, Andrea; Bonasoni, Paolo
2016-03-01
Quality of wind prediction is of great importance since a good wind forecast allows the prediction of available wind power, improving the penetration of renewable energies into the energy market. Here, a 1-year (1 December 2012 to 30 November 2013) three-model ensemble (TME) experiment for wind prediction is considered. The models employed, run operationally at National Research Council - Institute of Atmospheric Sciences and Climate (CNR-ISAC), are RAMS (Regional Atmospheric Modelling System), BOLAM (BOlogna Limited Area Model), and MOLOCH (MOdello LOCale in H coordinates). The area considered for the study is southern Italy and the measurements used for the forecast verification are those of the GTS (Global Telecommunication System). Comparison with observations is made every 3 h up to 48 h of forecast lead time. Results show that the three-model ensemble outperforms the forecast of each individual model. The RMSE improvement compared to the best model is between 22 and 30 %, depending on the season. It is also shown that the three-model ensemble outperforms the IFS (Integrated Forecasting System) of the ECMWF (European Centre for Medium-Range Weather Forecast) for the surface wind forecasts. Notably, the three-model ensemble forecast performs better than each unbiased model, showing the added value of the ensemble technique. Finally, the sensitivity of the three-model ensemble RMSE to the length of the training period is analysed.
The predictive performance and stability of six species distribution models.
Duan, Ren-Yan; Kong, Xiao-Quan; Huang, Min-Yi; Fan, Wei-Yi; Wang, Zhi-Gao
2014-01-01
Predicting species' potential geographical range by species distribution models (SDMs) is central to understand their ecological requirements. However, the effects of using different modeling techniques need further investigation. In order to improve the prediction effect, we need to assess the predictive performance and stability of different SDMs. We collected the distribution data of five common tree species (Pinus massoniana, Betula platyphylla, Quercus wutaishanica, Quercus mongolica and Quercus variabilis) and simulated their potential distribution area using 13 environmental variables and six widely used SDMs: BIOCLIM, DOMAIN, MAHAL, RF, MAXENT, and SVM. Each model run was repeated 100 times (trials). We compared the predictive performance by testing the consistency between observations and simulated distributions and assessed the stability by the standard deviation, coefficient of variation, and the 99% confidence interval of Kappa and AUC values. The mean values of AUC and Kappa from MAHAL, RF, MAXENT, and SVM trials were similar and significantly higher than those from BIOCLIM and DOMAIN trials (pSDMs (MAHAL, RF, MAXENT, and SVM) had higher prediction accuracy, smaller confidence intervals, and were more stable and less affected by the random variable (randomly selected pseudo-absence points). According to the prediction performance and stability of SDMs, we can divide these six SDMs into two categories: a high performance and stability group including MAHAL, RF, MAXENT, and SVM, and a low performance and stability group consisting of BIOCLIM, and DOMAIN. We highlight that choosing appropriate SDMs to address a specific problem is an important part of the modeling process.
SHMF: Interest Prediction Model with Social Hub Matrix Factorization
Directory of Open Access Journals (Sweden)
Chaoyuan Cui
2017-01-01
Full Text Available With the development of social networks, microblog has become the major social communication tool. There is a lot of valuable information such as personal preference, public opinion, and marketing in microblog. Consequently, research on user interest prediction in microblog has a positive practical significance. In fact, how to extract information associated with user interest orientation from the constantly updated blog posts is not so easy. Existing prediction approaches based on probabilistic factor analysis use blog posts published by user to predict user interest. However, these methods are not very effective for the users who post less but browse more. In this paper, we propose a new prediction model, which is called SHMF, using social hub matrix factorization. SHMF constructs the interest prediction model by combining the information of blogs posts published by both user and direct neighbors in user’s social hub. Our proposed model predicts user interest by integrating user’s historical behavior and temporal factor as well as user’s friendships, thus achieving accurate forecasts of user’s future interests. The experimental results on Sina Weibo show the efficiency and effectiveness of our proposed model.
Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models
Cuevas, Jaime; Crossa, José; Montesinos-López, Osval A.; Burgueño, Juan; Pérez-Rodríguez, Paulino; de los Campos, Gustavo
2016-01-01
The phenomenon of genotype × environment (G × E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects (u) that can be assessed by the Kronecker product of variance–covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP) and Gaussian (Gaussian kernel, GK). The other model has the same genetic component as the first model (u) plus an extra component, f, that captures random effects between environments that were not captured by the random effects u. We used five CIMMYT data sets (one maize and four wheat) that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with u and f over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect u. PMID:27793970
Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models
Directory of Open Access Journals (Sweden)
Jaime Cuevas
2017-01-01
Full Text Available The phenomenon of genotype × environment (G × E interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects ( u that can be assessed by the Kronecker product of variance–covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP and Gaussian (Gaussian kernel, GK. The other model has the same genetic component as the first model ( u plus an extra component, f, that captures random effects between environments that were not captured by the random effects u . We used five CIMMYT data sets (one maize and four wheat that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with u and f over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect u .
Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models.
Cuevas, Jaime; Crossa, José; Montesinos-López, Osval A; Burgueño, Juan; Pérez-Rodríguez, Paulino; de Los Campos, Gustavo
2017-01-05
The phenomenon of genotype × environment (G × E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects [Formula: see text] that can be assessed by the Kronecker product of variance-covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP) and Gaussian (Gaussian kernel, GK). The other model has the same genetic component as the first model [Formula: see text] plus an extra component, F: , that captures random effects between environments that were not captured by the random effects [Formula: see text] We used five CIMMYT data sets (one maize and four wheat) that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with [Formula: see text] over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect [Formula: see text]. Copyright © 2017 Cuevas et al.
Stochastic models for predicting pitting corrosion damage of HLRW containers
International Nuclear Information System (INIS)
Henshall, G.A.
1991-10-01
Stochastic models for predicting aqueous pitting corrosion damage of high-level radioactive-waste containers are described. These models could be used to predict the time required for the first pit to penetrate a container and the increase in the number of breaches at later times, both of which would be useful in the repository system performance analysis. Monte Carlo implementations of the stochastic models are described, and predictions of induction time, survival probability and pit depth distributions are presented. These results suggest that the pit nucleation probability decreases with exposure time and that pit growth may be a stochastic process. The advantages and disadvantages of the stochastic approach, methods for modeling the effects of environment, and plans for future work are discussed
Verification and improvement of a predictive model for radionuclide migration
International Nuclear Information System (INIS)
Miller, C.W.; Benson, L.V.; Carnahan, C.L.
1982-01-01
Prediction of the rates of migration of contaminant chemical species in groundwater flowing through toxic waste repositories is essential to the assessment of a repository's capability of meeting standards for release rates. A large number of chemical transport models, of varying degrees of complexity, have been devised for the purpose of providing this predictive capability. In general, the transport of dissolved chemical species through a water-saturated porous medium is influenced by convection, diffusion/dispersion, sorption, formation of complexes in the aqueous phase, and chemical precipitation. The reliability of predictions made with the models which omit certain of these processes is difficult to assess. A numerical model, CHEMTRN, has been developed to determine which chemical processes govern radionuclide migration. CHEMTRN builds on a model called MCCTM developed previously by Lichtner and Benson
A novel Bayesian hierarchical model for road safety hotspot prediction.
Fawcett, Lee; Thorpe, Neil; Matthews, Joseph; Kremer, Karsten
2017-02-01
In this paper, we propose a Bayesian hierarchical model for predicting accident counts in future years at sites within a pool of potential road safety hotspots. The aim is to inform road safety practitioners of the location of likely future hotspots to enable a proactive, rather than reactive, approach to road safety scheme implementation. A feature of our model is the ability to rank sites according to their potential to exceed, in some future time period, a threshold accident count which may be used as a criterion for scheme implementation. Our model specification enables the classical empirical Bayes formulation - commonly used in before-and-after studies, wherein accident counts from a single before period are used to estimate counterfactual counts in the after period - to be extended to incorporate counts from multiple time periods. This allows site-specific variations in historical accident counts (e.g. locally-observed trends) to offset estimates of safety generated by a global accident prediction model (APM), which itself is used to help account for the effects of global trend and regression-to-mean (RTM). The Bayesian posterior predictive distribution is exploited to formulate predictions and to properly quantify our uncertainty in these predictions. The main contributions of our model include (i) the ability to allow accident counts from multiple time-points to inform predictions, with counts in more recent years lending more weight to predictions than counts from time-points further in the past; (ii) where appropriate, the ability to offset global estimates of trend by variations in accident counts observed locally, at a site-specific level; and (iii) the ability to account for unknown/unobserved site-specific factors which may affect accident counts. We illustrate our model with an application to accident counts at 734 potential hotspots in the German city of Halle; we also propose some simple diagnostics to validate the predictive capability of our
Energy Technology Data Exchange (ETDEWEB)
Garzelli, M.V. [INFN (Italy); Granada Univ. (Spain). Dept. de Fisica Teorica y del Cosmos y CAFPE; Malamos, I. [Radboud Universiteit Nijmegen, Department of Theoretical High Energy Physics, Institute for Mathematics, Astrophysics and Particle Physics, Nijmegen (Netherlands)
2011-03-15
The analytical package written in FORM presented in this paper allows the computation of the complete set of Feynman Rules producing the Rational terms of kind R{sub 2} contributing to the virtual part of NLO corrections in the Standard Model of the Electroweak interactions. Building block topologies filled by means of generic scalars, vectors and fermions, allowing to build these Feynman Rules in terms of specific elementary particles, are explicitly given in the R{sub {xi}} gauge class, together with the automatic dressing procedure to obtain the Feynman Rules from them. The results in more specific gauges, like the 't Hooft Feynman one, follow as particular cases, in both the HV and the FDH dimensional regularization schemes. As a check on our formulas, the gauge independence of the total Rational contribution (R{sub 1}+R{sub 2}) to renormalized S-matrix elements is verified by considering the specific example of the H {yields}{gamma}{gamma} decay process at 1-loop. This package can be of interest for people aiming at a better understanding of the nature of the Rational terms. It is organized in a modular way, allowing a further use of some its files even in different contexts. Furthermore, it can be considered as a first seed in the effort towards a complete automation of the process of the analytical calculation of the R{sub 2} effective vertices, given the Lagrangian of a generic gauge theory of particle interactions. (orig.)
Energy Technology Data Exchange (ETDEWEB)
Novais, S.M.V., E-mail: suellenvaleriano@yahoo.com.br [Laboratory of Advanced Ceramic Materials, Physics Department, Federal University of Sergipe, 49.100-000, São Cristóvão, SE (Brazil); Dobrowolska, A.; Bos, A.J.J.; Dorenbos, P. [Faculty of Applied Sciences, Delft University of Technology, Mekelweg 15, 2629 JB Delft (Netherlands); Macedo, Z.S. [Laboratory of Advanced Ceramic Materials, Physics Department, Federal University of Sergipe, 49.100-000, São Cristóvão, SE (Brazil)
2014-04-15
Vacuum ultra-violet spectroscopy at low temperature was performed with undoped and Ln{sup 3+}-doped NaYP{sub 2}O{sub 7} (Ln=Ce, Sm, Eu, Tb and Yb). The emission observed from the NaYP{sub 2}O{sub 7} host was attributed to self-trapped exciton (STE) recombination. Dopant ions were well incorporated and responsible for characteristic luminescent emission in the doped samples. From excitation curves, 4f–5d transitions were identified for Ce{sup 3+}, Sm{sup 3+} and Tb{sup 3+} and charge transfer processes were noticed for Eu{sup 3+}, Sm{sup 3+} and Yb{sup 3+}. The experimental data obtained was used to determine the positions of the energy levels of divalent and trivalent lanthanides relative to the valence and conduction band of this compound. The experimentally derived levels are in good agreement with model predictions. The results are valuable to provide a better understanding on the performance of NaYP{sub 2}O{sub 7} and related diphosphates as luminescent materials. -- Highlights: • NaYP{sub 2}O{sub 7} polycrystalline samples were produced via the sol–gel method. • VUV excitation and emission spectra were successfully studied. • Luminescent properties were discussed. • The experimentally derived energy levels are in good agreement with model predictions.
Numerical weather prediction (NWP) and hybrid ARMA/ANN model to predict global radiation
International Nuclear Information System (INIS)
Voyant, Cyril; Muselli, Marc; Paoli, Christophe; Nivet, Marie-Laure
2012-01-01
We propose in this paper an original technique to predict global radiation using a hybrid ARMA/ANN model and data issued from a numerical weather prediction model (NWP). We particularly look at the multi-layer perceptron (MLP). After optimizing our architecture with NWP and endogenous data previously made stationary and using an innovative pre-input layer selection method, we combined it to an ARMA model from a rule based on the analysis of hourly data series. This model has been used to forecast the hourly global radiation for five places in Mediterranean area. Our technique outperforms classical models for all the places. The nRMSE for our hybrid model MLP/ARMA is 14.9% compared to 26.2% for the naïve persistence predictor. Note that in the standalone ANN case the nRMSE is 18.4%. Finally, in order to discuss the reliability of the forecaster outputs, a complementary study concerning the confidence interval of each prediction is proposed. -- Highlights: ► Time series forecasting with hybrid method based on the use of ALADIN numerical weather model, ANN and ARMA. ► Innovative pre-input layer selection method. ► Combination of optimized MLP and ARMA model obtained from a rule based on the analysis of hourly data series. ► Stationarity process (method and control) for the global radiation time series.
A simplified building airflow model for agent concentration prediction.
Jacques, David R; Smith, David A
2010-11-01
A simplified building airflow model is presented that can be used to predict the spread of a contaminant agent from a chemical or biological attack. If the dominant means of agent transport throughout the building is an air-handling system operating at steady-state, a linear time-invariant (LTI) model can be constructed to predict the concentration in any room of the building as a result of either an internal or external release. While the model does not capture weather-driven and other temperature-driven effects, it is suitable for concentration predictions under average daily conditions. The model is easily constructed using information that should be accessible to a building manager, supplemented with assumptions based on building codes and standard air-handling system design practices. The results of the model are compared with a popular multi-zone model for a simple building and are demonstrated for building examples containing one or more air-handling systems. The model can be used for rapid concentration prediction to support low-cost placement strategies for chemical and biological detection sensors.
Discrete fracture modelling for the Stripa tracer validation experiment predictions
International Nuclear Information System (INIS)
Dershowitz, W.; Wallmann, P.
1992-02-01
Groundwater flow and transport through three-dimensional networks of discrete fractures was modeled to predict the recovery of tracer from tracer injection experiments conducted during phase 3 of the Stripa site characterization and validation protect. Predictions were made on the basis of an updated version of the site scale discrete fracture conceptual model used for flow predictions and preliminary transport modelling. In this model, individual fractures were treated as stochastic features described by probability distributions of geometric and hydrologic properties. Fractures were divided into three populations: Fractures in fracture zones near the drift, non-fracture zone fractures within 31 m of the drift, and fractures in fracture zones over 31 meters from the drift axis. Fractures outside fracture zones are not modelled beyond 31 meters from the drift axis. Transport predictions were produced using the FracMan discrete fracture modelling package for each of five tracer experiments. Output was produced in the seven formats specified by the Stripa task force on fracture flow modelling. (au)
Predicting nucleic acid binding interfaces from structural models of proteins.
Dror, Iris; Shazman, Shula; Mukherjee, Srayanta; Zhang, Yang; Glaser, Fabian; Mandel-Gutfreund, Yael
2012-02-01
The function of DNA- and RNA-binding proteins can be inferred from the characterization and accurate prediction of their binding interfaces. However, the main pitfall of various structure-based methods for predicting nucleic acid binding function is that they are all limited to a relatively small number of proteins for which high-resolution three-dimensional structures are available. In this study, we developed a pipeline for extracting functional electrostatic patches from surfaces of protein structural models, obtained using the I-TASSER protein structure predictor. The largest positive patches are extracted from the protein surface using the patchfinder algorithm. We show that functional electrostatic patches extracted from an ensemble of structural models highly overlap the patches extracted from high-resolution structures. Furthermore, by testing our pipeline on a set of 55 known nucleic acid binding proteins for which I-TASSER produces high-quality models, we show that the method accurately identifies the nucleic acids binding interface on structural models of proteins. Employing a combined patch approach we show that patches extracted from an ensemble of models better predicts the real nucleic acid binding interfaces compared with patches extracted from independent models. Overall, these results suggest that combining information from a collection of low-resolution structural models could be a valuable approach for functional annotation. We suggest that our method will be further applicable for predicting other functional surfaces of proteins with unknown structure. Copyright © 2011 Wiley Periodicals, Inc.
Adaptive Gaussian Predictive Process Models for Large Spatial Datasets
Guhaniyogi, Rajarshi; Finley, Andrew O.; Banerjee, Sudipto; Gelfand, Alan E.
2011-01-01
Large point referenced datasets occur frequently in the environmental and natural sciences. Use of Bayesian hierarchical spatial models for analyzing these datasets is undermined by onerous computational burdens associated with parameter estimation. Low-rank spatial process models attempt to resolve this problem by projecting spatial effects to a lower-dimensional subspace. This subspace is determined by a judicious choice of “knots” or locations that are fixed a priori. One such representation yields a class of predictive process models (e.g., Banerjee et al., 2008) for spatial and spatial-temporal data. Our contribution here expands upon predictive process models with fixed knots to models that accommodate stochastic modeling of the knots. We view the knots as emerging from a point pattern and investigate how such adaptive specifications can yield more flexible hierarchical frameworks that lead to automated knot selection and substantial computational benefits. PMID:22298952
Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models.
Liu, Bowen; Ramsundar, Bharath; Kawthekar, Prasad; Shi, Jade; Gomes, Joseph; Luu Nguyen, Quang; Ho, Stephen; Sloane, Jack; Wender, Paul; Pande, Vijay
2017-10-25
We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem. The end-to-end trained model has an encoder-decoder architecture that consists of two recurrent neural networks, which has previously shown great success in solving other sequence-to-sequence prediction tasks such as machine translation. The model is trained on 50,000 experimental reaction examples from the United States patent literature, which span 10 broad reaction types that are commonly used by medicinal chemists. We find that our model performs comparably with a rule-based expert system baseline model, and also overcomes certain limitations associated with rule-based expert systems and with any machine learning approach that contains a rule-based expert system component. Our model provides an important first step toward solving the challenging problem of computational retrosynthetic analysis.
Pulsatile fluidic pump demonstration and predictive model application
International Nuclear Information System (INIS)
Morgan, J.G.; Holland, W.D.
1986-04-01
Pulsatile fluidic pumps were developed as a remotely controlled method of transferring or mixing feed solutions. A test in the Integrated Equipment Test facility demonstrated the performance of a critically safe geometry pump suitable for use in a 0.1-ton/d heavy metal (HM) fuel reprocessing plant. A predictive model was developed to calculate output flows under a wide range of external system conditions. Predictive and experimental flow rates are compared for both submerged and unsubmerged fluidic pump cases
Cloud Based Metalearning System for Predictive Modeling of Biomedical Data
Directory of Open Access Journals (Sweden)
Milan Vukićević
2014-01-01
Full Text Available Rapid growth and storage of biomedical data enabled many opportunities for predictive modeling and improvement of healthcare processes. On the other side analysis of such large amounts of data is a difficult and computationally intensive task for most existing data mining algorithms. This problem is addressed by proposing a cloud based system that integrates metalearning framework for ranking and selection of best predictive algorithms for data at hand and open source big data technologies for analysis of biomedical data.
Models for Predicting and Explaining Citation Count of Biomedical Articles
Fu, Lawrence D.; Aliferis, Constantin
2008-01-01
The single most important bibliometric criterion for judging the impact of biomedical papers and their authors’ work is the number of citations received which is commonly referred to as “citation count”. This metric however is unavailable until several years after publication time. In the present work, we build computer models that accurately predict citation counts of biomedical publications within a deep horizon of ten years using only predictive information available at publication time. O...
Predictive Models of Li-ion Battery Lifetime
Energy Technology Data Exchange (ETDEWEB)
Smith, Kandler; Wood, Eric; Santhanagopalan, Shriram; Kim, Gi-heon; Shi, Ying; Pesaran, Ahmad
2015-06-15
It remains an open question how best to predict real-world battery lifetime based on accelerated calendar and cycle aging data from the laboratory. Multiple degradation mechanisms due to (electro)chemical, thermal, and mechanical coupled phenomena influence Li-ion battery lifetime, each with different dependence on time, cycling and thermal environment. The standardization of life predictive models would benefit the industry by reducing test time and streamlining development of system controls.
Modelling personality, plasticity and predictability in shelter dogs
2017-01-01
Behavioural assessments of shelter dogs (Canis lupus familiaris) typically comprise standardized test batteries conducted at one time point, but test batteries have shown inconsistent predictive validity. Longitudinal behavioural assessments offer an alternative. We modelled longitudinal observational data on shelter dog behaviour using the framework of behavioural reaction norms, partitioning variance into personality (i.e. inter-individual differences in behaviour), plasticity (i.e. inter-individual differences in average behaviour) and predictability (i.e. individual differences in residual intra-individual variation). We analysed data on interactions of 3263 dogs (n = 19 281) with unfamiliar people during their first month after arrival at the shelter. Accounting for personality, plasticity (linear and quadratic trends) and predictability improved the predictive accuracy of the analyses compared to models quantifying personality and/or plasticity only. While dogs were, on average, highly sociable with unfamiliar people and sociability increased over days since arrival, group averages were unrepresentative of all dogs and predictions made at the individual level entailed considerable uncertainty. Effects of demographic variables (e.g. age) on personality, plasticity and predictability were observed. Behavioural repeatability was higher one week after arrival compared to arrival day. Our results highlight the value of longitudinal assessments on shelter dogs and identify measures that could improve the predictive validity of behavioural assessments in shelters. PMID:28989764
Prediction of type A behaviour: A structural equation model
Directory of Open Access Journals (Sweden)
René van Wyk
2009-05-01
Full Text Available The predictability of Type A behaviour was measured in a sample of 375 professionals with a shortened version of the Jenkins Activity Survey (JAS. Two structural equation models were constructed with the Type A behaviour achievement sub-scale and global (total Type A as the predictor variables. The indices showed a reasonable-to-promising fit with the data. Type A achievement was reasonably predicted by service-career orientation, internal locus of control, power self-concept and economic innovation. Type A global was also predicted by internal locus of control, power self-concept and the entrepreneurial attitude of achievement and personal control.
Modelling of physical properties - databases, uncertainties and predictive power
DEFF Research Database (Denmark)
Gani, Rafiqul
Physical and thermodynamic property in the form of raw data or estimated values for pure compounds and mixtures are important pre-requisites for performing tasks such as, process design, simulation and optimization; computer aided molecular/mixture (product) design; and, product-process analysis...... in the estimated/predicted property values, how to assess the quality and reliability of the estimated/predicted property values? The paper will review a class of models for prediction of physical and thermodynamic properties of organic chemicals and their mixtures based on the combined group contribution – atom...
Murari, A.; Peluso, E.; Vega, J.; Gelfusa, M.; Lungaroni, M.; Gaudio, P.; Martínez, F. J.; Contributors, JET
2017-01-01
Understanding the many aspects of tokamak physics requires the development of quite sophisticated models. Moreover, in the operation of the devices, prediction of the future evolution of discharges can be of crucial importance, particularly in the case of the prediction of disruptions, which can cause serious damage to various parts of the machine. The determination of the limits of predictability is therefore an important issue for modelling, classifying and forecasting. In all these cases, once a certain level of performance has been reached, the question typically arises as to whether all the information available in the data has been exploited, or whether there are still margins for improvement of the tools being developed. In this paper, a theoretical information approach is proposed to address this issue. The excellent properties of the developed indicator, called the prediction factor (PF), have been proved with the help of a series of numerical tests. Its application to some typical behaviour relating to macroscopic instabilities in tokamaks has shown very positive results. The prediction factor has also been used to assess the performance of disruption predictors running in real time in the JET system, including the one systematically deployed in the feedback loop for mitigation purposes. The main conclusion is that the most advanced predictors basically exploit all the information contained in the locked mode signal on which they are based. Therefore, qualitative improvements in disruption prediction performance in JET would need the processing of additional signals, probably profiles.
Dedes, I.; Dudek, J.
2018-03-01
We examine the effects of the parametric correlations on the predictive capacities of the theoretical modelling keeping in mind the nuclear structure applications. The main purpose of this work is to illustrate the method of establishing the presence and determining the form of parametric correlations within a model as well as an algorithm of elimination by substitution (see text) of parametric correlations. We examine the effects of the elimination of the parametric correlations on the stabilisation of the model predictions further and further away from the fitting zone. It follows that the choice of the physics case and the selection of the associated model are of secondary importance in this case. Under these circumstances we give priority to the relative simplicity of the underlying mathematical algorithm, provided the model is realistic. Following such criteria, we focus specifically on an important but relatively simple case of doubly magic spherical nuclei. To profit from the algorithmic simplicity we chose working with the phenomenological spherically symmetric Woods–Saxon mean-field. We employ two variants of the underlying Hamiltonian, the traditional one involving both the central and the spin orbit potential in the Woods–Saxon form and the more advanced version with the self-consistent density-dependent spin–orbit interaction. We compare the effects of eliminating of various types of correlations and discuss the improvement of the quality of predictions (‘predictive power’) under realistic parameter adjustment conditions.
GA-ARMA Model for Predicting IGS RTS Corrections
Directory of Open Access Journals (Sweden)
Mingyu Kim
2017-01-01
Full Text Available The global navigation satellite system (GNSS is widely used to estimate user positions. For precise positioning, users should correct for GNSS error components such as satellite orbit and clock errors as well as ionospheric delay. The international GNSS service (IGS real-time service (RTS can be used to correct orbit and clock errors in real-time. Since the IGS RTS provides real-time corrections via the Internet, intermittent data loss can occur due to software or hardware failures. We propose applying a genetic algorithm autoregressive moving average (GA-ARMA model to predict the IGS RTS corrections during data loss periods. The RTS orbit and clock corrections are predicted up to 900 s via the GA-ARMA model, and the prediction accuracies are compared with the results from a generic ARMA model. The orbit prediction performance of the GA-ARMA is nearly equivalent to that of ARMA, but GA-ARMA’s clock prediction performance is clearly better than that of ARMA, achieving a 32% error reduction. Predicted RTS corrections are applied to the broadcast ephemeris, and precise point positioning accuracies are compared. GA-ARMA shows a significant accuracy improvement over ARMA, particularly in terms of vertical positioning.
Modeling the prediction of business intelligence system effectiveness.
Weng, Sung-Shun; Yang, Ming-Hsien; Koo, Tian-Lih; Hsiao, Pei-I
2016-01-01
Although business intelligence (BI) technologies are continually evolving, the capability to apply BI technologies has become an indispensable resource for enterprises running in today's complex, uncertain and dynamic business environment. This study performed pioneering work by constructing models and rules for the prediction of business intelligence system effectiveness (BISE) in relation to the implementation of BI solutions. For enterprises, effectively managing critical attributes that determine BISE to develop prediction models with a set of rules for self-evaluation of the effectiveness of BI solutions is necessary to improve BI implementation and ensure its success. The main study findings identified the critical prediction indicators of BISE that are important to forecasting BI performance and highlighted five classification and prediction rules of BISE derived from decision tree structures, as well as a refined regression prediction model with four critical prediction indicators constructed by logistic regression analysis that can enable enterprises to improve BISE while effectively managing BI solution implementation and catering to academics to whom theory is important.
In silico modeling to predict drug-induced phospholipidosis
International Nuclear Information System (INIS)
Choi, Sydney S.; Kim, Jae S.; Valerio, Luis G.; Sadrieh, Nakissa
2013-01-01
Drug-induced phospholipidosis (DIPL) is a preclinical finding during pharmaceutical drug development that has implications on the course of drug development and regulatory safety review. A principal characteristic of drugs inducing DIPL is known to be a cationic amphiphilic structure. This provides evidence for a structure-based explanation and opportunity to analyze properties and structures of drugs with the histopathologic findings for DIPL. In previous work from the FDA, in silico quantitative structure–activity relationship (QSAR) modeling using machine learning approaches has shown promise with a large dataset of drugs but included unconfirmed data as well. In this study, we report the construction and validation of a battery of complementary in silico QSAR models using the FDA's updated database on phospholipidosis, new algorithms and predictive technologies, and in particular, we address high performance with a high-confidence dataset. The results of our modeling for DIPL include rigorous external validation tests showing 80–81% concordance. Furthermore, the predictive performance characteristics include models with high sensitivity and specificity, in most cases above ≥ 80% leading to desired high negative and positive predictivity. These models are intended to be utilized for regulatory toxicology applied science needs in screening new drugs for DIPL. - Highlights: • New in silico models for predicting drug-induced phospholipidosis (DIPL) are described. • The training set data in the models is derived from the FDA's phospholipidosis database. • We find excellent predictivity values of the models based on external validation. • The models can support drug screening and regulatory decision-making on DIPL
Cardiopulmonary Circuit Models for Predicting Injury to the Heart
Ward, Richard; Wing, Sarah; Bassingthwaighte, James; Neal, Maxwell
2004-11-01
Circuit models have been used extensively in physiology to describe cardiopulmonary function. Such models are being used in the DARPA Virtual Soldier (VS) Project* to predict the response to injury or physiological stress. The most complex model consists of systemic circulation, pulmonary circulation, and a four-chamber heart sub-model. This model also includes baroreceptor feedback, airway mechanics, gas exchange, and pleural pressure influence on the circulation. As part of the VS Project, Oak Ridge National Laboratory has been evaluating various cardiopulmonary circuit models for predicting the effects of injury to the heart. We describe, from a physicist's perspective, the concept of building circuit models, discuss both unstressed and stressed models, and show how the stressed models are used to predict effects of specific wounds. *This work was supported by a grant from the DARPA, executed by the U.S. Army Medical Research and Materiel Command/TATRC Cooperative Agreement, Contract # W81XWH-04-2-0012. The submitted manuscript has been authored by the U.S. Department of Energy, Office of Science of the Oak Ridge National Laboratory, managed for the U.S. DOE by UT-Battelle, LLC, under contract No. DE-AC05-00OR22725. Accordingly, the U.S. Government retains a non-exclusive, royalty-free license to publish or reproduce the published form of this contribution, or allow others to do so, for U.S. Government purpose.
Prediction of gas compressibility factor using intelligent models
Directory of Open Access Journals (Sweden)
Mohamad Mohamadi-Baghmolaei
2015-10-01
Full Text Available The gas compressibility factor, also known as Z-factor, plays the determinative role for obtaining thermodynamic properties of gas reservoir. Typically, empirical correlations have been applied to determine this important property. However, weak performance and some limitations of these correlations have persuaded the researchers to use intelligent models instead. In this work, prediction of Z-factor is aimed using different popular intelligent models in order to find the accurate one. The developed intelligent models are including Artificial Neural Network (ANN, Fuzzy Interface System (FIS and Adaptive Neuro-Fuzzy System (ANFIS. Also optimization of equation of state (EOS by Genetic Algorithm (GA is done as well. The validity of developed intelligent models was tested using 1038 series of published data points in literature. It was observed that the accuracy of intelligent predicting models for Z-factor is significantly better than conventional empirical models. Also, results showed the improvement of optimized EOS predictions when coupled with GA optimization. Moreover, of the three intelligent models, ANN model outperforms other models considering all data and 263 field data points of an Iranian offshore gas condensate with R2 of 0.9999, while the R2 for best empirical correlation was about 0.8334.
Non-SM Exotic Higgs: Beyond SM and MSSM
Directory of Open Access Journals (Sweden)
Lacaprara Stefano
2013-05-01
Full Text Available A review of the searches for exotic Higgs boson beyond standard model and minimal supersymmetric standard model (MSSM, from experiments at the Tevatron and LHC, is presented. Several different models have been considered, including extensions to standard model with fourth generation of fermions, fermiophobic Higgs, next-to-MSSM models, seesaw type-II, and rare decay of Higgs boson to hidden sector. For next-to- MSSM models several final states have been considered, including light pseudo-scalar Higgs decay into taus, muons, and photons, as well as charged Higgs boson. The searches has been performed with re-interpretation of results from standard model Higgs search as well as on new signatures.
Sahle, Berhe W; Owen, Alice J; Chin, Ken Lee; Reid, Christopher M
2017-09-01
Numerous models predicting the risk of incident heart failure (HF) have been developed; however, evidence of their methodological rigor and reporting remains unclear. This study critically appraises the methods underpinning incident HF risk prediction models. EMBASE and PubMed were searched for articles published between 1990 and June 2016 that reported at least 1 multivariable model for prediction of HF. Model development information, including study design, variable coding, missing data, and predictor selection, was extracted. Nineteen studies reporting 40 risk prediction models were included. Existing models have acceptable discriminative ability (C-statistics > 0.70), although only 6 models were externally validated. Candidate variable selection was based on statistical significance from a univariate screening in 11 models, whereas it was unclear in 12 models. Continuous predictors were retained in 16 models, whereas it was unclear how continuous variables were handled in 16 models. Missing values were excluded in 19 of 23 models that reported missing data, and the number of events per variable was models. Only 2 models presented recommended regression equations. There was significant heterogeneity in discriminative ability of models with respect to age (P prediction models that had sufficient discriminative ability, although few are externally validated. Methods not recommended for the conduct and reporting of risk prediction modeling were frequently used, and resulting algorithms should be applied with caution. Copyright © 2017 Elsevier Inc. All rights reserved.
Energy Technology Data Exchange (ETDEWEB)
Modi, Anchit, E-mail: anchitmodi87@gmail.com [Department of Physics, Barkatullah University, Bhopal 462026 (India); Bhat, Masroor Ahmad; Pandey, Devendra K. [Department of Physics, Barkatullah University, Bhopal 462026 (India); Tarachand [UGC-DAE, Consortium for Scientific Research, University Campus, Khandwa Road, Indore 452001 (India); Bhattacharya, Shovit [Technical Physics Division, Bhabha Atomic Research Center, Mumbai 400085 (India); Gaur, N.K. [Department of Physics, Barkatullah University, Bhopal 462026 (India); Okram, G.S. [UGC-DAE, Consortium for Scientific Research, University Campus, Khandwa Road, Indore 452001 (India)
2017-02-15
In the present paper we studied the structural, magnetotransport and thermopower properties of a series of polycrystalline La{sub 0.7−x}Sm{sub x}Ba{sub 0.3}MnO{sub 3} (0≤x≤0.2) compounds which were synthesized by conventional solid state reaction techniques. The Rietveld refinement of the X-ray diffraction pattern predicts the formation of single-phase compositions and doping of La{sup 3+} by Sm{sup 3+} ion converted the chemical structure form rhombohedral (R-3C) to orthorhombic (Imma) The scanning electron microscopy show clean smooth and densified structure images be a sign of good crystalline nature of the samples. The temperature dependent electrical resistivity is measured by standard four-probe method between 5 and 300 K exhibits Metal–Insulator Transition MIT which shows the decreasing trend with increasing Sm doping content and metallic state (T
Improving Saliency Models by Predicting Human Fixation Patches
Dubey, Rachit
2015-04-16
There is growing interest in studying the Human Visual System (HVS) to supplement and improve the performance of computer vision tasks. A major challenge for current visual saliency models is predicting saliency in cluttered scenes (i.e. high false positive rate). In this paper, we propose a fixation patch detector that predicts image patches that contain human fixations with high probability. Our proposed model detects sparse fixation patches with an accuracy of 84 % and eliminates non-fixation patches with an accuracy of 84 % demonstrating that low-level image features can indeed be used to short-list and identify human fixation patches. We then show how these detected fixation patches can be used as saliency priors for popular saliency models, thus, reducing false positives while maintaining true positives. Extensive experimental results show that our proposed approach allows state-of-the-art saliency methods to achieve better prediction performance on benchmark datasets.
Model Predictive Control of Wind Turbines using Uncertain LIDAR Measurements
DEFF Research Database (Denmark)
Mirzaei, Mahmood; Soltani, Mohsen; Poulsen, Niels Kjølstad
2013-01-01
The problem of Model predictive control (MPC) of wind turbines using uncertain LIDAR (LIght Detection And Ranging) measurements is considered. A nonlinear dynamical model of the wind turbine is obtained. We linearize the obtained nonlinear model for different operating points, which are determined......, we simplify state prediction for the MPC. Consequently, the control problem of the nonlinear system is simplified into a quadratic programming. We consider uncertainty in the wind propagation time, which is the traveling time of wind from the LIDAR measurement point to the rotor. An algorithm based...... by the effective wind speed on the rotor disc. We take the wind speed as a scheduling variable. The wind speed is measurable ahead of the turbine using LIDARs, therefore, the scheduling variable is known for the entire prediction horizon. By taking the advantage of having future values of the scheduling variable...
Data Quality Enhanced Prediction Model for Massive Plant Data
Energy Technology Data Exchange (ETDEWEB)
Park, Moon-Ghu [Nuclear Engr. Sejong Univ., Seoul (Korea, Republic of); Kang, Seong-Ki [Monitoring and Diagnosis, Suwon (Korea, Republic of); Shin, Hajin [Saint Paul Preparatory Seoul, Seoul (Korea, Republic of)
2016-10-15
This paper introduces an integrated signal preconditioning and model prediction mainly by kernel functions. The performance and benefits of the methods are demonstrated by a case study with measurement data from a power plant and its components transient data. The developed methods will be applied as a part of monitoring massive or big data platform where human experts cannot detect the fault behaviors due to too large size of the measurements. Recent extensive efforts for on-line monitoring implementation insists that a big surprise in the modeling for predicting process variables was the extent of data quality problems in measurement data especially for data-driven modeling. Bad data for training will be learned as normal and can make significant degrade in prediction performance. For this reason, the quantity and quality of measurement data in modeling phase need special care. Bad quality data must be removed from training sets to the bad data considered as normal system behavior. This paper presented an integrated structure of supervisory system for monitoring the plants or sensors performance. The quality of the data-driven model is improved with a bilateral kernel filter for preprocessing of the noisy data. The prediction module is also based on kernel regression having the same basis with noise filter. The model structure is optimized by a grouping process with nonlinear Hoeffding correlation function.
Data Quality Enhanced Prediction Model for Massive Plant Data
International Nuclear Information System (INIS)
Park, Moon-Ghu; Kang, Seong-Ki; Shin, Hajin
2016-01-01
This paper introduces an integrated signal preconditioning and model prediction mainly by kernel functions. The performance and benefits of the methods are demonstrated by a case study with measurement data from a power plant and its components transient data. The developed methods will be applied as a part of monitoring massive or big data platform where human experts cannot detect the fault behaviors due to too large size of the measurements. Recent extensive efforts for on-line monitoring implementation insists that a big surprise in the modeling for predicting process variables was the extent of data quality problems in measurement data especially for data-driven modeling. Bad data for training will be learned as normal and can make significant degrade in prediction performance. For this reason, the quantity and quality of measurement data in modeling phase need special care. Bad quality data must be removed from training sets to the bad data considered as normal system behavior. This paper presented an integrated structure of supervisory system for monitoring the plants or sensors performance. The quality of the data-driven model is improved with a bilateral kernel filter for preprocessing of the noisy data. The prediction module is also based on kernel regression having the same basis with noise filter. The model structure is optimized by a grouping process with nonlinear Hoeffding correlation function
Computational modeling of oligonucleotide positional densities for human promoter prediction.
Narang, Vipin; Sung, Wing-Kin; Mittal, Ankush
2005-01-01
The gene promoter region controls transcriptional initiation of a gene, which is the most important step in gene regulation. In-silico detection of promoter region in genomic sequences has a number of applications in gene discovery and understanding gene expression regulation. However, computational prediction of eukaryotic poly-II promoters has remained a difficult task. This paper introduces a novel statistical technique for detecting promoter regions in long genomic sequences. A number of existing techniques analyze the occurrence frequencies of oligonucleotides in promoter sequences as compared to other genomic regions. In contrast, the present work studies the positional densities of oligonucleotides in promoter sequences. The analysis does not require any non-promoter sequence dataset or any model of the background oligonucleotide content of the genome. The statistical model learnt from a dataset of promoter sequences automatically recognizes a number of transcription factor binding sites simultaneously with their occurrence positions relative to the transcription start site. Based on this model, a continuous naïve Bayes classifier is developed for the detection of human promoters and transcription start sites in genomic sequences. The present study extends the scope of statistical models in general promoter modeling and prediction. Promoter sequence features learnt by the model correlate well with known biological facts. Results of human transcription start site prediction compare favorably with existing 2nd generation promoter prediction tools.
Predictive modeling of mosquito abundance and dengue transmission in Kenya
Caldwell, J.; Krystosik, A.; Mutuku, F.; Ndenga, B.; LaBeaud, D.; Mordecai, E.
2017-12-01
Approximately 390 million people are exposed to dengue virus every year, and with no widely available treatments or vaccines, predictive models of disease risk are valuable tools for vector control and disease prevention. The aim of this study was to modify and improve climate-driven predictive models of dengue vector abundance (Aedes spp. mosquitoes) and viral transmission to people in Kenya. We simulated disease transmission using a temperature-driven mechanistic model and compared model predictions with vector trap data for larvae, pupae, and adult mosquitoes collected between 2014 and 2017 at four sites across urban and rural villages in Kenya. We tested predictive capacity of our models using four temperature measurements (minimum, maximum, range, and anomalies) across daily, weekly, and monthly time scales. Our results indicate seasonal temperature variation is a key driving factor of Aedes mosquito abundance and disease transmission. These models can help vector control programs target specific locations and times when vectors are likely to be present, and can be modified for other Aedes-transmitted diseases and arboviral endemic regions around the world.
International Nuclear Information System (INIS)
Moon, Jin Woo; Yoon, Younju; Jeon, Young-Hoon; Kim, Sooyoung
2017-01-01
Highlights: • Initial ANN model was developed for predicting the time to the setback temperature. • Initial model was optimized for producing accurate output. • Optimized model proved its prediction accuracy. • ANN-based algorithms were developed and tested their performance. • ANN-based algorithms presented superior thermal comfort or energy efficiency. - Abstract: In this study, a temperature control algorithm was developed to apply a setback temperature predictively for the cooling system of a residential building during occupied periods by residents. An artificial neural network (ANN) model was developed to determine the required time for increasing the current indoor temperature to the setback temperature. This study involved three phases: development of the initial ANN-based prediction model, optimization and testing of the initial model, and development and testing of three control algorithms. The development and performance testing of the model and algorithm were conducted using TRNSYS and MATLAB. Through the development and optimization process, the final ANN model employed indoor temperature and the temperature difference between the current and target setback temperature as two input neurons. The optimal number of hidden layers, number of neurons, learning rate, and moment were determined to be 4, 9, 0.6, and 0.9, respectively. The tangent–sigmoid and pure-linear transfer function was used in the hidden and output neurons, respectively. The ANN model used 100 training data sets with sliding-window method for data management. Levenberg-Marquart training method was employed for model training. The optimized model had a prediction accuracy of 0.9097 root mean square errors when compared with the simulated results. Employing the ANN model, ANN-based algorithms maintained indoor temperatures better within target ranges. Compared to the conventional algorithm, the ANN-based algorithms reduced the duration of time, in which the indoor temperature
Relative sensitivity analysis of the predictive properties of sloppy models.
Myasnikova, Ekaterina; Spirov, Alexander
2018-01-25
Commonly among the model parameters characterizing complex biological systems are those that do not significantly influence the quality of the fit to experimental data, so-called "sloppy" parameters. The sloppiness can be mathematically expressed through saturating response functions (Hill's, sigmoid) thereby embodying biological mechanisms responsible for the system robustness to external perturbations. However, if a sloppy model is used for the prediction of the system behavior at the altered input (e.g. knock out mutations, natural expression variability), it may demonstrate the poor predictive power due to the ambiguity in the parameter estimates. We introduce a method of the predictive power evaluation under the parameter estimation uncertainty, Relative Sensitivity Analysis. The prediction problem is addressed in the context of gene circuit models describing the dynamics of segmentation gene expression in Drosophila embryo. Gene regulation in these models is introduced by a saturating sigmoid function of the concentrations of the regulatory gene products. We show how our approach can be applied to characterize the essential difference between the sensitivity properties of robust and non-robust solutions and select among the existing solutions those providing the correct system behavior at any reasonable input. In general, the method allows to uncover the sources of incorrect predictions and proposes the way to overcome the estimation uncertainties.
New Temperature-based Models for Predicting Global Solar Radiation
International Nuclear Information System (INIS)
Hassan, Gasser E.; Youssef, M. Elsayed; Mohamed, Zahraa E.; Ali, Mohamed A.; Hanafy, Ahmed A.
2016-01-01
Highlights: • New temperature-based models for estimating solar radiation are investigated. • The models are validated against 20-years measured data of global solar radiation. • The new temperature-based model shows the best performance for coastal sites. • The new temperature-based model is more accurate than the sunshine-based models. • The new model is highly applicable with weather temperature forecast techniques. - Abstract: This study presents new ambient-temperature-based models for estimating global solar radiation as alternatives to the widely used sunshine-based models owing to the unavailability of sunshine data at all locations around the world. Seventeen new temperature-based models are established, validated and compared with other three models proposed in the literature (the Annandale, Allen and Goodin models) to estimate the monthly average daily global solar radiation on a horizontal surface. These models are developed using a 20-year measured dataset of global solar radiation for the case study location (Lat. 30°51′N and long. 29°34′E), and then, the general formulae of the newly suggested models are examined for ten different locations around Egypt. Moreover, the local formulae for the models are established and validated for two coastal locations where the general formulae give inaccurate predictions. Mostly common statistical errors are utilized to evaluate the performance of these models and identify the most accurate model. The obtained results show that the local formula for the most accurate new model provides good predictions for global solar radiation at different locations, especially at coastal sites. Moreover, the local and general formulas of the most accurate temperature-based model also perform better than the two most accurate sunshine-based models from the literature. The quick and accurate estimations of the global solar radiation using this approach can be employed in the design and evaluation of performance for
Coherent potential approximation treatment of the Sm valence transition in SmS induced by alloying
International Nuclear Information System (INIS)
Spronken, G.
1982-01-01
The Sm valence transition, similar to the pressure-induced transition in pure SmS, has been observed in a large number of cases by substituting a trivalent rare earth, B, for Sm, thereby forming isostructural alloys with a lattice constant smaller than that of semiconducting SmS. Such substitutions are expected to exert a 'chemical' pressure which simulates the external pressure. However, divalent substitutions (Yb, Eu and Ca), although having a favourable 'size' factor, do not induce any valence transition. Accordingly, band structure effects, essentially due to the relative position of the conduction bands of SmS and BS, should also be taken into consideration. In order to clarify the role which the lattice constant and the electronic structure play in the valence transition, these alloys have been studied using the coherent potential approximation (CPA) including both the crystal field effect and the Coulomb interaction between localised and itinerant states as driving mechanisms. For Sm, the 'homogeneous' picture is considered, each Sm site having the average valence; then the Smsub(1-x)Bsub(x)S system is reduced to a binary alloy. Charge transfer between the f states of Sm and the alloy conduction band (i.e. Sm valence change) is determined self-consistently. The calculations are consistent with the experimental behaviour and allow us to understand qualitatively the variation of the critical concentration xsub(c) for the transition as a function of the difference Δa between the lattice parameters of SmS and BS. (author)
Predicting turns in proteins with a unified model.
Directory of Open Access Journals (Sweden)
Qi Song
Full Text Available MOTIVATION: Turns are a critical element of the structure of a protein; turns play a crucial role in loops, folds, and interactions. Current prediction methods are well developed for the prediction of individual turn types, including α-turn, β-turn, and γ-turn, etc. However, for further protein structure and function prediction it is necessary to develop a uniform model that can accurately predict all types of turns simultaneously. RESULTS: In this study, we present a novel approach, TurnP, which offers the ability to investigate all the turns in a protein based on a unified model. The main characteristics of TurnP are: (i using newly exploited features of structural evolution information (secondary structure and shape string of protein based on structure homologies, (ii considering all types of turns in a unified model, and (iii practical capability of accurate prediction of all turns simultaneously for a query. TurnP utilizes predicted secondary structures and predicted shape strings, both of which have greater accuracy, based on innovative technologies which were both developed by our group. Then, sequence and structural evolution features, which are profile of sequence, profile of secondary structures and profile of shape strings are generated by sequence and structure alignment. When TurnP was validated on a non-redundant dataset (4,107 entries by five-fold cross-validation, we achieved an accuracy of 88.8% and a sensitivity of 71.8%, which exceeded the most state-of-the-art predictors of certain type of turn. Newly determined sequences, the EVA and CASP9 datasets were used as independent tests and the results we achieved were outstanding for turn predictions and confirmed the good performance of TurnP for practical applications.
Directory of Open Access Journals (Sweden)
Guohui Li
2017-01-01
Full Text Available Aiming at the irregularity of nonlinear signal and its predicting difficulty, a deep learning prediction model based on extreme-point symmetric mode decomposition (ESMD and clustering analysis is proposed. Firstly, the original data is decomposed by ESMD to obtain the finite number of intrinsic mode functions (IMFs and residuals. Secondly, the fuzzy c-means is used to cluster the decomposed components, and then the deep belief network (DBN is used to predict it. Finally, the reconstructed IMFs and residuals are the final prediction results. Six kinds of prediction models are compared, which are DBN prediction model, EMD-DBN prediction model, EEMD-DBN prediction model, CEEMD-DBN prediction model, ESMD-DBN prediction model, and the proposed model in this paper. The same sunspots time series are predicted with six kinds of prediction models. The experimental results show that the proposed model has better prediction accuracy and smaller error.
4K Video Traffic Prediction using Seasonal Autoregressive Modeling
Directory of Open Access Journals (Sweden)
D. R. Marković
2017-06-01
Full Text Available From the perspective of average viewer, high definition video streams such as HD (High Definition and UHD (Ultra HD are increasing their internet presence year over year. This is not surprising, having in mind expansion of HD streaming services, such as YouTube, Netflix etc. Therefore, high definition video streams are starting to challenge network resource allocation with their bandwidth requirements and statistical characteristics. Need for analysis and modeling of this demanding video traffic has essential importance for better quality of service and experience support. In this paper we use an easy-to-apply statistical model for prediction of 4K video traffic. Namely, seasonal autoregressive modeling is applied in prediction of 4K video traffic, encoded with HEVC (High Efficiency Video Coding. Analysis and modeling were performed within R programming environment using over 17.000 high definition video frames. It is shown that the proposed methodology provides good accuracy in high definition video traffic modeling.
Selection of References in Wind Turbine Model Predictive Control Design
DEFF Research Database (Denmark)
Odgaard, Peter Fogh; Hovgaard, Tobias
2015-01-01
a model predictive controller for a wind turbine. One of the important aspects for a tracking control problem is how to setup the optimal reference tracking problem, as it might be relevant to track, e.g., the three concurrent references: optimal pitch angle, optimal rotational speed, and optimal power....... The importance if the individual references differ depending in particular on the wind speed. In this paper we investigate the performance of a reference tracking model predictive controller with two different setups of the used optimal reference signals. The controllers are evaluated using an industrial high...
A predictive model of music preference using pairwise comparisons
DEFF Research Database (Denmark)
Jensen, Bjørn Sand; Gallego, Javier Saez; Larsen, Jan
2012-01-01
Music recommendation is an important aspect of many streaming services and multi-media systems, however, it is typically based on so-called collaborative filtering methods. In this paper we consider the recommendation task from a personal viewpoint and examine to which degree music preference can...... be elicited and predicted using simple and robust queries such as pairwise comparisons. We propose to model - and in turn predict - the pairwise music preference using a very flexible model based on Gaussian Process priors for which we describe the required inference. We further propose a specific covariance...
Evaluating the reliability of predictions made using environmental transfer models
International Nuclear Information System (INIS)
1989-01-01
The development and application of mathematical models for predicting the consequences of releases of radionuclides into the environment from normal operations in the nuclear fuel cycle and in hypothetical accident conditions has increased dramatically in the last two decades. This Safety Practice publication has been prepared to provide guidance on the available methods for evaluating the reliability of environmental transfer model predictions. It provides a practical introduction of the subject and a particular emphasis has been given to worked examples in the text. It is intended to supplement existing IAEA publications on environmental assessment methodology. 60 refs, 17 figs, 12 tabs
Physical/chemical modeling for photovoltaic module life prediction
Moacanin, J.; Carroll, W. F.; Gupta, A.
1979-01-01
The paper presents a generalized methodology for identification and evaluation of potential degradation and failure of terrestrial photovoltaic encapsulation. Failure progression modeling and an interaction matrix are utilized to complement the conventional approach to failure degradation mode identification. Comparison of the predicted performance based on these models can produce: (1) constraints on system or component design, materials or operating conditions, (2) qualification (predicted satisfactory function), and (3) uncertainty. The approach has been applied to an investigation of an unexpected delamination failure; it is being used to evaluate thermomechanical interactions in photovoltaic modules and to study corrosion of contacts and interconnects.
A neural network model for olfactory glomerular activity prediction
Soh, Zu; Tsuji, Toshio; Takiguchi, Noboru; Ohtake, Hisao
2012-12-01
Recently, the importance of odors and methods for their evaluation have seen increased emphasis, especially in the fragrance and food industries. Although odors can be characterized by their odorant components, their chemical information cannot be directly related to the flavors we perceive. Biological research has revealed that neuronal activity related to glomeruli (which form part of the olfactory system) is closely connected to odor qualities. Here we report on a neural network model of the olfactory system that can predict glomerular activity from odorant molecule structures. We also report on the learning and prediction ability of the proposed model.
Predicted and measured velocity distribution in a model heat exchanger
International Nuclear Information System (INIS)
Rhodes, D.B.; Carlucci, L.N.
1984-01-01
This paper presents a comparison between numerical predictions, using the porous media concept, and measurements of the two-dimensional isothermal shell-side velocity distributions in a model heat exchanger. Computations and measurements were done with and without tubes present in the model. The effect of tube-to-baffle leakage was also investigated. The comparison was made to validate certain porous media concepts used in a computer code being developed to predict the detailed shell-side flow in a wide range of shell-and-tube heat exchanger geometries
Mathematical modeling to predict residential solid waste generation.
Benítez, Sara Ojeda; Lozano-Olvera, Gabriela; Morelos, Raúl Adalberto; Vega, Carolina Armijo de
2008-01-01
One of the challenges faced by waste management authorities is determining the amount of waste generated by households in order to establish waste management systems, as well as trying to charge rates compatible with the principle applied worldwide, and design a fair payment system for households according to the amount of residential solid waste (RSW) they generate. The goal of this research work was to establish mathematical models that correlate the generation of RSW per capita to the following variables: education, income per household, and number of residents. This work was based on data from a study on generation, quantification and composition of residential waste in a Mexican city in three stages. In order to define prediction models, five variables were identified and included in the model. For each waste sampling stage a different mathematical model was developed, in order to find the model that showed the best linear relation to predict residential solid waste generation. Later on, models to explore the combination of included variables and select those which showed a higher R(2) were established. The tests applied were normality, multicolinearity and heteroskedasticity. Another model, formulated with four variables, was generated and the Durban-Watson test was applied to it. Finally, a general mathematical model is proposed to predict residential waste generation, which accounts for 51% of the total.
Using Deep Learning Model for Meteorological Satellite Cloud Image Prediction
Su, X.
2017-12-01
A satellite cloud image contains much weather information such as precipitation information. Short-time cloud movement forecast is important for precipitation forecast and is the primary means for typhoon monitoring. The traditional methods are mostly using the cloud feature matching and linear extrapolation to predict the cloud movement, which makes that the nonstationary process such as inversion and deformation during the movement of the cloud is basically not considered. It is still a hard task to predict cloud movement timely and correctly. As deep learning model could perform well in learning spatiotemporal features, to meet this challenge, we could regard cloud image prediction as a spatiotemporal sequence forecasting problem and introduce deep learning model to solve this problem. In this research, we use a variant of Gated-Recurrent-Unit(GRU) that has convolutional structures to deal with spatiotemporal features and build an end-to-end model to solve this forecast problem. In this model, both the input and output are spatiotemporal sequences. Compared to Convolutional LSTM(ConvLSTM) model, this model has lower amount of parameters. We imply this model on GOES satellite data and the model perform well.
Montresor, Antonio; Deol, Arminder; À Porta, Natacha; Lethanh, Nam; Jankovic, Dina
2016-04-01
Estimating the reduction in levels of infection during implementation of soil-transmitted helminth (STH) control programmes is important to measure their performance and to plan interventions. Markov modelling techniques have been used with some success to predict changes in STH prevalence following treatment in Viet Nam. The model is stationary and to date, the prediction has been obtained by calculating the transition probabilities between the different classes of intensity following the first year of drug distribution and assuming that these remain constant in subsequent years. However, to run this model longitudinal parasitological data (including intensity of infection) are required for two consecutive years from at least 200 individuals. Since this amount of data is not often available from STH control programmes, the possible application of the model in control programme is limited. The present study aimed to address this issue by adapting the existing Markov model to allow its application when a more limited amount of data is available and to test the predictive capacities of these simplified models. We analysed data from field studies conducted with different combination of three parameters: (i) the frequency of drug administration; (ii) the drug distributed; and (iii) the target treatment population (entire population or school-aged children only). This analysis allowed us to define 10 sets of standard transition probabilities to be used to predict prevalence changes when only baseline data are available (simplified model 1). We also formulated three equations (one for each STH parasite) to calculate the predicted prevalence of the different classes of intensity from the total prevalence. These equations allowed us to design a simplified model (SM2) to obtain predictions when the classes of intensity at baseline were not known. To evaluate the performance of the simplified models, we collected data from the scientific literature on changes in STH
Directory of Open Access Journals (Sweden)
Antonio Montresor
2016-04-01
Full Text Available Estimating the reduction in levels of infection during implementation of soil-transmitted helminth (STH control programmes is important to measure their performance and to plan interventions. Markov modelling techniques have been used with some success to predict changes in STH prevalence following treatment in Viet Nam. The model is stationary and to date, the prediction has been obtained by calculating the transition probabilities between the different classes of intensity following the first year of drug distribution and assuming that these remain constant in subsequent years. However, to run this model longitudinal parasitological data (including intensity of infection are required for two consecutive years from at least 200 individuals. Since this amount of data is not often available from STH control programmes, the possible application of the model in control programme is limited. The present study aimed to address this issue by adapting the existing Markov model to allow its application when a more limited amount of data is available and to test the predictive capacities of these simplified models.We analysed data from field studies conducted with different combination of three parameters: (i the frequency of drug administration; (ii the drug distributed; and (iii the target treatment population (entire population or school-aged children only. This analysis allowed us to define 10 sets of standard transition probabilities to be used to predict prevalence changes when only baseline data are available (simplified model 1. We also formulated three equations (one for each STH parasite to calculate the predicted prevalence of the different classes of intensity from the total prevalence. These equations allowed us to design a simplified model (SM2 to obtain predictions when the classes of intensity at baseline were not known. To evaluate the performance of the simplified models, we collected data from the scientific literature on changes in
Predictive models in cancer management: A guide for clinicians.
Kazem, Mohammed Ali
2017-04-01
Predictive tools in cancer management are used to predict different outcomes including survival probability or risk of recurrence. The uptake of these tools by clinicians involved in cancer management has not been as common as other clinical tools, which may be due to the complexity of some of these tools or a lack of understanding of how they can aid decision-making in particular clinical situations. The aim of this article is to improve clinicians' knowledge and understanding of predictive tools used in cancer management, including how they are built, how they can be applied to medical practice, and what their limitations may be. Literature review was conducted to investigate the role of predictive tools in cancer management. All predictive models share similar characteristics, but depending on the type of the tool its ability to predict an outcome will differ. Each type has its own pros and cons, and its generalisability will depend on the cohort used to build the tool. These factors will affect the clinician's decision whether to apply the model to their cohort or not. Before a model is used in clinical practice, it is important to appreciate how the model is constructed, what its use may add over and above traditional decision-making tools, and what problems or limitations may be associated with it. Understanding all the above is an important step for any clinician who wants to decide whether or not use predictive tools in their practice. Copyright © 2016 Royal College of Surgeons of Edinburgh (Scottish charity number SC005317) and Royal College of Surgeons in Ireland. Published by Elsevier Ltd. All rights reserved.
Ensemble ecosystem modeling for predicting ecosystem response to predator reintroduction.
Baker, Christopher M; Gordon, Ascelin; Bode, Michael
2017-04-01
Introducing a new or extirpated species to an ecosystem is risky, and managers need quantitative methods that can predict the consequences for the recipient ecosystem. Proponents of keystone predator reintroductions commonly argue that the presence of the predator will restore ecosystem function, but this has not always been the case, and mathematical modeling has an important role to play in predicting how reintroductions will likely play out. We devised an ensemble modeling method that integrates species interaction networks and dynamic community simulations and used it to describe the range of plausible consequences of 2 keystone-predator reintroductions: wolves (Canis lupus) to Yellowstone National Park and dingoes (Canis dingo) to a national park in Australia. Although previous methods for predicting ecosystem responses to such interventions focused on predicting changes around a given equilibrium, we used Lotka-Volterra equations to predict changing abundances through time. We applied our method to interaction networks for wolves in Yellowstone National Park and for dingoes in Australia. Our model replicated the observed dynamics in Yellowstone National Park and produced a larger range of potential outcomes for the dingo network. However, we also found that changes in small vertebrates or invertebrates gave a good indication about the potential future state of the system. Our method allowed us to predict when the systems were far from equilibrium. Our results showed that the method can also be used to predict which species may increase or decrease following a reintroduction and can identify species that are important to monitor (i.e., species whose changes in abundance give extra insight into broad changes in the system). Ensemble ecosystem modeling can also be applied to assess the ecosystem-wide implications of other types of interventions including assisted migration, biocontrol, and invasive species eradication. © 2016 Society for Conservation Biology.
Importance of fines in smart water enhanced oil recovery (SmW-EOR) for chalk outcrops
DEFF Research Database (Denmark)
Chakravarty, Krishna Hara; Fosbøl, Philip Loldrup; Thomsen, Kaj
2015-01-01
In SmW-EOR it is generally believed that precipitation of brines must be avoided since it can have a negative impact on the SmW sweep efficiency. But substitution of Mg2+ by Ca2+ on calcite surfaces (a well-accepted phenomenon) can change the brine combination and enhance the possibility of fine...... formation at speciation. Considering this phenomenon we analyze the possibility of fines formation and its influence in SmW-EOR. To calculate the brine speciation and the amount of precipitate formed at different pressure and temperature conditions, we use the Extended UNIQUAC model for 61 Sm......W-EOR experiments reported in literature. Both the amount of available soluble SO4 2- (aq) in the solution and the amount of CaSO4 precipitation has been calculated and correlated to the corresponding oil recovery....
Hybrid multiscale modeling and prediction of cancer cell behavior.
Directory of Open Access Journals (Sweden)
Mohammad Hossein Zangooei
Full Text Available Understanding cancer development crossing several spatial-temporal scales is of great practical significance to better understand and treat cancers. It is difficult to tackle this challenge with pure biological means. Moreover, hybrid modeling techniques have been proposed that combine the advantages of the continuum and the discrete methods to model multiscale problems.In light of these problems, we have proposed a new hybrid vascular model to facilitate the multiscale modeling and simulation of cancer development with respect to the agent-based, cellular automata and machine learning methods. The purpose of this simulation is to create a dataset that can be used for prediction of cell phenotypes. By using a proposed Q-learning based on SVR-NSGA-II method, the cells have the capability to predict their phenotypes autonomously that is, to act on its own without external direction in response to situations it encounters.Computational simulations of the model were performed in order to analyze its performance. The most striking feature of our results is that each cell can select its phenotype at each time step according to its condition. We provide evidence that the prediction of cell phenotypes is reliable.Our proposed model, which we term a hybrid multiscale modeling of cancer cell behavior, has the potential to combine the best features of both continuum and discrete models. The in silico results indicate that the 3D model can represent key features of cancer growth, angiogenesis, and its related micro-environment and show that the findings are in good agreement with biological tumor behavior. To the best of our knowledge, this paper is the first hybrid vascular multiscale modeling of cancer cell behavior that has the capability to predict cell phenotypes individually by a self-generated dataset.
Predicting Power Outages Using Multi-Model Ensemble Forecasts
Cerrai, D.; Anagnostou, E. N.; Yang, J.; Astitha, M.
2017-12-01
Power outages affect every year millions of people in the United States, affecting the economy and conditioning the everyday life. An Outage Prediction Model (OPM) has been developed at the University of Connecticut for helping utilities to quickly restore outages and to limit their adverse consequences on the population. The OPM, operational since 2015, combines several non-parametric machine learning (ML) models that use historical weather storm simulations and high-resolution weather forecasts, satellite remote sensing data, and infrastructure and land cover data to predict the number and spatial distribution of power outages. A new methodology, developed for improving the outage model performances by combining weather- and soil-related variables using three different weather models (WRF 3.7, WRF 3.8 and RAMS/ICLAMS), will be presented in this study. First, we will present a performance evaluation of each model variable, by comparing historical weather analyses with station data or reanalysis over the entire storm data set. Hence, each variable of the new outage model version is extracted from the best performing weather model for that variable, and sensitivity tests are performed for investigating the most efficient variable combination for outage prediction purposes. Despite that the final variables combination is extracted from different weather models, this ensemble based on multi-weather forcing and multi-statistical model power outage prediction outperforms the currently operational OPM version that is based on a single weather forcing variable (WRF 3.7), because each model component is the closest to the actual atmospheric state.
Candido Dos Reis, Francisco J; Wishart, Gordon C; Dicks, Ed M; Greenberg, David; Rashbass, Jem; Schmidt, Marjanka K; van den Broek, Alexandra J; Ellis, Ian O; Green, Andrew; Rakha, Emad; Maishman, Tom; Eccles, Diana M; Pharoah, Paul D P
2017-05-22
PREDICT is a breast cancer prognostic and treatment benefit model implemented online. The overall fit of the model has been good in multiple independent case series, but PREDICT has been shown to underestimate breast cancer specific mortality in women diagnosed under the age of 40. Another limitation is the use of discrete categories for tumour size and node status resulting in 'step' changes in risk estimates on moving between categories. We have refitted the PREDICT prognostic model using the original cohort of cases from East Anglia with updated survival time in order to take into account age at diagnosis and to smooth out the survival function for tumour size and node status. Multivariable Cox regression models were used to fit separate models for ER negative and ER positive disease. Continuous variables were fitted using fractional polynomials and a smoothed baseline hazard was obtained by regressing the baseline cumulative hazard for each patients against time using fractional polynomials. The fit of the prognostic models were then tested in three independent data sets that had also been used to validate the original version of PREDICT. In the model fitting data, after adjusting for other prognostic variables, there is an increase in risk of breast cancer specific mortality in younger and older patients with ER positive disease, with a substantial increase in risk for women diagnosed before the age of 35. In ER negative disease the risk increases slightly with age. The association between breast cancer specific mortality and both tumour size and number of positive nodes was non-linear with a more marked increase in risk with increasing size and increasing number of nodes in ER positive disease. The overall calibration and discrimination of the new version of PREDICT (v2) was good and comparable to that of the previous version in both model development and validation data sets. However, the calibration of v2 improved over v1 in patients diagnosed under the age
Mathematical modelling methodologies in predictive food microbiology: a SWOT analysis.
Ferrer, Jordi; Prats, Clara; López, Daniel; Vives-Rego, Josep
2009-08-31
Predictive microbiology is the area of food microbiology that attempts to forecast the quantitative evolution of microbial populations over time. This is achieved to a great extent through models that include the mechanisms governing population dynamics. Traditionally, the models used in predictive microbiology are whole-system continuous models that describe population dynamics by means of equations applied to extensive or averaged variables of the whole system. Many existing models can be classified by specific criteria. We can distinguish between survival and growth models by seeing whether they tackle mortality or cell duplication. We can distinguish between empirical (phenomenological) models, which mathematically describe specific behaviour, and theoretical (mechanistic) models with a biological basis, which search for the underlying mechanisms driving already observed phenomena. We can also distinguish between primary, secondary and tertiary models, by examining their treatment of the effects of external factors and constraints on the microbial community. Recently, the use of spatially explicit Individual-based Models (IbMs) has spread through predictive microbiology, due to the current technological capacity of performing measurements on single individual cells and thanks to the consolidation of computational modelling. Spatially explicit IbMs are bottom-up approaches to microbial communities that build bridges between the description of micro-organisms at the cell level and macroscopic observations at the population level. They provide greater insight into the mesoscale phenomena that link unicellular and population levels. Every model is built in response to a particular question and with different aims. Even so, in this research we conducted a SWOT (Strength, Weaknesses, Opportunities and Threats) analysis of the different approaches (population continuous modelling and Individual-based Modelling), which we hope will be helpful for current and future
International Nuclear Information System (INIS)
Korenko, M.; Kubikova, B.; Stulov, Y.V.; Kuznetsov, S.A.; Ambrova, M.
2014-01-01
This article is focused on the electrochemical investigation (cyclic voltammetry and related studies) of the redox couple Sm(III)/Sm(II) in an eutectic LiF-CaF 2 melt containing SmF 3 . The first step of reduction for Sm(III) ions involving one electron exchange in soluble/soluble Sm(III)/Sm(II) system was found on a tungsten electrode. The study of the Sm(II)/Sm(0) electrode reaction was not feasible, due to insufficient electrochemical stability of LiF-CaF 2 . The first step was found reversible at temperatures 1,075 and 1,125 K up to polarization rate 1 V/s and at temperature 1,175 K the process was reversible at all sweep rates applied in this study. The diffusion coefficients (D) of Sm(II) and Sm(III) ions were determined by cyclic voltammetry, showing that D decreases when oxidation state increase, while the activation energy of diffusion (E a ) increases. The standard rate constants of charge transfer (k s) were calculated for the redox couple Sm(III)/Sm(II) at 1,075 and 1,125 K based on the data of cyclic voltammetry. (author)
Clinical and epidemiological round: Approach to clinical prediction models
Directory of Open Access Journals (Sweden)
Isaza-Jaramillo, Sandra
2017-01-01
Full Text Available Research related to prognosis can be classified as follows: fundamental, which shows differences in health outcomes; prognostic factors, which identifies and characterizes variables; development, validation and impact of predictive models; and finally, stratified medicine, to establish groups that share a risk factor associated with the outcome of interest. The outcome of a person regarding health or disease status can be predicted considering certain characteristics associated, before or simultaneously, with that outcome. This can be done by means of prognostic or diagnostic predictive models. The development of a predictive model requires to be careful in the selection, definition, measurement and categorization of predictor variables; in the exploration of interactions; in the number of variables to be included; in the calculation of sample size; in the handling of lost data; in the statistical tests to be used, and in the presentation of the model. The model thus developed must be validated in a different group of patients to establish its calibration, discrimination and usefulness.
Neural Network Modeling to Predict Shelf Life of Greenhouse Lettuce
Directory of Open Access Journals (Sweden)
Wei-Chin Lin
2009-04-01
Full Text Available Greenhouse-grown butter lettuce (Lactuca sativa L. can potentially be stored for 21 days at constant 0°C. When storage temperature was increased to 5°C or 10°C, shelf life was shortened to 14 or 10 days, respectively, in our previous observations. Also, commercial shelf life of 7 to 10 days is common, due to postharvest temperature fluctuations. The objective of this study was to establish neural network (NN models to predict the remaining shelf life (RSL under fluctuating postharvest temperatures. A box of 12 - 24 lettuce heads constituted a sample unit. The end of the shelf life of each head was determined when it showed initial signs of decay or yellowing. Air temperatures inside a shipping box were recorded. Daily average temperatures in storage and averaged shelf life of each box were used as inputs, and the RSL was modeled as an output. An R2 of 0.57 could be observed when a simple NN structure was employed. Since the "future" (or remaining storage temperatures were unavailable at the time of making a prediction, a second NN model was introduced to accommodate a range of future temperatures and associated shelf lives. Using such 2-stage NN models, an R2 of 0.61 could be achieved for predicting RSL. This study indicated that NN modeling has potential for cold chain quality control and shelf life prediction.
A new, accurate predictive model for incident hypertension.
Völzke, Henry; Fung, Glenn; Ittermann, Till; Yu, Shipeng; Baumeister, Sebastian E; Dörr, Marcus; Lieb, Wolfgang; Völker, Uwe; Linneberg, Allan; Jørgensen, Torben; Felix, Stephan B; Rettig, Rainer; Rao, Bharat; Kroemer, Heyo K
2013-11-01
Data mining represents an alternative approach to identify new predictors of multifactorial diseases. This work aimed at building an accurate predictive model for incident hypertension using data mining procedures. The primary study population consisted of 1605 normotensive individuals aged 20-79 years with 5-year follow-up from the population-based study, that is the Study of Health in Pomerania (SHIP). The initial set was randomly split into a training and a testing set. We used a probabilistic graphical model applying a Bayesian network to create a predictive model for incident hypertension and compared the predictive performance with the established Framingham risk score for hypertension. Finally, the model was validated in 2887 participants from INTER99, a Danish community-based intervention study. In the training set of SHIP data, the Bayesian network used a small subset of relevant baseline features including age, mean arterial pressure, rs16998073, serum glucose and urinary albumin concentrations. Furthermore, we detected relevant interactions between age and serum glucose as well as between rs16998073 and urinary albumin concentrations [area under the receiver operating characteristic (AUC 0.76)]. The model was confirmed in the SHIP validation set (AUC 0.78) and externally replicated in INTER99 (AUC 0.77). Compared to the established Framingham risk score for hypertension, the predictive performance of the new model was similar in the SHIP validation set and moderately better in INTER99. Data mining procedures identified a predictive model for incident hypertension, which included innovative and easy-to-measure variables. The findings promise great applicability in screening settings and clinical practice.
An international model to predict recurrent cardiovascular disease.
Wilson, Peter W F; D'Agostino, Ralph; Bhatt, Deepak L; Eagle, Kim; Pencina, Michael J; Smith, Sidney C; Alberts, Mark J; Dallongeville, Jean; Goto, Shinya; Hirsch, Alan T; Liau, Chiau-Suong; Ohman, E Magnus; Röther, Joachim; Reid, Christopher; Mas, Jean-Louis; Steg, Ph Gabriel
2012-07-01
Prediction models for cardiovascular events and cardiovascular death in patients with established cardiovascular disease are not generally available. Participants from the prospective REduction of Atherothrombosis for Continued Health (REACH) Registry provided a global outpatient population with known cardiovascular disease at entry. Cardiovascular prediction models were estimated from the 2-year follow-up data of 49,689 participants from around the world. A developmental prediction model was estimated from 33,419 randomly selected participants (2394 cardiovascular events with 1029 cardiovascular deaths) from the pool of 49,689. The number of vascular beds with clinical disease, diabetes, smoking, low body mass index, history of atrial fibrillation, cardiac failure, and history of cardiovascular event(s) <1 year before baseline examination increased risk of a subsequent cardiovascular event. Statin (hazard ratio 0.75; 95% confidence interval, 0.69-0.82) and acetylsalicylic acid therapy (hazard ratio 0.90; 95% confidence interval, 0.83-0.99) also were significantly associated with reduced risk of cardiovascular events. The prediction model was validated in the remaining 16,270 REACH subjects (1172 cardiovascular events, 494 cardiovascular deaths). Risk of cardiovascular death was similarly estimated with the same set of risk factors. Simple algorithms were developed for prediction of overall cardiovascular events and for cardiovascular death. This study establishes and validates a risk model to predict secondary cardiovascular events and cardiovascular death in outpatients with established atherothrombotic disease. Traditional risk factors, burden of disease, lack of treatment, and geographic location all are related to an increased risk of subsequent cardiovascular morbidity and cardiovascular mortality. Copyright © 2012 Elsevier Inc. All rights reserved.
Curtis, Gary P.; Lu, Dan; Ye, Ming
2015-01-01
While Bayesian model averaging (BMA) has been widely used in groundwater modeling, it is infrequently applied to groundwater reactive transport modeling because of multiple sources of uncertainty in the coupled hydrogeochemical processes and because of the long execution time of each model run. To resolve these problems, this study analyzed different levels of uncertainty in a hierarchical way, and used the maximum likelihood version of BMA, i.e., MLBMA, to improve the computational efficiency. This study demonstrates the applicability of MLBMA to groundwater reactive transport modeling in a synthetic case in which twenty-seven reactive transport models were designed to predict the reactive transport of hexavalent uranium (U(VI)) based on observations at a former uranium mill site near Naturita, CO. These reactive transport models contain three uncertain model components, i.e., parameterization of hydraulic conductivity, configuration of model boundary, and surface complexation reactions that simulate U(VI) adsorption. These uncertain model components were aggregated into the alternative models by integrating a hierarchical structure into MLBMA. The modeling results of the individual models and MLBMA were analyzed to investigate their predictive performance. The predictive logscore results show that MLBMA generally outperforms the best model, suggesting that using MLBMA is a sound strategy to achieve more robust model predictions relative to a single model. MLBMA works best when the alternative models are structurally distinct and have diverse model predictions. When correlation in model structure exists, two strategies were used to improve predictive performance by retaining structurally distinct models or assigning smaller prior model probabilities to correlated models. Since the synthetic models were designed using data from the Naturita site, the results of this study are expected to provide guidance for real-world modeling. Limitations of applying MLBMA to the
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.
Molina-Mora, J A; Kop-Montero, M; Quirós-Fernández, I; Quiros, S; Crespo-Mariño, J L; Mora-Rodríguez, R A
2018-04-13
Sphingolipid (SL) metabolism is a complex biological system that produces and transforms ceramides and other molecules able to modulate other cellular processes, including survival or death pathways key to cell fate decisions. This signaling pathway integrates several types of stress signals, including chemotherapy, into changes in the activity of its metabolic enzymes, altering thereby the cellular composition of bioactive SLs. Therefore, the SL pathway is a promising sensor of chemosensitivity in cancer and a target hub to overcome resistance. However, there is still a gap in our understanding of how chemotherapeutic drugs can disturb the SL pathway in order to control cellular fate. We propose to bridge this gap by a systems biology approach to integrate i) a dynamic model of SL analogue (BODIPY-FL fluorescent-sphingomyelin analogue, SM-BOD) metabolism, ii) a Gaussian mixture model (GMM) of the fluorescence features to identify how the SL pathway senses the effect of chemotherapy and iii) a fuzzy logic model (FLM) to associate SL composition with cell viability by semi-quantitative rules. Altogether, this hybrid model approach was able to predict the cell viability of double experimental perturbations with chemotherapy, indicating that the SL pathway is a promising sensor to design strategies to overcome drug resistance in cancer. Copyright © 2018 Elsevier Ltd. All rights reserved.
Evolutionary neural network modeling for software cumulative failure time prediction
International Nuclear Information System (INIS)
Tian Liang; Noore, Afzel
2005-01-01
An evolutionary neural network modeling approach for software cumulative failure time prediction based on multiple-delayed-input single-output architecture is proposed. Genetic algorithm is used to globally optimize the number of the delayed input neurons and the number of neurons in the hidden layer of the neural network architecture. Modification of Levenberg-Marquardt algorithm with Bayesian regularization is used to improve the ability to predict software cumulative failure time. The performance of our proposed approach has been compared using real-time control and flight dynamic application data sets. Numerical results show that both the goodness-of-fit and the next-step-predictability of our proposed approach have greater accuracy in predicting software cumulative failure time compared to existing approaches
Intra prediction based on Markov process modeling of images.
Kamisli, Fatih
2013-10-01
In recent video coding standards, intraprediction of a block of pixels is performed by copying neighbor pixels of the block along an angular direction inside the block. Each block pixel is predicted from only one or few directionally aligned neighbor pixels of the block. Although this is a computationally efficient approach, it ignores potentially useful correlation of other neighbor pixels of the block. To use this correlation, a general linear prediction approach is proposed, where each block pixel is predicted using a weighted sum of all neighbor pixels of the block. The disadvantage of this approach is the increased complexity because of the large number of weights. In this paper, we propose an alternative approach to intraprediction, where we model image pixels with a Markov process. The Markov process model accounts for the ignored correlation in standard intraprediction methods, but uses few neighbor pixels and enables a computationally efficient recursive prediction algorithm. Compared with the general linear prediction approach that has a large number of independent weights, the Markov process modeling approach uses a much smaller number of independent parameters and thus offers significantly reduced memory or computation requirements, while achieving similar coding gains with offline computed parameters.
Nonlinear mixed-effects modeling: individualization and prediction.
Olofsen, Erik; Dinges, David F; Van Dongen, Hans P A
2004-03-01
The development of biomathematical models for the prediction of fatigue and performance relies on statistical techniques to analyze experimental data and model simulations. Statistical models of empirical data have adjustable parameters with a priori unknown values. Interindividual variability in estimates of those values requires a form of smoothing. This traditionally consists of averaging observations across subjects, or fitting a model to the data of individual subjects first and subsequently averaging the parameter estimates. However, the standard errors of the parameter estimates are assessed inaccurately by such averaging methods. The reason is that intra- and inter-individual variabilities are intertwined. They can be separated by mixed-effects modeling in which model predictions are not only determined by fixed effects (usually constant parameters or functions of time) but also by random effects, describing the sampling of subject-specific parameter values from probability distributions. By estimating the parameters of the distributions of the random effects, mixed-effects models can describe experimental observations involving multiple subjects properly (i.e., yielding correct estimates of the standard errors) and parsimoniously (i.e., estimating no more parameters than necessary). Using a Bayesian approach, mixed-effects models can be "individualized" as observations are acquired that capture the unique characteristics of the individual at hand. Mixed-effects models, therefore, have unique advantages in research on human neurobehavioral functions, which frequently show large inter-individual differences. To illustrate this we analyzed laboratory neurobehavioral performance data acquired during sleep deprivation, using a nonlinear mixed-effects model. The results serve to demonstrate the usefulness of mixed-effects modeling for data-driven development of individualized predictive models of fatigue and performance.
Conceptual Design Plan SM-43 Replacement Project
Energy Technology Data Exchange (ETDEWEB)
University of California, Los Alamos National Laboratory, SCC Project Office
2000-11-01
The Los Alamos National Laboratory Conceptual Design Plan for the SM-43 Replacement Project outlines plans for replacing the SM-43 Administration Building. Topics include the reasons that replacement is considered a necessity; the roles of the various project sponsors; and descriptions of the proposed site and facilities. Also covered in this proposal is preliminary information on the project schedule, cost estimates, acquisition strategy, risk assessment, NEPA strategy, safety strategy, and safeguards and security. Spreadsheets provide further detail on space requirements, project schedules, and cost estimates.
Predictive assessment of models for dynamic functional connectivity.
Nielsen, Søren F V; Schmidt, Mikkel N; Madsen, Kristoffer H; Mørup, Morten
2018-05-01
In neuroimaging, it has become evident that models of dynamic functional connectivity (dFC), which characterize how intrinsic brain organization changes over time, can provide a more detailed representation of brain function than traditional static analyses. Many dFC models in the literature represent functional brain networks as a meta-stable process with a discrete number of states; however, there is a lack of consensus on how to perform model selection and learn the number of states, as well as a lack of understanding of how different modeling assumptions influence the estimated state dynamics. To address these issues, we consider a predictive likelihood approach to model assessment, where models are evaluated based on their predictive performance on held-out test data. Examining several prominent models of dFC (in their probabilistic formulations) we demonstrate our framework on synthetic data, and apply it on two real-world examples: a face recognition EEG experiment and resting-state fMRI. Our results evidence that both EEG and fMRI are better characterized using dynamic modeling approaches than by their static counterparts, but we also demonstrate that one must be cautious when interpreting dFC because parameter settings and modeling assumptions, such as window lengths and emission models, can have a large impact on the estimated states and consequently on the interpretation of the brain dynamics. Copyright © 2018 Elsevier Inc. All rights reserved.
Models to predict the start of the airborne pollen season
Siniscalco, Consolata; Caramiello, Rosanna; Migliavacca, Mirco; Busetto, Lorenzo; Mercalli, Luca; Colombo, Roberto; Richardson, Andrew D.
2015-07-01
Aerobiological data can be used as indirect but reliable measures of flowering phenology to analyze the response of plant species to ongoing climate changes. The aims of this study are to evaluate the performance of several phenological models for predicting the pollen start of season (PSS) in seven spring-flowering trees ( Alnus glutinosa, Acer negundo, Carpinus betulus, Platanus occidentalis, Juglans nigra, Alnus viridis, and Castanea sativa) and in two summer-flowering herbaceous species ( Artemisia vulgaris and Ambrosia artemisiifolia) by using a 26-year aerobiological data set collected in Turin (Northern Italy). Data showed a reduced interannual variability of the PSS in the summer-flowering species compared to the spring-flowering ones. Spring warming models with photoperiod limitation performed best for the greater majority of the studied species, while chilling class models were selected only for the early spring flowering species. For Ambrosia and Artemisia, spring warming models were also selected as the best models, indicating that temperature sums are positively related to flowering. However, the poor variance explained by the models suggests that further analyses have to be carried out in order to develop better models for predicting the PSS in these two species. Modeling the pollen season start on a very wide data set provided a new opportunity to highlight the limits of models in elucidating the environmental factors driving the pollen season start when some factors are always fulfilled, as chilling or photoperiod or when the variance is very poor and is not explained by the models.
Estimation and prediction under local volatility jump-diffusion model
Kim, Namhyoung; Lee, Younhee
2018-02-01
Volatility is an important factor in operating a company and managing risk. In the portfolio optimization and risk hedging using the option, the value of the option is evaluated using the volatility model. Various attempts have been made to predict option value. Recent studies have shown that stochastic volatility models and jump-diffusion models reflect stock price movements accurately. However, these models have practical limitations. Combining them with the local volatility model, which is widely used among practitioners, may lead to better performance. In this study, we propose a more effective and efficient method of estimating option prices by combining the local volatility model with the jump-diffusion model and apply it using both artificial and actual market data to evaluate its performance. The calibration process for estimating the jump parameters and local volatility surfaces is divided into three stages. We apply the local volatility model, stochastic volatility model, and local volatility jump-diffusion model estimated by the proposed method to KOSPI 200 index option pricing. The proposed method displays good estimation and prediction performance.
Modeling and Prediction of Soil Water Vapor Sorption Isotherms
DEFF Research Database (Denmark)
Arthur, Emmanuel; Tuller, Markus; Moldrup, Per
2015-01-01
Soil water vapor sorption isotherms describe the relationship between water activity (aw) and moisture content along adsorption and desorption paths. The isotherms are important for modeling numerous soil processes and are also used to estimate several soil (specific surface area, clay content.......93) for a wide range of soils; and (ii) develop and test regression models for estimating the isotherms from clay content. Preliminary results show reasonable fits of the majority of the investigated empirical and theoretical models to the measured data although some models were not capable to fit both sorption...... directions accurately. Evaluation of the developed prediction equations showed good estimation of the sorption/desorption isotherms for tested soils....
Prediction of ultrasonic probe characteristics through modeling and simulation
International Nuclear Information System (INIS)
Amry Amin Abas; Mohamad Pauzi Ismail; Suhairy Sani
2004-01-01
One of the main component in an ultrasonic probe is piezoelectric material. It converts electrical energy supplied to it into mechanical energy (i.e. sound waves) and vice versa. In industrial application, the characteristic of ultrasonic probes is important as it will affect the results obtained. The probes fabricated must possess the characteristics suitable to the intended application. Through modeling and simulation, we can predict the characteristics of the probes. Mason equivalent circuit is used to make a model and simulation of the probes. In this model, the probes will be treated and simplified as a one dimensional electrical line. From simulation, the electrical properties such as impedance, operating frequency bandwidth and others can be predicted. From this model, the correct material to be used for actual probe construction can be obtained. The limitation of this method is details such as bond line between layers is not taken into consideration. (Author)
Predictions of titanium alloy properties using thermodynamic modeling tools
Zhang, F.; Xie, F.-Y.; Chen, S.-L.; Chang, Y. A.; Furrer, D.; Venkatesh, V.
2005-12-01
Thermodynamic modeling tools have become essential in understanding the effect of alloy chemistry on the final microstructure of a material. Implementation of such tools to improve titanium processing via parameter optimization has resulted in significant cost savings through the elimination of shop/laboratory trials and tests. In this study, a thermodynamic modeling tool developed at CompuTherm, LLC, is being used to predict β transus, phase proportions, phase chemistries, partitioning coefficients, and phase boundaries of multicomponent titanium alloys. This modeling tool includes Pandat, software for multicomponent phase equilibrium calculations, and PanTitanium, a thermodynamic database for titanium alloys. Model predictions are compared with experimental results for one α-β alloy (Ti-64) and two near-β alloys (Ti-17 and Ti-10-2-3). The alloying elements, especially the interstitial elements O, N, H, and C, have been shown to have a significant effect on the β transus temperature, and are discussed in more detail herein.
[A predictive model on turnover intention of nurses in Korea].
Moon, Sook Ja; Han, Sang Sook
2011-10-01
The purpose of this study was to propose and test a predictive model that could explain and predict Korean nurses' turnover intentions. A survey using a structured questionnaire was conducted with 445 nurses in Korea. Six instruments were used in this model. The data were analyzed using SPSS 15.0 and Amos 7.0 program. Based on the constructed model, organizational commitment, and burnout were found to have a significant direct effect on turnover intention of nurses. In addition, factors such as empowerment, job satisfaction, and organizational commitment were found to indirectly affect turnover intention of nurse. The final modified model yielded χ²=402.30, pturnover intention in Korean nurses. Findings from this study can be used to design appropriate strategies to further decrease the nurses' turnover intention in Korea.
PVT characterization and viscosity modeling and prediction of crude oils
DEFF Research Database (Denmark)
Cisneros, Eduardo Salvador P.; Dalberg, Anders; Stenby, Erling Halfdan
2004-01-01
method based on an accurate description of the fluid mass distribution is presented. The characterization procedure accurately matches the fluid saturation pressure. Additionally, a Peneloux volume translation scheme, capable of accurately reproducing the fluid density above and below the saturation...... deliver accurate viscosity predictions. The modeling approach presented in this work can deliver accurate viscosity and density modeling and prediction results over wide ranges of reservoir conditions, including the compositional changes induced by recovery processes such as gas injection.......In previous works, the general, one-parameter friction theory (f-theory), models have been applied to the accurate viscosity modeling of reservoir fluids. As a base, the f-theory approach requires a compositional characterization procedure for the application of an equation of state (EOS), in most...