Continuous real-time in vivo measurement of cerebral nitric oxide supports theoretical predictions of an irreversible switching in cerebral ROS after sufficient exposure to external toxins.

Science.gov (United States)

Finnerty, Niall J; O'Riordan, Saidhbhe L; Lowry, John P; Cloutier, Mathieu; Wellstead, Peter

2013-01-01

Mathematical models of the interactions between alphasynuclein (αS) and reactive oxygen species (ROS) predict a systematic and irreversible switching to damagingly high levels of ROS after sufficient exposure to risk factors associated with Parkinson's disease (PD). We tested this prediction by continuously monitoring real-time changes in neurochemical levels over periods of several days in animals exposed to a toxin known to cause Parkinsonian symptoms. Nitric oxide (NO) sensors were implanted in the brains of freely moving rats and the NO levels continuously recorded while the animals were exposed to paraquat (PQ) injections of various amounts and frequencies. Long-term, real-time measurement of NO in a cohort of animals showed systematic switching in levels when PQ injections of sufficient size and frequency were administered. The experimental observations of changes in NO imply a corresponding switching in endogenous ROS levels and support theoretical predictions of an irreversible change to damagingly high levels of endogenous ROS when PD risks are sufficiently large. Our current results only consider one form of PD risk, however, we are sufficiently confident in them to conclude that: (i) continuous long-term measurement of neurochemical dynamics provide a novel way to measure the temporal change and system dynamics which determine Parkinsonian damage, and (ii) the bistable feedback switching predicted by mathematical modelling seems to exist and that a deeper analysis of its characteristics would provide a way of understanding the pathogenic mechanisms that initiate Parkinsonian cell damage.

Decision support models for solid waste management: Review and game-theoretic approaches

International Nuclear Information System (INIS)

Karmperis, Athanasios C.; Aravossis, Konstantinos; Tatsiopoulos, Ilias P.; Sotirchos, Anastasios

2013-01-01

Highlights: ► The mainly used decision support frameworks for solid waste management are reviewed. ► The LCA, CBA and MCDM models are presented and their strengths, weaknesses, similarities and possible combinations are analyzed. ► The game-theoretic approach in a solid waste management context is presented. ► The waste management bargaining game is introduced as a specific decision support framework. ► Cooperative and non-cooperative game-theoretic approaches to decision support for solid waste management are discussed. - Abstract: This paper surveys decision support models that are commonly used in the solid waste management area. Most models are mainly developed within three decision support frameworks, which are the life-cycle assessment, the cost–benefit analysis and the multi-criteria decision-making. These frameworks are reviewed and their strengths and weaknesses as well as their critical issues are analyzed, while their possible combinations and extensions are also discussed. Furthermore, the paper presents how cooperative and non-cooperative game-theoretic approaches can be used for the purpose of modeling and analyzing decision-making in situations with multiple stakeholders. Specifically, since a waste management model is sustainable when considering not only environmental and economic but also social aspects, the waste management bargaining game is introduced as a specific decision support framework in which future models can be developed

Decision support models for solid waste management: Review and game-theoretic approaches

Energy Technology Data Exchange (ETDEWEB)

Karmperis, Athanasios C., E-mail: athkarmp@mail.ntua.gr [Sector of Industrial Management and Operational Research, School of Mechanical Engineering, National Technical University of Athens, Iroon Polytechniou 9, 15780 Athens (Greece); Army Corps of Engineers, Hellenic Army General Staff, Ministry of Defence (Greece); Aravossis, Konstantinos; Tatsiopoulos, Ilias P.; Sotirchos, Anastasios [Sector of Industrial Management and Operational Research, School of Mechanical Engineering, National Technical University of Athens, Iroon Polytechniou 9, 15780 Athens (Greece)

2013-05-15

Highlights: ► The mainly used decision support frameworks for solid waste management are reviewed. ► The LCA, CBA and MCDM models are presented and their strengths, weaknesses, similarities and possible combinations are analyzed. ► The game-theoretic approach in a solid waste management context is presented. ► The waste management bargaining game is introduced as a specific decision support framework. ► Cooperative and non-cooperative game-theoretic approaches to decision support for solid waste management are discussed. - Abstract: This paper surveys decision support models that are commonly used in the solid waste management area. Most models are mainly developed within three decision support frameworks, which are the life-cycle assessment, the cost–benefit analysis and the multi-criteria decision-making. These frameworks are reviewed and their strengths and weaknesses as well as their critical issues are analyzed, while their possible combinations and extensions are also discussed. Furthermore, the paper presents how cooperative and non-cooperative game-theoretic approaches can be used for the purpose of modeling and analyzing decision-making in situations with multiple stakeholders. Specifically, since a waste management model is sustainable when considering not only environmental and economic but also social aspects, the waste management bargaining game is introduced as a specific decision support framework in which future models can be developed.

Point-counterpoint in physics: theoretical prediction and experimental discovery of elementary particles

International Nuclear Information System (INIS)

Leite Lopes, J.

1984-01-01

A report is given on the theoretical prediction and the experimental discovery of elementary particles from the electron to the weak intermediate vector bosons. The work of Lattes, Occhialini and Powell which put in evidence the pions predicted by Yukawa was the starting point of the modern experimental particle physics

Point-counterpoint in physics: theoretical prediction and experimental discovery of elementary particles

International Nuclear Information System (INIS)

Lopes, J.L.

1984-01-01

A report is given on the theoretical prediction and the experimental discovery of elementary particles from the electron to the weak intermediate vector bosons. The work of Lattes, Occhialini and Powell which put in evidence the pions predicted by Yukawa was the starting point of the modern experimental particle physics. (Author) [pt

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 ...

Experimental tests and theoretical predictions for electroweak processes

International Nuclear Information System (INIS)

Martinelli, G.; Istituto Nazionale di Fisica Nucleare, Frascati

1987-01-01

In sect. 2, I will briefly recall the basic ingredients of the standard model and I will define the relevant parameters. Low-energy processes which enter into the determination of neutral-current couplings to fermions (in particular sin 2 θ W ) are presented in sect. 3. Radiative corrections to these processes are discussed in sect. 4. In sect. 5 the measurements of the W and Z 0 masses at the SPS collider are described and compared with theoretical predictions including one-loop radiative corrections. (orig./BBO)

A comparison of SAR ATR performance with information theoretic predictions

Science.gov (United States)

Blacknell, David

2003-09-01

Performance assessment of automatic target detection and recognition algorithms for SAR systems (or indeed any other sensors) is essential if the military utility of the system / algorithm mix is to be quantified. This is a relatively straightforward task if extensive trials data from an existing system is used. However, a crucial requirement is to assess the potential performance of novel systems as a guide to procurement decisions. This task is no longer straightforward since a hypothetical system cannot provide experimental trials data. QinetiQ has previously developed a theoretical technique for classification algorithm performance assessment based on information theory. The purpose of the study presented here has been to validate this approach. To this end, experimental SAR imagery of targets has been collected using the QinetiQ Enhanced Surveillance Radar to allow algorithm performance assessments as a number of parameters are varied. In particular, performance comparisons can be made for (i) resolutions up to 0.1m, (ii) single channel versus polarimetric (iii) targets in the open versus targets in scrubland and (iv) use versus non-use of camouflage. The change in performance as these parameters are varied has been quantified from the experimental imagery whilst the information theoretic approach has been used to predict the expected variation of performance with parameter value. A comparison of these measured and predicted assessments has revealed the strengths and weaknesses of the theoretical technique as will be discussed in the paper.

Mesoscopic structure prediction of nanoparticle assembly and coassembly: Theoretical foundation

KAUST Repository

Hur, Kahyun

2010-01-01

In this work, we present a theoretical framework that unifies polymer field theory and density functional theory in order to efficiently predict ordered nanostructure formation of systems having considerable complexity in terms of molecular structures and interactions. We validate our approach by comparing its predictions with previous simulation results for model systems. We illustrate the flexibility of our approach by applying it to hybrid systems composed of block copolymers and ligand coated nanoparticles. We expect that our approach will enable the treatment of multicomponent self-assembly with a level of molecular complexity that approaches experimental systems. © 2010 American Institute of Physics.

A theoretical model to predict customer satisfaction in relation to service quality in selected university libraries in Sri Lanka

Directory of Open Access Journals (Sweden)

Chaminda Jayasundara

2009-01-01

Full Text Available University library administrators in Sri Lanka have begun to search for alternative ways to satisfy their clientele on the basis of service quality. This article aims at providing a theoretical model to facilitate the identification of service quality attributes and domains that may be used to predict customer satisfaction from a service quality perspective. The effectiveness of existing service quality models such as LibQUAL, SERVQUAL and SERVPREF have been questioned. In that regard, this study developed a theoretical model for academic libraries in Sri Lanka based on the disconfirmation and performance-only paradigms. These perspectives were considered by researchers to be the core mechanism to develop service quality/customer satisfaction models. The attributes and domain identification of service quality was carried out with a stratified sample of 263 participants selected from postgraduate and undergraduate students and academic staff members from the faculties of Arts in four universities in Sri Lanka. The study established that responsiveness, supportiveness, building environment, collection and access, furniture and facilities, technology, Web services and service delivery were quality domains which can be used to predict customer satisfaction. The theoretical model is unique in its domain structure compared to the existing models. The model needs to be statistically tested to make it valid and parsimonious.

Efficient Prediction of Progesterone Receptor Interactome Using a Support Vector Machine Model

Directory of Open Access Journals (Sweden)

Ji-Long Liu

2015-03-01

Full Text Available Protein-protein interaction (PPI is essential for almost all cellular processes and identification of PPI is a crucial task for biomedical researchers. So far, most computational studies of PPI are intended for pair-wise prediction. Theoretically, predicting protein partners for a single protein is likely a simpler problem. Given enough data for a particular protein, the results can be more accurate than general PPI predictors. In the present study, we assessed the potential of using the support vector machine (SVM model with selected features centered on a particular protein for PPI prediction. As a proof-of-concept study, we applied this method to identify the interactome of progesterone receptor (PR, a protein which is essential for coordinating female reproduction in mammals by mediating the actions of ovarian progesterone. We achieved an accuracy of 91.9%, sensitivity of 92.8% and specificity of 91.2%. Our method is generally applicable to any other proteins and therefore may be of help in guiding biomedical experiments.

A THEORETICAL MODEL OF SUPPORTING OPEN SOURCE FRONT END INNOVATION THROUGH IDEA MANAGEMENT

DEFF Research Database (Denmark)

Aagaard, Annabeth

2013-01-01

to overcome these various challenges companies are looking for new models to support FEI. This theoretical paper explores in what way idea management may be applied as a tool in facilitation of front end innovation and how this facilitation may be captured in a conceptual model. First, I show through...... a literature study, how idea management and front end innovation are related and how they may support each other. Secondly, I present a theoretical model of how idea management may be applied in support of the open source front end of new product innovations. Thirdly, I present different venues of further...... exploration of active facilitation of open source front end innovation through idea management....

Large Hadron Collider (LHC) phenomenology, operational challenges and theoretical predictions

CERN Document Server

Gilles, Abelin R

2013-01-01

The Large Hadron Collider (LHC) is the highest-energy particle collider ever constructed and is considered "one of the great engineering milestones of mankind." It was built by the European Organization for Nuclear Research (CERN) from 1998 to 2008, with the aim of allowing physicists to test the predictions of different theories of particle physics and high-energy physics, and particularly prove or disprove the existence of the theorized Higgs boson and of the large family of new particles predicted by supersymmetric theories. In this book, the authors study the phenomenology, operational challenges and theoretical predictions of LHC. Topics discussed include neutral and charged black hole remnants at the LHC; the modified statistics approach for the thermodynamical model of multiparticle production; and astroparticle physics and cosmology in the LHC era.

Predicting catalyst-support interactions between metal nanoparticles and amorphous silica supports

Science.gov (United States)

Ewing, Christopher S.; Veser, Götz; McCarthy, Joseph J.; Lambrecht, Daniel S.; Johnson, J. Karl

2016-10-01

Metal-support interactions significantly affect the stability and activity of supported catalytic nanoparticles (NPs), yet there is no simple and reliable method for estimating NP-support interactions, especially for amorphous supports. We present an approach for rapid prediction of catalyst-support interactions between Pt NPs and amorphous silica supports for NPs of various sizes and shapes. We use density functional theory calculations of 13 atom Pt clusters on model amorphous silica supports to determine linear correlations relating catalyst properties to NP-support interactions. We show that these correlations can be combined with fast discrete element method simulations to predict adhesion energy and NP net charge for NPs of larger sizes and different shapes. Furthermore, we demonstrate that this approach can be successfully transferred to Pd, Au, Ni, and Fe NPs. This approach can be used to quickly screen stability and net charge transfer and leads to a better fundamental understanding of catalyst-support interactions.

Revisioning Theoretical Framework of Electronic Performance Support Systems (EPSS within the Software Application Examples

Directory of Open Access Journals (Sweden)

Dr. Servet BAYRAM,

2004-04-01

Full Text Available Revisioning Theoretical Framework of Electronic Performance Support Systems (EPSS within the Software Application Examples Assoc. Prof. Dr. Servet BAYRAM Computer Education & Instructional Technologies Marmara University , TURKEY ABSTRACT EPSS provides electronic support to learners in achieving a performance objective; a feature which makes it universally and consistently available on demand any time, any place, regardless of situation, without unnecessary intermediaries involved in the process. The aim of this review is to develop a set of theoretical construct that provide descriptive power for explanation of EPSS and its roots and features within the software application examples (i.e., Microsoft SharePoint Server”v2.0” Beta 2, IBM Lotus Notes 6 & Domino 6, Oracle 9i Collaboration Suite, and Mac OS X v10.2. From the educational and training point of view, the paper visualizes a pentagon model for the interrelated domains of the theoretical framework of EPSS. These domains are: learning theories, information processing theories, developmental theories, instructional theories, and acceptance theories. This descriptive framework explains a set of descriptions as to which outcomes occur under given theoretical conditions for a given EPSS model within software examples. It summarizes some of the theoretical concepts supporting to the EPSS’ related features and explains how such concepts sharing same features with the example software programs in education and job training.

Search for an interstellar Si2C molecule: A theoretical prediction

Indian Academy of Sciences (India)

63, No. 3. — journal of. September 2004 physics pp. 627–631. Search for an interstellar Si2C molecule: A theoretical prediction. SURESH CHANDRA. School of ... top molecule as its electric dipole moment µ lies along the axis of intermediate moment of inertia. Because of differences between the molecular parameters of.

Predicting Child Abuse Potential: An Empirical Investigation of Two Theoretical Frameworks

Science.gov (United States)

Begle, Angela Moreland; Dumas, Jean E.; Hanson, Rochelle F.

2010-01-01

This study investigated two theoretical risk models predicting child maltreatment potential: (a) Belsky's (1993) developmental-ecological model and (b) the cumulative risk model in a sample of 610 caregivers (49% African American, 46% European American; 53% single) with a child between 3 and 6 years old. Results extend the literature by using a…

Theoretical models to predict the mechanical behavior of thick composite tubes

Directory of Open Access Journals (Sweden)

Volnei Tita

2012-02-01

Full Text Available This paper shows theoretical models (analytical formulations to predict the mechanical behavior of thick composite tubes and how some parameters can influence this behavior. Thus, firstly, it was developed the analytical formulations for a pressurized tube made of composite material with a single thick ply and only one lamination angle. For this case, the stress distribution and the displacement fields are investigated as function of different lamination angles and reinforcement volume fractions. The results obtained by the theoretical model are physic consistent and coherent with the literature information. After that, the previous formulations are extended in order to predict the mechanical behavior of a thick laminated tube. Both analytical formulations are implemented as a computational tool via Matlab code. The results obtained by the computational tool are compared to the finite element analyses, and the stress distribution is considered coherent. Moreover, the engineering computational tool is used to perform failure analysis, using different types of failure criteria, which identifies the damaged ply and the mode of failure.

Physics of mind: Experimental confirmations of theoretical predictions.

Science.gov (United States)

Schoeller, Félix; Perlovsky, Leonid; Arseniev, Dmitry

2018-02-02

What is common among Newtonian mechanics, statistical physics, thermodynamics, quantum physics, the theory of relativity, astrophysics and the theory of superstrings? All these areas of physics have in common a methodology, which is discussed in the first few lines of the review. Is a physics of the mind possible? Is it possible to describe how a mind adapts in real time to changes in the physical world through a theory based on a few basic laws? From perception and elementary cognition to emotions and abstract ideas allowing high-level cognition and executive functioning, at nearly all levels of study, the mind shows variability and uncertainties. Is it possible to turn psychology and neuroscience into so-called "hard" sciences? This review discusses several established first principles for the description of mind and their mathematical formulations. A mathematical model of mind is derived from these principles. This model includes mechanisms of instincts, emotions, behavior, cognition, concepts, language, intuitions, and imagination. We clarify fundamental notions such as the opposition between the conscious and the unconscious, the knowledge instinct and aesthetic emotions, as well as humans' universal abilities for symbols and meaning. In particular, the review discusses in length evolutionary and cognitive functions of aesthetic emotions and musical emotions. Several theoretical predictions are derived from the model, some of which have been experimentally confirmed. These empirical results are summarized and we introduce new theoretical developments. Several unsolved theoretical problems are proposed, as well as new experimental challenges for future research. Copyright © 2017. Published by Elsevier B.V.

Information-theoretic model selection for optimal prediction of stochastic dynamical systems from data

Science.gov (United States)

Darmon, David

2018-03-01

In the absence of mechanistic or phenomenological models of real-world systems, data-driven models become necessary. The discovery of various embedding theorems in the 1980s and 1990s motivated a powerful set of tools for analyzing deterministic dynamical systems via delay-coordinate embeddings of observations of their component states. However, in many branches of science, the condition of operational determinism is not satisfied, and stochastic models must be brought to bear. For such stochastic models, the tool set developed for delay-coordinate embedding is no longer appropriate, and a new toolkit must be developed. We present an information-theoretic criterion, the negative log-predictive likelihood, for selecting the embedding dimension for a predictively optimal data-driven model of a stochastic dynamical system. We develop a nonparametric estimator for the negative log-predictive likelihood and compare its performance to a recently proposed criterion based on active information storage. Finally, we show how the output of the model selection procedure can be used to compare candidate predictors for a stochastic system to an information-theoretic lower bound.

Prediction of Machine Tool Condition Using Support Vector Machine

International Nuclear Information System (INIS)

Wang Peigong; Meng Qingfeng; Zhao Jian; Li Junjie; Wang Xiufeng

2011-01-01

Condition monitoring and predicting of CNC machine tools are investigated in this paper. Considering the CNC machine tools are often small numbers of samples, a condition predicting method for CNC machine tools based on support vector machines (SVMs) is proposed, then one-step and multi-step condition prediction models are constructed. The support vector machines prediction models are used to predict the trends of working condition of a certain type of CNC worm wheel and gear grinding machine by applying sequence data of vibration signal, which is collected during machine processing. And the relationship between different eigenvalue in CNC vibration signal and machining quality is discussed. The test result shows that the trend of vibration signal Peak-to-peak value in surface normal direction is most relevant to the trend of surface roughness value. In trends prediction of working condition, support vector machine has higher prediction accuracy both in the short term ('One-step') and long term (multi-step) prediction compared to autoregressive (AR) model and the RBF neural network. Experimental results show that it is feasible to apply support vector machine to CNC machine tool condition prediction.

Hierarchical representations of the five-factor model of personality in predicting job performance: integrating three organizing frameworks with two theoretical perspectives.

Science.gov (United States)

Judge, Timothy A; Rodell, Jessica B; Klinger, Ryan L; Simon, Lauren S; Crawford, Eean R

2013-11-01

Integrating 2 theoretical perspectives on predictor-criterion relationships, the present study developed and tested a hierarchical framework in which each five-factor model (FFM) personality trait comprises 2 DeYoung, Quilty, and Peterson (2007) facets, which in turn comprise 6 Costa and McCrae (1992) NEO facets. Both theoretical perspectives-the bandwidth-fidelity dilemma and construct correspondence-suggest that lower order traits would better predict facets of job performance (task performance and contextual performance). They differ, however, as to the relative merits of broad and narrow traits in predicting a broad criterion (overall job performance). We first meta-analyzed the relationship of the 30 NEO facets to overall job performance and its facets. Overall, 1,176 correlations from 410 independent samples (combined N = 406,029) were coded and meta-analyzed. We then formed the 10 DeYoung et al. facets from the NEO facets, and 5 broad traits from those facets. Overall, results provided support for the 6-2-1 framework in general and the importance of the NEO facets in particular. (c) 2013 APA, all rights reserved.

Theoretical predictions for side-chain liquid-crystal polymers and comparison to experiment

International Nuclear Information System (INIS)

Dowell, F.

1988-01-01

This paper presents results from a new unique microscopic molecular theory for side-chain liquid-crystalline polymers (LCPs) in the nematic (N) and multiple smectic-A (SA) LC phases and the isotropic (I) liquid phase. There are no ad hoc or arbitrarily adjustable parameters in this theory. The agreement between the theoretical and experimental values for various properties (including transition temperatures and quadratic characteristic radii) is very good (relative deviations between 0% and less than 6.2%). The theoretical results also show--for the first time--that the N and I phases for these LCPs involve the packing of plate-like sections of backbones and side chains and that the local bilayer SA phase involves packing of side-chains within a plate-like section. This type of packing is predicted to be typical for side-chain LCPs. This theory can predict--for the first time--whether the side chains of a molecule pack on the same or alternating opposite sides of the backbone and whether side chains on different molecules interdigitate (overlap) with each other. 13 refs., 1 fig., 4 tabs

Prediction of the theoretical capacity of non-aqueous lithium-air batteries

International Nuclear Information System (INIS)

Tan, Peng; Wei, Zhaohuan; Shyy, W.; Zhao, T.S.

2013-01-01

Highlights: • The theoretical capacity of non-aqueous lithium-air batteries is predicted. • Key battery design parameters are defined and considered. • The theoretical battery capacity is about 10% of the lithium capacity. • The battery mass and volume changes after discharge are also studied. - Abstract: In attempt to realistically assess the high-capacity feature of emerging lithium-air batteries, a model is developed for predicting the theoretical capacity of non-aqueous lithium-air batteries. Unlike previous models that were formulated by assuming that the active materials and electrolyte are perfectly balanced according to the electrochemical reaction, the present model takes account of the fraction of the reaction products (Li 2 O 2 and Li 2 O), the utilization of the onboard lithium metal, the utilization of the void volume of the porous cathode, and the onboard excess electrolyte. Results show that the gravimetric capacity increases from 1033 to 1334 mA h/g when the reaction product varies from pure Li 2 O 2 to pure Li 2 O. It is further demonstrated that the capacity declines drastically from 1080 to 307 mA h/g when the case of full utilization of the onboard lithium is altered to that only 10% of the metal is utilized. Similarly, the capacity declines from 1080 to 144 mA h/g when the case of full occupation of the cathode void volume by the reaction products is varied to that only 10% of the void volume is occupied. In general, the theoretical gravimetric capacity of typical non-aqueous lithium-air batteries falls in the range of 380–450 mA h/g, which is about 10–12% of the gravimetric capacity calculated based on the energy density of the lithium metal. The present model also facilitates the study of the effects of different parameters on the mass and volume change of non-aqueous lithium-air batteries

Indonesian Stock Prediction using Support Vector Machine (SVM

Directory of Open Access Journals (Sweden)

Santoso Murtiyanto

2018-01-01

Full Text Available This project is part of developing software to provide predictive information technology-based services artificial intelligence (Machine Intelligence or Machine Learning that will be utilized in the money market community. The prediction method used in this early stages uses the combination of Gaussian Mixture Model and Support Vector Machine with Python programming. The system predicts the price of Astra International (stock code: ASII.JK stock data. The data used was taken during 17 yr period of January 2000 until September 2017. Some data was used for training/modeling (80 % of data and the remainder (20 % was used for testing. An integrated model comprising Gaussian Mixture Model and Support Vector Machine system has been tested to predict stock market of ASII.JK for l d in advance. This model has been compared with the Market Cummulative Return. From the results, it is depicts that the Gaussian Mixture Model-Support Vector Machine based stock predicted model, offers significant improvement over the compared models resulting sharpe ratio of 3.22.

Theoretical model for cavitation erosion prediction in centrifugal pump impeller

International Nuclear Information System (INIS)

Rayan, M.A.; Mahgob, M.M.; Mostafa, N.H.

1990-01-01

Cavitation is known to have great effects on pump hydraulic and mechanical characteristics. These effects are mainly described by deviation in pump performance, increasing vibration and noise level as well as erosion of blade and casing materials. In the present work, only the hydrodynamic aspect of cavitation was considered. The efforts were directed toward the study of cavitation inception, cavity mechanics and material erosion in order to clarify the macrohydrodynamic aspects of cavitation erosive wear in real machines. As a result of this study, it was found that cavitation damage can be predicted from model data. The obtained theoretical results show good agreement with the experimental results obtained in this investigation and with results of some other investigations. The application of the findings of this work will help the design engineer in predicting the erosion rate, according to the different operating conditions. (author)

On the problem of synthesis of superheavy nuclei. A short historical review on first theoretical predictions

International Nuclear Information System (INIS)

Kalinkin, B.N.; Gareev, F.A.

1999-01-01

It is shown that it is just Dubna that possesses the priority both in the recent synthesis of a superheavy nucleus with charge Z=114 (Flerov Laboratory of Nuclear Reactions, JINR) and in its theoretical prediction (Bogolyubov Laboratory of Theoretical Physics, JINR) made 33 years ago. Possible sizes of the 'island of stability' of superheavy nuclei are discussed

A Balanced Theoretical and Empirical Approach for the Development of a Design Support Tool

DEFF Research Database (Denmark)

Jensen, Thomas Aakjær; Hansen, Claus Thorp

1996-01-01

The introduction of a new design support system may change the engineering designer's work situation. Therefore, it may not be possible to derive all the functionalities for a design support system from solely empirical studies of manual design work. Alternatively the design support system could ...... system, indicating a proposal for how to balance a theoretical and empirical approach. The result of this research will be utilized in the development of a Designer's Workbench to support the synthesis activity in mechanical design....

Decision Support for Flood Event Prediction and Monitoring

DEFF Research Database (Denmark)

Mioc, Darka; Anton, François; Liang, Gengsheng

2007-01-01

In this paper the development of Web GIS based decision support system for flood events is presented. To improve flood prediction we developed the decision support system for flood prediction and monitoring that integrates hydrological modelling and CARIS GIS. We present the methodology for data...... integration, floodplain delineation, and online map interfaces. Our Web-based GIS model can dynamically display observed and predicted flood extents for decision makers and the general public. The users can access Web-based GIS that models current flood events and displays satellite imagery and digital...... elevation model integrated with flood plain area. The system can show how the flooding prediction based on the output from hydrological modeling for the next 48 hours along the lower Saint John River Valley....

Theoretical predictions for pp and panti p elastic scattering in the TeV energy domain

International Nuclear Information System (INIS)

Bourrely, C.; Martin, A.

1984-01-01

We present theoretical predictions on total cross-sections and elastic scattering in the TeV energy domain obtained from the present experimental situation at the ISR and the panti p Collider. (orig.)

Theoretical prediction of the electronic transport properties of the Al-Cu alloys based on the first-principle calculation and Boltzmann transport equation

Science.gov (United States)

Choi, Garam; Lee, Won Bo

Metal alloys, especially Al-based, are commonly-used materials for various industrial applications. In this paper, the Al-Cu alloys with varying the Al-Cu ratio were investigated based on the first-principle calculation using density functional theory. And the electronic transport properties of the Al-Cu alloys were carried out using Boltzmann transport theory. From the results, the transport properties decrease with Cu-containing ratio at the temperature from moderate to high, but with non-linearity. It is inferred by various scattering effects from the calculation results with relaxation time approximation. For the Al-Cu alloy system, where it is hard to find the reliable experimental data for various alloys, it supports understanding and expectation for the thermal electrical properties from the theoretical prediction. Theoretical and computational soft matters laboratory.

Evidence - competence - discourse: the theoretical framework of the multi-centre clinical ethics support project METAP.

Science.gov (United States)

Reiter-Theil, Stella; Mertz, Marcel; Schürmann, Jan; Stingelin Giles, Nicola; Meyer-Zehnder, Barbara

2011-09-01

In this paper we assume that 'theory' is important for Clinical Ethics Support Services (CESS). We will argue that the underlying implicit theory should be reflected. Moreover, we suggest that the theoretical components on which any clinical ethics support (CES) relies should be explicitly articulated in order to enhance the quality of CES. A theoretical framework appropriate for CES will be necessarily complex and should include ethical (both descriptive and normative), metaethical and organizational components. The various forms of CES that exist in North-America and in Europe show their underlying theory more or less explicitly, with most of them referring to some kind of theoretical components including 'how-to' questions (methodology), organizational issues (implementation), problem analysis (phenomenology or typology of problems), and related ethical issues such as end-of-life decisions (major ethical topics). In order to illustrate and explain the theoretical framework that we are suggesting for our own CES project METAP, we will outline this project which has been established in a multi-centre context in several healthcare institutions. We conceptualize three 'pillars' as the major components of our theoretical framework: (1) evidence, (2) competence, and (3) discourse. As a whole, the framework is aimed at developing a foundation of our CES project METAP. We conclude that this specific integration of theoretical components is a promising model for the fruitful further development of CES. © 2011 Blackwell Publishing Ltd.

Theoretical Predictions of Cross-Sections of the Super-Heavy Elements

Science.gov (United States)

Bouriquet, B.; Kosenko, G.; Abe, Y.

The evaluation of the residue cross-sections of reactionssynthesising superheavy elements has been achieved by the combination of the two-step model for fusion and the evaporation code (KEWPIE) for survival probability. The theoretical scheme of those calculations is presented, and some encouraging results are given, together with some difficulties. With this approach, the measured excitation functions of the 1n reactions producing elements with Z=108, 110, 111 and 112 are well reproduced. Thus, the model has been used to predict the cross-sections of the reactions leading to the formation of the elements with Z=113 and Z=114.

Theoretical predictions of cross-sections of the super-heavy elements

International Nuclear Information System (INIS)

Bouriquet, B.; Abe, Y.; Kosenko, G.

2004-01-01

The evaluation of the residue cross-sections of reactions synthesising superheavy elements has been achieved by the combination of the two-step model for fusion and the evaporation code (KEWPIE) for survival probability. The theoretical scheme of those calculations is presented, and some encouraging results are given, together with some difficulties. With this approach, the measured excitation functions of the 1n reactions producing elements with Z = 108, 110, 111 and 112 are well reproduced. Thus, the model has been used to predict the cross-sections of the reactions leading to the formation of the elements with Z = 113 and Z = 114. (author)

Theoretical pattern of supporting continuity in physical education of students' personality.

Directory of Open Access Journals (Sweden)

Vovk V.M.

2011-04-01

Full Text Available Methodological approaches and principles on which theoretical pattern of supporting of continuity in physical education of senior pupil and students' personality are considered. It is proved that effective process of continuity in physical education is impossible without construction of patterns. It is ascertained that continuity is a condition and mechanism of realization for others principles in teaching process that represent itself as major factors in realization of continuity in physical education.

Gender and Autonomy-Supportive Contexts: Theoretical Perspectives of Self-Determination and Goal Setting

Science.gov (United States)

Lin, Shinyi; Chen, Yu-Chuan

2013-01-01

In integrating theoretical perspectives of self-determination and goal-setting, this study proposes a conceptual model with moderating and mediating effects exploring gender issue in autonomy-supportive learning in higher education as research context. In the proposed model, goal-setting attributes, i.e., individual determinants, social…

A carbon risk prediction model for Chinese heavy-polluting industrial enterprises based on support vector machine

International Nuclear Information System (INIS)

Zhou, Zhifang; Xiao, Tian; Chen, Xiaohong; Wang, Chang

2016-01-01

Chinese heavy-polluting industrial enterprises, especially petrochemical or chemical industry, labeled low carbon efficiency and high emission load, are facing the tremendous pressure of emission reduction under the background of global shortage of energy supply and constrain of carbon emission. However, due to the limited amount of theoretic and practical research in this field, problems like lacking prediction indicators or models, and the quantified standard of carbon risk remain unsolved. In this paper, the connotation of carbon risk and an assessment index system for Chinese heavy-polluting industrial enterprises (eg. coal enterprise, petrochemical enterprises, chemical enterprises et al.) based on support vector machine are presented. By using several heavy-polluting industrial enterprises’ related data, SVM model is trained to predict the carbon risk level of a specific enterprise, which allows the enterprise to identify and manage its carbon risks. The result shows that this method can predict enterprise’s carbon risk level in an efficient, accurate way with high practical application and generalization value.

Theoretical prediction of crystallization kinetics of a supercooled Lennard-Jones fluid

Science.gov (United States)

Gunawardana, K. G. S. H.; Song, Xueyu

2018-05-01

The first order curvature correction to the crystal-liquid interfacial free energy is calculated using a theoretical model based on the interfacial excess thermodynamic properties. The correction parameter (δ), which is analogous to the Tolman length at a liquid-vapor interface, is found to be 0.48 ± 0.05 for a Lennard-Jones (LJ) fluid. We show that this curvature correction is crucial in predicting the nucleation barrier when the size of the crystal nucleus is small. The thermodynamic driving force (Δμ) corresponding to available simulated nucleation conditions is also calculated by combining the simulated data with a classical density functional theory. In this paper, we show that the classical nucleation theory is capable of predicting the nucleation barrier with excellent agreement to the simulated results when the curvature correction to the interfacial free energy is accounted for.

A theoretical model for predicting the Peak Cutting Force of conical picks

Directory of Open Access Journals (Sweden)

Gao Kuidong

2014-01-01

Full Text Available In order to predict the PCF (Peak Cutting Force of conical pick in rock cutting process, a theoretical model is established based on elastic fracture mechanics theory. The vertical fracture model of rock cutting fragment is also established based on the maximum tensile criterion. The relation between vertical fracture angle and associated parameters (cutting parameter  and ratio B of rock compressive strength to tensile strength is obtained by numerical analysis method and polynomial regression method, and the correctness of rock vertical fracture model is verified through experiments. Linear regression coefficient between the PCF of prediction and experiments is 0.81, and significance level less than 0.05 shows that the model for predicting the PCF is correct and reliable. A comparative analysis between the PCF obtained from this model and Evans model reveals that the result of this prediction model is more reliable and accurate. The results of this work could provide some guidance for studying the rock cutting theory of conical pick and designing the cutting mechanism.

Predictive power of theoretical modelling of the nuclear mean field: examples of improving predictive capacities

Science.gov (United States)

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.

Within tree variation of lignin, extractives, and microfibril angle coupled with the theoretical and near infrared modeling of microfibril angle

Science.gov (United States)

Brian K. Via; chi L. So; Leslie H. Groom; Todd F. Shupe; michael Stine; Jan. Wikaira

2007-01-01

A theoretical model was built predicting the relationship between microfibril angle and lignin content at the Angstrom (A) level. Both theoretical and statistical examination of experimental data supports a square root transformation of lignin to predict microfibril angle. The experimental material used came from 10 longleaf pine (Pinus palustris)...

Slope Deformation Prediction Based on Support Vector Machine

Directory of Open Access Journals (Sweden)

Lei JIA

2013-07-01

Full Text Available This paper principally studies the prediction of slope deformation based on Support Vector Machine (SVM. In the prediction process，explore how to reconstruct the phase space. The geological body’s displacement data obtained from chaotic time series are used as SVM’s training samples. Slope displacement caused by multivariable coupling is predicted by means of single variable. Results show that this model is of high fitting accuracy and generalization, and provides reference for deformation prediction in slope engineering.

Toward Predicting Social Support Needs in Online Health Social Networks.

Science.gov (United States)

Choi, Min-Je; Kim, Sung-Hee; Lee, Sukwon; Kwon, Bum Chul; Yi, Ji Soo; Choo, Jaegul; Huh, Jina

2017-08-02

While online health social networks (OHSNs) serve as an effective platform for patients to fulfill their various social support needs, predicting the needs of users and providing tailored information remains a challenge. The objective of this study was to discriminate important features for identifying users' social support needs based on knowledge gathered from survey data. This study also provides guidelines for a technical framework, which can be used to predict users' social support needs based on raw data collected from OHSNs. We initially conducted a Web-based survey with 184 OHSN users. From this survey data, we extracted 34 features based on 5 categories: (1) demographics, (2) reading behavior, (3) posting behavior, (4) perceived roles in OHSNs, and (5) values sought in OHSNs. Features from the first 4 categories were used as variables for binary classification. For the prediction outcomes, we used features from the last category: the needs for emotional support, experience-based information, unconventional information, and medical facts. We compared 5 binary classifier algorithms: gradient boosting tree, random forest, decision tree, support vector machines, and logistic regression. We then calculated the scores of the area under the receiver operating characteristic (ROC) curve (AUC) to understand the comparative effectiveness of the used features. The best performance was AUC scores of 0.89 for predicting users seeking emotional support, 0.86 for experience-based information, 0.80 for unconventional information, and 0.83 for medical facts. With the gradient boosting tree as our best performing model, we analyzed the strength of individual features in predicting one's social support need. Among other discoveries, we found that users seeking emotional support tend to post more in OHSNs compared with others. We developed an initial framework for automatically predicting social support needs in OHSNs using survey data. Future work should involve nonsurvey

Delayed hydride cracking: theoretical model testing to predict cracking velocity

International Nuclear Information System (INIS)

Mieza, Juan I.; Vigna, Gustavo L.; Domizzi, Gladys

2009-01-01

Pressure tubes from Candu nuclear reactors as any other component manufactured with Zr alloys are prone to delayed hydride cracking. That is why it is important to be able to predict the cracking velocity during the component lifetime from parameters easy to be measured, such as: hydrogen concentration, mechanical and microstructural properties. Two of the theoretical models reported in literature to calculate the DHC velocity were chosen and combined, and using the appropriate variables allowed a comparison with experimental results of samples from Zr-2.5 Nb tubes with different mechanical and structural properties. In addition, velocities measured by other authors in irradiated materials could be reproduced using the model described above. (author)

Empirical and theoretical challenges in aboveground-belowground ecology

DEFF Research Database (Denmark)

W.H. van der Putten,; R.D. Bardgett; P.C. de Ruiter

2009-01-01

of the current conceptual succession models into more predictive models can help targeting empirical studies and generalising their results. Then, we discuss how understanding succession may help to enhance managing arable crops, grasslands and invasive plants, as well as provide insights into the effects...... and environmental settings, we explore where and how they can be supported by theoretical approaches to develop testable predictions and to generalise empirical results. We review four key areas where a combined aboveground-belowground approach offers perspectives for enhancing ecological understanding, namely...

Rolling force prediction for strip casting using theoretical model and artificial intelligence

Institute of Scientific and Technical Information of China (English)

CAO Guang-ming; LI Cheng-gang; ZHOU Guo-ping; LIU Zhen-yu; WU Di; WANG Guo-dong; LIU Xiang-hua

2010-01-01

Rolling force for strip casting of 1Cr17 ferritic stainless steel was predicted using theoretical model and artificial intelligence.Solution zone was classified into two parts by kiss point position during casting strip.Navier-Stokes equation in fluid mechanics and stream function were introduced to analyze the rheological property of liquid zone and mushy zone,and deduce the analytic equation of unit compression stress distribution.The traditional hot rolling model was still used in the solid zone.Neural networks based on feedforward training algorithm in Bayesian regularization were introduced to build model for kiss point position.The results show that calculation accuracy for verification data of 94.67% is in the range of+7.0%,which indicates that the predicting accuracy of this model is very high.

Probing the CuO planes with positrons in high Tc cuprates: theoretical predictions

International Nuclear Information System (INIS)

Barbiellini, B.; Jarlborg, T.; Massidda, S.; Peter, M.

1995-01-01

Positron annihilation spectroscopy is a useful tool to investigate the Fermi surface in high T c superconductors. To study the physics of the copper-oxygen subsystem that forms the Cu-O layers, it is important to provide theoretical predictions, on materials where there is a large overlap between the positron and the interesting Cu-O planes. We have performed first-principle electronic structure calculations obtained using the linear muffin-tin orbital and the full-potential linearized augmented plane wave methods. The positron charge distributions and their sensitivity to different potentials are calculated. Secondly, we have computed the annihilation rates and the electron-positron momentum density in order to give predictions of the Fermi surface signals. (orig.)

Physical violence and psychological abuse among siblings :a theoretical and empirical analysis

OpenAIRE

Hoffman, Kristi L.

1996-01-01

This study develops and evaluates a theoretical model based on social learning, conflict, and feminist perspectives to explain teenage sibling physical violence and psychological abuse. Using regression analysis and data from 796 young adults, considerable support is found for all three theoretical approaches and suggests an integrated model best predicts acts of violence and abuse among siblings. For physical violence, males and brothers had significantly higher rates. Spousal...

Theoretical predictions for alpha particle spectroscopic strengths

International Nuclear Information System (INIS)

Draayer, J.P.

1975-01-01

Multinucleon transfers induced in heavy-ion reactions of the type ( 6 Li,d) furnish a selective probe with which to study the interplay between rotational and clustering phenomena so characteristic of the structure of the light sd-shell nuclei. For these nuclei, theoretical predictions for inter-band as well as intra-band transfer strengths can be made using recently tabulated results for angular momentum dependent SU 3 inclusion R 3 relative spectroscopic strengths and angular momentum independent SU 6 inclusion SU 3 coefficients of fractional parentage. The pure SU 3 (oscillator)-SU 4 (supermultiplet) symmetry limit agrees well with results obtained using available eigenfunctions determined in large shell model calculations. In particular, the scalar nature of a transferred ''alpha''-cluster insures that the effect of spatial symmetry admixtures in the initial and final states of the target and residual nuclei are minimized. Sum rule quantities provide a measure of the probable effects of symmetry breaking. Strength variations within a band are expected; transfers to core excited states are often favored. Results extracted from exact finite range DWBA analyses of ( 6 Li,d) data on 16 , 18 O, 20 , 21 , 22 Ne, 24 , 25 Mg show some anomalies in our understanding of the structure and/or reaction mechanisms. (18 figures) (U.S.)

The lack of theoretical support for using person trade-offs in QALY-type models

DEFF Research Database (Denmark)

Østerdal, Lars Peter Raahave

2009-01-01

-adjusted life years (DALYs). This paper discusses the theoretical support for the use of person trade-offs in QALY-type measurement of (changes in) population health. It argues that measures of this type based on such quality-adjustment factors almost always violate the Pareto principle, and so lack normative...

A theoretical perspective on road safety communication campaigns.

Science.gov (United States)

Elvik, Rune

2016-12-01

This paper proposes a theoretical perspective on road safety communication campaigns, which may help in identifying the conditions under which such campaigns can be effective. The paper proposes that, from a theoretical point of view, it is reasonable to assume that road user behaviour is, by and large, subjectively rational. This means that road users are assumed to behave the way they think is best. If this assumption is accepted, the best theoretical prediction is that road safety campaigns consisting of persuasive messages only will have no effect on road user behaviour and accordingly no effect on accidents. This theoretical prediction is not supported by meta-analyses of studies that have evaluated the effects of road safety communication campaigns. These analyses conclude that, on the average, such campaigns are associated with an accident reduction. The paper discusses whether this finding can be explained theoretically. The discussion relies on the distinction made by many modern theorists between bounded and perfect rationality. Road user behaviour is characterised by bounded rationality. Hence, if road users can gain insight into the bounds of their rationality, so that they see advantages to themselves of changing behaviour, they are likely to do so. It is, however, largely unknown whether such a mechanism explains why some road safety communication campaigns have been found to be more effective than others. Copyright © 2015 Elsevier Ltd. All rights reserved.

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

Directory of Open Access Journals (Sweden)

Fang Su

2013-01-01

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

Child Support Payment: A Structural Model of Predictive Variables.

Science.gov (United States)

Wright, David W.; Price, Sharon J.

A major area of concern in divorced families is compliance with child support payments. Aspects of the former spouse relationship that are predictive of compliance with court-ordered payment of child support were investigated in a sample of 58 divorced persons all of whom either paid or received child support. Structured interviews and…

Sodium fires: French strategy - theoretical and experimental developments

International Nuclear Information System (INIS)

Descombes; Thomann; Malet, J.C.; Rzekiecki, R.

1985-01-01

After a description of the needs relating to LMFBR safety analysis and design in terms of prevention, detection and protection, the French strategy concerning sodium fires it presented. It includes theoretical developments supported with relevant experimental program, to allow reliable calculations and predictions for safety and design. The following physical phenomena are detailed: (1) sodium fire (mechanical and thermal effects); (2) sodium-structures interactions; (3) aerosols behavior

Predicting Tunnel Squeezing Using Multiclass Support Vector Machines

Directory of Open Access Journals (Sweden)

Yang Sun

2018-01-01

Full Text Available Tunnel squeezing is one of the major geological disasters that often occur during the construction of tunnels in weak rock masses subjected to high in situ stresses. It could cause shield jamming, budget overruns, and construction delays and could even lead to tunnel instability and casualties. Therefore, accurate prediction or identification of tunnel squeezing is extremely important in the design and construction of tunnels. This study presents a modified application of a multiclass support vector machine (SVM to predict tunnel squeezing based on four parameters, that is, diameter (D, buried depth (H, support stiffness (K, and rock tunneling quality index (Q. We compiled a database from the literature, including 117 case histories obtained from different countries such as India, Nepal, and Bhutan, to train the multiclass SVM model. The proposed model was validated using 8-fold cross validation, and the average error percentage was approximately 11.87%. Compared with existing approaches, the proposed multiclass SVM model yields a better performance in predictive accuracy. More importantly, one could estimate the severity of potential squeezing problems based on the predicted squeezing categories/classes.

Molecular approach of uranyl/mineral surfaces: theoretical approach

International Nuclear Information System (INIS)

Roques, J.

2009-01-01

As migration of radio-toxic elements through the geosphere is one of the processes which may affect the safety of a radioactive waste storage site, the author shows that numerical modelling is a support to experimental result exploitation, and allows the development of new interpretation and prediction codes. He shows that molecular modelling can be used to study processes of interaction between an actinide ion (notably a uranyl ion) and a mineral surface (a TiO 2 substrate). He also reports the predictive theoretical study of the interaction between an uranyl ion and a gibbsite substrate

A THEORETICAL MODEL OF SOCIO-PSYCHOLOGICAL SUPPORT WORK PROCESSES FOR MANAGEMENT OF PRODUCTION TEAM

Directory of Open Access Journals (Sweden)

Tatyana Gennadevna Pronyushkina

2015-10-01

Full Text Available This article discusses the management of production team, in particular the developed theoretical model of socio-psychological support work processes for management of production team. The author of the research are formulated the purpose and objectives of social-psychological work on management of the production team. Developed in the study a theoretical model aimed at determining the conditions and the identification of features of effective management of the enterprise taking into account the socio-psychological characteristics of its staff. Tasks include: definition of the main characteristics of the production team and their severity, the analysis of these characteristics and identifying opportunities for their transformation, development of recommendations for management of social-psychological work on effects on the characteristics of the collective enterprise.Practical study of the activities of a number of businesses have shown the need to improve socio-psychological support of management processes production team: introducing a social and psychological planning team and develop the practice of sociological research on the state of the team, to ensure the smoothing of relations between workers and management through periodic meetings, creations of conditions for feedback, maintaining healthy competition among team members.

Accelerator simulation and theoretical modelling of radiation effects (SMoRE)

CERN Document Server

2018-01-01

This publication summarizes the findings and conclusions of the IAEA coordinated research project (CRP) on accelerator simulation and theoretical modelling of radiation effects, aimed at supporting Member States in the development of advanced radiation-resistant structural materials for implementation in innovative nuclear systems. This aim can be achieved through enhancement of both experimental neutron-emulation capabilities of ion accelerators and improvement of the predictive efficiency of theoretical models and computer codes. This dual approach is challenging but necessary, because outputs of accelerator simulation experiments need adequate theoretical interpretation, and theoretical models and codes need high dose experimental data for their verification. Both ion irradiation investigations and computer modelling have been the specific subjects of the CRP, and the results of these studies are presented in this publication which also includes state-ofthe- art reviews of four major aspects of the project...

DNBR Prediction Using a Support Vector Regression

International Nuclear Information System (INIS)

Yang, Heon Young; Na, Man Gyun

2008-01-01

PWRs (Pressurized Water Reactors) generally operate in the nucleate boiling state. However, the conversion of nucleate boiling into film boiling with conspicuously reduced heat transfer induces a boiling crisis that may cause the fuel clad melting in the long run. This type of boiling crisis is called Departure from Nucleate Boiling (DNB) phenomena. Because the prediction of minimum DNBR in a reactor core is very important to prevent the boiling crisis such as clad melting, a lot of research has been conducted to predict DNBR values. The object of this research is to predict minimum DNBR applying support vector regression (SVR) by using the measured signals of a reactor coolant system (RCS). The SVR has extensively and successfully been applied to nonlinear function approximation like the proposed problem for estimating DNBR values that will be a function of various input variables such as reactor power, reactor pressure, core mass flowrate, control rod positions and so on. The minimum DNBR in a reactor core is predicted using these various operating condition data as the inputs to the SVR. The minimum DBNR values predicted by the SVR confirm its correctness compared with COLSS values

Theoretical prediction of the energy stability of graphene nanoblisters

Science.gov (United States)

Glukhova, O. E.; Slepchenkov, M. M.; Barkov, P. V.

2018-04-01

The paper presents the results of a theoretical prediction of the energy stability of graphene nanoblisters with various geometrical parameters. As a criterion for the evaluation of the stability of investigated carbon objects we propose to consider the value of local stress of the nanoblister atomic grid. Numerical evaluation of stresses experienced by atoms of the graphene blister framework was carried out by means of an original method for calculation of local stresses that is based on energy approach. Atomistic models of graphene nanoblisters corresponding to the natural experiment data were built for the first time in this work. New physical regularities of the influence of topology on the thermodynamic stability of nanoblisters were established as a result of the analysis of the numerical experiment data. We built the distribution of local stresses for graphene blister structures, whose atomic grid contains a variety of structural defects. We have shown how the concentration and location of defects affect the picture of the distribution of the maximum stresses experienced by the atoms of the nanoblisters.

Predictive Analytics to Support Real-Time Management in Pathology Facilities.

Science.gov (United States)

Lessard, Lysanne; Michalowski, Wojtek; Chen Li, Wei; Amyot, Daniel; Halwani, Fawaz; Banerjee, Diponkar

2016-01-01

Predictive analytics can provide valuable support to the effective management of pathology facilities. The introduction of new tests and technologies in anatomical pathology will increase the volume of specimens to be processed, as well as the complexity of pathology processes. In order for predictive analytics to address managerial challenges associated with the volume and complexity increases, it is important to pinpoint the areas where pathology managers would most benefit from predictive capabilities. We illustrate common issues in managing pathology facilities with an analysis of the surgical specimen process at the Department of Pathology and Laboratory Medicine (DPLM) at The Ottawa Hospital, which processes all surgical specimens for the Eastern Ontario Regional Laboratory Association. We then show how predictive analytics could be used to support management. Our proposed approach can be generalized beyond the DPLM, contributing to a more effective management of pathology facilities and in turn to quicker clinical diagnoses.

Predicting Resilience via Social Support and Illness Perceptions Among Patients Undergoing Hemodialysis

Directory of Open Access Journals (Sweden)

Reihane Hajmohammadi

2017-07-01

Full Text Available Background and Objectives Chronic renal disease is a threatening condition for the health, economic, and social status of the affected person and his/her family. Patients undergoing hemodialysis encounter mental and health problems; the current study aimed at predicting resilience via social support and illness perceptions among patients undergoing hemodialysis. Methods The current descriptive-correlational study had a statistical population including 308 patients undergoing hemodialysis in Kerman, Iran, in 2017. Based on the Krejcie-Morgan table, the minimum required sample size was 169. The sample was selected using a convenience sampling method. Data collection tools were the Connor-Davidson resilience scale, the medical outcome study (MOS social support survey developed by Sherbourne and Stewart, and the brief illness perception questionnaire developed by Broadbent et al. Data were analyzed using a Pearson correlation coefficient and a stepwise regression analysis via SPSS version 19. Results Results indicated that resilience was significantly and positively related to social support (r = 0.318, P < 0.05 and illness perceptions (r = 0.165, P < 0.05. Among the subscales of social support, emotional support, tangible support, and social interaction could predict resilience, and among the subscales of illness perceptions, only cognitive representation could predict resilience. Conclusions The obtained results demonstrated that resilience was significantly and positively related to social support and illness perceptions. Additionally, the subscales of social support and illness perceptions could predict resilience among the patients undergoing hemodialysis.

Theoretical prediction of thermal conductivity for thermal protection systems

International Nuclear Information System (INIS)

Gori, F.; Corasaniti, S.; Worek, W.M.; Minkowycz, W.J.

2012-01-01

The present work is aimed to evaluate the effective thermal conductivity of an ablative composite material in the state of virgin material and in three paths of degradation. The composite material is undergoing ablation with formation of void pores or char and void pores. The one dimensional effective thermal conductivity is evaluated theoretically by the solution of heat conduction under two assumptions, i.e. parallel isotherms and parallel heat fluxes. The paper presents the theoretical model applied to an elementary cubic cell of the composite material which is made of two crossed fibres and a matrix. A numerical simulation is carried out to compare the numerical results with the theoretical ones for different values of the filler volume fraction. - Highlights: ► Theoretical models of the thermal conductivity of an ablative composite. ► Composite material is made of two crossed fibres and a matrix. ► Three mechanisms of degradation are investigated. ► One dimensional thermal conductivity is evaluated by the heat conduction equation. ► Numerical simulations to be compared with the theoretical models.

Lithium-ion battery remaining useful life prediction based on grey support vector machines

Directory of Open Access Journals (Sweden)

Xiaogang Li

2015-12-01

Full Text Available In this article, an improved grey prediction model is proposed to address low-accuracy prediction issue of grey forecasting model. The first step is using a trigonometric function to transform the original data sequence to smooth the data, which is called smoothness of grey prediction model, and then a grey support vector machine model by integrating the improved grey model with support vector machine is introduced. At the initial stage of the model, trigonometric functions and accumulation generation operation can be used to preprocess the data, which enhances the smoothness of the data and reduces the associated randomness. In addition, support vector machine is implemented to establish a prediction model for the pre-processed data and select the optimal model parameters via genetic algorithms. Finally, the data are restored through the ‘regressive generate’ operation to obtain the forecasting data. To prove that the grey support vector machine model is superior to the other models, the battery life data from the Center for Advanced Life Cycle Engineering are selected, and the presented model is used to predict the remaining useful life of the battery. The predicted result is compared to that of grey model and support vector machines. For a more intuitive comparison of the three models, this article quantifies the root mean square errors for these three different models in the case of different ratio of training samples and prediction samples. The results show that the effect of grey support vector machine model is optimal, and the corresponding root mean square error is only 3.18%.

Predictive Analytics to Support Real-Time Management in Pathology Facilities

Science.gov (United States)

Lessard, Lysanne; Michalowski, Wojtek; Chen Li, Wei; Amyot, Daniel; Halwani, Fawaz; Banerjee, Diponkar

2016-01-01

Predictive analytics can provide valuable support to the effective management of pathology facilities. The introduction of new tests and technologies in anatomical pathology will increase the volume of specimens to be processed, as well as the complexity of pathology processes. In order for predictive analytics to address managerial challenges associated with the volume and complexity increases, it is important to pinpoint the areas where pathology managers would most benefit from predictive capabilities. We illustrate common issues in managing pathology facilities with an analysis of the surgical specimen process at the Department of Pathology and Laboratory Medicine (DPLM) at The Ottawa Hospital, which processes all surgical specimens for the Eastern Ontario Regional Laboratory Association. We then show how predictive analytics could be used to support management. Our proposed approach can be generalized beyond the DPLM, contributing to a more effective management of pathology facilities and in turn to quicker clinical diagnoses. PMID:28269873

A theoretical prediction of critical heat flux in saturated pool boiling during power transients

International Nuclear Information System (INIS)

Pasamehmetoglu, K.O.; Nelson, R.A.; Gunnerson, F.S.

1987-01-01

Understanding and predicting critical heat flux (CHF) behavior during steady-state and transient conditions is of fundamental interest in the design, operation, and safety of boiling and two-phase flow devices. Presented within this paper are the results of a comprehensive theoretical study specifically conducted to model transient CHF behavior in saturated pool boiling. Thermal energy conduction within a heating element and its influence on the CHF are also discussed. The resultant theory provides new insight into the basic physics of the CHF phenomenon and indicates favorable agreement with the experimental data from cylindrical heaters with small radii. However, the flat-ribbon heater data compared poorly with the present theory, although the general trend was predicted. Finally, various factors that affect the discrepency between the data and the theory are listed

Fault trend prediction of device based on support vector regression

International Nuclear Information System (INIS)

Song Meicun; Cai Qi

2011-01-01

The research condition of fault trend prediction and the basic theory of support vector regression (SVR) were introduced. SVR was applied to the fault trend prediction of roller bearing, and compared with other methods (BP neural network, gray model, and gray-AR model). The results show that BP network tends to overlearn and gets into local minimum so that the predictive result is unstable. It also shows that the predictive result of SVR is stabilization, and SVR is superior to BP neural network, gray model and gray-AR model in predictive precision. SVR is a kind of effective method of fault trend prediction. (authors)

A simple equilibrium theoretical model and predictions for a continuous wave exciplex pumped alkali laser

International Nuclear Information System (INIS)

Carroll, David L; Verdeyen, Joseph T

2013-01-01

The exciplex pumped alkali laser (XPAL) system has been demonstrated in mixtures of Cs vapour, Ar, with and without ethane, by pumping Cs-Ar atomic collision pairs and subsequent dissociation of diatomic, electronically excited CsAr molecules (exciplexes or excimers). The blue satellites of the alkali D 2 lines provide an advantageous pathway for optically pumping atomic alkali lasers on the principal series (resonance) transitions with broad linewidth (>2 nm) semiconductor diode lasers. The development of a simple theoretical analysis of continuous-wave XPAL systems is presented along with predictions as a function of temperature and pump intensity. The model predicts that an optical-to-optical efficiency in the range of 40-50% can be achieved for XPAL.

The Emergence of Family-specific Support Constructs: Cross-level Effects of Family-supportive Supervision and Family-Supportive Organization Perceptions on Individual Outcomes.

Science.gov (United States)

Hill, Rachel T; Matthews, Russell A; Walsh, Benjamin M

2016-12-01

Implicit to the definitions of both family-supportive supervision (FSS) and family-supportive organization perceptions (FSOP) is the argument that these constructs may manifest at a higher (e.g. group or organizational) level. In line with these conceptualizations, grounded in tenants of conservation of resources theory, we argue that FSS and FSOP, as universal resources, are emergent constructs at the organizational level, which have cross-level effects on work-family conflict and turnover intentions. To test our theoretically derived hypotheses, a multilevel model was examined in which FSS and FSOP at the unit level predict individual work-to-family conflict, which in turn predicts turnover intentions. Our hypothesized model was generally supported. Collectively, our results point to FSOP serving as an explanatory mechanism of the effects that mutual perceptions of FSS have on individual experiences of work-to-family conflict and turnover intentions. Lagged (i.e. overtime) cross-level effects of the model were also confirmed in supplementary analyses. Our results extend our theoretical understanding of FSS and FSOP by demonstrating the utility of conceptualizing them as universal resources, opening up a variety of avenues for future research. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Prediction of Banking Systemic Risk Based on Support Vector Machine

Directory of Open Access Journals (Sweden)

Shouwei Li

2013-01-01

Full Text Available Banking systemic risk is a complex nonlinear phenomenon and has shed light on the importance of safeguarding financial stability by recent financial crisis. According to the complex nonlinear characteristics of banking systemic risk, in this paper we apply support vector machine (SVM to the prediction of banking systemic risk in an attempt to suggest a new model with better explanatory power and stability. We conduct a case study of an SVM-based prediction model for Chinese banking systemic risk and find the experiment results showing that support vector machine is an efficient method in such case.

Predicting changes in posttraumatic growth and subjective well-being among breast cancer survivors: the role of social support and stress.

Science.gov (United States)

McDonough, Meghan H; Sabiston, Catherine M; Wrosch, Carsten

2014-01-01

Social support is theoretically expected to be positively associated with posttraumatic growth (PTG) and subjective well-being, and stress is expected to be positively associated with PTG and negatively associated with subjective well-being among breast cancer (BC) survivors. However, empirical evidence is mixed, predominantly cross-sectional, and few studies have examined the unique effects of these predictors on positive changes in psychological experiences post cancer diagnosis and systemic treatment. This study examined both general and BC-specific social support and stress as predictors of change in PTG and subjective well-being among BC survivors. Women (N = 173, Mage  = 55.40, SD = 10.99) who had recently finished treatment completed demographic and treatment measures at baseline (T1); general and cancer-specific social support and stress, PTG and subjective well-being at 3 months (T2); and PTG and subjective well-being again at 6 months (T3). Longitudinal predictors of change in PTG and subjective well-being were examined using hierarchical multiple regression. The BC-specific social support (β = .12) and stress (cancer worry; β = .10) predicted increasing levels of PTG. Improvements in subjective well-being were predicted by higher levels of general social support (β = .21) and lower levels of general stress (β = -.59). There are distinct predictors of change in PTG and subjective well-being among BC survivors, supporting the distinction between the trauma-specific process of PTG and well-being. Copyright © 2013 John Wiley & Sons, Ltd.

A theoretical adaptive model of thermal comfort - Adaptive Predicted Mean Vote (aPMV)

Energy Technology Data Exchange (ETDEWEB)

Yao, Runming [School of Construction Management and Engineering, The University of Reading (United Kingdom); Faculty of Urban Construction and Environmental Engineering, Chongqing University (China); Li, Baizhan [Key Laboratory of the Three Gorges Reservoir Region' s Eco-Environment (Ministry of Education), Chongqing University (China); Faculty of Urban Construction and Environmental Engineering, Chongqing University (China); Liu, Jing [School of Construction Management and Engineering, The University of Reading (United Kingdom)

2009-10-15

This paper presents in detail a theoretical adaptive model of thermal comfort based on the ''Black Box'' theory, taking into account factors such as culture, climate, social, psychological and behavioural adaptations, which have an impact on the senses used to detect thermal comfort. The model is called the Adaptive Predicted Mean Vote (aPMV) model. The aPMV model explains, by applying the cybernetics concept, the phenomena that the Predicted Mean Vote (PMV) is greater than the Actual Mean Vote (AMV) in free-running buildings, which has been revealed by many researchers in field studies. An Adaptive coefficient ({lambda}) representing the adaptive factors that affect the sense of thermal comfort has been proposed. The empirical coefficients in warm and cool conditions for the Chongqing area in China have been derived by applying the least square method to the monitored onsite environmental data and the thermal comfort survey results. (author)

Computational tools for experimental determination and theoretical prediction of protein structure

Energy Technology Data Exchange (ETDEWEB)

O`Donoghue, S.; Rost, B.

1995-12-31

This tutorial was one of eight tutorials selected to be presented at the Third International Conference on Intelligent Systems for Molecular Biology which was held in the United Kingdom from July 16 to 19, 1995. The authors intend to review the state of the art in the experimental determination of protein 3D structure (focus on nuclear magnetic resonance), and in the theoretical prediction of protein function and of protein structure in 1D, 2D and 3D from sequence. All the atomic resolution structures determined so far have been derived from either X-ray crystallography (the majority so far) or Nuclear Magnetic Resonance (NMR) Spectroscopy (becoming increasingly more important). The authors briefly describe the physical methods behind both of these techniques; the major computational methods involved will be covered in some detail. They highlight parallels and differences between the methods, and also the current limitations. Special emphasis will be given to techniques which have application to ab initio structure prediction. Large scale sequencing techniques increase the gap between the number of known proteins sequences and that of known protein structures. They describe the scope and principles of methods that contribute successfully to closing that gap. Emphasis will be given on the specification of adequate testing procedures to validate such methods.

Modeling and prediction of Turkey's electricity consumption using Support Vector Regression

International Nuclear Information System (INIS)

Kavaklioglu, Kadir

2011-01-01

Support Vector Regression (SVR) methodology is used to model and predict Turkey's electricity consumption. Among various SVR formalisms, ε-SVR method was used since the training pattern set was relatively small. Electricity consumption is modeled as a function of socio-economic indicators such as population, Gross National Product, imports and exports. In order to facilitate future predictions of electricity consumption, a separate SVR model was created for each of the input variables using their current and past values; and these models were combined to yield consumption prediction values. A grid search for the model parameters was performed to find the best ε-SVR model for each variable based on Root Mean Square Error. Electricity consumption of Turkey is predicted until 2026 using data from 1975 to 2006. The results show that electricity consumption can be modeled using Support Vector Regression and the models can be used to predict future electricity consumption. (author)

Prediction of Spirometric Forced Expiratory Volume (FEV1) Data Using Support Vector Regression

Science.gov (United States)

Kavitha, A.; Sujatha, C. M.; Ramakrishnan, S.

2010-01-01

In this work, prediction of forced expiratory volume in 1 second (FEV1) in pulmonary function test is carried out using the spirometer and support vector regression analysis. Pulmonary function data are measured with flow volume spirometer from volunteers (N=175) using a standard data acquisition protocol. The acquired data are then used to predict FEV1. Support vector machines with polynomial kernel function with four different orders were employed to predict the values of FEV1. The performance is evaluated by computing the average prediction accuracy for normal and abnormal cases. Results show that support vector machines are capable of predicting FEV1 in both normal and abnormal cases and the average prediction accuracy for normal subjects was higher than that of abnormal subjects. Accuracy in prediction was found to be high for a regularization constant of C=10. Since FEV1 is the most significant parameter in the analysis of spirometric data, it appears that this method of assessment is useful in diagnosing the pulmonary abnormalities with incomplete data and data with poor recording.

Theoretical nuclear physics

CERN Document Server

Blatt, John M

1979-01-01

A classic work by two leading physicists and scientific educators endures as an uncommonly clear and cogent investigation and correlation of key aspects of theoretical nuclear physics. It is probably the most widely adopted book on the subject. The authors approach the subject as ""the theoretical concepts, methods, and considerations which have been devised in order to interpret the experimental material and to advance our ability to predict and control nuclear phenomena.""The present volume does not pretend to cover all aspects of theoretical nuclear physics. Its coverage is restricted to

Daily Autonomy Support and Sexual Identity Disclosure Predicts Daily Mental and Physical Health Outcomes.

Science.gov (United States)

Legate, Nicole; Ryan, Richard M; Rogge, Ronald D

2017-06-01

Using a daily diary methodology, we examined how social environments support or fail to support sexual identity disclosure, and associated mental and physical health outcomes. Results showed that variability in disclosure across the diary period related to greater psychological well-being and fewer physical symptoms, suggesting potential adaptive benefits to selectively disclosing. A multilevel path model indicated that perceiving autonomy support in conversations predicted more disclosure, which in turn predicted more need satisfaction, greater well-being, and fewer physical symptoms that day. Finally, mediation analyses revealed that disclosure and need satisfaction explained why perceiving autonomy support in a conversation predicted greater well-being and fewer physical symptoms. That is, perceiving autonomy support in conversations indirectly predicted greater wellness through sexual orientation disclosure, along with feeling authentic and connected in daily interactions with others. Discussion highlights the role of supportive social contexts and everyday opportunities to disclose in affecting sexual minority mental and physical health.

Information-Theoretic Evidence for Predictive Coding in the Face-Processing System.

Science.gov (United States)

Brodski-Guerniero, Alla; Paasch, Georg-Friedrich; Wollstadt, Patricia; Özdemir, Ipek; Lizier, Joseph T; Wibral, Michael

2017-08-23

Predictive coding suggests that the brain infers the causes of its sensations by combining sensory evidence with internal predictions based on available prior knowledge. However, the neurophysiological correlates of (pre)activated prior knowledge serving these predictions are still unknown. Based on the idea that such preactivated prior knowledge must be maintained until needed, we measured the amount of maintained information in neural signals via the active information storage (AIS) measure. AIS was calculated on whole-brain beamformer-reconstructed source time courses from MEG recordings of 52 human subjects during the baseline of a Mooney face/house detection task. Preactivation of prior knowledge for faces showed as α-band-related and β-band-related AIS increases in content-specific areas; these AIS increases were behaviorally relevant in the brain's fusiform face area. Further, AIS allowed decoding of the cued category on a trial-by-trial basis. Our results support accounts indicating that activated prior knowledge and the corresponding predictions are signaled in low-frequency activity (information our eyes/retina and other sensory organs receive from the outside world, but strongly depends also on information already present in our brains, such as prior knowledge about specific situations or objects. A currently popular theory in neuroscience, predictive coding theory, suggests that this prior knowledge is used by the brain to form internal predictions about upcoming sensory information. However, neurophysiological evidence for this hypothesis is rare, mostly because this kind of evidence requires strong a priori assumptions about the specific predictions the brain makes and the brain areas involved. Using a novel, assumption-free approach, we find that face-related prior knowledge and the derived predictions are represented in low-frequency brain activity. Copyright © 2017 the authors 0270-6474/17/378273-11$15.00/0.

Discussion About Nonlinear Time Series Prediction Using Least Squares Support Vector Machine

International Nuclear Information System (INIS)

Xu Ruirui; Bian Guoxing; Gao Chenfeng; Chen Tianlun

2005-01-01

The least squares support vector machine (LS-SVM) is used to study the nonlinear time series prediction. First, the parameter γ and multi-step prediction capabilities of the LS-SVM network are discussed. Then we employ clustering method in the model to prune the number of the support values. The learning rate and the capabilities of filtering noise for LS-SVM are all greatly improved.

A new theoretical approach to analyze complex processes in cytoskeleton proteins.

Science.gov (United States)

Li, Xin; Kolomeisky, Anatoly B

2014-03-20

Cytoskeleton proteins are filament structures that support a large number of important biological processes. These dynamic biopolymers exist in nonequilibrium conditions stimulated by hydrolysis chemical reactions in their monomers. Current theoretical methods provide a comprehensive picture of biochemical and biophysical processes in cytoskeleton proteins. However, the description is only qualitative under biologically relevant conditions because utilized theoretical mean-field models neglect correlations. We develop a new theoretical method to describe dynamic processes in cytoskeleton proteins that takes into account spatial correlations in the chemical composition of these biopolymers. Our approach is based on analysis of probabilities of different clusters of subunits. It allows us to obtain exact analytical expressions for a variety of dynamic properties of cytoskeleton filaments. By comparing theoretical predictions with Monte Carlo computer simulations, it is shown that our method provides a fully quantitative description of complex dynamic phenomena in cytoskeleton proteins under all conditions.

The voltammetric responses of nanometer-sized electrodes in weakly supported electrolyte: A theoretical study

International Nuclear Information System (INIS)

Liu Yuwen; Zhang Qianfan; Chen Shengli

2010-01-01

The effect of the supporting electrolyte concentration on the interfacial profiles and voltammetric responses of nanometer-sized disk electrodes have been investigated theoretically by combining the Poisson-Nernst-Planck (PNP) theory and Butler-Volmer (BV) equation. The PNP-theory is used to treat the nonlinear couplings of electric field, concentration field and dielectric field at electrochemical interface without the electroneutrality assumption that has been long adopted in various voltammetric theories for macro/microelectrodes. The BV equation is modified by using the Frumkin correction to account for the effect of the diffuse double layer potential on interfacial electron-transfer (ET) rate and by including a distance-dependent ET probability in the expression of rate constant to describe the radial heterogeneity of the ET rate constant at nanometer-sized disk electrodes. The computed voltammetric responses for disk electrodes larger than 200 nm in radii in the absence of the excess of the supporting electrolyte using the present theoretical scheme show reasonable agreements with the predications of the conventional microelectrode voltammetric theory which uses the combined Nernst-Planck equation and electroneutrality equation to describe the mixed electromigration-diffusion mass transport without including the possible effects of the diffuse double layer (Amatore et al. ). For electrodes smaller than 200 nm, however, the voltammetric responses predicated by the present theory exhibit significant deviation from the microelectrode theory. It is shown that the deviations are mainly resulted from the overlap between the diffuse double layer and the concentration depletion layer (CDL) at nanoscale electrochemical interfaces in weakly supported media, which will result in the invalidation of the electroneutrality condition in CDL, and from the radial inhomogeneity of ET probability at nanometer-sized disk electrodes.

The voltammetric responses of nanometer-sized electrodes in weakly supported electrolyte: A theoretical study

Energy Technology Data Exchange (ETDEWEB)

Liu Yuwen; Zhang Qianfan [Hubei Electrochemical Power Sources Key Laboratory, Key Laboratory of Analytical Chemistry for Biology and Medicine (Ministry of Education), Department of Chemistry, Wuhan University, Wuhan 430072 (China); Chen Shengli, E-mail: slchen@whu.edu.c [Hubei Electrochemical Power Sources Key Laboratory, Key Laboratory of Analytical Chemistry for Biology and Medicine (Ministry of Education), Department of Chemistry, Wuhan University, Wuhan 430072 (China)

2010-11-30

The effect of the supporting electrolyte concentration on the interfacial profiles and voltammetric responses of nanometer-sized disk electrodes have been investigated theoretically by combining the Poisson-Nernst-Planck (PNP) theory and Butler-Volmer (BV) equation. The PNP-theory is used to treat the nonlinear couplings of electric field, concentration field and dielectric field at electrochemical interface without the electroneutrality assumption that has been long adopted in various voltammetric theories for macro/microelectrodes. The BV equation is modified by using the Frumkin correction to account for the effect of the diffuse double layer potential on interfacial electron-transfer (ET) rate and by including a distance-dependent ET probability in the expression of rate constant to describe the radial heterogeneity of the ET rate constant at nanometer-sized disk electrodes. The computed voltammetric responses for disk electrodes larger than 200 nm in radii in the absence of the excess of the supporting electrolyte using the present theoretical scheme show reasonable agreements with the predications of the conventional microelectrode voltammetric theory which uses the combined Nernst-Planck equation and electroneutrality equation to describe the mixed electromigration-diffusion mass transport without including the possible effects of the diffuse double layer (Amatore et al. ). For electrodes smaller than 200 nm, however, the voltammetric responses predicated by the present theory exhibit significant deviation from the microelectrode theory. It is shown that the deviations are mainly resulted from the overlap between the diffuse double layer and the concentration depletion layer (CDL) at nanoscale electrochemical interfaces in weakly supported media, which will result in the invalidation of the electroneutrality condition in CDL, and from the radial inhomogeneity of ET probability at nanometer-sized disk electrodes.

Tourist activity planning in congested urban tourism environments : towards a game theoretic model and decision support system

NARCIS (Netherlands)

Han, Q.; Dellaert, B.G.C.; Raaij, van W.F.; Timmermans, H.J.P.; Crouch, G.I.; Perdue, R/R/; Timmermans, H.J.P.; Uysal, M.

2005-01-01

Urban tourism has positive effects on the city such as generating financial support and improving the city's atmosphere, but may also have negative impacts such as overuse of resources. In this chapter, an activity-based approach to tourists' behaviour analysis is combined with a game-theoretic

Sensory predictions during action support perception of imitative reactions across suprasecond delays.

Science.gov (United States)

Yon, Daniel; Press, Clare

2018-04-01

Perception during action is optimized by sensory predictions about the likely consequences of our movements. Influential theories in social cognition propose that we use the same predictions during interaction, supporting perception of similar reactions in our social partners. However, while our own action outcomes typically occur at short, predictable delays after movement execution, the reactions of others occur at longer, variable delays in the order of seconds. To examine whether we use sensorimotor predictions to support perception of imitative reactions, we therefore investigated the temporal profile of sensory prediction during action in two psychophysical experiments. We took advantage of an influence of prediction on apparent intensity, whereby predicted visual stimuli appear brighter (more intense). Participants performed actions (e.g., index finger lift) and rated the brightness of observed outcomes congruent (index finger lift) or incongruent (middle finger lift) with their movements. Observed action outcomes could occur immediately after execution, or at longer delays likely reflective of those in natural social interaction (1800 or 3600 ms). Consistent with the previous literature, Experiment 1 revealed that congruent action outcomes were rated as brighter than incongruent outcomes. Importantly, this facilitatory perceptual effect was found irrespective of whether outcomes occurred immediately or at delay. Experiment 2 replicated this finding and demonstrated that it was not the result of response bias. These findings therefore suggest that visual predictions generated during action are sufficiently general across time to support our perception of imitative reactions in others, likely generating a range of benefits during social interaction. Copyright © 2017 Elsevier B.V. All rights reserved.

Predicting cyberbullying perpetration in emerging adults: A theoretical test of the Barlett Gentile Cyberbullying Model.

Science.gov (United States)

Barlett, Christopher; Chamberlin, Kristina; Witkower, Zachary

2017-04-01

The Barlett and Gentile Cyberbullying Model (BGCM) is a learning-based theory that posits the importance of positive cyberbullying attitudes predicting subsequent cyberbullying perpetration. Furthermore, the tenants of the BGCM state that cyberbullying attitude are likely to form when the online aggressor believes that the online environment allows individuals of all physical sizes to harm others and they are perceived as anonymous. Past work has tested parts of the BGCM; no study has used longitudinal methods to examine this model fully. The current study (N = 161) employed a three-wave longitudinal design to test the BGCM. Participants (age range: 18-24) completed measures of the belief that physical strength is irrelevant online and anonymity perceptions at Wave 1, cyberbullying attitudes at Wave 2, and cyberbullying perpetration at Wave 3. Results showed strong support for the BGCM: anonymity perceptions and the belief that physical attributes are irrelevant online at Wave 1 predicted Wave 2 cyberbullying attitudes, which predicted subsequent Wave 3 cyberbullying perpetration. These results support the BGCM and are the first to show empirical support for this model. Aggr. Behav. 43:147-154, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Transmembrane protein diffusion in gel-supported dual-leaflet membranes.

Science.gov (United States)

Wang, Chih-Ying; Hill, Reghan J

2014-11-18

Tools to measure transmembrane-protein diffusion in lipid bilayer membranes have advanced in recent decades, providing a need for predictive theoretical models that account for interleaflet leaflet friction on tracer mobility. Here we address the fully three-dimensional flows driven by a (nonprotruding) transmembrane protein embedded in a dual-leaflet membrane that is supported above and below by soft porous supports (e.g., hydrogel or extracellular matrix), each of which has a prescribed permeability and solvent viscosity. For asymmetric configurations, i.e., supports with contrasting permeability, as realized for cells in contact with hydrogel scaffolds or culture media, the diffusion coefficient can reflect interleaflet friction. Reasonable approximations, for sufficiently large tracers on low-permeability supports, are furnished by a recent phenomenological theory from the literature. Interpreting literature data, albeit for hard-supported membranes, provides a theoretical basis for the phenomenological Stokes drag law as well as strengthening assertions that nonhydrodynamic interactions are important in supported bilayer systems, possibly leading to overestimates of the membrane/leaflet viscosity. Our theory provides a theoretical foundation for future experimental studies of tracer diffusion in gel-supported membranes.

Lamb shift in muonic hydrogen-I. Verification and update of theoretical predictions

International Nuclear Information System (INIS)

Jentschura, U.D.

2011-01-01

Research highlights: → The QED theory of muonic hydrogen energy levels is verified and updated. → Previously obtained results of Pachucki and Borie are confirmed. → The influence of the vacuum polarization potential onto the Bethe logarithm is calculated nonperturbatively. → A model-independent estimate of the Zemach moment correction is given. → Parametrically, the observed discrepancy of theory and experiment is shown to be substantial and large. - Abstract: In view of the recently observed discrepancy of theory and experiment for muonic hydrogen [R. Pohl et al., Nature 466 (2010) 213], we reexamine the theory on which the quantum electrodynamic (QED) predictions are based. In particular, we update the theory of the 2P-2S Lamb shift, by calculating the self-energy of the bound muon in the full Coulomb + vacuum polarization (Uehling) potential. We also investigate the relativistic two-body corrections to the vacuum polarization shift, and we analyze the influence of the shape of the nuclear charge distribution on the proton radius determination. The uncertainty associated with the third Zemach moment 3 > 2 in the determination of the proton radius from the measurement is estimated. An updated theoretical prediction for the 2S-2P transition is given.

"You've Changed": Low Self-Concept Clarity Predicts Lack of Support for Partner Change.

Science.gov (United States)

Emery, Lydia F; Gardner, Wendi L; Finkel, Eli J; Carswell, Kathleen L

2018-03-01

People often pursue self-change, and having a romantic partner who supports these changes increases relationship satisfaction. However, most existing research focuses only on the experience of the person who is changing. What predicts whether people support their partner's change? People with low self-concept clarity resist self-change, so we hypothesized that they would be unsupportive of their partner's changes. People with low self-concept clarity did not support their partner's change (Study 1a), because they thought they would have to change, too (Study 1b). Low self-concept clarity predicted failing to support a partner's change, but not vice versa (Studies 2 and 3), and only for larger changes (Study 3). Not supporting a partner's change predicted decreases in relationship quality for both members of the couple (Studies 2 and 3). This research underscores the role of partners in self-change, suggesting that failing to support a partner's change may stem from self-concept confusion.

Phase Space Prediction of Chaotic Time Series with Nu-Support Vector Machine Regression

International Nuclear Information System (INIS)

Ye Meiying; Wang Xiaodong

2005-01-01

A new class of support vector machine, nu-support vector machine, is discussed which can handle both classification and regression. We focus on nu-support vector machine regression and use it for phase space prediction of chaotic time series. The effectiveness of the method is demonstrated by applying it to the Henon map. This study also compares nu-support vector machine with back propagation (BP) networks in order to better evaluate the performance of the proposed methods. The experimental results show that the nu-support vector machine regression obtains lower root mean squared error than the BP networks and provides an accurate chaotic time series prediction. These results can be attributable to the fact that nu-support vector machine implements the structural risk minimization principle and this leads to better generalization than the BP networks.

Using support vector regression to predict PM10 and PM2.5

International Nuclear Information System (INIS)

Weizhen, Hou; Zhengqiang, Li; Yuhuan, Zhang; Hua, Xu; Ying, Zhang; Kaitao, Li; Donghui, Li; Peng, Wei; Yan, Ma

2014-01-01

Support vector machine (SVM), as a novel and powerful machine learning tool, can be used for the prediction of PM 10 and PM 2.5 (particulate matter less or equal than 10 and 2.5 micrometer) in the atmosphere. This paper describes the development of a successive over relaxation support vector regress (SOR-SVR) model for the PM 10 and PM 2.5 prediction, based on the daily average aerosol optical depth (AOD) and meteorological parameters (atmospheric pressure, relative humidity, air temperature, wind speed), which were all measured in Beijing during the year of 2010–2012. The Gaussian kernel function, as well as the k-fold crosses validation and grid search method, are used in SVR model to obtain the optimal parameters to get a better generalization capability. The result shows that predicted values by the SOR-SVR model agree well with the actual data and have a good generalization ability to predict PM 10 and PM 2.5 . In addition, AOD plays an important role in predicting particulate matter with SVR model, which should be included in the prediction model. If only considering the meteorological parameters and eliminating AOD from the SVR model, the prediction results of predict particulate matter will be not satisfying

Infinite ensemble of support vector machines for prediction of ...

African Journals Online (AJOL)

Many researchers have demonstrated the use of artificial neural networks (ANNs) to predict musculoskeletal disorders risk associated with occupational exposures. In order to improve the accuracy of LBDs risk classification, this paper proposes to use the support vector machines (SVMs), a machine learning algorithm used ...

A theoretical framework to support research of health service innovation.

Science.gov (United States)

Fox, Amanda; Gardner, Glenn; Osborne, Sonya

2015-02-01

Health service managers and policy makers are increasingly concerned about the sustainability of innovations implemented in health care settings. The increasing demand on health services requires that innovations are both effective and sustainable; however, research in this field is limited, with multiple disciplines, approaches and paradigms influencing the field. These variations prevent a cohesive approach, and therefore the accumulation of research findings, in the development of a body of knowledge. The purpose of this paper is to provide a thorough examination of the research findings and provide an appropriate theoretical framework to examine sustainability of health service innovation. This paper presents an integrative review of the literature available in relation to sustainability of health service innovation and provides the development of a theoretical framework based on integration and synthesis of the literature. A theoretical framework serves to guide research, determine variables, influence data analysis and is central to the quest for ongoing knowledge development. This research outlines the sustainability of innovation framework; a theoretical framework suitable for examining the sustainability of health service innovation. If left unaddressed, health services research will continue in an ad hoc manner, preventing full utilisation of outcomes, recommendations and knowledge for effective provision of health services. The sustainability of innovation theoretical framework provides an operational basis upon which reliable future research can be conducted.

Social support and employee well-being: the conditioning effect of perceived patterns of supportive exchange.

Science.gov (United States)

Nahum-Shani, Inbal; Bamberger, Peter A; Bacharach, Samuel B

2011-03-01

Seeking to explain divergent empirical findings regarding the direct effect of social support on well-being, the authors posit that the pattern of supportive exchange (i.e., reciprocal, under-, or over-reciprocating) determines the impact of receiving support on well-being. Findings generated on the basis of longitudinal data collected from a sample of older blue-collar workers support the authors' predictions, indicating that receiving emotional support is associated with enhanced well-being when the pattern of supportive exchange is perceived by an individual as being reciprocal (support received equals support given), with this association being weaker when the exchange of support is perceived as being under-reciprocating (support given exceeds support received). Moreover, receiving support was found to adversely affect well-being when the pattern of exchange was perceived as being over-reciprocating (support received exceeds support given). Theoretical and practical implications of these findings are discussed.

Large-scale ligand-based predictive modelling using support vector machines.

Science.gov (United States)

Alvarsson, Jonathan; Lampa, Samuel; Schaal, Wesley; Andersson, Claes; Wikberg, Jarl E S; Spjuth, Ola

2016-01-01

The increasing size of datasets in drug discovery makes it challenging to build robust and accurate predictive models within a reasonable amount of time. In order to investigate the effect of dataset sizes on predictive performance and modelling time, ligand-based regression models were trained on open datasets of varying sizes of up to 1.2 million chemical structures. For modelling, two implementations of support vector machines (SVM) were used. Chemical structures were described by the signatures molecular descriptor. Results showed that for the larger datasets, the LIBLINEAR SVM implementation performed on par with the well-established libsvm with a radial basis function kernel, but with dramatically less time for model building even on modest computer resources. Using a non-linear kernel proved to be infeasible for large data sizes, even with substantial computational resources on a computer cluster. To deploy the resulting models, we extended the Bioclipse decision support framework to support models from LIBLINEAR and made our models of logD and solubility available from within Bioclipse.

What predicts depression in cardiac patients: sociodemographic factors, disease severity or theoretical vulnerabilities?

Science.gov (United States)

Doyle, F; McGee, H M; Conroy, R M; Delaney, M

2011-05-01

Depression is associated with increased cardiovascular risk in acute coronary syndrome (ACS) patients, but some argue that elevated depression is actually a marker of cardiovascular disease severity. Therefore, disease indices should better predict depression than established theoretical causes of depression (interpersonal life events, reinforcing events, cognitive distortions, type D personality). However, little theory-based research has been conducted in this area. In a cross-sectional design, ACS patients (n = 336) completed questionnaires assessing depression and psychosocial vulnerabilities. Nested logistic regression assessed the relative contribution of demographic or vulnerability factors, or disease indices or vulnerabilities to depression. In multivariate analysis, all vulnerabilities were independent significant predictors of depression (scoring above threshold on any scale, 48%). Demographic variables accounted for vulnerabilities accounting for significantly more (pseudo R² = 0.16, χ²(change) = 150.9, df = 4, p vulnerabilities increased the overall variance explained to 22% (pseudo R² = 0.22, χ² = 58.6, df = 4, p vulnerabilities predicted depression status better than did either demographic or disease indices. The presence of these proximal causes of depression suggests that depression in ACS patients is not simply a result of cardiovascular disease severity.

What roles do errors serve in motor skill learning? An examination of two theoretical predictions.

Science.gov (United States)

Sanli, Elizabeth A; Lee, Timothy D

2014-01-01

Easy-to-difficult and difficult-to-easy progressions of task difficulty during skill acquisition were examined in 2 experiments that assessed retention, dual-task, and transfer tests of learning. Findings of the first experiment suggest that an easy-to difficult progression did not consistently induce implicit learning processes and was not consistently beneficial to performance under a secondary-task load. The findings of experiment two did not support the predictions made based on schema theory and only partially supported predictions based on reinvestment theory. The authors interpret these findings to suggest that the timing of error in relation to the difficulty of the task (functional task difficulty) plays a role in the transfer of learning to novel versions of a task.

Perceived support from a caregiver's social ties predicts subsequent care-recipient health

Directory of Open Access Journals (Sweden)

Dannielle E. Kelley

2017-12-01

Full Text Available Most social support research has examined support from an individual patient perspective and does not model the broader social context of support felt by caregivers. Understanding how social support networks may complement healthcare services is critical, considering the aging population, as social support networks may be a valuable resource to offset some of the demands placed on the healthcare system. We sought to identify how caregivers' perceived organizational and interpersonal support from their social support network influences care-recipient health.We created a dyadic dataset of care-recipient and caregivers from the first two rounds of the National Health and Aging Trends survey (2011, 2012 and the first round of the associated National Study of Caregivers survey (2011. Using structural equation modeling, we explored how caregivers' perceived social support is associated with caregiver confidence to provide care, and is associated with care-recipient health outcomes at two time points. All data were analyzed in 2016.Social engagement with members from caregivers' social support networks was positively associated with caregiver confidence, and social engagement and confidence were positively associated with care-recipient health at time 1. Social engagement positively predicted patient health at time 2 controlling for time 1. Conversely, use of organizational support negatively predicted care-recipient health at time 2.Care-recipients experience better health outcomes when caregivers are able to be more engaged with members of their social support network. Keywords: Informal caregiving, Social support, Social support network, Patient-caregiver dyads

Chaotic advection at large Péclet number: Electromagnetically driven experiments, numerical simulations, and theoretical predictions

International Nuclear Information System (INIS)

Figueroa, Aldo; Meunier, Patrice; Villermaux, Emmanuel; Cuevas, Sergio; Ramos, Eduardo

2014-01-01

We present a combination of experiment, theory, and modelling on laminar mixing at large Péclet number. The flow is produced by oscillating electromagnetic forces in a thin electrolytic fluid layer, leading to oscillating dipoles, quadrupoles, octopoles, and disordered flows. The numerical simulations are based on the Diffusive Strip Method (DSM) which was recently introduced (P. Meunier and E. Villermaux, “The diffusive strip method for scalar mixing in two-dimensions,” J. Fluid Mech. 662, 134–172 (2010)) to solve the advection-diffusion problem by combining Lagrangian techniques and theoretical modelling of the diffusion. Numerical simulations obtained with the DSM are in reasonable agreement with quantitative dye visualization experiments of the scalar fields. A theoretical model based on log-normal Probability Density Functions (PDFs) of stretching factors, characteristic of homogeneous turbulence in the Batchelor regime, allows to predict the PDFs of scalar in agreement with numerical and experimental results. This model also indicates that the PDFs of scalar are asymptotically close to log-normal at late stages, except for the large concentration levels which correspond to low stretching factors

Joining the Dots: Theoretically Connecting the Vona du Toit Model of Creative Ability (VdTMoCA) with Supported Employment

OpenAIRE

de Bruyn, Marna; Wright, Jon

2017-01-01

The Vona du Toit Model of Creative Ability (VdTMoCA) presents a framework for understanding client motivation and action in occupational therapy, emphasising the relationship between motivation and action. Similarly, motivation to work is regarded as the primary and in some instances, the only eligibility criterion for inclusion in supported employment services. This commentary explores the potential theoretical link between the VdTMoCA and supported employment, primarily applied to the South...

Comparisons of theoretically predicted transport from ion temperature gradient instabilities to L-mode tokamak experiments

International Nuclear Information System (INIS)

Kotschenreuther, M.; Wong, H.V.; Lyster, P.L.; Berk, H.L.; Denton, R.; Miner, W.H.; Valanju, P.

1991-12-01

The theoretical transport from kinetic micro-instabilities driven by ion temperature gradients is a sheared slab is compared to experimentally inferred transport in L-mode tokamaks. Low noise gyrokinetic simulation techniques are used to obtain the ion thermal transport coefficient X. This X is much smaller than in experiments, and so cannot explain L-mode confinement. Previous predictions based on fluid models gave much greater X than experiments. Linear and nonlinear comparisons with the fluid model show that it greatly overestimates transport for experimental parameters. In addition, disagreements among previous analytic and simulation calculations of X in the fluid model are reconciled

Perceived support from a caregiver's social ties predicts subsequent care-recipient health.

Science.gov (United States)

Kelley, Dannielle E; Lewis, Megan A; Southwell, Brian G

2017-12-01

Most social support research has examined support from an individual patient perspective and does not model the broader social context of support felt by caregivers. Understanding how social support networks may complement healthcare services is critical, considering the aging population, as social support networks may be a valuable resource to offset some of the demands placed on the healthcare system. We sought to identify how caregivers' perceived organizational and interpersonal support from their social support network influences care-recipient health. We created a dyadic dataset of care-recipient and caregivers from the first two rounds of the National Health and Aging Trends survey (2011, 2012) and the first round of the associated National Study of Caregivers survey (2011). Using structural equation modeling, we explored how caregivers' perceived social support is associated with caregiver confidence to provide care, and is associated with care-recipient health outcomes at two time points. All data were analyzed in 2016. Social engagement with members from caregivers' social support networks was positively associated with caregiver confidence, and social engagement and confidence were positively associated with care-recipient health at time 1. Social engagement positively predicted patient health at time 2 controlling for time 1. Conversely, use of organizational support negatively predicted care-recipient health at time 2. Care-recipients experience better health outcomes when caregivers are able to be more engaged with members of their social support network.

Fundamental studies of aluminum corrosion in acidic and basic environments: Theoretical predictions and experimental observations

International Nuclear Information System (INIS)

Lashgari, Mohsen; Malek, Ali M.

2010-01-01

Using quantum electrochemical approaches based on density functional theory and cluster/polarized continuum model, we investigated the corrosion behavior of aluminum in HCl and NaOH media containing phenol inhibitor. In this regard, we determined the geometry and electronic structure of the species at metal/solution interface. The investigations revealed that the interaction energies of hydroxide corrosive agents with aluminum surface should be more negative than those of chloride ones. The inhibitor adsorption in acid is more likely to have a physical nature while it appears as though to be chemical in basic media. To verify these predictions, using Tafel plots, we studied the phenomena from experimental viewpoint. The studies confirmed that the rate of corrosion in alkaline solution is substantially greater than in HCl media. Moreover, phenol is a potential-molecule having mixed-type inhibition mechanism. The relationship between inhibitory action and molecular parameters was discussed and the activity in alkaline media was also theoretically anticipated. This prediction was in accord with experiment.

Measurement and prediction of voice support and room gain

DEFF Research Database (Denmark)

Pelegrin Garcia, David; Brunskog, Jonas; Lyberg-Åhlander, Viveka

2012-01-01

and good acoustical quality lies in the range between 14 and 9 dB, whereas the room gain is in the range between 0.2 and 0.5 dB. The prediction model for voice support describes the measurements in the classrooms with a coefficient of determination of 0.84 and a standard deviation of 1.2 dB....

Innovation value chain capability in Malaysian-owned company: A theoretical framework

Science.gov (United States)

Abidin, Norkisme Zainal; Suradi, Nur Riza Mohd

2014-09-01

Good quality products or services are no longer adequate to guarantee the sustainability of a company in the present competitive business. Prior research has developed various innovation models with the hope to better understand the innovativeness of the company. Due to countless definitions, indicators, factors, parameter and approaches in the study of innovation, it is difficult to ensure which one will best suit the Malaysian-owned company innovativeness. This paper aims to provide a theoretical background to support the framework of the innovation value chain capability in Malaysian-owned Company. The theoretical framework was based on the literature reviews, expert interviews and focus group study. The framework will be used to predict and assess the innovation value chain capability in Malaysian-owned company.

Theoretical prediction of thermodynamic activities of liquid Au-Sn-X (X=Bi, Sb, Zn) solder systems

Energy Technology Data Exchange (ETDEWEB)

Awe, O.E., E-mail: draweoe2004@yahoo.com [Department of Physics, University of Ibadan, Ibadan (Nigeria); Department of Physics and Engineering Physics, Obafemi Awolowo University, Ile-Ife (Nigeria); Oshakuade, O.M. [Department of Physics, University of Ibadan, Ibadan (Nigeria)

2017-02-15

Molecular interaction volume model has been theoretically used to predict the thermodynamic activities of tin in Au-Sn-Bi and Au-Sn-Sb and the thermodynamic activity of zinc in Au-Sn-Zn at experimental temperatures 800 K, 873 K and 973 K, respectively. On the premise of agreement between the predicted and experimental values, we predicted the activities of the remaining two components in each of the three systems. This prediction was extended from three cross-sections to five cross-sections, and to temperature range 400–600 K, relevant for applications. Iso-activities were plotted. Results show that addition of tin reduces the tendency for chemical short range order in both Au-Sb and Au-Zn systems, while addition of gold and bismuth, respectively, reduce the tendency for chemical short range order in Sn-Sb and Au-Sn systems. Also, we found that, in the desired high-temperature region for applications, while a combination of chemical order and miscibility of components exist in both Au-Sn-Bi and Au-Sn-Zn systems, only chemical order exist in the Au-Sn-Sb system. Results, further show that increase in temperature reduces the phase separation tendency in Au-Sn-Bi system.

The First Galaxies Theoretical Predictions and Observational Clues

CERN Document Server

Mobasher, Bahram; Bromm, Volker

2013-01-01

New observations of the period between the cosmic recombination and the end of reionization are posing intriguing questions about where the first generations of stars were formed, how the first galaxies were assembled, whether these galaxies have low redshift counterparts, and what role the early galaxies played in the reionization process. Combining the new observational data with theoretical models can shed new light on open issues regarding the star formation process, its role in the reionization of the Universe, and the metal enrichment in galaxies at those early epochs. This volume brings together leading experts in the field to discuss our current level of understanding and what may come in the near future as our observational as well as theoretical tools improve. The book confronts the theory of how the first stars, black holes, and galaxies formed with current and planned observations. This synthesis is very timely, just ahead of the establishment of major new facilities, such as the James Webb Space ...

Modeling and prediction of flotation performance using support vector regression

Directory of Open Access Journals (Sweden)

Despotović Vladimir

2017-01-01

Full Text Available Continuous efforts have been made in recent year to improve the process of paper recycling, as it is of critical importance for saving the wood, water and energy resources. Flotation deinking is considered to be one of the key methods for separation of ink particles from the cellulose fibres. Attempts to model the flotation deinking process have often resulted in complex models that are difficult to implement and use. In this paper a model for prediction of flotation performance based on Support Vector Regression (SVR, is presented. Representative data samples were created in laboratory, under a variety of practical control variables for the flotation deinking process, including different reagents, pH values and flotation residence time. Predictive model was created that was trained on these data samples, and the flotation performance was assessed showing that Support Vector Regression is a promising method even when dataset used for training the model is limited.

Predictive coding in Agency Detection

DEFF Research Database (Denmark)

Andersen, Marc Malmdorf

2017-01-01

Agency detection is a central concept in the cognitive science of religion (CSR). Experimental studies, however, have so far failed to lend support to some of the most common predictions that follow from current theories on agency detection. In this article, I argue that predictive coding, a highly...... promising new framework for understanding perception and action, may solve pending theoretical inconsistencies in agency detection research, account for the puzzling experimental findings mentioned above, and provide hypotheses for future experimental testing. Predictive coding explains how the brain......, unbeknownst to consciousness, engages in sophisticated Bayesian statistics in an effort to constantly predict the hidden causes of sensory input. My fundamental argument is that most false positives in agency detection can be seen as the result of top-down interference in a Bayesian system generating high...

Financial Distress Prediction using Linear Discriminant Analysis and Support Vector Machine

Science.gov (United States)

Santoso, Noviyanti; Wibowo, Wahyu

2018-03-01

A financial difficulty is the early stages before the bankruptcy. Bankruptcies caused by the financial distress can be seen from the financial statements of the company. The ability to predict financial distress became an important research topic because it can provide early warning for the company. In addition, predicting financial distress is also beneficial for investors and creditors. This research will be made the prediction model of financial distress at industrial companies in Indonesia by comparing the performance of Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) combined with variable selection technique. The result of this research is prediction model based on hybrid Stepwise-SVM obtains better balance among fitting ability, generalization ability and model stability than the other models.

Theoretical Characterizaiton of Visual Signatures (Muzzle Flash)

Science.gov (United States)

Kashinski, D. O.; Scales, A. N.; Vanderley, D. L.; Chase, G. M.; di Nallo, O. E.; Byrd, E. F. C.

2014-05-01

We are investigating the accuracy of theoretical models used to predict the visible, ultraviolet and infrared spectra of product materials ejected from the muzzle of currently fielded systems. Recent advances in solid propellants has made the management of muzzle signature (flash) a principle issue in weapons development across the calibers. A priori prediction of the electromagnetic spectra of formulations will allow researchers to tailor blends that yield desired signatures and determine spectrographic detection ranges. We are currently employing quantum chemistry methods at various levels of sophistication to optimize molecular geometries, compute vibrational frequencies, and determine the optical spectra of specific gas-phase molecules and radicals of interest. Electronic excitations are being computed using Time Dependent Density Functional Theory (TD-DFT). A comparison of computational results to experimental values found in the literature is used to assess the affect of basis set and functional choice on calculation accuracy. The current status of this work will be presented at the conference. Work supported by the ARL, and USMA.

Software Infrastructure to Support DSAP (Dynamic Situational Awareness and Prediction) Capabilities

National Research Council Canada - National Science Library

McGraw, Robert

2006-01-01

Today's C4I systems will be required to support faster-than-real-time predictive simulation that can determine possible outcomes by re-calibrating with real-time sensor data or extracted knowledge in real-time...

Prediction and analysis of beta-turns in proteins by support vector machine.

Science.gov (United States)

Pham, Tho Hoan; Satou, Kenji; Ho, Tu Bao

2003-01-01

Tight turn has long been recognized as one of the three important features of proteins after the alpha-helix and beta-sheet. Tight turns play an important role in globular proteins from both the structural and functional points of view. More than 90% tight turns are beta-turns. Analysis and prediction of beta-turns in particular and tight turns in general are very useful for the design of new molecules such as drugs, pesticides, and antigens. In this paper, we introduce a support vector machine (SVM) approach to prediction and analysis of beta-turns. We have investigated two aspects of applying SVM to the prediction and analysis of beta-turns. First, we developed a new SVM method, called BTSVM, which predicts beta-turns of a protein from its sequence. The prediction results on the dataset of 426 non-homologous protein chains by sevenfold cross-validation technique showed that our method is superior to the other previous methods. Second, we analyzed how amino acid positions support (or prevent) the formation of beta-turns based on the "multivariable" classification model of a linear SVM. This model is more general than the other ones of previous statistical methods. Our analysis results are more comprehensive and easier to use than previously published analysis results.

Experimental data and theoretical predictions for the rate of electrophoretic clarification of colloidal suspensions

Energy Technology Data Exchange (ETDEWEB)

Johnson, T.J.; Davis, E.J.

2000-05-01

An experimental and theoretical investigation of the electrophoretic clarification rate of colloidal suspensions was conducted. The suspensions included a coal-washing effluent and a model system of TiO{sub 2} particles. A parametric study of TiO{sub 2} suspensions was performed to validate and analysis of the electrophoretic motion of the clarification front formed between a clear zone and the suspension. To measure the electric field strength needed in the prediction of the location of the front, a moveable probe and salt bridge were connected to a reference electrode. Using the measured electric field strengths, it was found that the numerical solution to the unit cell electrophoresis model agrees with the measured clarification rates. For suspensions with moderately thick electric double layers and high particle volume fractions the deviations from classical Smoluchowski theory are substantial, and the numerical analysis is in somewhat better agreement with the data than a prior solution of the problem. The numerical model reduces to the predictions of previous theories as the thickness of the electric double layer decreases, and it is in good agreement with the clarification rate measured for a coal-washing effluent suspension with thin electric double layers.

Theoretical predictions for α -decay chains of 118 290 -298Og isotopes using a finite-range nucleon-nucleon interaction

Science.gov (United States)

Ismail, M.; Adel, A.

2018-04-01

The α -decay half-lives of the recently synthesized superheavy nuclei (SHN) are investigated by employing the density dependent cluster model. A realistic nucleon-nucleon (NN ) interaction with a finite-range exchange part is used to calculate the microscopic α -nucleus potential in the well-established double-folding model. The calculated potential is then implemented to find both the assault frequency and the penetration probability of the α particle by means of the Wentzel-Kramers-Brillouin (WKB) approximation in combination with the Bohr-Sommerfeld quantization condition. The calculated values of α -decay half-lives of the recently synthesized Og isotopes and its decay products are in good agreement with the experimental data. Moreover, the calculated values of α -decay half-lives have been compared with those values evaluated using other theoretical models, and it was found that our theoretical values match well with their counterparts. The competition between α decay and spontaneous fission is investigated and predictions for possible decay modes for the unknown nuclei 118 290 -298Og are presented. We studied the behavior of the α -decay half-lives of Og isotopes and their decay products as a function of the mass number of the parent nuclei. We found that the behavior of the curves is governed by proton and neutron magic numbers found from previous studies. The proton numbers Z =114 , 116, 108, 106 and the neutron numbers N =172 , 164, 162, 158 show some magic character. We hope that the theoretical prediction of α -decay chains provides a new perspective to experimentalists.

Predicting Social Support for Grieving Persons: A Theory of Planned Behavior Perspective

Science.gov (United States)

Bath, Debra M.

2009-01-01

Research has consistently reported that social support from family, friends, and colleagues is an important factor in the bereaved person's ability to cope after the loss of a loved one. This study used a Theory of Planned Behavior framework to identify those factors that predict a person's intention to interact with, and support, a grieving…

Perceived social support predicts increased conscientiousness during older adulthood.

Science.gov (United States)

Hill, Patrick L; Payne, Brennan R; Jackson, Joshua J; Stine-Morrow, Elizabeth A L; Roberts, Brent W

2014-07-01

This study examined whether perceived social support predicted adaptive personality change in older adulthood, focusing on the trait of conscientiousness. We tested this hypothesis both at the broad domain level and with respect to the specific lower order facets that comprise conscientiousness: order, self-control, industriousness, responsibility, and traditionalism. A sample of 143 older adults (aged 60-91) completed measures of conscientiousness and social support during 2 assessments 7 months apart. Social support and conscientiousness were positively correlated among older adults. Moreover, older adults who perceived greater social support at baseline were more likely to gain in conscientiousness over time. The magnitude of this effect was relatively similar across the order, self-control, and industriousness facets. Perceived social support provides multiple benefits later in life, and the current results add to this literature by showing that it also promotes conscientiousness. As conscientiousness is linked to a variety of positive outcomes later in life, including health, future research should examine whether conscientiousness change may be an important mechanism through which social support enhances resilience in older adulthood. © The Author 2013. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

A Maximum Muscle Strength Prediction Formula Using Theoretical Grade 3 Muscle Strength Value in Daniels et al.’s Manual Muscle Test, in Consideration of Age: An Investigation of Hip and Knee Joint Flexion and Extension

Directory of Open Access Journals (Sweden)

Hideyuki Usa

2017-01-01

Full Text Available This study attempted to develop a formula for predicting maximum muscle strength value for young, middle-aged, and elderly adults using theoretical Grade 3 muscle strength value (moment fair: Mf—the static muscular moment to support a limb segment against gravity—from the manual muscle test by Daniels et al. A total of 130 healthy Japanese individuals divided by age group performed isometric muscle contractions at maximum effort for various movements of hip joint flexion and extension and knee joint flexion and extension, and the accompanying resisting force was measured and maximum muscle strength value (moment max, Mm was calculated. Body weight and limb segment length (thigh and lower leg length were measured, and Mf was calculated using anthropometric measures and theoretical calculation. There was a linear correlation between Mf and Mm in each of the four movement types in all groups, excepting knee flexion in elderly. However, the formula for predicting maximum muscle strength was not sufficiently compatible in middle-aged and elderly adults, suggesting that the formula obtained in this study is applicable in young adults only.

Theoretical prediction of ion conductivity in solid state HfO2

Science.gov (United States)

Zhang, Wei; Chen, Wen-Zhou; Sun, Jiu-Yu; Jiang, Zhen-Yi

2013-01-01

A theoretical prediction of ion conductivity for solid state HfO2 is carried out in analogy to ZrO2 based on the density functional calculation. Geometric and electronic structures of pure bulks exhibit similarity for the two materials. Negative formation enthalpy and negative vacancy formation energy are found for YSH (yttria-stabilized hafnia) and YSZ (yttria-stabilized zirconia), suggesting the stability of both materials. Low activation energies (below 0.7 eV) of diffusion are found in both materials, and YSH's is a little higher than that of YSZ. In addition, for both HfO2 and ZrO2, the supercells with native oxygen vacancies are also studied. The so-called defect states are observed in the supercells with neutral and +1 charge native vacancy but not in the +2 charge one. It can give an explanation to the relatively lower activation energies of yttria-doped oxides and +2 charge vacancy supercells. A brief discussion is presented to explain the different YSH ion conductivities in the experiment and obtained by us, and we attribute this to the different ion vibrations at different temperatures.

Curiosity and reward: Valence predicts choice and information prediction errors enhance learning.

Science.gov (United States)

Marvin, Caroline B; Shohamy, Daphna

2016-03-01

Curiosity drives many of our daily pursuits and interactions; yet, we know surprisingly little about how it works. Here, we harness an idea implied in many conceptualizations of curiosity: that information has value in and of itself. Reframing curiosity as the motivation to obtain reward-where the reward is information-allows one to leverage major advances in theoretical and computational mechanisms of reward-motivated learning. We provide new evidence supporting 2 predictions that emerge from this framework. First, we find an asymmetric effect of positive versus negative information, with positive information enhancing both curiosity and long-term memory for information. Second, we find that it is not the absolute value of information that drives learning but, rather, the gap between the reward expected and reward received, an "information prediction error." These results support the idea that information functions as a reward, much like money or food, guiding choices and driving learning in systematic ways. (c) 2016 APA, all rights reserved).

The predictive role of support in the birth experience: A longitudinal cohort study.

Science.gov (United States)

Sigurdardottir, Valgerdur Lisa; Gamble, Jennifer; Gudmundsdottir, Berglind; Kristjansdottir, Hildur; Sveinsdottir, Herdis; Gottfredsdottir, Helga

2017-12-01

Several risk factors for negative birth experience have been identified, but little is known regarding the influence of social and midwifery support on the birth experience over time. The aim of this study was to describe women's birth experience up to two years after birth and to detect the predictive role of satisfaction with social and midwifery support in the birth experience. A longitudinal cohort study was conducted with a convenience sample of pregnant women from 26 community health care centres. Data was gathered using questionnaires at 11-16 weeks of pregnancy (T1, n=1111), at five to six months (T2, n=765), and at 18-24 months after birth (T3, n=657). Data about sociodemographic factors, reproductive history, birth outcomes, social and midwifery support, depressive symptoms, and birth experience were collected. The predictive role of midwifery support in the birth experience was examined using binary logistic regression. The prevalence of negative birth experience was 5% at T2 and 5.7% at T3. Women who were not satisfied with midwifery support during pregnancy and birth were more likely to have negative birth experience at T2 than women who were satisfied with midwifery support. Operative birth, perception of prolonged birth and being a student predicted negative birth experience at both T2 and T3. Perception of negative birth experience was relatively consistent during the study period and the role of support from midwives during pregnancy and birth had a significant impact on women's perception of birth experience. Copyright © 2017 Australian College of Midwives. Published by Elsevier Ltd. All rights reserved.

Theoretical predictions for spatially-focused heating of magnetic nanoparticles guided by magnetic particle imaging field gradients

Energy Technology Data Exchange (ETDEWEB)

Dhavalikar, Rohan [Department of Chemical Engineering, University of Florida, 1030 Center Drive, Gainesville, FL 32611 (United States); Rinaldi, Carlos, E-mail: carlos.rinaldi@bme.ufl.edu [Department of Chemical Engineering, University of Florida, 1030 Center Drive, Gainesville, FL 32611 (United States); J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, 1275 Center Drive, Gainesville, FL 32611 (United States)

2016-12-01

Magnetic nanoparticles in alternating magnetic fields (AMFs) transfer some of the field's energy to their surroundings in the form of heat, a property that has attracted significant attention for use in cancer treatment through hyperthermia and in developing magnetic drug carriers that can be actuated to release their cargo externally using magnetic fields. To date, most work in this field has focused on the use of AMFs that actuate heat release by nanoparticles over large regions, without the ability to select specific nanoparticle-loaded regions for heating while leaving other nanoparticle-loaded regions unaffected. In parallel, magnetic particle imaging (MPI) has emerged as a promising approach to image the distribution of magnetic nanoparticle tracers in vivo, with sub-millimeter spatial resolution. The underlying principle in MPI is the application of a selection magnetic field gradient, which defines a small region of low bias field, superimposed with an AMF (of lower frequency and amplitude than those normally used to actuate heating by the nanoparticles) to obtain a signal which is proportional to the concentration of particles in the region of low bias field. Here we extend previous models for estimating the energy dissipation rates of magnetic nanoparticles in uniform AMFs to provide theoretical predictions of how the selection magnetic field gradient used in MPI can be used to selectively actuate heating by magnetic nanoparticles in the low bias field region of the selection magnetic field gradient. Theoretical predictions are given for the spatial decay in energy dissipation rate under magnetic field gradients representative of those that can be achieved with current MPI technology. These results underscore the potential of combining MPI and higher amplitude/frequency actuation AMFs to achieve selective magnetic fluid hyperthermia (MFH) guided by MPI. - Highlights: • SAR predictions based on a field-dependent magnetization relaxation model. �

Theoretical study of the lowest-lying electronic states of Aluminium monoiodide

International Nuclear Information System (INIS)

Taher, F.; Kabbani, A.; Ani-El Houte, W.

2004-01-01

Full text.The spectroscopic study of Aluminium monohalides, especially the Aluminium monoiodide, is important for monitoring such species in high temperature fast-flow reactors. Theoretical calculations of AlI are not available, whereas several studies have been done for the other aluminium monohalides. In this work, CAS-SCF/MRCI calculations are performed for the lowest-lying electronic states of AlI in a range of internuclear distance between 2.30 A and 2.80 A. Ab-initio calculations have been effectuated by using the computational chemistry program Molpro. The basis set used in this study for aluminium atom is that used by Langhoff for aluminium monohalides, of contractions using atomic natural orbitals and a pseudopotential is used for iode. Accurate theoretical spectroscopic constants and potential curves are obtained for the ground state X 1 Σ + and the first excited states a 3 Π and A 1 Π. The calculated values of Te, ωe, ωexe and re of these states are compatible with the experimental results. An ordering of states is represented for the lowest five predicted singlet and lowest five predicted triplet states. These results provide a big support to determine the analogy in the ordering of the electronic states in AlF, AlBr and AlI respectively at lower energies. These theoretical results identify a set of electronic singlet and triplet states unobserved experimentally

Theoretical predictions for glass flow into an evacuated canister

International Nuclear Information System (INIS)

Routt, K.R.; Crow, K.R.

1983-01-01

Radioactive waste currently stored at the Savannah River Plant in liquid form is to be immobilized by incorporating it into a borosilicate glass. The glass melter for this process will consist of a refractory lined, steel vessel operated at a glass temperature of 1150 0 C. At the end of a two-year projected melter lifetime, the glass inside the melter is to be drained prior to disposition of the melter vessel. One proposed technique for accomplishing this drainage is by sucking the glass into an evacuated canister. The theoretical bases for design of an evacuated canister for draining a glass melter have been developed and tested. The theoretical equations governing transient and steady-state flow were substantiated with both a silicone glass simulant and molten glass

Theoretically predicted soft x-ray emission and absorption spectra of graphitic-structured BC2N

Science.gov (United States)

Muramatsu, Yasuji

Theoretical B K, C K and N K x-ray emission/absorption spectra of three possible graphitic-structured BC2N clusters are predicted based on the B2p-, C2p-, and N2p- density-of-states (DOS) calculated by discrete variational (DV)-X[alpha] molecular orbital calculations. Several prominent differences in DOS spectral features among BC2Ns, h-BN, and graphite are confirmed from comparison of calculated B2p-, C2p-, and N2p-DOS spectra. These variations in the spectra allow BC2N structures to be positively identified by high-resolution x-ray emission/absorption spectroscopy in the B K, C K, and N K regions.

A theoretical prediction of critical heat flux in subcooled pool boiling during power transients

International Nuclear Information System (INIS)

Pasamehmetoglu, K.O.; Nelson, R.A.; Gunnerson, F.S.

1988-01-01

Understanding and predicting critical heat flux (CHF) behavior during steady-state and transient conditions are of fundamenatal interest in the design, operation, safety of boiling and two-phase flow devices. This paper discusses the results of a comprehensive theoretical study made specifically to model transient CHF behavior in subcooled pool boiling. This study is based upon a simplified steady-state CHF model in terms of the vapor mass growth period. The results obtained from this theory indicate favorable agreement with the experimental data from cylindrical heaters with small radii. The statistical nature of the vapor mass behavior in transient boiling also is considered and upper and lower limits for the current theory are established. Various factors that affect the discrepancy between the data and the theory are discussed

Testing a Theoretical Model Predicting Uncertainty and Depression in Patients Undergoing Renal Replacement Therapy in Korea

Directory of Open Access Journals (Sweden)

Heeyoung Lee, PhD, APRN

2008-06-01

Conclusion: The effectiveness of social support in relieving experiences of uncertainty and consequently depression was shown in this study. Moreover, depression in this population could be predicted by direct social support, economic status, and frequency of admission. The study was needed to investigate the relationship between depression and experiences of uncertainty with time covariates, as well as to find the factors that influence depression in patients with chronic renal failure.

Intelligent Quality Prediction Using Weighted Least Square Support Vector Regression

Science.gov (United States)

Yu, Yaojun

A novel quality prediction method with mobile time window is proposed for small-batch producing process based on weighted least squares support vector regression (LS-SVR). The design steps and learning algorithm are also addressed. In the method, weighted LS-SVR is taken as the intelligent kernel, with which the small-batch learning is solved well and the nearer sample is set a larger weight, while the farther is set the smaller weight in the history data. A typical machining process of cutting bearing outer race is carried out and the real measured data are used to contrast experiment. The experimental results demonstrate that the prediction accuracy of the weighted LS-SVR based model is only 20%-30% that of the standard LS-SVR based one in the same condition. It provides a better candidate for quality prediction of small-batch producing process.

Research on bearing life prediction based on support vector machine and its application

International Nuclear Information System (INIS)

Sun Chuang; Zhang Zhousuo; He Zhengjia

2011-01-01

Life prediction of rolling element bearing is the urgent demand in engineering practice, and the effective life prediction technique is beneficial to predictive maintenance. Support vector machine (SVM) is a novel machine learning method based on statistical learning theory, and is of advantage in prediction. This paper develops SVM-based model for bearing life prediction. The inputs of the model are features of bearing vibration signal and the output is the bearing running time-bearing failure time ratio. The model is built base on a few failed bearing data, and it can fuse information of the predicted bearing. So it is of advantage to bearing life prediction in practice. The model is applied to life prediction of a bearing, and the result shows the proposed model is of high precision.

Theoretical basis of the new particles

International Nuclear Information System (INIS)

Rujula, A.

1977-01-01

The four-quark standard gauge field theory of weak, electromagnetic and strong interactions is reviewed and placed into a historical perspective since as early as 1961. Theoretical predictions of the model are compared to experimental observations available as of the Conference date, charm production in e + e - annihilation being in the spotlight. Virtues and shortcomings of the standard model are discussed. The model is concluded to have been an incredibly successful predictive tool. Some theoretical developments around the standard model are also discussed in view of CP violation in SU(2)xU(1) gauge theories, the Higgs' bosons and superunification of weak, strong and electromagnetic interactions

A theoretical model for prediction of deposition efficiency in cold spraying

International Nuclear Information System (INIS)

Li Changjiu; Li Wenya; Wang Yuyue; Yang Guanjun; Fukanuma, H.

2005-01-01

The deposition behavior of a spray particle stream with a particle size distribution was theoretically examined for cold spraying in terms of deposition efficiency as a function of particle parameters and spray angle. The theoretical relation was established between the deposition efficiency and spray angle. The experiments were conducted by measuring deposition efficiency at different driving gas conditions and different spray angles using gas-atomized copper powder. It was found that the theoretically estimated results agreed reasonably well with the experimental ones. Based on the theoretical model and experimental results, it was revealed that the distribution of particle velocity resulting from particle size distribution influences significantly the deposition efficiency in cold spraying. It was necessary for the majority of particles to achieve a velocity higher than the critical velocity in order to improve the deposition efficiency. The normal component of particle velocity contributed to the deposition of the particle under the off-nomal spray condition. The deposition efficiency of sprayed particles decreased owing to the decrease of the normal velocity component as spray was performed at off-normal angle

Prediction of Hydrocarbon Reservoirs Permeability Using Support Vector Machine

Directory of Open Access Journals (Sweden)

R. Gholami

2012-01-01

Full Text Available Permeability is a key parameter associated with the characterization of any hydrocarbon reservoir. In fact, it is not possible to have accurate solutions to many petroleum engineering problems without having accurate permeability value. The conventional methods for permeability determination are core analysis and well test techniques. These methods are very expensive and time consuming. Therefore, attempts have usually been carried out to use artificial neural network for identification of the relationship between the well log data and core permeability. In this way, recent works on artificial intelligence techniques have led to introduce a robust machine learning methodology called support vector machine. This paper aims to utilize the SVM for predicting the permeability of three gas wells in the Southern Pars field. Obtained results of SVM showed that the correlation coefficient between core and predicted permeability is 0.97 for testing dataset. Comparing the result of SVM with that of a general regression neural network (GRNN revealed that the SVM approach is faster and more accurate than the GRNN in prediction of hydrocarbon reservoirs permeability.

Predicting respiratory tumor motion with multi-dimensional adaptive filters and support vector regression

International Nuclear Information System (INIS)

Riaz, Nadeem; Wiersma, Rodney; Mao Weihua; Xing Lei; Shanker, Piyush; Gudmundsson, Olafur; Widrow, Bernard

2009-01-01

Intra-fraction tumor tracking methods can improve radiation delivery during radiotherapy sessions. Image acquisition for tumor tracking and subsequent adjustment of the treatment beam with gating or beam tracking introduces time latency and necessitates predicting the future position of the tumor. This study evaluates the use of multi-dimensional linear adaptive filters and support vector regression to predict the motion of lung tumors tracked at 30 Hz. We expand on the prior work of other groups who have looked at adaptive filters by using a general framework of a multiple-input single-output (MISO) adaptive system that uses multiple correlated signals to predict the motion of a tumor. We compare the performance of these two novel methods to conventional methods like linear regression and single-input, single-output adaptive filters. At 400 ms latency the average root-mean-square-errors (RMSEs) for the 14 treatment sessions studied using no prediction, linear regression, single-output adaptive filter, MISO and support vector regression are 2.58, 1.60, 1.58, 1.71 and 1.26 mm, respectively. At 1 s, the RMSEs are 4.40, 2.61, 3.34, 2.66 and 1.93 mm, respectively. We find that support vector regression most accurately predicts the future tumor position of the methods studied and can provide a RMSE of less than 2 mm at 1 s latency. Also, a multi-dimensional adaptive filter framework provides improved performance over single-dimension adaptive filters. Work is underway to combine these two frameworks to improve performance.

A theoretical model predicting the intensity of emitted light per unit of x-ray exposure in radiographic screens

Energy Technology Data Exchange (ETDEWEB)

Tsoukos, S; Kateris, A; Kalivas, N; Spyrou, G; Panayiotakis, G [Department of Medical Physics, School of Medicine, University of Patras, 265 00 pAtras (Greece); Kandarakis, I; Gavouras, D [Department of Medical Instrumentation Technology, Technological Educational Institution of Athens (Greece)

1999-12-31

A theoretical model predicting the intensity of light emitted by x-ray imaging phosphor screens per unit of area and time over incident x-ray flux (absolute efficiency) was developed. The model takes into account : A) the structure of the screens which consists of luminescent grains embedded in a binding matrix. B) the direct deposition of energy by x-ray absorption effects.. C) the re-absorption of K fluorescence characteristic x-rays produced when the x-ray energy exceeds the energy of the K absorption edge of the phosphor material. To test the model a set of (Gd,La)2O2S:Tb phosphor screens was prepared by sedimentation in the laboratory. Experimental absolute efficiency data were obtained at x-ray tube voltage range from 40 to 160 kVp. The coincidence between experimental and theoretical results were satisfactory. (authors) 7 refs., 4 figs.

Profiled support vector machines for antisense oligonucleotide efficacy prediction

Directory of Open Access Journals (Sweden)

Martín-Guerrero José D

2004-09-01

Full Text Available Abstract Background This paper presents the use of Support Vector Machines (SVMs for prediction and analysis of antisense oligonucleotide (AO efficacy. The collected database comprises 315 AO molecules including 68 features each, inducing a problem well-suited to SVMs. The task of feature selection is crucial given the presence of noisy or redundant features, and the well-known problem of the curse of dimensionality. We propose a two-stage strategy to develop an optimal model: (1 feature selection using correlation analysis, mutual information, and SVM-based recursive feature elimination (SVM-RFE, and (2 AO prediction using standard and profiled SVM formulations. A profiled SVM gives different weights to different parts of the training data to focus the training on the most important regions. Results In the first stage, the SVM-RFE technique was most efficient and robust in the presence of low number of samples and high input space dimension. This method yielded an optimal subset of 14 representative features, which were all related to energy and sequence motifs. The second stage evaluated the performance of the predictors (overall correlation coefficient between observed and predicted efficacy, r; mean error, ME; and root-mean-square-error, RMSE using 8-fold and minus-one-RNA cross-validation methods. The profiled SVM produced the best results (r = 0.44, ME = 0.022, and RMSE= 0.278 and predicted high (>75% inhibition of gene expression and low efficacy (http://aosvm.cgb.ki.se/. Conclusions The SVM approach is well suited to the AO prediction problem, and yields a prediction accuracy superior to previous methods. The profiled SVM was found to perform better than the standard SVM, suggesting that it could lead to improvements in other prediction problems as well.

Theoretical predictions of the two-dimensional solid-state NMR spectra: a case study of the 13C-1H correlations in metergoline

Czech Academy of Sciences Publication Activity Database

Czernek, Jiří; Brus, Jiří

2013-01-01

Roč. 586, 24 October (2013), s. 56-60 ISSN 0009-2614 Institutional support: RVO:61389013 Keywords : NMR * shielding * metergoline Subject RIV: CF - Physical ; Theoretical Chemistry Impact factor: 1.991, year: 2013

Experimental data and theoretical predictions of the rate of electrophoretic clarification of colloidal suspensions

Energy Technology Data Exchange (ETDEWEB)

Johnson, T.J.; Davis, E.J. [University of Washington, Seattle, WA (USA). Dept. of Chemical Engineering

2000-05-01

An experimental and theoretical investigation of the electrophoretic clarification rate of colloidal suspensions was conducted. The suspensions included a coal-washing effluent and a model system of TiO{sub 2} particles. A parametric study of TiO{sub 2} suspensions was performed to validate an analysis of the electrophoretic motion of the clarification front formed between a clear zone and the suspension. To measure the electric field strength needed in the prediction of the location of the front, a moveable probe and salt bridge were connected to a reference electrode. Using the measured electric field strength, it was found that the numerical solution to the unit cell electrophoresis model agrees with the measured clarification rates. For suspensions with moderately thick electric double layers and high particle volume fractions the deviations from classical Smoluchowski theory are substantial, and the numerical analysis is in somewhat better agreement with the data than a prior solution of the problem. The numerical model reduces to the predictions of previous theories as the thickness of the electric double layer decreases, and it is in good agreement with the clarification rate measured for a coal-washing effluent suspension with thin electric double layers. 21 refs., 8 figs., 4 tabs.

Theoretical Predictions of Freestanding Honeycomb Sheets of Cadmium Chalcogenides

Energy Technology Data Exchange (ETDEWEB)

Zhou, Jia [ORNL; Huang, Jingsong [ORNL; Sumpter, Bobby G [ORNL; Kent, Paul R [ORNL; Xie, Yu [ORNL; Terrones Maldonado, Humberto [ORNL; Smith, Sean C [ORNL

2014-01-01

Two-dimensional (2D) nanocrystals of CdX (X = S, Se, Te) typically grown by colloidal synthesis are coated with organic ligands. Recent experimental work on ZnSe showed that the organic ligands can be removed at elevated temperature, giving a freestanding 2D sheet of ZnSe. In this theoretical work, freestanding single- to few-layer sheets of CdX, each possessing a pseudo honeycomb lattice, are considered by cutting along all possible lattice planes of the bulk zinc blende (ZB) and wurtzite (WZ) phases. Using density functional theory, we have systematically studied their geometric structures, energetics, and electronic properties. A strong surface distortion is found to occur for all of the layered sheets, and yet all of the pseudo honeycomb lattices are preserved, giving unique types of surface corrugations and different electronic properties. The energetics, in combination with phonon mode calculations and molecular dynamics simulations, indicate that the syntheses of these freestanding 2D sheets could be selective, with the single- to few-layer WZ110, WZ100, and ZB110 sheets being favored. Through the GW approximation, it is found that all single-layer sheets have large band gaps falling into the ultraviolet range, while thicker sheets in general have reduced band gaps in the visible and ultraviolet range. On the basis of the present work and the experimental studies on freestanding double-layer sheets of ZnSe, we envision that the freestanding 2D layered sheets of CdX predicted herein are potential synthesis targets, which may offer tunable band gaps depending on their structural features including surface corrugations, stacking motifs, and number of layers.

The prediction of candidate genes for cervix related cancer through gene ontology and graph theoretical approach.

Science.gov (United States)

Hindumathi, V; Kranthi, T; Rao, S B; Manimaran, P

2014-06-01

With rapidly changing technology, prediction of candidate genes has become an indispensable task in recent years mainly in the field of biological research. The empirical methods for candidate gene prioritization that succors to explore the potential pathway between genetic determinants and complex diseases are highly cumbersome and labor intensive. In such a scenario predicting potential targets for a disease state through in silico approaches are of researcher's interest. The prodigious availability of protein interaction data coupled with gene annotation renders an ease in the accurate determination of disease specific candidate genes. In our work we have prioritized the cervix related cancer candidate genes by employing Csaba Ortutay and his co-workers approach of identifying the candidate genes through graph theoretical centrality measures and gene ontology. With the advantage of the human protein interaction data, cervical cancer gene sets and the ontological terms, we were able to predict 15 novel candidates for cervical carcinogenesis. The disease relevance of the anticipated candidate genes was corroborated through a literature survey. Also the presence of the drugs for these candidates was detected through Therapeutic Target Database (TTD) and DrugMap Central (DMC) which affirms that they may be endowed as potential drug targets for cervical cancer.

Perceived Threat Associated with Police Officers and Black Men Predicts Support for Policing Policy Reform

Directory of Open Access Journals (Sweden)

Allison Louise Skinner

2016-07-01

Full Text Available Racial disparities in policing and recent high-profile incidents resulting in the deaths of Black men have ignited a national debate on policing policies. Given evidence that both police officers and Black men may be associated with threat, we examined the impact of perceived threat on support for reformed policing policies. Across three studies we found correlational evidence that perceiving police officers as threatening predicts increased support for reformed policing practices (e.g., limiting the use of lethal force and matching police force demographics to those of the community. In contrast, perceiving Black men as threatening predicted reduced support for policing policy reform. Perceived threat also predicted willingness to sign a petition calling for police reform. Experimental evidence indicated that priming participants to associate Black men with threat could also reduce support for policing policy reform, and this effect was moderated by internal motivation to respond without prejudice. Priming participants to associate police officers with threat did not increase support for policing policy reform. Results indicate that resistance to policing policy reform is associated with perceiving Black men as threatening. Moreover, findings suggest that publicizing racially charged police encounters, which may conjure associations between Black men and threat, could reduce support for policing policy reform.

Linear and support vector regressions based on geometrical correlation of data

Directory of Open Access Journals (Sweden)

Kaijun Wang

2007-10-01

Full Text Available Linear regression (LR and support vector regression (SVR are widely used in data analysis. Geometrical correlation learning (GcLearn was proposed recently to improve the predictive ability of LR and SVR through mining and using correlations between data of a variable (inner correlation. This paper theoretically analyzes prediction performance of the GcLearn method and proves that GcLearn LR and SVR will have better prediction performance than traditional LR and SVR for prediction tasks when good inner correlations are obtained and predictions by traditional LR and SVR are far away from their neighbor training data under inner correlation. This gives the applicable condition of GcLearn method.

Prediction of sport adherence through the influence of autonomy-supportive coaching among spanish adolescent athletes.

Science.gov (United States)

Almagro, Bartolomé J; Sáenz-López, Pedro; Moreno, Juan A

2010-01-01

The purpose of this study was to test a motivational model of the coach-athlete relationship, based on self-determination theory and on the hierarchical model of intrinsic and extrinsic motivation. The sample comprised of 608 athletes (ages of 12-17 years) completed the following measures: interest in athlete's input, praise for autonomous behavior, perceived autonomy, intrinsic motivation, and the intention to be physically active. Structural equation modeling results demonstrated that interest in athletes' input and praise for autonomous behavior predicted perceived autonomy, and perceived autonomy positively predicted intrinsic motivation. Finally, intrinsic motivation predicted the intention to be physically active in the future. The results are discussed in relation to the importance of the climate of autonomy support created by the coach on intrinsic motivation and adherence to sport by adolescent athletes. Further, the results provide information related to the possible objectives of future interventions for the education of coaches, with the goal of providing them with tools and strategies to favor the development of intrinsic motivation among their athletes. In conclusion, the climate of autonomy support created by the coach can predict the autonomy perceived by the athletes which predicts the intrinsic motivation experienced by the athletes, and therefore, their adherence to athletic practice. Key pointsImportance of the climate of autonomy support created by the coach on intrinsic motivation and adherence to sport by adolescent athletes.Interest in athletes' input and praise for autonomous behavior predicted perceived autonomy, and perceived autonomy positively predicted intrinsic motivation.Intrinsic motivation predicted the intention to be physically active in the future.

Theoretical Prediction of Melting Relations in the Deep Mantle: the Phase Diagram Approach

Science.gov (United States)

Belmonte, D.; Ottonello, G. A.; Vetuschi Zuccolini, M.; Attene, M.

2016-12-01

Despite the outstanding progress in computer technology and experimental facilities, understanding melting phase relations in the deep mantle is still an open challenge. In this work a novel computational scheme to predict melting relations at HP-HT by a combination of first principles DFT calculations, polymer chemistry and equilibrium thermodynamics is presented and discussed. The adopted theoretical framework is physically-consistent and allows to compute multi-component phase diagrams relevant to Earth's deep interior in a broad range of P-T conditions by a convex-hull algorithm for Gibbs free energy minimisation purposely developed for high-rank simplexes. The calculated phase diagrams are in turn used as a source of information to gain new insights on the P-T-X evolution of magmas in the deep mantle, providing some thermodynamic constraints to both present-day and early Earth melting processes. High-pressure melting curves of mantle silicates are also obtained as by-product of phase diagram calculation. Application of the above method to the MgO-Al2O3-SiO2 (MAS) ternary system highlights as pressure effects are not only able to change the nature of melting of some minerals (like olivine and pyroxene) from eutectic to peritectic (and vice versa), but also simplify melting relations by drastically reducing the number of phases with a primary phase field at HP-HT conditions. It turns out that mineral phases like Majorite-Pyrope garnet and Anhydrous Phase B (Mg14Si5O24), which are often disregarded in modelling melting processes of mantle assemblages, are stable phases at solidus or liquidus conditions in a P-T range compatible with the mantle transition zone (i.e. P = 16 - 23 GPa and T = 2200 - 2700 °C) when their thermodynamic and thermophysical properties are properly assessed. Financial support to the Senior Author (D.B.) during his stay as Invited Scientist at the Institut de Physique du Globe de Paris (IPGP, Paris) is warmly acknowledged.

Theoretical prediction of hysteretic rubber friction in ball on plate configuration by finite element method

Directory of Open Access Journals (Sweden)

2009-11-01

Full Text Available This paper has investigated theoretically the influence of sliding speed and temperature on the hysteretic friction in case of a smooth, reciprocating steel ball sliding on smooth rubber plate by finite element method (FEM. Generalized Maxwell-models combined with Mooney-Rivlin model have been used to describe the material behaviour of the ethylenepropylene-diene-monomer (EPDM rubber studied. Additionally, the effect of the technique applied at the parameter identification of the material model and the number of Maxwell elements on the coefficient of friction (COF was also investigated. Finally, the open parameter of the Greenwood-Tabor analytical model has been determined from a fit to the FE results. By fitting, as usual, the Maxwell-model to the storage modulus master curve the predicted COF, in a broad frequency range, will be underestimated even in case of 40-term Maxwell-model. To obtain more accurate numerical prediction or to provide an upper limit for the hysteretic friction, in the interesting frequency range, the Maxwell parameters should be determined, as proposed, from a fit to the measured loss factor master curve. This conclusion can be generalized for all the FE simulations where the hysteresis plays an important role.

Integrating transition theory and bioecological theory: a theoretical perspective for nurses supporting the transition to adulthood for young people with medical complexity.

Science.gov (United States)

Joly, Elizabeth

2016-06-01

To present a discussion of a theoretical perspective developed through integrating Meleis' Transition Theory and Bronfenbrenner's Bioecological Theory of Human Development to inform nursing and advanced nursing practice supporting the transition to adulthood for young people with medical complexity. Theoretical perspectives to inform nursing practice in supporting successful transition are limited, yet nurses frequently encounter young people with medical complexity during the transition to adulthood. Discussion paper. A literature search of CINAHL and Medline was conducted in 2014 and included articles from 2003-2014; informal discussions with families; the author's experiences in a transition program. The integrated theoretical perspective described in this paper can inform nurses and advanced practice nurses on contextual influences, program and intervention development across spheres of influence and outcomes for the transition to adulthood for young people with medical complexity. Young people and their families require effective reciprocal interactions with individuals and services across sectors to successfully transition to adulthood and become situated in the adult world. Intervention must also extend beyond the young person to include providers, services and health and social policy. Nurses can take a leadership role in supporting the transition to adulthood for young people with medical complexity through direct care, case management, education and research. It is integral that nurses holistically consider developmental processes, complexity and contextual conditions that promote positive outcomes during and beyond the transition to adulthood. © 2016 John Wiley & Sons Ltd.

A novel representation for apoptosis protein subcellular localization prediction using support vector machine.

Science.gov (United States)

Zhang, Li; Liao, Bo; Li, Dachao; Zhu, Wen

2009-07-21

Apoptosis, or programmed cell death, plays an important role in development of an organism. Obtaining information on subcellular location of apoptosis proteins is very helpful to understand the apoptosis mechanism. In this paper, based on the concept that the position distribution information of amino acids is closely related with the structure and function of proteins, we introduce the concept of distance frequency [Matsuda, S., Vert, J.P., Ueda, N., Toh, H., Akutsu, T., 2005. A novel representation of protein sequences for prediction of subcellular location using support vector machines. Protein Sci. 14, 2804-2813] and propose a novel way to calculate distance frequencies. In order to calculate the local features, each protein sequence is separated into p parts with the same length in our paper. Then we use the novel representation of protein sequences and adopt support vector machine to predict subcellular location. The overall prediction accuracy is significantly improved by jackknife test.

The Theoretical and Practical Aspects of Forming the Financial Support for the Health Care System

Directory of Open Access Journals (Sweden)

Goncharuk Svitlana M.

2017-04-01

Full Text Available The objectives of the article are: defining the theoretical and methodological foundations for financial support for health care institutions; disclosure of the concept and substance of the targeted budget programs in the health care system; a critical analysis of the current practice in the use of performance indicators for the targeted budget programs; improving the methods for managerial decision-making in the course of implementation of the targeted budget programs; determining ways to improve the effectiveness and efficiency of the targeted budget programs in the health care system. In order to develop the health care sector, there’s a necessity to define the order and mechanisms for the priority financing, as well as the personnel and material-technical provision of health care institutions. There is also a need for the State support and regulation of an adequate financing for health care programs to achieve equal access in different regions of Ukraine. It is important further to define the specifics of the health sector’s targeted programs that will facilitate management of them.

Communication competence, social support, and depression among college students: a model of facebook and face-to-face support network influence.

Science.gov (United States)

Wright, Kevin B; Rosenberg, Jenny; Egbert, Nicole; Ploeger, Nicole A; Bernard, Daniel R; King, Shawn

2013-01-01

This study examined the influence of the social networking site Facebook and face-to-face support networks on depression among (N = 361) college students. The authors used the Relational Health Communication Competence Model as a framework for examining the influence of communication competence on social support network satisfaction and depression. Moreover, they examined the influence of interpersonal and social integrative motives as exogenous variables. On the basis of previous work, the authors propose and test a theoretical model using structural equation modeling. The results indicated empirical support for the model, with interpersonal motives predicting increased face-to-face and computer-mediated competence, increased social support satisfaction with face-to-face and Facebook support, and lower depression scores. The implications of the findings for theory, key limitations, and directions for future research are discussed.

Centrality of positive and negative deployment memories predicts posttraumatic growth in danish veterans

DEFF Research Database (Denmark)

Staugaard, Søren Risløv; Johannessen, Kim Berg; Thomsen, Yvonne Duval

2015-01-01

OBJECTIVE: The purpose of the present study was to examine theoretically motivated predictors for the development of positive changes following potentially traumatic experiences (i.e., posttraumatic growth). Specifically, we wanted to examine the prediction that memories of highly negative......-sectional analyses of the data. RESULTS: The main findings were that the centrality of highly emotional memories from deployment predicted growth alongside openness to experience, combat exposure, and social support. Importantly, the centrality of both positive and negative memories predicted growth equally well...

PREDICTION OF SPORT ADHERENCE THROUGH THE INFLUENCE OF AUTONOMY-SUPPORTIVE COACHING AMONG SPANISH ADOLESCENT ATHLETES

Directory of Open Access Journals (Sweden)

Bartolomé J. Almagro

2010-03-01

Full Text Available The purpose of this study was to test a motivational model of the coach-athlete relationship, based on self-determination theory and on the hierarchical model of intrinsic and extrinsic motivation. The sample comprised of 608 athletes (ages of 12-17 years completed the following measures: interest in athlete's input, praise for autonomous behavior, perceived autonomy, intrinsic motivation, and the intention to be physically active. Structural equation modeling results demonstrated that interest in athletes' input and praise for autonomous behavior predicted perceived autonomy, and perceived autonomy positively predicted intrinsic motivation. Finally, intrinsic motivation predicted the intention to be physically active in the future. The results are discussed in relation to the importance of the climate of autonomy support created by the coach on intrinsic motivation and adherence to sport by adolescent athletes. Further, the results provide information related to the possible objectives of future interventions for the education of coaches, with the goal of providing them with tools and strategies to favor the development of intrinsic motivation among their athletes. In conclusion, the climate of autonomy support created by the coach can predict the autonomy perceived by the athletes which predicts the intrinsic motivation experienced by the athletes, and therefore, their adherence to athletic practice. Key words: Autonomy support, perceived autonomy, intrinsic motivation, sport adherence

Predictive validity of social support relative to psychological well-being in men with spinal cord injury.

Science.gov (United States)

Rintala, Diana H

2013-11-01

Compare predictive validity (relative to psychological well-being) of long and short versions of 2 measures of social support for persons with spinal cord injury (SCI). Sixty-nine men with SCI completed (a) a long and short version of the Interpersonal Support Evaluation List (ISEL), (b) a structured interview regarding the frequency with which a person receives 11 kinds of support from each of their most important supporters (maximum of 5), and (c) a global measure of the same 11 kinds of support. Approximately 3 years later they completed 4 measures of psychological well-being--the Center for Epidemiologic Studies Depression scale (CESD), the Life Satisfaction Index A (LSIA), the Perceived Stress Scale (PSS), and the Rosenberg Self-Esteem Scale (RSES). Comparisons were made among the social support measures with regard to their ability to predict each of the 4 measures of psychological well-being at a later point in time. The long version of the ISEL had more predictive power than the long version of the structured interview. The long version of the ISEL is a good choice for measuring social support in persons with SCI and the short ISEL may be an acceptable choice when minimizing respondent burden is critical if the number of response options is increased to 4. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Using leg muscles as shock absorbers: theoretical predictions and experimental results of drop landing performance.

Science.gov (United States)

Minetti, A E; Ardigò, L P; Susta, D; Cotelli, F

1998-12-01

The use of muscles as power dissipators is investigated in this study, both from the modellistic and the experimental points of view. Theoretical predictions of the drop landing manoeuvre for a range of initial conditions have been obtained by accounting for the mechanical characteristics of knee extensor muscles, the limb geometry and assuming maximum neural activation. Resulting dynamics have been represented in the phase plane (vertical displacement versus speed) to better classify the damping performance. Predictions of safe landing in sedentary subjects were associated to dropping from a maximum (feet) height of 1.6-2.0 m (about 11 m on the moon). Athletes can extend up to 2.6-3.0 m, while for obese males (m = 100 kg, standard stature) the limit should reduce to 0.9-1.3 m. These results have been calculated by including in the model the estimated stiffness of the 'global elastic elements' acting below the squat position. Experimental landings from a height of 0.4, 0.7, 1.1 m (sedentary males (SM) and male (AM) and female (AF) athletes from the alpine ski national team) showed dynamics similar to the model predictions. While the peak power (for a drop height of about 0.7 m) was similar in SM and AF (AM shows a +40% increase, about 33 W/kg), AF stopped the downward movement after a time interval (0.219 +/- 0.030 s) from touch-down 20% significantly shorter than SM. Landing strategy and the effect of anatomical constraints are discussed in the paper.

Tau decays: A theoretical perspective

International Nuclear Information System (INIS)

Marciano, W.J.

1992-11-01

Theoretical predictions for various tau decay rates are reviewed. Effects of electroweak radiative corrections are described. Implications for precision tests of the standard model and ''new physics'' searches are discussed. A perspective on the tau decay puzzle and 1-prong problem is given

Predicting Jakarta composite index using hybrid of fuzzy time series and support vector regression models

Science.gov (United States)

Febrian Umbara, Rian; Tarwidi, Dede; Budi Setiawan, Erwin

2018-03-01

The paper discusses the prediction of Jakarta Composite Index (JCI) in Indonesia Stock Exchange. The study is based on JCI historical data for 1286 days to predict the value of JCI one day ahead. This paper proposes predictions done in two stages., The first stage using Fuzzy Time Series (FTS) to predict values of ten technical indicators, and the second stage using Support Vector Regression (SVR) to predict the value of JCI one day ahead, resulting in a hybrid prediction model FTS-SVR. The performance of this combined prediction model is compared with the performance of the single stage prediction model using SVR only. Ten technical indicators are used as input for each model.

Tyrosine Kinase Ligand-Receptor Pair Prediction by Using Support Vector Machine

Directory of Open Access Journals (Sweden)

Masayuki Yarimizu

2015-01-01

Full Text Available Receptor tyrosine kinases are essential proteins involved in cellular differentiation and proliferation in vivo and are heavily involved in allergic diseases, diabetes, and onset/proliferation of cancerous cells. Identifying the interacting partner of this protein, a growth factor ligand, will provide a deeper understanding of cellular proliferation/differentiation and other cell processes. In this study, we developed a method for predicting tyrosine kinase ligand-receptor pairs from their amino acid sequences. We collected tyrosine kinase ligand-receptor pairs from the Database of Interacting Proteins (DIP and UniProtKB, filtered them by removing sequence redundancy, and used them as a dataset for machine learning and assessment of predictive performance. Our prediction method is based on support vector machines (SVMs, and we evaluated several input features suitable for tyrosine kinase for machine learning and compared and analyzed the results. Using sequence pattern information and domain information extracted from sequences as input features, we obtained 0.996 of the area under the receiver operating characteristic curve. This accuracy is higher than that obtained from general protein-protein interaction pair predictions.

Ab-initio theoretical predictions of structural properties of semiconductors

International Nuclear Information System (INIS)

Rodriguez, C.O.; Peltzer y Blanca, E.L.; Cappannini, O.M.

1983-01-01

Calculations of the total energies of Si, GaP and C together with related structural properties are presented. The results show good agreement with experimental values (differences of less than 6%). They also agree with other recent theoretical results. Calculations for Si and GaP have already been reported and are given here as a reference. (L.C.) [pt

Matching the results of a theoretical model with failure rates obtained from a population of non-nuclear pressure vessels

International Nuclear Information System (INIS)

Harrop, L.P.

1982-02-01

Failure rates for non-nuclear pressure vessel populations are often regarded as showing a decrease with time. Empirical evidence can be cited which supports this view. On the other hand theoretical predictions of PWR type reactor pressure vessel failure rates have shown an increasing failure rate with time. It is shown that these two situations are not necessarily incompatible. If adjustments are made to the input data of the theoretical model to treat a non-nuclear pressure vessel population, the model can produce a failure rate which decreases with time. These adjustments are explained and the results obtained are shown. (author)

Sediment sorting along tidal sand waves: A comparison between field observations and theoretical predictions

Science.gov (United States)

Van Oyen, Tomas; Blondeaux, Paolo; Van den Eynde, Dries

2013-07-01

A site-by-site comparison between field observations and theoretical predictions of sediment sorting patterns along tidal sand waves is performed for ten locations in the North Sea. At each site, the observed grain size distribution along the bottom topography and the geometry of the bed forms is described in detail and the procedure used to obtain the model parameters is summarized. The model appears to accurately describe the wavelength of the observed sand waves for the majority of the locations; still providing a reliable estimate for the other sites. In addition, it is found that for seven out of the ten locations, the qualitative sorting process provided by the model agrees with the observed grain size distribution. A discussion of the site-by-site comparison is provided which, taking into account uncertainties in the field data, indicates that the model grasps the major part of the key processes controlling the phenomenon.

Predicting treatment noncompliance among criminal justice-mandated clients: a theoretical and empirical exploration.

Science.gov (United States)

Sung, Hung-En; Belenko, Steven; Feng, Li; Tabachnick, Carrie

2004-01-01

Compliance with therapeutic regimens constitutes an important but infrequently studied precursor of treatment engagement and is a necessary condition of successful treatment. This study builds on recent treatment process research and provides a theory-driven analysis of treatment compliance. Five hypotheses are formulated to predict treatment noncompliance among criminal justice-mandated clients. These hypotheses tap different determinants of treatment progress, including physical prime, supportive social network, conventional social involvement, treatment motivation, and risk-taking propensity. Data from 150 addicted felons participating in a diversion program are analyzed to test the hypotheses. Predictors related to these hypotheses correctly identify 58% of the fully compliant clients and 55-88% of the noncompliant clients. Most hypotheses are at least partially corroborated and a few strong correlates emerge across analyses. Clients in their physical prime, those with poorer social support, and those lacking internal desires for change were found especially likely to violate treatment program rules. Clinical implications are discussed.

Prediction of Carbohydrate-Binding Proteins from Sequences Using Support Vector Machines

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Seizi Someya

2010-01-01

Full Text Available Carbohydrate-binding proteins are proteins that can interact with sugar chains but do not modify them. They are involved in many physiological functions, and we have developed a method for predicting them from their amino acid sequences. Our method is based on support vector machines (SVMs. We first clarified the definition of carbohydrate-binding proteins and then constructed positive and negative datasets with which the SVMs were trained. By applying the leave-one-out test to these datasets, our method delivered 0.92 of the area under the receiver operating characteristic (ROC curve. We also examined two amino acid grouping methods that enable effective learning of sequence patterns and evaluated the performance of these methods. When we applied our method in combination with the homology-based prediction method to the annotated human genome database, H-invDB, we found that the true positive rate of prediction was improved.

Predicting South Korean University Students' Happiness through Social Support and Efficacy Beliefs

Science.gov (United States)

Lee, Diane Sookyoung; Padilla, Amado M.

2016-01-01

This study investigated the adversity and coping experiences of 198 South Korean university students and takes a cultural lens in understanding how social and individual factors shape their happiness. Hierarchical linear regression analyses suggest that Korean students' perceptions of social support significantly predicted their happiness,…

Screw Remaining Life Prediction Based on Quantum Genetic Algorithm and Support Vector Machine

Directory of Open Access Journals (Sweden)

Xiaochen Zhang

2017-01-01

Full Text Available To predict the remaining life of ball screw, a screw remaining life prediction method based on quantum genetic algorithm (QGA and support vector machine (SVM is proposed. A screw accelerated test bench is introduced. Accelerometers are installed to monitor the performance degradation of ball screw. Combined with wavelet packet decomposition and isometric mapping (Isomap, the sensitive feature vectors are obtained and stored in database. Meanwhile, the sensitive feature vectors are randomly chosen from the database and constitute training samples and testing samples. Then the optimal kernel function parameter and penalty factor of SVM are searched with the method of QGA. Finally, the training samples are used to train optimized SVM while testing samples are adopted to test the prediction accuracy of the trained SVM so the screw remaining life prediction model can be got. The experiment results show that the screw remaining life prediction model could effectively predict screw remaining life.

Intellect: a theoretical framework for personality traits related to intellectual achievements.

Science.gov (United States)

Mussel, Patrick

2013-05-01

The present article develops a theoretical framework for the structure of personality traits related to intellectual achievements. We postulate a 2-dimensional model, differentiating between 2 processes (Seek and Conquer) and 3 operations (Think, Learn, and Create). The framework was operationalized by a newly developed measure, which was validated based on 2 samples. Subsequently, in 3 studies (overall N = 1,478), the 2-dimensional structure of the Intellect framework was generally supported. Additionally, subdimensions of the Intellect framework specifically predicted conceptually related criteria, including scholastic performance, vocational interest, and leisure activities. Furthermore, results from multidimensional scaling and higher order confirmatory factor analyses show that the framework allows for the incorporation of several constructs that have been proposed on different theoretical backgrounds, such as need for cognition, typical intellectual engagement, curiosity, intrinsic motivation, goal orientation, and openness to ideas. It is concluded that based on the Intellect framework, these constructs, which have been researched separately in the literature, can be meaningfully integrated.

Theoretical-experimental comparison of vitrified glass container behavior using the Castem system

International Nuclear Information System (INIS)

Moncouyoux, J.P.; Jamet, P.; Combescure, A.; Millard, A.

1989-01-01

This paper compares theoretical predictions of vitrified nuclear waste glass package collapse with experimental values in order to qualify the mathematical models describing canister deformation under external pressure loads. After briefly outlining the program and describing the experiments performed, the paper discusses the theoretical predictions based on the INCA code from the CEA's CASTEM system

Predicting SVOC Emissions into Air and Foods in Support of ...

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The release of semi-volatile organic compounds (SVOCs) from consumer articles may be a critical human exposure pathway. In addition, the migration of SVOCs from food packaging materials into foods may also be a dominant source of exposure for some chemicals. Here we describe recent efforts to characterize emission-related parameters for these exposure pathways to support prediction of aggregate exposures for thousands of chemicals For chemicals in consumer articles, Little et al. (2012) developed a screening-level indoor exposure prediction model which, for a given SVOC, principally depends on steady-state gas-phase concentrations (y0). We have developed a model that predicts y0 for SVOCs in consumer articles, allowing exposure predictions for 274 ToxCast chemicals. Published emissions data for 31 SVOCs found in flooring materials, provided a training set where both chemical-specific physicochemical properties, article specific formulation properties, and experimental design aspects were available as modeling descriptors. A linear regression yielded R2- and p- values of approximately 0.62 and 3.9E-05, respectively. A similar model was developed based upon physicochemical properties alone, since article information is often not available for a given SVOC or product. This latter model yielded R2 - and p- values of approximately 0.47 and 1.2E-10, respectively. Many SVOCs are also used as additives (e.g. plasticizers, antioxidants, lubricants) in plastic food pac

Perceived parenting and social support: can they predict academic achievement in Argentinean college students?

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de la Iglesia, Guadalupe; Freiberg Hoffmann, Agustin; Fernández Liporace, Mercedes

2014-01-01

The aim of this study was to test the ability to predict academic achievement through the perception of parenting and social support in a sample of 354 Argentinean college students. Their mean age was 23.50 years (standard deviation =2.62 years) and most of them (83.3%) were females. As a prerequisite for admission to college, students are required to pass a series of mandatory core classes and are expected to complete them in two semesters. Delay in completing the curriculum is considered low academic achievement. Parenting was assessed taking into account the mother and the father and considering two dimensions: responsiveness and demandingness. Perceived social support was analyzed considering four sources: parents, teachers, classmates, and best friend or boyfriend/girlfriend. Path analysis showed that, as hypothesized, responsiveness had a positive indirect effect on the perception of social support and enhanced achievement. Demandingness had a different effect in the case of the mother as compared to the father. In the mother model, demandingness had a positive direct effect on achievement. In the case of the father, however, the effect of demandingness had a negative and indirect impact on the perception of social support. Teachers were the only source of perceived social support that significantly predicted achievement. The pathway that belongs to teachers as a source of support was positive and direct. Implications for possible interventions are discussed.

Physical Violence between Siblings: A Theoretical and Empirical Analysis

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Hoffman, Kristi L.; Kiecolt, K. Jill; Edwards, John N.

2005-01-01

This study develops and tests a theoretical model to explain sibling violence based on the feminist, conflict, and social learning theoretical perspectives and research in psychology and sociology. A multivariate analysis of data from 651 young adults generally supports hypotheses from all three theoretical perspectives. Males with brothers have…

Building a Predictive Capability for Decision-Making that Supports MultiPEM

Energy Technology Data Exchange (ETDEWEB)

Carmichael, Joshua Daniel [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2017-11-20

Multi-phenomenological explosion monitoring (multiPEM) is a developing science that uses multiple geophysical signatures of explosions to better identify and characterize their sources. MultiPEM researchers seek to integrate explosion signatures together to provide stronger detection, parameter estimation, or screening capabilities between different sources or processes. This talk will address forming a predictive capability for screening waveform explosion signatures to support multiPEM.

Prediction by graph theoretic measures of structural effects in proteins arising from non-synonymous single nucleotide polymorphisms.

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Tammy M K Cheng

Full Text Available Recent analyses of human genome sequences have given rise to impressive advances in identifying non-synonymous single nucleotide polymorphisms (nsSNPs. By contrast, the annotation of nsSNPs and their links to diseases are progressing at a much slower pace. Many of the current approaches to analysing disease-associated nsSNPs use primarily sequence and evolutionary information, while structural information is relatively less exploited. In order to explore the potential of such information, we developed a structure-based approach, Bongo (Bonds ON Graph, to predict structural effects of nsSNPs. Bongo considers protein structures as residue-residue interaction networks and applies graph theoretical measures to identify the residues that are critical for maintaining structural stability by assessing the consequences on the interaction network of single point mutations. Our results show that Bongo is able to identify mutations that cause both local and global structural effects, with a remarkably low false positive rate. Application of the Bongo method to the prediction of 506 disease-associated nsSNPs resulted in a performance (positive predictive value, PPV, 78.5% similar to that of PolyPhen (PPV, 77.2% and PANTHER (PPV, 72.2%. As the Bongo method is solely structure-based, our results indicate that the structural changes resulting from nsSNPs are closely associated to their pathological consequences.

Weighted K-means support vector machine for cancer prediction.

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Kim, SungHwan

2016-01-01

To date, the support vector machine (SVM) has been widely applied to diverse bio-medical fields to address disease subtype identification and pathogenicity of genetic variants. In this paper, I propose the weighted K-means support vector machine (wKM-SVM) and weighted support vector machine (wSVM), for which I allow the SVM to impose weights to the loss term. Besides, I demonstrate the numerical relations between the objective function of the SVM and weights. Motivated by general ensemble techniques, which are known to improve accuracy, I directly adopt the boosting algorithm to the newly proposed weighted KM-SVM (and wSVM). For predictive performance, a range of simulation studies demonstrate that the weighted KM-SVM (and wSVM) with boosting outperforms the standard KM-SVM (and SVM) including but not limited to many popular classification rules. I applied the proposed methods to simulated data and two large-scale real applications in the TCGA pan-cancer methylation data of breast and kidney cancer. In conclusion, the weighted KM-SVM (and wSVM) increases accuracy of the classification model, and will facilitate disease diagnosis and clinical treatment decisions to benefit patients. A software package (wSVM) is publicly available at the R-project webpage (https://www.r-project.org).

Support vector machine incremental learning triggered by wrongly predicted samples

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Tang, Ting-long; Guan, Qiu; Wu, Yi-rong

2018-05-01

According to the classic Karush-Kuhn-Tucker (KKT) theorem, at every step of incremental support vector machine (SVM) learning, the newly adding sample which violates the KKT conditions will be a new support vector (SV) and migrate the old samples between SV set and non-support vector (NSV) set, and at the same time the learning model should be updated based on the SVs. However, it is not exactly clear at this moment that which of the old samples would change between SVs and NSVs. Additionally, the learning model will be unnecessarily updated, which will not greatly increase its accuracy but decrease the training speed. Therefore, how to choose the new SVs from old sets during the incremental stages and when to process incremental steps will greatly influence the accuracy and efficiency of incremental SVM learning. In this work, a new algorithm is proposed to select candidate SVs and use the wrongly predicted sample to trigger the incremental processing simultaneously. Experimental results show that the proposed algorithm can achieve good performance with high efficiency, high speed and good accuracy.

Theoretical prediction and impact of fundamental electric dipole moments

International Nuclear Information System (INIS)

Ellis, Sebastian A.R.; Kane, Gordon L.

2016-01-01

The predicted Standard Model (SM) electric dipole moments (EDMs) of electrons and quarks are tiny, providing an important window to observe new physics. Theories beyond the SM typically allow relatively large EDMs. The EDMs depend on the relative phases of terms in the effective Lagrangian of the extended theory, which are generally unknown. Underlying theories, such as string/M-theories compactified to four dimensions, could predict the phases and thus EDMs in the resulting supersymmetric (SUSY) theory. Earlier one of us, with collaborators, made such a prediction and found, unexpectedly, that the phases were predicted to be zero at tree level in the theory at the unification or string scale ∼O(10 16 GeV). Electroweak (EW) scale EDMs still arise via running from the high scale, and depend only on the SM Yukawa couplings that also give the CKM phase. Here we extend the earlier work by studying the dependence of the low scale EDMs on the constrained but not fully known fundamental Yukawa couplings. The dominant contribution is from two loop diagrams and is not sensitive to the choice of Yukawa texture. The electron EDM should not be found to be larger than about 5×10 −30 e cm, and the neutron EDM should not be larger than about 5×10 −29 e cm. These values are quite a bit smaller than the reported predictions from Split SUSY and typical effective theories, but much larger than the Standard Model prediction. Also, since models with random phases typically give much larger EDMs, it is a significant testable prediction of compactified M-theory that the EDMs should not be above these upper limits. The actual EDMs can be below the limits, so once they are measured they could provide new insight into the fundamental Yukawa couplings of leptons and quarks. We comment also on the role of strong CP violation. EDMs probe fundamental physics near the Planck scale.

Theoretical prediction and impact of fundamental electric dipole moments

Energy Technology Data Exchange (ETDEWEB)

Ellis, Sebastian A.R.; Kane, Gordon L. [Michigan Center for Theoretical Physics (MCTP),Department of Physics, University of Michigan,Ann Arbor, MI 48109 (United States)

2016-01-13

The predicted Standard Model (SM) electric dipole moments (EDMs) of electrons and quarks are tiny, providing an important window to observe new physics. Theories beyond the SM typically allow relatively large EDMs. The EDMs depend on the relative phases of terms in the effective Lagrangian of the extended theory, which are generally unknown. Underlying theories, such as string/M-theories compactified to four dimensions, could predict the phases and thus EDMs in the resulting supersymmetric (SUSY) theory. Earlier one of us, with collaborators, made such a prediction and found, unexpectedly, that the phases were predicted to be zero at tree level in the theory at the unification or string scale ∼O(10{sup 16} GeV). Electroweak (EW) scale EDMs still arise via running from the high scale, and depend only on the SM Yukawa couplings that also give the CKM phase. Here we extend the earlier work by studying the dependence of the low scale EDMs on the constrained but not fully known fundamental Yukawa couplings. The dominant contribution is from two loop diagrams and is not sensitive to the choice of Yukawa texture. The electron EDM should not be found to be larger than about 5×10{sup −30}e cm, and the neutron EDM should not be larger than about 5×10{sup −29}e cm. These values are quite a bit smaller than the reported predictions from Split SUSY and typical effective theories, but much larger than the Standard Model prediction. Also, since models with random phases typically give much larger EDMs, it is a significant testable prediction of compactified M-theory that the EDMs should not be above these upper limits. The actual EDMs can be below the limits, so once they are measured they could provide new insight into the fundamental Yukawa couplings of leptons and quarks. We comment also on the role of strong CP violation. EDMs probe fundamental physics near the Planck scale.

Perceived Organizational Support for Enhancing Welfare at Work: A Regression Tree Model

Science.gov (United States)

Giorgi, Gabriele; Dubin, David; Perez, Javier Fiz

2016-01-01

When trying to examine outcomes such as welfare and well-being, research tends to focus on main effects and take into account limited numbers of variables at a time. There are a number of techniques that may help address this problem. For example, many statistical packages available in R provide easy-to-use methods of modeling complicated analysis such as classification and tree regression (i.e., recursive partitioning). The present research illustrates the value of recursive partitioning in the prediction of perceived organizational support in a sample of more than 6000 Italian bankers. Utilizing the tree function party package in R, we estimated a regression tree model predicting perceived organizational support from a multitude of job characteristics including job demand, lack of job control, lack of supervisor support, training, etc. The resulting model appears particularly helpful in pointing out several interactions in the prediction of perceived organizational support. In particular, training is the dominant factor. Another dimension that seems to influence organizational support is reporting (perceived communication about safety and stress concerns). Results are discussed from a theoretical and methodological point of view. PMID:28082924

Theoretical isochrones with decreasing gravitational constant

International Nuclear Information System (INIS)

Vandenberg, D.A.

1976-01-01

Van Flandern has postulated a variation of the gravitational constant at the rate approximately -8 x 10 -11 /yr. This variation, consistent with Hoyle-Narlikar and Dirac cosmologies, has been assumed in the computation of a 5 x 10 9 yr theoretical isochrone. Present results show that, even for this age, theory predicts a cluster turn-off luminosity approximately 0.5 to 1.0 mag fainter than the observed turn-offs of globular clusters. Unsatisfactory agreement between theoretical and observed luminosity functions is also indicated. (author)

Predictive value of age for coping: the role of self-efficacy, social support satisfaction and perceived stress.

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Trouillet, Raphaël; Gana, Kamel; Lourel, Marcel; Fort, Isabelle

2009-05-01

The present study was prompted by the lack of agreement on how coping changes with age. We postulate that the effect of age on coping is mediated by coping resources, such as self-efficacy, perceived stress and social support satisfaction. The participants in the study were community dwelling and aged between 22 and 88 years old. Data were collected using the General Self Efficacy Scale, the Social Support Questionnaire, the Perceived Stress Scale, the Geriatric Depression Scale, the Social Readjustment Rating Scale (life-events) and the Way of Coping Checklist. We performed path analyses for two competitive structural models: M1 (age does not directly affect coping processes) and M2 (age directly affects coping processes). Our results supported a modified version of M2. Age was not found to predict either of two coping strategies: problem-focused coping is predicted by self-efficacy and social support satisfaction; emotion-focused coping is predicted by social support satisfaction and perceived stress. Changes in coping over the lifespan reflect the effectiveness with which a person's adaptive processes deal with age-associated changes in self-referred beliefs and environment perception.

Predicting beta-turns in proteins using support vector machines with fractional polynomials.

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Elbashir, Murtada; Wang, Jianxin; Wu, Fang-Xiang; Wang, Lusheng

2013-11-07

β-turns are secondary structure type that have essential role in molecular recognition, protein folding, and stability. They are found to be the most common type of non-repetitive structures since 25% of amino acids in protein structures are situated on them. Their prediction is considered to be one of the crucial problems in bioinformatics and molecular biology, which can provide valuable insights and inputs for the fold recognition and drug design. We propose an approach that combines support vector machines (SVMs) and logistic regression (LR) in a hybrid prediction method, which we call (H-SVM-LR) to predict β-turns in proteins. Fractional polynomials are used for LR modeling. We utilize position specific scoring matrices (PSSMs) and predicted secondary structure (PSS) as features. Our simulation studies show that H-SVM-LR achieves Qtotal of 82.87%, 82.84%, and 82.32% on the BT426, BT547, and BT823 datasets respectively. These values are the highest among other β-turns prediction methods that are based on PSSMs and secondary structure information. H-SVM-LR also achieves favorable performance in predicting β-turns as measured by the Matthew's correlation coefficient (MCC) on these datasets. Furthermore, H-SVM-LR shows good performance when considering shape strings as additional features. In this paper, we present a comprehensive approach for β-turns prediction. Experiments show that our proposed approach achieves better performance compared to other competing prediction methods.

Using support vector machine to predict beta- and gamma-turns in proteins.

Science.gov (United States)

Hu, Xiuzhen; Li, Qianzhong

2008-09-01

By using the composite vector with increment of diversity, position conservation scoring function, and predictive secondary structures to express the information of sequence, a support vector machine (SVM) algorithm for predicting beta- and gamma-turns in the proteins is proposed. The 426 and 320 nonhomologous protein chains described by Guruprasad and Rajkumar (Guruprasad and Rajkumar J. Biosci 2000, 25,143) are used for training and testing the predictive model of the beta- and gamma-turns, respectively. The overall prediction accuracy and the Matthews correlation coefficient in 7-fold cross-validation are 79.8% and 0.47, respectively, for the beta-turns. The overall prediction accuracy in 5-fold cross-validation is 61.0% for the gamma-turns. These results are significantly higher than the other algorithms in the prediction of beta- and gamma-turns using the same datasets. In addition, the 547 and 823 nonhomologous protein chains described by Fuchs and Alix (Fuchs and Alix Proteins: Struct Funct Bioinform 2005, 59, 828) are used for training and testing the predictive model of the beta- and gamma-turns, and better results are obtained. This algorithm may be helpful to improve the performance of protein turns' prediction. To ensure the ability of the SVM method to correctly classify beta-turn and non-beta-turn (gamma-turn and non-gamma-turn), the receiver operating characteristic threshold independent measure curves are provided. (c) 2008 Wiley Periodicals, Inc.

Coal demand prediction based on a support vector machine model

Energy Technology Data Exchange (ETDEWEB)

Jia, Cun-liang; Wu, Hai-shan; Gong, Dun-wei [China University of Mining & Technology, Xuzhou (China). School of Information and Electronic Engineering

2007-01-15

A forecasting model for coal demand of China using a support vector regression was constructed. With the selected embedding dimension, the output vectors and input vectors were constructed based on the coal demand of China from 1980 to 2002. After compared with lineal kernel and Sigmoid kernel, a radial basis function(RBF) was adopted as the kernel function. By analyzing the relationship between the error margin of prediction and the model parameters, the proper parameters were chosen. The support vector machines (SVM) model with multi-input and single output was proposed. Compared the predictor based on RBF neural networks with test datasets, the results show that the SVM predictor has higher precision and greater generalization ability. In the end, the coal demand from 2003 to 2006 is accurately forecasted. l0 refs., 2 figs., 4 tabs.

Prediction of backbone dihedral angles and protein secondary structure using support vector machines

Directory of Open Access Journals (Sweden)

Hirst Jonathan D

2009-12-01

Full Text Available Abstract Background The prediction of the secondary structure of a protein is a critical step in the prediction of its tertiary structure and, potentially, its function. Moreover, the backbone dihedral angles, highly correlated with secondary structures, provide crucial information about the local three-dimensional structure. Results We predict independently both the secondary structure and the backbone dihedral angles and combine the results in a loop to enhance each prediction reciprocally. Support vector machines, a state-of-the-art supervised classification technique, achieve secondary structure predictive accuracy of 80% on a non-redundant set of 513 proteins, significantly higher than other methods on the same dataset. The dihedral angle space is divided into a number of regions using two unsupervised clustering techniques in order to predict the region in which a new residue belongs. The performance of our method is comparable to, and in some cases more accurate than, other multi-class dihedral prediction methods. Conclusions We have created an accurate predictor of backbone dihedral angles and secondary structure. Our method, called DISSPred, is available online at http://comp.chem.nottingham.ac.uk/disspred/.

Predictive based monitoring of nuclear plant component degradation using support vector regression

International Nuclear Information System (INIS)

Agarwal, Vivek; Alamaniotis, Miltiadis; Tsoukalas, Lefteri H.

2015-01-01

Nuclear power plants (NPPs) are large installations comprised of many active and passive assets. Degradation monitoring of all these assets is expensive (labor cost) and highly demanding task. In this paper a framework based on Support Vector Regression (SVR) for online surveillance of critical parameter degradation of NPP components is proposed. In this case, on time replacement or maintenance of components will prevent potential plant malfunctions, and reduce the overall operational cost. In the current work, we apply SVR equipped with a Gaussian kernel function to monitor components. Monitoring includes the one-step-ahead prediction of the component's respective operational quantity using the SVR model, while the SVR model is trained using a set of previous recorded degradation histories of similar components. Predictive capability of the model is evaluated upon arrival of a sensor measurement, which is compared to the component failure threshold. A maintenance decision is based on a fuzzy inference system that utilizes three parameters: (i) prediction evaluation in the previous steps, (ii) predicted value of the current step, (iii) and difference of current predicted value with components failure thresholds. The proposed framework will be tested on turbine blade degradation data.

Blood glucose level prediction based on support vector regression using mobile platforms.

Science.gov (United States)

Reymann, Maximilian P; Dorschky, Eva; Groh, Benjamin H; Martindale, Christine; Blank, Peter; Eskofier, Bjoern M

2016-08-01

The correct treatment of diabetes is vital to a patient's health: Staying within defined blood glucose levels prevents dangerous short- and long-term effects on the body. Mobile devices informing patients about their future blood glucose levels could enable them to take counter-measures to prevent hypo or hyper periods. Previous work addressed this challenge by predicting the blood glucose levels using regression models. However, these approaches required a physiological model, representing the human body's response to insulin and glucose intake, or are not directly applicable to mobile platforms (smart phones, tablets). In this paper, we propose an algorithm for mobile platforms to predict blood glucose levels without the need for a physiological model. Using an online software simulator program, we trained a Support Vector Regression (SVR) model and exported the parameter settings to our mobile platform. The prediction accuracy of our mobile platform was evaluated with pre-recorded data of a type 1 diabetes patient. The blood glucose level was predicted with an error of 19 % compared to the true value. Considering the permitted error of commercially used devices of 15 %, our algorithm is the basis for further development of mobile prediction algorithms.

An AP endonuclease 1-DNA polymerase beta complex: theoretical prediction of interacting surfaces.

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Alexej Abyzov

2008-04-01

Full Text Available Abasic (AP sites in DNA arise through both endogenous and exogenous mechanisms. Since AP sites can prevent replication and transcription, the cell contains systems for their identification and repair. AP endonuclease (APEX1 cleaves the phosphodiester backbone 5' to the AP site. The cleavage, a key step in the base excision repair pathway, is followed by nucleotide insertion and removal of the downstream deoxyribose moiety, performed most often by DNA polymerase beta (pol-beta. While yeast two-hybrid studies and electrophoretic mobility shift assays provide evidence for interaction of APEX1 and pol-beta, the specifics remain obscure. We describe a theoretical study designed to predict detailed interacting surfaces between APEX1 and pol-beta based on published co-crystal structures of each enzyme bound to DNA. Several potentially interacting complexes were identified by sliding the protein molecules along DNA: two with pol-beta located downstream of APEX1 (3' to the damaged site and three with pol-beta located upstream of APEX1 (5' to the damaged site. Molecular dynamics (MD simulations, ensuring geometrical complementarity of interfaces, enabled us to predict interacting residues and calculate binding energies, which in two cases were sufficient (approximately -10.0 kcal/mol to form a stable complex and in one case a weakly interacting complex. Analysis of interface behavior during MD simulation and visual inspection of interfaces allowed us to conclude that complexes with pol-beta at the 3'-side of APEX1 are those most likely to occur in vivo. Additional multiple sequence analyses of APEX1 and pol-beta in related organisms identified a set of correlated mutations of specific residues at the predicted interfaces. Based on these results, we propose that pol-beta in the open or closed conformation interacts and makes a stable interface with APEX1 bound to a cleaved abasic site on the 3' side. The method described here can be used for analysis in

The Ability–Motivation–Opportunity Framework for Team Innovation: Efficacy Beliefs, Proactive Personalities, Supportive Supervision and Team Innovation

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Jana Krapež Trošt

2016-01-01

Full Text Available Based on ability–motivation–opportunity theoretical framework, the study explores the interplay among team members’ proactive personalities (abilities, collective efficacy (motivation, and supportive supervision (opportunity, and their interaction in predicting team innovation. Multi-level study of 249 employees nested within 64 teams from one German and three Slovenian hi-tech companies showed that collective efficacy was positively related to team innovation. However, the effect of collective efficacy on team innovation was weaker when high levels of supportive supervision and proactivity moderated this relationship. When teams perceived lower levels of collective efficacy, team proactivity, and supportive supervision were more important for achieving higher levels of team innovation as they were when teams perceived lower levels of motivation. We discuss theoretical and practical implications

The need for international nursing diagnosis research and a theoretical framework.

Science.gov (United States)

Lunney, Margaret

2008-01-01

To describe the need for nursing diagnosis research and a theoretical framework for such research. A linguistics theory served as the foundation for the theoretical framework. Reasons for additional nursing diagnosis research are: (a) file names are needed for implementation of electronic health records, (b) international consensus is needed for an international classification, and (c) continuous changes occur in clinical practice. A theoretical framework used by the author is explained. Theoretical frameworks provide support for nursing diagnosis research. Linguistics theory served as an appropriate exemplar theory to support nursing research. Additional nursing diagnosis studies based upon a theoretical framework are needed and linguistics theory can provide an appropriate structure for this research.

Prediction of hourly PM2.5 using a space-time support vector regression model

Science.gov (United States)

Yang, Wentao; Deng, Min; Xu, Feng; Wang, Hang

2018-05-01

Real-time air quality prediction has been an active field of research in atmospheric environmental science. The existing methods of machine learning are widely used to predict pollutant concentrations because of their enhanced ability to handle complex non-linear relationships. However, because pollutant concentration data, as typical geospatial data, also exhibit spatial heterogeneity and spatial dependence, they may violate the assumptions of independent and identically distributed random variables in most of the machine learning methods. As a result, a space-time support vector regression model is proposed to predict hourly PM2.5 concentrations. First, to address spatial heterogeneity, spatial clustering is executed to divide the study area into several homogeneous or quasi-homogeneous subareas. To handle spatial dependence, a Gauss vector weight function is then developed to determine spatial autocorrelation variables as part of the input features. Finally, a local support vector regression model with spatial autocorrelation variables is established for each subarea. Experimental data on PM2.5 concentrations in Beijing are used to verify whether the results of the proposed model are superior to those of other methods.

Enthalpy/entropy contributions to conformational KIEs: theoretical predictions and comparison with experiment.

Science.gov (United States)

Fong, Aaron; Meyer, Matthew P; O'Leary, Daniel J

2013-02-18

Previous theoretical studies of Mislow's doubly-bridged biphenyl ketone 1 and dihydrodimethylphenanthrene 2 have determined significant entropic contributions to their normal (1) and inverse (2) conformational kinetic isotope effects (CKIEs). To broaden our investigation, we have used density functional methods to characterize the potential energy surfaces and vibrational frequencies for ground and transition structures of additional systems with measured CKIEs, including [2.2]-metaparacyclophane-d (3), 1,1'-binaphthyl (4), 2,2'-dibromo-[1,1'-biphenyl]-4,4'-dicarboxylic acid (5), and the 2-(N,N,N-trimethyl)-2'-(N,N-dimethyl)-diaminobiphenyl cation (6). We have also computed CKIEs in a number of systems whose experimental CKIEs are unknown. These include analogs of 1 in which the C=O groups have been replaced with CH₂ (7), O (8), and S (9) atoms and ring-expanded variants of 2 containing CH₂ (10), O (11), S (12), or C=O (13) groups. Vibrational entropy contributes to the CKIEs in all of these systems with the exception of cyclophane 3, whose isotope effect is predicted to be purely enthalpic in origin and whose Bigeleisen-Mayer ZPE term is equivalent to DDH‡. There is variable correspondence between these terms in the other molecules studied, thus identifying additional examples of systems in which the Bigeleisen-Mayer formalism does not correlate with DH/DS dissections.

Enthalpy/Entropy Contributions to Conformational KIEs: Theoretical Predictions and Comparison with Experiment

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Aaron Fong

2013-02-01

Full Text Available Previous theoretical studies of Mislow’s doubly-bridged biphenyl ketone 1 and dihydrodimethylphenanthrene 2 have determined significant entropic contributions to their normal (1 and inverse (2 conformational kinetic isotope effects (CKIEs. To broaden our investigation, we have used density functional methods to characterize the potential energy surfaces and vibrational frequencies for ground and transition structures of additional systems with measured CKIEs, including [2.2]-metaparacyclophane-d (3, 1,1'-binaphthyl (4, 2,2'-dibromo-[1,1'-biphenyl]-4,4'-dicarboxylic acid (5, and the 2-(N,N,N-trimethyl-2'-(N,N-dimethyl-diaminobiphenyl cation (6. We have also computed CKIEs in a number of systems whose experimental CKIEs are unknown. These include analogs of 1 in which the C=O groups have been replaced with CH2 (7, O (8, and S (9 atoms and ring-expanded variants of 2 containing CH2 (10, O (11, S (12, or C=O (13 groups. Vibrational entropy contributes to the CKIEs in all of these systems with the exception of cyclophane 3, whose isotope effect is predicted to be purely enthalpic in origin and whose Bigeleisen-Mayer ZPE term is equivalent to ΔΔ H‡. There is variable correspondence between these terms in the other molecules studied, thus identifying additional examples of systems in which the Bigeleisen-Mayer formalism does not correlate with ΔH/ΔS dissections.

Preliminary Findings in the Development of a Theoretical Framework for Investigating ICT Integration in Teacher Education

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Suthagar Narasuman

2012-06-01

Full Text Available The following report is the result of a preliminary investigation in the development of a theoretical framework for investigating ICT integration, particularly in TESL (Teaching of English as a Second Language teacher training. The study is primarily an empirical effort to develop a theoretical framework for investigating ICT integration in TESL teacher training. In identifying the predictive variables for the framework, the researchers conducted an intensive review of the literature which included a review of various models used in studies on ICT integration. The contributing variables identified in the present study were age, gender, experience, ICT proficiency, attitude, access to ICT infrastructure, support services, and exposure to ICT professional development programmes. In developing the framework, the study sought to determine the extent to which the observed variability in ICT integration could be predicted by these factors. The sample comprised 266 respondents working at the faculty or English Language Unit in various teacher training institutions across the country. The study predominantly employed quantitative methods of data collection. Interview data was used to corroborate information derived from the survey data.

A Bayesian least-squares support vector machine method for predicting the remaining useful life of a microwave component

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Fuqiang Sun

2017-01-01

Full Text Available Rapid and accurate lifetime prediction of critical components in a system is important to maintaining the system’s reliable operation. To this end, many lifetime prediction methods have been developed to handle various failure-related data collected in different situations. Among these methods, machine learning and Bayesian updating are the most popular ones. In this article, a Bayesian least-squares support vector machine method that combines least-squares support vector machine with Bayesian inference is developed for predicting the remaining useful life of a microwave component. A degradation model describing the change in the component’s power gain over time is developed, and the point and interval remaining useful life estimates are obtained considering a predefined failure threshold. In our case study, the radial basis function neural network approach is also implemented for comparison purposes. The results indicate that the Bayesian least-squares support vector machine method is more precise and stable in predicting the remaining useful life of this type of components.

Theoretical models of DNA flexibility

Czech Academy of Sciences Publication Activity Database

Dršata, Tomáš; Lankaš, Filip

2013-01-01

Roč. 3, č. 4 (2013), s. 355-363 ISSN 1759-0876 Institutional support: RVO:61388963 Keywords : molecular dynamics simulations * base pair level * indirect readout Subject RIV: CF - Physical ; Theoretical Chemistry Impact factor: 9.041, year: 2013

Support for the Logical Execution Time Model on a Time-predictable Multicore Processor

DEFF Research Database (Denmark)

Kluge, Florian; Schoeberl, Martin; Ungerer, Theo

2016-01-01

The logical execution time (LET) model increases the compositionality of real-time task sets. Removal or addition of tasks does not influence the communication behavior of other tasks. In this work, we extend a multicore operating system running on a time-predictable multicore processor to support...... the LET model. For communication between tasks we use message passing on a time-predictable network-on-chip to avoid the bottleneck of shared memory. We report our experiences and present results on the costs in terms of memory and execution time....

Prediction of toxicity of nitrobenzenes using ab initio and least squares support vector machines

International Nuclear Information System (INIS)

Niazi, Ali; Jameh-Bozorghi, Saeed; Nori-Shargh, Davood

2008-01-01

A quantitative structure-property relationship (QSPR) study is suggested for the prediction of toxicity (IGC 50 ) of nitrobenzenes. Ab initio theory was used to calculate some quantum chemical descriptors including electrostatic potentials and local charges at each atom, HOMO and LUMO energies, etc. Modeling of the IGC 50 of nitrobenzenes as a function of molecular structures was established by means of the least squares support vector machines (LS-SVM). This model was applied for the prediction of the toxicity (IGC 50 ) of nitrobenzenes, which were not in the modeling procedure. The resulted model showed high prediction ability with root mean square error of prediction of 0.0049 for LS-SVM. Results have shown that the introduction of LS-SVM for quantum chemical descriptors drastically enhances the ability of prediction in QSAR studies superior to multiple linear regression and partial least squares

Theoretical perspective for baryon number violation

International Nuclear Information System (INIS)

Langacker, P.

1982-01-01

In this talk I describe the theoretical predictions for proton decay and other baryon number violating processes, emphasizing that there are many models and theories involving baryon number violation and that it is an experimental problem to distinguish between them. I first review the the theoretical predictions for the unification mass M/sub X/ and for the weak angle sin 2 theta/sub W/. It will be seen that the class of models involving an Su 3 x SU 2 x U 1 invariant desert between M/sub W/ and M/sub X/ are strongly favored. I then turn to baryon number violation. The proton lifetime and branching ratio predictions for the SU 5 and other 3-2-1 desert models are reviewed, with emphasis on distinguishing between models and on the implications of the small value of the QCD parameter lambda/sub anti MS/ that seems to be favored by the data. I then discuss the consequences of low energy supersymmetry for proton decay, nuclear effects, and models with low mass scales. Finally, I mention possible implications of the anomalously large flux of cosmic ray antiprotons that has recently been reported

The Role of Family Expressed Emotion and Perceived Social Support in Predicting Addiction Relapse

OpenAIRE

Atadokht, Akbar; Hajloo, Nader; Karimi, Masoud; Narimani, Mohammad

2015-01-01

Background: Emotional conditions governing the family and patients? perceived social support play important roles in the treatment or relapse process of the chronic disease. Objectives: The current study aimed to investigate the role of family expressed emotion and perceived social support in prediction of addiction relapse. Patients and Methods: The descriptive-correlation method was used in the current study. The study population consisted of the individuals referred to the addiction treatm...

OrderRex: clinical order decision support and outcome predictions by data-mining electronic medical records.

Science.gov (United States)

Chen, Jonathan H; Podchiyska, Tanya; Altman, Russ B

2016-03-01

To answer a "grand challenge" in clinical decision support, the authors produced a recommender system that automatically data-mines inpatient decision support from electronic medical records (EMR), analogous to Netflix or Amazon.com's product recommender. EMR data were extracted from 1 year of hospitalizations (>18K patients with >5.4M structured items including clinical orders, lab results, and diagnosis codes). Association statistics were counted for the ∼1.5K most common items to drive an order recommender. The authors assessed the recommender's ability to predict hospital admission orders and outcomes based on initial encounter data from separate validation patients. Compared to a reference benchmark of using the overall most common orders, the recommender using temporal relationships improves precision at 10 recommendations from 33% to 38% (P < 10(-10)) for hospital admission orders. Relative risk-based association methods improve inverse frequency weighted recall from 4% to 16% (P < 10(-16)). The framework yields a prediction receiver operating characteristic area under curve (c-statistic) of 0.84 for 30 day mortality, 0.84 for 1 week need for ICU life support, 0.80 for 1 week hospital discharge, and 0.68 for 30-day readmission. Recommender results quantitatively improve on reference benchmarks and qualitatively appear clinically reasonable. The method assumes that aggregate decision making converges appropriately, but ongoing evaluation is necessary to discern common behaviors from "correct" ones. Collaborative filtering recommender algorithms generate clinical decision support that is predictive of real practice patterns and clinical outcomes. Incorporating temporal relationships improves accuracy. Different evaluation metrics satisfy different goals (predicting likely events vs. "interesting" suggestions). Published by Oxford University Press on behalf of the American Medical Informatics Association 2015. This work is written by US Government

Social support, self-care, and quality of life in cancer patients receiving radiotherapy in Thailand

International Nuclear Information System (INIS)

Hanucharurnkul, S.

1988-01-01

The purpose of the study was two-fold: (1) to examine the relationships among self-care, social support, and quality of life in adult cancer patients receiving radiotherapy while the selected basic conditioning factors of age, marital and socio-economic status, living arrangement, stage and site of cancer were statistically controlled; and (2) to test a theoretical model which postulated that (a) quality of life was predicted jointly by the selected basic conditioning factors, social support and self-care, and (b) self-care was predicted jointly by the selected basic conditioning factors and social support. A convenience sample of 112 adult cervical and head/neck cancer patients receiving radiotherapy was obtained from radiotherapy outpatient clinic in three hospitals located in Bangkok, Thailand. Results of the study indicated positive relationships among self-care, social support, and quality of life. Socio-economic status, site of cancer, and self-care were significant predictors for reported quality of life. Social support appeared to be a significant predictor of quality of life indirectly through self-care. Socio-economic status and social support were also significant predictors of self-care, whereas, stage and site of cancer seemed to predict self-care indirectly through social support

Breaking the theoretical scaling limit for predicting quasiparticle energies: the stochastic GW approach.

Science.gov (United States)

Neuhauser, Daniel; Gao, Yi; Arntsen, Christopher; Karshenas, Cyrus; Rabani, Eran; Baer, Roi

2014-08-15

We develop a formalism to calculate the quasiparticle energy within the GW many-body perturbation correction to the density functional theory. The occupied and virtual orbitals of the Kohn-Sham Hamiltonian are replaced by stochastic orbitals used to evaluate the Green function G, the polarization potential W, and, thereby, the GW self-energy. The stochastic GW (sGW) formalism relies on novel theoretical concepts such as stochastic time-dependent Hartree propagation, stochastic matrix compression, and spatial or temporal stochastic decoupling techniques. Beyond the theoretical interest, the formalism enables linear scaling GW calculations breaking the theoretical scaling limit for GW as well as circumventing the need for energy cutoff approximations. We illustrate the method for silicon nanocrystals of varying sizes with N_{e}>3000 electrons.

Reservoir Inflow Prediction under GCM Scenario Downscaled by Wavelet Transform and Support Vector Machine Hybrid Models

Directory of Open Access Journals (Sweden)

Gusfan Halik

2015-01-01

Full Text Available Climate change has significant impacts on changing precipitation patterns causing the variation of the reservoir inflow. Nowadays, Indonesian hydrologist performs reservoir inflow prediction according to the technical guideline of Pd-T-25-2004-A. This technical guideline does not consider the climate variables directly, resulting in significant deviation to the observation results. This research intends to predict the reservoir inflow using the statistical downscaling (SD of General Circulation Model (GCM outputs. The GCM outputs are obtained from the National Center for Environmental Prediction/National Center for Atmospheric Research Reanalysis (NCEP/NCAR Reanalysis. A new proposed hybrid SD model named Wavelet Support Vector Machine (WSVM was utilized. It is a combination of the Multiscale Principal Components Analysis (MSPCA and nonlinear Support Vector Machine regression. The model was validated at Sutami Reservoir, Indonesia. Training and testing were carried out using data of 1991–2008 and 2008–2012, respectively. The results showed that MSPCA produced better extracting data than PCA. The WSVM generated better reservoir inflow prediction than the one of technical guideline. Moreover, this research also applied WSVM for future reservoir inflow prediction based on GCM ECHAM5 and scenario SRES A1B.

Theoretical foundations for collaboration engineering

NARCIS (Netherlands)

Kolfschoten, G.L.

2007-01-01

Collaboration is often presented as the solution to numerous problems in business and society. However, collaboration is challenging, and collaboration support is not an off-the-shelf-product. This research offers theoretical foundations for Collaboration Engineering. Collaboration Engineering is an

Perceived parenting and social support: can they predict academic achievement in Argentinean college students?

Directory of Open Access Journals (Sweden)

de la Iglesia G

2014-09-01

Full Text Available Guadalupe de la Iglesia,1,2 Agustin Freiberg Hoffmann,2 Mercedes Fernández Liporace1,2 1National Council of Scientific and Technical Research (CONICET, 2University of Buenos Aires, Buenos Aires, Argentina Abstract: The aim of this study was to test the ability to predict academic achievement through the perception of parenting and social support in a sample of 354 Argentinean college students. Their mean age was 23.50 years (standard deviation =2.62 years and most of them (83.3% were females. As a prerequisite for admission to college, students are required to pass a series of mandatory core classes and are expected to complete them in two semesters. Delay in completing the curriculum is considered low academic achievement. Parenting was assessed taking into account the mother and the father and considering two dimensions: responsiveness and demandingness. Perceived social support was analyzed considering four sources: parents, teachers, classmates, and best friend or boyfriend/girlfriend. Path analysis showed that, as hypothesized, responsiveness had a positive indirect effect on the perception of social support and enhanced achievement. Demandingness had a different effect in the case of the mother as compared to the father. In the mother model, demandingness had a positive direct effect on achievement. In the case of the father, however, the effect of demandingness had a negative and indirect impact on the perception of social support. Teachers were the only source of perceived social support that significantly predicted achievement. The pathway that belongs to teachers as a source of support was positive and direct. Implications for possible interventions are discussed. Keywords: academic achievement, parenting, social support, college

Computing confidence and prediction intervals of industrial equipment degradation by bootstrapped support vector regression

International Nuclear Information System (INIS)

Lins, Isis Didier; Droguett, Enrique López; Moura, Márcio das Chagas; Zio, Enrico; Jacinto, Carlos Magno

2015-01-01

Data-driven learning methods for predicting the evolution of the degradation processes affecting equipment are becoming increasingly attractive in reliability and prognostics applications. Among these, we consider here Support Vector Regression (SVR), which has provided promising results in various applications. Nevertheless, the predictions provided by SVR are point estimates whereas in order to take better informed decisions, an uncertainty assessment should be also carried out. For this, we apply bootstrap to SVR so as to obtain confidence and prediction intervals, without having to make any assumption about probability distributions and with good performance even when only a small data set is available. The bootstrapped SVR is first verified on Monte Carlo experiments and then is applied to a real case study concerning the prediction of degradation of a component from the offshore oil industry. The results obtained indicate that the bootstrapped SVR is a promising tool for providing reliable point and interval estimates, which can inform maintenance-related decisions on degrading components. - Highlights: • Bootstrap (pairs/residuals) and SVR are used as an uncertainty analysis framework. • Numerical experiments are performed to assess accuracy and coverage properties. • More bootstrap replications does not significantly improve performance. • Degradation of equipment of offshore oil wells is estimated by bootstrapped SVR. • Estimates about the scale growth rate can support maintenance-related decisions

Theoretical performance of cross-wind axis turbines with results for a catenary vertical axis configuration

Science.gov (United States)

Muraca, R. J.; Stephens, M. V.; Dagenhart, J. R.

1975-01-01

A general analysis capable of predicting performance characteristics of cross-wind axis turbines was developed, including the effects of airfoil geometry, support struts, blade aspect ratio, windmill solidity, blade interference and curved flow. The results were compared with available wind tunnel results for a catenary blade shape. A theoretical performance curve for an aerodynamically efficient straight blade configuration was also presented. In addition, a linearized analytical solution applicable for straight configurations was developed. A listing of the computer program developed for numerical solutions of the general performance equations is included in the appendix.

Prediction of protein binding sites using physical and chemical descriptors and the support vector machine regression method

International Nuclear Information System (INIS)

Sun Zhong-Hua; Jiang Fan

2010-01-01

In this paper a new continuous variable called core-ratio is defined to describe the probability for a residue to be in a binding site, thereby replacing the previous binary description of the interface residue using 0 and 1. So we can use the support vector machine regression method to fit the core-ratio value and predict the protein binding sites. We also design a new group of physical and chemical descriptors to characterize the binding sites. The new descriptors are more effective, with an averaging procedure used. Our test shows that much better prediction results can be obtained by the support vector regression (SVR) method than by the support vector classification method. (rapid communication)

Bearing Degradation Process Prediction Based on the Support Vector Machine and Markov Model

Directory of Open Access Journals (Sweden)

Shaojiang Dong

2014-01-01

Full Text Available Predicting the degradation process of bearings before they reach the failure threshold is extremely important in industry. This paper proposed a novel method based on the support vector machine (SVM and the Markov model to achieve this goal. Firstly, the features are extracted by time and time-frequency domain methods. However, the extracted original features are still with high dimensional and include superfluous information, and the nonlinear multifeatures fusion technique LTSA is used to merge the features and reduces the dimension. Then, based on the extracted features, the SVM model is used to predict the bearings degradation process, and the CAO method is used to determine the embedding dimension of the SVM model. After the bearing degradation process is predicted by SVM model, the Markov model is used to improve the prediction accuracy. The proposed method was validated by two bearing run-to-failure experiments, and the results proved the effectiveness of the methodology.

Predicting suicide ideation through intrapersonal and interpersonal factors: The interplay of Big-Five personality traits and social support.

Science.gov (United States)

Ayub, Nailah

2015-11-01

While a specific personality trait may escalate suicide ideation, contextual factors such as social support, when provided effectively, may alleviate the effects of such personality traits. This study examined the moderating role of social support in the relationship between the Big-Five personality traits and suicide ideation. Significant interactions were found between social support and extraversion and emotional stability. Specifically, the relationship between emotional stability and extraversion to suicide ideation was exacerbated when social support was low. Slope analysis showed openness also interacted with low social support. Results were computed for frequency, duration and attitude dimensions of suicide ideation. Extraversion interacted with social support to predict all three dimensions. Social support moderated emotional stability to predict frequency and duration, moderated conscientiousness towards frequency and attitude, and moderated openness towards attitude. The results imply that whereas personality traits may be difficult to alter, social support may play a significant role in saving a life. Psychologists should include family and friends when treating a suicidal youth, guiding them to awareness of one's personality and being more supportive. Copyright © 2015 John Wiley & Sons, Ltd.

Developing a theoretical predictive model for cellular response to combined actions of low radiation and hyperthermia

International Nuclear Information System (INIS)

Jin Kyu Kim; Petin, V.G.; Mishra, K.P.

2007-01-01

Complete text of publication follows. Background: Organisms in their living environment are not exposed to merely a single stress agent. Several factors such as radiation and heat may simultaneously exert their stressful effect to the organisms. The combined exposure to two stressors can result in an enhanced effect that would be expected from the addition of the separate exposures to individual agents. Objective: This study has been undertaken to develop a theoretical model for assessment of combined effects of low dose radiation and mild heat for predictive cellular response assay. Rationale: Present study was motivated from the belief that synergism may occur in terms of lethal lesions arising from the interaction of non-lethal sub-lesions induced by individual agents. The sub-lesions induced by each agent may be negligible or undetectable. But, there exists a possibility of some cross talk between sublesions produced by radiation and heat. These processes may reflect the real mechanisms for inflicting the lethal damage by otherwise ignorable or undetectable insults to exposed organisms. Results: A theoretically developed mathematical model of the synergy was formulated which was tested for validation on the experimental data. The model predictions fairly closely corresponded with several experimental results. .The significance of synergistic effects for radiation biology has been demonstrated. A number of common peculiarities of synergistic interactions were found to play their roles. A unified biophysical concept for synergistic interaction has been suggested. Conclusions: For a constant dose rate, synergistic interaction between radiation and hyperthermia especially at low intensity is realized only within a certain range of temperature, independently of the target object analyzed. For temperatures below the range, the synergistic effect was not observed and cell killing was mainly determined by the damage induced by ionizing radiation. On the contrary, the

Predicting supportive behavior of parents and siblings to a family member with intellectual disability living in institutional care.

Science.gov (United States)

Rimmerman, Arie; Chen, Ariel

2012-01-01

This feasibility study examines whether the theory of planned behavior can predict supportive behavior provided by either parents to their offspring--or adult siblings to their brothers and sisters--with an intellectual disability living in 2 Israeli institutional care facilities. Participants were 67 parents and 63 siblings who were interviewed at baseline regarding their intentions to visit their offspring or sibling in the institutional care facility, to contact the caregiving staff, and to accept visits at home. Parents' and siblings' behavior regarding visitation and supportive behavior was examined after 6 months by caregiving staff. Core findings indicated that subjective norms in siblings and parents predicted frequency of home visits. Perceived behavioral control predicted frequency of contact between siblings and staff. Differences between parents and siblings regarding their supportive behaviors are discussed with respect to social work practice.

Theoretical Prediction of the Forming Limit Band

International Nuclear Information System (INIS)

Banabic, D.; Paraianu, L.; Vos, M.; Jurco, P.

2007-01-01

Forming Limit Band (FLB) is a very useful tool to improve the sheet metal forming simulation robustness. Until now, the study of the FLB was only experimental. This paper presents the first attempt to model the FLB. The authors have established an original method for predicting the two margins of the limit band. The method was illustrated on the AA6111-T43 aluminum alloy. A good agreement with the experiments has been obtained

Theoretical Prediction of the Forming Limit Band

Science.gov (United States)

Banabic, D.; Vos, M.; Paraianu, L.; Jurco, P.

2007-04-01

Forming Limit Band (FLB) is a very useful tool to improve the sheet metal forming simulation robustness. Until now, the study of the FLB was only experimental. This paper presents the first attempt to model the FLB. The authors have established an original method for predicting the two margins of the limit band. The method was illustrated on the AA6111-T43 aluminum alloy. A good agreement with the experiments has been obtained.

PREDICTIVE MODELS FOR SUPPORT OF INCIDENT MANAGEMENT PROCESS IN IT SERVICE MANAGEMENT

Directory of Open Access Journals (Sweden)

Martin SARNOVSKY

2018-03-01

Full Text Available ABSTRACT The work presented in this paper is focused on creating of predictive models that help in the process of incident resolution and implementation of IT infrastructure changes to increase the overall support of IT management. Our main objective was to build the predictive models using machine learning algorithms and CRISP-DM methodology. We used the incident and related changes database obtained from the IT environment of the Rabobank Group company, which contained information about the processing of the incidents during the incident management process. We decided to investigate the dependencies between the incident observation on particular infrastructure component and the actual source of the incident as well as the dependency between the incidents and related changes in the infrastructure. We used Random Forests and Gradient Boosting Machine classifiers in the process of identification of incident source as well as in the prediction of possible impact of the observed incident. Both types of models were tested on testing set and evaluated using defined metrics.

Support vector machines for prediction and analysis of beta and gamma-turns in proteins.

Science.gov (United States)

Pham, Tho Hoan; Satou, Kenji; Ho, Tu Bao

2005-04-01

Tight turns have long been recognized as one of the three important features of proteins, together with alpha-helix and beta-sheet. Tight turns play an important role in globular proteins from both the structural and functional points of view. More than 90% tight turns are beta-turns and most of the rest are gamma-turns. Analysis and prediction of beta-turns and gamma-turns is very useful for design of new molecules such as drugs, pesticides, and antigens. In this paper we investigated two aspects of applying support vector machine (SVM), a promising machine learning method for bioinformatics, to prediction and analysis of beta-turns and gamma-turns. First, we developed two SVM-based methods, called BTSVM and GTSVM, which predict beta-turns and gamma-turns in a protein from its sequence. When compared with other methods, BTSVM has a superior performance and GTSVM is competitive. Second, we used SVMs with a linear kernel to estimate the support of amino acids for the formation of beta-turns and gamma-turns depending on their position in a protein. Our analysis results are more comprehensive and easier to use than the previous results in designing turns in proteins.

Research in theoretical particle physics. Technical summary of research supported under grant DE-FG02-85ER40214 from 5/1/86 to 4/30/98

International Nuclear Information System (INIS)

McKay, Douglas W.; Ralston, John P.

1999-01-01

This research proposes new theoretical models and experimental tests for new phenomena that range from realms deep within the atomic nucleus to those spanning the universe. Further study of data from distant galaxies clarifies the evidence published by JPR that there may be preferred ''axis'' to the Universe. Also at astronomical scales is the work to discover evidence of ultra high energy neutrinos that are predicted to be streaming line of sight from the collapsing cores of distant galaxies. At the subnuclear level the authors have proposed theoretical motivation and experimental texts for new forms of cooperative effects and new forms of that matter itself

Theoretical Support of Heat Exchanger Experiments of the EU-CONGA Project

International Nuclear Information System (INIS)

Herranz, L. E.; Lopez Jimenez, J.; Munoz-Cobo, J. L.; Palomo, M. J.

1999-01-01

In this report the work carried out within the Work Package 5 of the CONGA project under the auspices of the European Union has been presented. Primarily focused on studying from a theoretical perspective the degradation of heat exchangers to be used in next generation of European reactor containments under accident conditions, and particularly the effect of aerosols, the objective has been met quite satisfactorily and the results can be summed up in three specific items: - A mathematical model of a mechanistic nature that has been encapsulated into a FORTRAN code (HTCFOUL) capable of simulating condensation heat transfer to a horizontal finned tube internally cooled. - A theoretical correlation depending upon non-dimensional variables and numbers which embodies most of the HTCFOUL physics and gives results not beyond 20% of actual HTCFOUL estimates. - A reasonable interpretation of the major measurements and observations obtained in the heat exchanger experiments performed within the Work Package 2 of the CONGA project. (Author) 55 refs

A dynamic particle filter-support vector regression method for reliability prediction

International Nuclear Information System (INIS)

Wei, Zhao; Tao, Tao; ZhuoShu, Ding; Zio, Enrico

2013-01-01

Support vector regression (SVR) has been applied to time series prediction and some works have demonstrated the feasibility of its use to forecast system reliability. For accuracy of reliability forecasting, the selection of SVR's parameters is important. The existing research works on SVR's parameters selection divide the example dataset into training and test subsets, and tune the parameters on the training data. However, these fixed parameters can lead to poor prediction capabilities if the data of the test subset differ significantly from those of training. Differently, the novel method proposed in this paper uses particle filtering to estimate the SVR model parameters according to the whole measurement sequence up to the last observation instance. By treating the SVR training model as the observation equation of a particle filter, our method allows updating the SVR model parameters dynamically when a new observation comes. Because of the adaptability of the parameters to dynamic data pattern, the new PF–SVR method has superior prediction performance over that of standard SVR. Four application results show that PF–SVR is more robust than SVR to the decrease of the number of training data and the change of initial SVR parameter values. Also, even if there are trends in the test data different from those in the training data, the method can capture the changes, correct the SVR parameters and obtain good predictions. -- Highlights: •A dynamic PF–SVR method is proposed to predict the system reliability. •The method can adjust the SVR parameters according to the change of data. •The method is robust to the size of training data and initial parameter values. •Some cases based on both artificial and real data are studied. •PF–SVR shows superior prediction performance over standard SVR

Information-Theoretic Properties of Auditory Sequences Dynamically Influence Expectation and Memory.

Science.gov (United States)

Agres, Kat; Abdallah, Samer; Pearce, Marcus

2018-01-01

A basic function of cognition is to detect regularities in sensory input to facilitate the prediction and recognition of future events. It has been proposed that these implicit expectations arise from an internal predictive coding model, based on knowledge acquired through processes such as statistical learning, but it is unclear how different types of statistical information affect listeners' memory for auditory stimuli. We used a combination of behavioral and computational methods to investigate memory for non-linguistic auditory sequences. Participants repeatedly heard tone sequences varying systematically in their information-theoretic properties. Expectedness ratings of tones were collected during three listening sessions, and a recognition memory test was given after each session. Information-theoretic measures of sequential predictability significantly influenced listeners' expectedness ratings, and variations in these properties had a significant impact on memory performance. Predictable sequences yielded increasingly better memory performance with increasing exposure. Computational simulations using a probabilistic model of auditory expectation suggest that listeners dynamically formed a new, and increasingly accurate, implicit cognitive model of the information-theoretic structure of the sequences throughout the experimental session. Copyright © 2017 Cognitive Science Society, Inc.

Decisions at hand: a decision support system on handhelds.

Science.gov (United States)

Zupan, B; Porenta, A; Vidmar, G; Aoki, N; Bratko, I; Beck, J R

2001-01-01

One of the applications of clinical information systems is decision support. Although the advantages of utilizing such aids have never been theoretically disputed, they have been rarely used in practice. The factor that probably often limits the utility of clinical decision support systems is the need for computing power at the very site of decision making--at the place where the patient is interviewed, in discussion rooms, etc. The paper reports on a possible solution to this problem. A decision-support shell LogReg is presented, which runs on a handheld computer. A general schema for handheld-based decision support is also proposed, where decision models are developed on personal computers/workstations, encoded in XML and then transferred to handhelds, where the models are used within a decision support shell. A use case where LogReg has been applied to clinical outcome prediction in crush injury is presented.

Theoretical integration and the psychology of sport injury prevention.

Science.gov (United States)

Chan, Derwin King-Chung; Hagger, Martin S

2012-09-01

Integrating different theories of motivation to facilitate or predict behaviour change has received an increasing amount of attention within the health, sport and exercise science literature. A recent review article in Sports Medicine, by Keats, Emery and Finch presented an integrated model using two prominent theories in social psychology, self-determination theory (SDT) and the theory of planned behaviour (TPB), aimed at explaining and enhancing athletes' adherence to sport injury prevention. While echoing their optimistic views about the utility of these two theories to explain adherence in this area and the virtues of theoretical integration, we would like to seize this opportunity to clarify several conceptual principles arising from the authors' integration of the theories. Clarifying the theoretical assumptions and explaining precisely how theoretical integration works is crucial not only for improving the comprehensiveness of the integrated framework for predicting injury prevention behaviour, but also to aid the design of effective intervention strategies targeting behavioural adherence. In this article, we use the integration of SDT and TPB as an example to demonstrate how theoretical integration can advance the understanding of injury prevention behaviour in sport.

Prediction of flare activity of stellar aggregates. I. Theoretical part

International Nuclear Information System (INIS)

Mnatsakanyan, M.A.; Mirzoyan, A.L.

1989-01-01

The problem is posed of predicting the number n k (t) of flare stars that have exhibited precisely k flares by the time t on the basis of data on these quantities known during the total time T of observations of the aggregate. The problem posed by Ambartsumyan of determining the distribution function f(ν) of the true frequency of stellar flares from known chronology of these data is equivalent to the limiting form of their formulation - prediction in the future over an infinitely long time. An exact analytic solution of the problem obtained without any assumption about the function f(ν) is given. It permits prediction of the steady flare activity of the aggregate into both the future and the (known) past. It follows from this solution that prediction into the future is in principle impossible to times that exceed the doubled time 2T of the available observations (this means that the problem of determining of the function f(ν) cannot be solved). Moreover, because of the unavoidable fluctuations in the observational data n k (T), such prediction is limited to even shorter times, and these are shorter the larger the value of k. Prediction into the past and into the future on the basis of the data n k (T) at the present time and its possible errors due to small fluctuations in these data are illustrated for the examples of the Pleiades and the Orion aggregate

Experimental and theoretical investigation of high gradient acceleration

International Nuclear Information System (INIS)

Wurtele, J.S.; Bekefi, G.; Chen, C.; Chen, S.C.; Temkin, R.J.

1993-01-01

This report contains a technical progress summary of the research conducted under the auspices of DOE Grant No. DE-AC02-91-ER40648, ''Experimental and Theoretical Investigations of High Gradient Acceleration''. This grant supports three research tasks: Task A consists of the design, fabrication and testing of a 17GHz RF photocathode gun, which can produce 2ps electron pulses with up to 1nC of charge at 2MeV energy and at a 1OHz repetition rate. Task B supports the testing of high gradient acceleration at 33GHz structure, and Task C comprises theoretical investigations, both in support of the experimental tasks and on critical physics issues for the development of high energy linear colliders

Neural Elements for Predictive Coding

Directory of Open Access Journals (Sweden)

Stewart SHIPP

2016-11-01

Full Text Available Predictive coding theories of sensory brain function interpret the hierarchical construction of the cerebral cortex as a Bayesian, generative model capable of predicting the sensory data consistent with any given percept. Predictions are fed backwards in the hierarchy and reciprocated by prediction error in the forward direction, acting to modify the representation of the outside world at increasing levels of abstraction, and so to optimize the nature of perception over a series of iterations. This accounts for many ‘illusory’ instances of perception where what is seen (heard, etc is unduly influenced by what is expected, based on past experience. This simple conception, the hierarchical exchange of prediction and prediction error, confronts a rich cortical microcircuitry that is yet to be fully documented. This article presents the view that, in the current state of theory and practice, it is profitable to begin a two-way exchange: that predictive coding theory can support an understanding of cortical microcircuit function, and prompt particular aspects of future investigation, whilst existing knowledge of microcircuitry can, in return, influence theoretical development. As an example, a neural inference arising from the earliest formulations of predictive coding is that the source populations of forwards and backwards pathways should be completely separate, given their functional distinction; this aspect of circuitry – that neurons with extrinsically bifurcating axons do not project in both directions – has only recently been confirmed. Here, the computational architecture prescribed by a generalized (free-energy formulation of predictive coding is combined with the classic ‘canonical microcircuit’ and the laminar architecture of hierarchical extrinsic connectivity to produce a template schematic, that is further examined in the light of (a updates in the microcircuitry of primate visual cortex, and (b rapid technical advances made

Neural Elements for Predictive Coding.

Science.gov (United States)

Shipp, Stewart

2016-01-01

Predictive coding theories of sensory brain function interpret the hierarchical construction of the cerebral cortex as a Bayesian, generative model capable of predicting the sensory data consistent with any given percept. Predictions are fed backward in the hierarchy and reciprocated by prediction error in the forward direction, acting to modify the representation of the outside world at increasing levels of abstraction, and so to optimize the nature of perception over a series of iterations. This accounts for many 'illusory' instances of perception where what is seen (heard, etc.) is unduly influenced by what is expected, based on past experience. This simple conception, the hierarchical exchange of prediction and prediction error, confronts a rich cortical microcircuitry that is yet to be fully documented. This article presents the view that, in the current state of theory and practice, it is profitable to begin a two-way exchange: that predictive coding theory can support an understanding of cortical microcircuit function, and prompt particular aspects of future investigation, whilst existing knowledge of microcircuitry can, in return, influence theoretical development. As an example, a neural inference arising from the earliest formulations of predictive coding is that the source populations of forward and backward pathways should be completely separate, given their functional distinction; this aspect of circuitry - that neurons with extrinsically bifurcating axons do not project in both directions - has only recently been confirmed. Here, the computational architecture prescribed by a generalized (free-energy) formulation of predictive coding is combined with the classic 'canonical microcircuit' and the laminar architecture of hierarchical extrinsic connectivity to produce a template schematic, that is further examined in the light of (a) updates in the microcircuitry of primate visual cortex, and (b) rapid technical advances made possible by transgenic neural

Freshwater Algal Bloom Prediction by Support Vector Machine in Macau Storage Reservoirs

Directory of Open Access Journals (Sweden)

Zhengchao Xie

2012-01-01

Full Text Available Understanding and predicting dynamic change of algae population in freshwater reservoirs is particularly important, as algae-releasing cyanotoxins are carcinogens that would affect the health of public. However, the high complex nonlinearity of water variables and their interactions makes it difficult to model the growth of algae species. Recently, support vector machine (SVM was reported to have advantages of only requiring a small amount of samples, high degree of prediction accuracy, and long prediction period to solve the nonlinear problems. In this study, the SVM-based prediction and forecast models for phytoplankton abundance in Macau Storage Reservoir (MSR are proposed, in which the water parameters of pH, SiO2, alkalinity, bicarbonate (HCO3 -, dissolved oxygen (DO, total nitrogen (TN, UV254, turbidity, conductivity, nitrate, total nitrogen (TN, orthophosphate (PO4 3−, total phosphorus (TP, suspended solid (SS and total organic carbon (TOC selected from the correlation analysis of the 23 monthly water variables were included, with 8-year (2001–2008 data for training and the most recent 3 years (2009–2011 for testing. The modeling results showed that the prediction and forecast powers were estimated as approximately 0.76 and 0.86, respectively, showing that the SVM is an effective new way that can be used for monitoring algal bloom in drinking water storage reservoir.

Predicting binary choices from probability phrase meanings.

Science.gov (United States)

Wallsten, Thomas S; Jang, Yoonhee

2008-08-01

The issues of how individuals decide which of two events is more likely and of how they understand probability phrases both involve judging relative likelihoods. In this study, we investigated whether derived scales representing probability phrase meanings could be used within a choice model to predict independently observed binary choices. If they can, this simultaneously provides support for our model and suggests that the phrase meanings are measured meaningfully. The model assumes that, when deciding which of two events is more likely, judges take a single sample from memory regarding each event and respond accordingly. The model predicts choice probabilities by using the scaled meanings of individually selected probability phrases as proxies for confidence distributions associated with sampling from memory. Predictions are sustained for 34 of 41 participants but, nevertheless, are biased slightly low. Sequential sampling models improve the fit. The results have both theoretical and applied implications.

Design of a Clinical Decision Support System for Fracture Prediction Using Imbalanced Dataset

Directory of Open Access Journals (Sweden)

Yung-Fu Chen

2018-01-01

Full Text Available More than 1 billion people suffer from chronic respiratory diseases worldwide, accounting for more than 4 million deaths annually. Inhaled corticosteroid is a popular medication for treating chronic respiratory diseases. Its side effects include decreased bone mineral density and osteoporosis. The aims of this study are to investigate the association of inhaled corticosteroids and fracture and to design a clinical support system for fracture prediction. The data of patients aged 20 years and older, who had visited healthcare centers and been prescribed with inhaled corticosteroids within 2002–2010, were retrieved from the National Health Insurance Research Database (NHIRD. After excluding patients diagnosed with hip fracture or vertebrate fractures before using inhaled corticosteroid, a total of 11645 patients receiving inhaled corticosteroid therapy were included for this study. Among them, 1134 (9.7% were diagnosed with hip fracture or vertebrate fracture. The statistical results showed that demographic information, chronic respiratory diseases and comorbidities, and corticosteroid-related variables (cumulative dose, mean exposed daily dose, follow-up duration, and exposed duration were significantly different between fracture and nonfracture patients. The clinical decision support systems (CDSSs were designed with integrated genetic algorithm (GA and support vector machine (SVM by training and validating the models with balanced training sets obtained by random and cluster-based undersampling methods and testing with the imbalanced NHIRD dataset. Two different objective functions were adopted for obtaining optimal models with best predictive performance. The predictive performance of the CDSSs exhibits a sensitivity of 69.84–77.00% and an AUC of 0.7495–0.7590. It was concluded that long-term use of inhaled corticosteroids may induce osteoporosis and exhibit higher incidence of hip or vertebrate fractures. The accumulated dose of ICS and

Supporting change processes in design: Complexity, prediction and reliability

Energy Technology Data Exchange (ETDEWEB)

Eckert, Claudia M. [Engineering Design Centre, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ (United Kingdom)]. E-mail: cme26@cam.ac.uk; Keller, Rene [Engineering Design Centre, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ (United Kingdom)]. E-mail: rk313@cam.ac.uk; Earl, Chris [Open University, Department of Design and Innovation, Walton Hall, Milton Keynes MK7 6AA (United Kingdom)]. E-mail: C.F.Earl@open.ac.uk; Clarkson, P. John [Engineering Design Centre, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ (United Kingdom)]. E-mail: pjc10@cam.ac.uk

2006-12-15

Change to existing products is fundamental to design processes. New products are often designed through change or modification to existing products. Specific parts or subsystems are changed to similar ones whilst others are directly reused. Design by modification applies particularly to safety critical products where the reuse of existing working parts and subsystems can reduce cost and risk. However change is rarely a matter of just reusing or modifying parts. Changing one part can propagate through the entire design leading to costly rework or jeopardising the integrity of the whole product. This paper characterises product change based on studies in the aerospace and automotive industry and introduces tools to aid designers in understanding the potential effects of change. Two ways of supporting designers are described: probabilistic prediction of the effects of change and visualisation of change propagation through product connectivities. Change propagation has uncertainties which are amplified by the choices designers make in practice as they implement change. Change prediction and visualisation is discussed with reference to complexity in three areas of product development: the structural backcloth of connectivities in the existing product (and its processes), the descriptions of the product used in design and the actions taken to carry out changes.

Predicting Freshman Persistence and Voluntary Dropout Decisions from a Theoretical Model.

Science.gov (United States)

Pascarella, Ernest T.; Terenzini, Patrick T.

1980-01-01

A five-scale instrument developed from a theoretical model of college attrition correctly identified the persistence/voluntary withdrawal decisions of 78.5 percent of 773 freshmen in a large, residential university. Findings showed that student relationships with faculty were particularly important. (Author/PHR)

Theoretical spectroscopic study of the conjugate microcystin-LR-europium cryptate

Energy Technology Data Exchange (ETDEWEB)

Santos, Julio G.; Dutra, Jose Diogo L.; Costa Junior, Nivan B. da; Freire, Ricardo O., E-mail: rfreire@ufs.br [Universidade Federal de Sergipe (UFS), Sao Cristovao, SE (Brazil). Departamento de Quimica; Alves Junior, Severino; Sa, Gilberto F. de [Universidade Federal de Pernambuco (UFPE), Recife, PE (Brazil). Departamento de Quimica Fundamental

2013-02-15

In this work, theoretical tools were used to study spectroscopic properties of the conjugate microcystin-LR-europium cryptate. The Sparkle/AM1 model was applied to predict the geometry of the system and the INDO/S-CIS model was used to calculate the excited state energies. Based on the Judd-Ofelt theory, the intensity parameters were predicted and a theoretical model based on the theory of the 4f-4f transitions was applied to calculate energy transfer and backtransfer rates, radiative and non-radiative decay rates, quantum efficiency and quantum yield. A detailed study of the luminescent properties of the conjugate Microcystin-LR-europium cryptate was carried out. The results show that the theoretical quantum yield of luminescence of 23% is in good agreement with the experimental value published. This fact suggests that this theoretical protocol can be used to design new systems in order to improve their luminescence properties. The results suggest that this luminescent system may be a good conjugate for using in assay ELISA for detection by luminescence of the Microcystin-LR in water. (author)

An Experimental and Theoretical Study on Cavitating Propellers.

Science.gov (United States)

1982-10-01

34 And Identfyp eV &to" nMeeJ cascade flow theoretical supercavitating flow performance prediction method partially cavitating flow supercavitating ...the present work was to develop an analytical tool for predicting the off-design performance of supercavitating propellers over a wide range of...operating conditions. Due to the complex nature of the flow phenomena, a lifting line theory sirply combined with the two-dimensional supercavitating

Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach

International Nuclear Information System (INIS)

Chen, Kuilin; Yu, Jie

2014-01-01

Highlights: • A novel hybrid modeling method is proposed for short-term wind speed forecasting. • Support vector regression model is constructed to formulate nonlinear state-space framework. • Unscented Kalman filter is adopted to recursively update states under random uncertainty. • The new SVR–UKF approach is compared to several conventional methods for short-term wind speed prediction. • The proposed method demonstrates higher prediction accuracy and reliability. - Abstract: Accurate wind speed forecasting is becoming increasingly important to improve and optimize renewable wind power generation. Particularly, reliable short-term wind speed prediction can enable model predictive control of wind turbines and real-time optimization of wind farm operation. However, this task remains challenging due to the strong stochastic nature and dynamic uncertainty of wind speed. In this study, unscented Kalman filter (UKF) is integrated with support vector regression (SVR) based state-space model in order to precisely update the short-term estimation of wind speed sequence. In the proposed SVR–UKF approach, support vector regression is first employed to formulate a nonlinear state-space model and then unscented Kalman filter is adopted to perform dynamic state estimation recursively on wind sequence with stochastic uncertainty. The novel SVR–UKF method is compared with artificial neural networks (ANNs), SVR, autoregressive (AR) and autoregressive integrated with Kalman filter (AR-Kalman) approaches for predicting short-term wind speed sequences collected from three sites in Massachusetts, USA. The forecasting results indicate that the proposed method has much better performance in both one-step-ahead and multi-step-ahead wind speed predictions than the other approaches across all the locations

The Role of Perceived Social Support and Coping Styles in Predicting Adolescents' Positivity

Science.gov (United States)

Çevik, Gülsen Büyüksahin; Yildiz, Mehmet Ali

2017-01-01

The current research aims to examine the perceived social support and coping styles predicting positivity. Research participants included 268 adolescents, attending high school, with 147 females (54.9%) and 121 males (45.1%). Adolescents participating in the research were 14 to 18 years old and their average age was 16.12 with SD = 1.01. Research…

Clinical Decision Support Model to Predict Occlusal Force in Bruxism Patients.

Science.gov (United States)

Thanathornwong, Bhornsawan; Suebnukarn, Siriwan

2017-10-01

The aim of this study was to develop a decision support model for the prediction of occlusal force from the size and color of articulating paper markings in bruxism patients. We used the information from the datasets of 30 bruxism patients in which digital measurements of the size and color of articulating paper markings (12-µm Hanel; Coltene/Whaledent GmbH, Langenau, Germany) on canine protected hard stabilization splints were measured in pixels (P) and in red (R), green (G), and blue (B) values using Adobe Photoshop software (Adobe Systems, San Jose, CA, USA). The occlusal force (F) was measured using T-Scan III (Tekscan Inc., South Boston, MA, USA). The multiple regression equation was applied to predict F from the P and RGB. Model evaluation was performed using the datasets from 10 new patients. The patient's occlusal force measured by T-Scan III was used as a 'gold standard' to compare with the occlusal force predicted by the multiple regression model. The results demonstrate that the correlation between the occlusal force and the pixels and RGB of the articulating paper markings was positive (F = 1.62×P + 0.07×R -0.08×G + 0.08×B + 4.74; R 2 = 0.34). There was a high degree of agreement between the occlusal force of the patient measured using T-Scan III and the occlusal force predicted by the model (kappa value = 0.82). The results obtained demonstrate that the multiple regression model can predict the occlusal force using the digital values for the size and color of the articulating paper markings in bruxism patients.

Clinical Decision Support Model to Predict Occlusal Force in Bruxism Patients

Science.gov (United States)

Thanathornwong, Bhornsawan

2017-01-01

Objectives The aim of this study was to develop a decision support model for the prediction of occlusal force from the size and color of articulating paper markings in bruxism patients. Methods We used the information from the datasets of 30 bruxism patients in which digital measurements of the size and color of articulating paper markings (12-µm Hanel; Coltene/Whaledent GmbH, Langenau, Germany) on canine protected hard stabilization splints were measured in pixels (P) and in red (R), green (G), and blue (B) values using Adobe Photoshop software (Adobe Systems, San Jose, CA, USA). The occlusal force (F) was measured using T-Scan III (Tekscan Inc., South Boston, MA, USA). The multiple regression equation was applied to predict F from the P and RGB. Model evaluation was performed using the datasets from 10 new patients. The patient's occlusal force measured by T-Scan III was used as a ‘gold standard’ to compare with the occlusal force predicted by the multiple regression model. Results The results demonstrate that the correlation between the occlusal force and the pixels and RGB of the articulating paper markings was positive (F = 1.62×P + 0.07×R –0.08×G + 0.08×B + 4.74; R2 = 0.34). There was a high degree of agreement between the occlusal force of the patient measured using T-Scan III and the occlusal force predicted by the model (kappa value = 0.82). Conclusions The results obtained demonstrate that the multiple regression model can predict the occlusal force using the digital values for the size and color of the articulating paper markings in bruxism patients. PMID:29181234

Silicene: Recent theoretical advances

KAUST Repository

Lew Yan Voon, L. C.

2016-04-14

Silicene is a two-dimensional allotrope of silicon with a puckered hexagonal structure closely related to the structure of graphene and that has been predicted to be stable. To date, it has been successfully grown in solution (functionalized) and on substrates. The goal of this review is to provide a summary of recent theoretical advances in the properties of both free-standing silicene as well as in interaction with molecules and substrates, and of proposed device applications.

Silicene: Recent theoretical advances

KAUST Repository

Lew Yan Voon, L. C.; Zhu, Jiajie; Schwingenschlö gl, Udo

2016-01-01

Silicene is a two-dimensional allotrope of silicon with a puckered hexagonal structure closely related to the structure of graphene and that has been predicted to be stable. To date, it has been successfully grown in solution (functionalized) and on substrates. The goal of this review is to provide a summary of recent theoretical advances in the properties of both free-standing silicene as well as in interaction with molecules and substrates, and of proposed device applications.

Predicting sumoylation sites using support vector machines based on various sequence features, conformational flexibility and disorder.

Science.gov (United States)

Yavuz, Ahmet Sinan; Sezerman, Osman Ugur

2014-01-01

Sumoylation, which is a reversible and dynamic post-translational modification, is one of the vital processes in a cell. Before a protein matures to perform its function, sumoylation may alter its localization, interactions, and possibly structural conformation. Abberations in protein sumoylation has been linked with a variety of disorders and developmental anomalies. Experimental approaches to identification of sumoylation sites may not be effective due to the dynamic nature of sumoylation, laborsome experiments and their cost. Therefore, computational approaches may guide experimental identification of sumoylation sites and provide insights for further understanding sumoylation mechanism. In this paper, the effectiveness of using various sequence properties in predicting sumoylation sites was investigated with statistical analyses and machine learning approach employing support vector machines. These sequence properties were derived from windows of size 7 including position-specific amino acid composition, hydrophobicity, estimated sub-window volumes, predicted disorder, and conformational flexibility. 5-fold cross-validation results on experimentally identified sumoylation sites revealed that our method successfully predicts sumoylation sites with a Matthew's correlation coefficient, sensitivity, specificity, and accuracy equal to 0.66, 73%, 98%, and 97%, respectively. Additionally, we have showed that our method compares favorably to the existing prediction methods and basic regular expressions scanner. By using support vector machines, a new, robust method for sumoylation site prediction was introduced. Besides, the possible effects of predicted conformational flexibility and disorder on sumoylation site recognition were explored computationally for the first time to our knowledge as an additional parameter that could aid in sumoylation site prediction.

A STUDY OF PREDICTING THE NEED FOR VENTILATOR SUPPORT AND OUTCOME IN ORGANOPHOSPHORUS POISONING

Directory of Open Access Journals (Sweden)

Kalinga Bommankatte Eranaik

2017-04-01

Full Text Available BACKGROUND Organophosphorus compound poisoning is the most common poisonings in India because of easy availability, often requiring ICU care and ventilator support. Clinical research has indicated that respiratory failure is the most important cause of death due to Organophosphorus poisoning. It results in respiratory muscle weakness, pulmonary oedema, respiratory depression, increased secretions and bronchospasm. These complications and death can be prevented with timely Institution of ventilator support. The aim of present study was to identify the factors and predicting the need for ventilator support and outcome. Aim of the Study- To predict the need for ventilator support and outcome in organophosphate poisoning. MATERIALS AND METHODS Seventy consecutive patients admitted with a history of organophosphorus poisoning at KIMS, Hubli were taken for study after considering the inclusion and exclusion criteria. Detailed history, confirmation of poisoning, examination and other than routine investigations serum pseudocholinesterase and arterial blood gas analysis was done. The severity of organophosphorus poisoning was graded as mild, moderate and severe based on the factors which influence the need for ventilator support. RESULTS This study was conducted in 70 patients, out of which 48 (68.6% were male patients and 22 (31.4% were female patients. Among them 37 (53% patients required ventilation and 33 (47% expired. Chlorpyrifos, Dichlorvos and Monocrotophos were most commonly consumed poisons. 74% patients who consumed these compounds required ventilator support and 73% patients expired. 100% of patients presented with pin point pupil, fasciculation score > 4, respiratory rate > 20, GCS score < 7 and severe grade of poisoning required ventilator support and pseudocholinesterase < 900 U/L, 70% of metabolic acidosis and atropine requirement more than 180 mg within 48 hours required ventilator support and associated with high mortality. CONCLUSION

Support vector regression model based predictive control of water level of U-tube steam generators

Energy Technology Data Exchange (ETDEWEB)

Kavaklioglu, Kadir, E-mail: kadir.kavaklioglu@pau.edu.tr

2014-10-15

Highlights: • Water level of U-tube steam generators was controlled in a model predictive fashion. • Models for steam generator water level were built using support vector regression. • Cost function minimization for future optimal controls was performed by using the steepest descent method. • The results indicated the feasibility of the proposed method. - Abstract: A predictive control algorithm using support vector regression based models was proposed for controlling the water level of U-tube steam generators of pressurized water reactors. Steam generator data were obtained using a transfer function model of U-tube steam generators. Support vector regression based models were built using a time series type model structure for five different operating powers. Feedwater flow controls were calculated by minimizing a cost function that includes the level error, the feedwater change and the mismatch between feedwater and steam flow rates. Proposed algorithm was applied for a scenario consisting of a level setpoint change and a steam flow disturbance. The results showed that steam generator level can be controlled at all powers effectively by the proposed method.

Personality predicts perceived availability of social support and satisfaction with social support in women with early stage breast cancer.

Science.gov (United States)

Den Oudsten, Brenda L; Van Heck, Guus L; Van der Steeg, Alida F W; Roukema, Jan A; De Vries, Jolanda

2010-04-01

This study examines the relationships between personality, on the one hand, and perceived availability of social support (PASS) and satisfaction with received social support (SRSS), on the other hand, in women with early stage breast cancer (BC). In addition, this study examined whether a stressful event (i.e., diagnosis) is associated with quality of life (QOL), when controlling for PASS and SRSS. Women were assessed on PASS and SRSS (World Health Organization QOL assessment instrument-100) before diagnosis (time 1) and 1 (time 2), 3 (time 3), 6 (time 4), 12 (time 5), and 24 months (time 6) after surgical treatment. Personality (neuroticism extraversion openness five-factor inventory and state trait anxiety inventory-trait scale) and fatigue (fatigue assessment scale) were assessed at time 1. Agreeableness and fatigue predicted PASS and SRSS at time 5 and time 6. Trait anxiety had a negative effect on SRSS (ss = -0.22, p personality factors substantially influence the way women with early stage BC perceive social support. Knowledge about these underlying mechanisms of social support is useful for the development of tailor-made interventions. Professionals should be aware of the importance of social support. They should check whether patients have sufficient significant others in their social environment and be sensitive to potential discrepancies patients might experience between availability and adequacy of social support.

Familial social support predicts a reduced cortisol response to stress in sexual minority young adults.

Science.gov (United States)

Burton, C L; Bonanno, G A; Hatzenbuehler, M L

2014-09-01

Social support has been repeatedly associated with mental and physical health outcomes, with hypothalamic-pituitary-adrenocortical (HPA) axis activity posited as a potential mechanism. The influence of social bonds appears particularly important in the face of stigma-related stress; however, there is a dearth of research examining social support and HPA axis response among members of a stigmatized group. To address this gap in the literature, we tested in a sample of 70 lesbian, gay, and bisexual (LGB) young adults whether family support or peer support differentially predict cortisol reactivity in response to a laboratory stressor, the Trier Social Stress Test. While greater levels of family support were associated with reduced cortisol reactivity, neither peer support nor overall support satisfaction was associated with cortisol response. These findings suggest that the association between social support and neuroendocrine functioning differs according to the source of support among members of one stigmatized group. Copyright © 2014 Elsevier Ltd. All rights reserved.

Pre-Operative Prediction of Advanced Prostatic Cancer Using Clinical Decision Support Systems: Accuracy Comparison between Support Vector Machine and Artificial Neural Network

Energy Technology Data Exchange (ETDEWEB)

Kim, Sang Youn; Moon, Sung Kyoung; Hwang, Sung Il; Sung, Chang Kyu; Cho, Jeong Yeon; Kim, Seung Hyup; Lee, Hak Jong [Seoul National University College of Medicine, Seoul (Korea, Republic of); Jung, Dae Chul [National Cancer Center, Ilsan (Korea, Republic of); Lee, Ji Won [Kangwon National University College of Medicine, Chuncheon (Korea, Republic of)

2011-10-15

The purpose of the current study was to develop support vector machine (SVM) and artificial neural network (ANN) models for the pre-operative prediction of advanced prostate cancer by using the parameters acquired from transrectal ultrasound (TRUS)-guided prostate biopsies, and to compare the accuracies between the two models. Five hundred thirty-two consecutive patients who underwent prostate biopsies and prostatectomies for prostate cancer were divided into the training and test groups (n = 300 versus n 232). From the data in the training group, two clinical decision support systems (CDSSs-[SVM and ANN]) were constructed with input (age, prostate specific antigen level, digital rectal examination, and five biopsy parameters) and output data (the probability for advanced prostate cancer [> pT3a]). From the data of the test group, the accuracy of output data was evaluated. The areas under the receiver operating characteristic (ROC) curve (AUC) were calculated to summarize the overall performances, and a comparison of the ROC curves was performed (p < 0.05). The AUC of SVM and ANN is 0.805 and 0.719, respectively (p = 0.020), in the pre-operative prediction of advanced prostate cancer. Te performance of SVM is superior to ANN in the pre-operative prediction of advanced prostate cancer.

Theoretical prediction of experimental jump and pull-in dynamics in a MEMS sensor

KAUST Repository

Ruzziconi, Laura; Lenci, Stefano; Ramini, Abdallah; Younis, Mohammad I.

2014-01-01

The present research study deals with an electrically actuated MEMS device. An experimental investigation is performed, via frequency sweeps in a neighbourhood of the first natural frequency. Resonant behavior is explored, with special attention devoted to jump and pull-in dynamics. A theoretical single degree-of-freedom spring-mass model is derived. Classical numerical simulations are observed to properly predict the main nonlinear features. Nevertheless, some discrepancies arise, which are particularly visible in the resonant branch. They mainly concern the practical range of existence of each attractor and the final outcome after its disappearance. These differences are likely due to disturbances, which are unavoidable in practice, but have not been included in the model. To take disturbances into account, in addition to the classical local investigations, we consider the global dynamics and explore the robustness of the obtained results by performing a dynamical integrity analysis. Our aim is that of developing an applicable confident estimate of the system response. Integrity profiles and integrity charts are built to detect the parameter range where reliability is practically strong and where it becomes weak. Integrity curves exactly follow the experimental data. They inform about the practical range of actuality. We discuss the combined use of integrity charts in the engineering design. Although we refer to a particular case-study, the approach is very general.

Theoretical prediction of experimental jump and pull-in dynamics in a MEMS sensor

KAUST Repository

Ruzziconi, Laura

2014-09-15

The present research study deals with an electrically actuated MEMS device. An experimental investigation is performed, via frequency sweeps in a neighbourhood of the first natural frequency. Resonant behavior is explored, with special attention devoted to jump and pull-in dynamics. A theoretical single degree-of-freedom spring-mass model is derived. Classical numerical simulations are observed to properly predict the main nonlinear features. Nevertheless, some discrepancies arise, which are particularly visible in the resonant branch. They mainly concern the practical range of existence of each attractor and the final outcome after its disappearance. These differences are likely due to disturbances, which are unavoidable in practice, but have not been included in the model. To take disturbances into account, in addition to the classical local investigations, we consider the global dynamics and explore the robustness of the obtained results by performing a dynamical integrity analysis. Our aim is that of developing an applicable confident estimate of the system response. Integrity profiles and integrity charts are built to detect the parameter range where reliability is practically strong and where it becomes weak. Integrity curves exactly follow the experimental data. They inform about the practical range of actuality. We discuss the combined use of integrity charts in the engineering design. Although we refer to a particular case-study, the approach is very general.

SAMEX: A severe accident management support expert

International Nuclear Information System (INIS)

Park, Soo-Yong; Ahn, Kwang-Il

2010-01-01

A decision support system for use in a severe accident management following an incident at a nuclear power plant is being developed which is aided by a severe accident risk database module and a severe accident management simulation module. The severe accident management support expert (SAMEX) system can provide the various types of diagnostic and predictive assistance based on the real-time plant specific safety parameters. It consists of four major modules as sub-systems: (a) severe accident risk data base module (SARDB), (b) risk-informed severe accident risk data base management module (RI-SARD), (c) severe accident management simulation module (SAMS), and (d) on-line severe accident management guidance module (on-line SAMG). The modules are integrated into a code package that executes within a WINDOWS XP operating environment, using extensive user friendly graphics control. In Korea, the integrated approach of the decision support system is being carried out under the nuclear R and D program planned by the Korean Ministry of Education, Science and Technology (MEST). An objective of the project is to develop the support system which can show a theoretical possibility. If the system is feasible, the project team will recommend the radiation protection technical support center of a national regulatory body to implement a plant specific system, which is applicable to a real accident, for the purpose of immediate and various diagnosis based on the given plant status information and of prediction of an expected accident progression under a severe accident situation.

Emotional Intelligence: A Theoretical Framework for Individual Differences in Affective Forecasting

Science.gov (United States)

Hoerger, Michael; Chapman, Benjamin P.; Epstein, Ronald M.; Duberstein, Paul R.

2011-01-01

Only recently have researchers begun to examine individual differences in affective forecasting. The present investigation was designed to make a theoretical contribution to this emerging literature by examining the role of emotional intelligence in affective forecasting. Emotional intelligence was hypothesized to be associated with affective forecasting accuracy, memory for emotional reactions, and subsequent improvement on an affective forecasting task involving emotionally-evocative pictures. Results from two studies (N = 511) supported our hypotheses. Emotional intelligence was associated with accuracy in predicting, encoding, and consolidating emotional reactions. Furthermore, emotional intelligence was associated with greater improvement on a second affective forecasting task, with the relationship explained by basic memory processes. Implications for future research on basic and applied decision making are discussed. PMID:22251053

Genetic Learning of Fuzzy Parameters in Predictive and Decision Support Modelling

Directory of Open Access Journals (Sweden)

Nebot

2012-04-01

Full Text Available In this research a genetic fuzzy system (GFS is proposed that performs discretization parameter learning in the context of the Fuzzy Inductive Reasoning (FIR methodology and the Linguistic Rule FIR (LR-FIR algorithm. The main goal of the GFS is to take advantage of the potentialities of GAs to learn the fuzzification parameters of the FIR and LR-FIR approaches in order to obtain reliable and useful predictive (FIR models and decision support (LR-FIR models. The GFS is evaluated in an e-learning context.

Prediction of Agriculture Drought Using Support Vector Regression Incorporating with Climatology Indices

Science.gov (United States)

Tian, Y.; Xu, Y. P.

2017-12-01

In this paper, the Support Vector Regression (SVR) model incorporating climate indices and drought indices are developed to predict agriculture drought in Xiangjiang River basin, Central China. The agriculture droughts are presented with the Precipitation-Evapotranspiration Index (SPEI). According to the analysis of the relationship between SPEI with different time scales and soil moisture, it is found that SPEI of six months time scales (SPEI-6) could reflect the soil moisture better than that of three and one month time scale from the drought features including drought duration, severity and peak. Climate forcing like El Niño Southern Oscillation and western Pacific subtropical high (WPSH) are represented by climate indices such as MEI and series indices of WPSH. Ridge Point of WPSH is found to be the key factor that influences the agriculture drought mainly through the control of temperature. Based on the climate indices analysis, the predictions of SPEI-6 are conducted using the SVR model. The results show that the SVR model incorperating climate indices, especially ridge point of WPSH, could improve the prediction accuracy compared to that using drought index only. The improvement was more significant for the prediction of one month lead time than that of three months lead time. However, it needs to be cautious in selection of the input parameters, since adding more useless information could have a counter effect in attaining a better prediction.

Segregation of Brain Structural Networks Supports Spatio-Temporal Predictive Processing

Directory of Open Access Journals (Sweden)

Valentina Ciullo

2018-05-01

Full Text Available The ability to generate probabilistic expectancies regarding when and where sensory stimuli will occur, is critical to derive timely and accurate inferences about updating contexts. However, the existence of specialized neural networks for inferring predictive relationships between events is still debated. Using graph theoretical analysis applied to structural connectivity data, we tested the extent of brain connectivity properties associated with spatio-temporal predictive performance across 29 healthy subjects. Participants detected visual targets appearing at one out of three locations after one out of three intervals; expectations about stimulus location (spatial condition or onset (temporal condition were induced by valid or invalid symbolic cues. Connectivity matrices and centrality/segregation measures, expressing the relative importance of, and the local interactions among specific cerebral areas respect to the behavior under investigation, were calculated from whole-brain tractography and cortico-subcortical parcellation.Results: Response preparedness to cued stimuli relied on different structural connectivity networks for the temporal and spatial domains. Significant covariance was observed between centrality measures of regions within a subcortical-fronto-parietal-occipital network -comprising the left putamen, the right caudate nucleus, the left frontal operculum, the right inferior parietal cortex, the right paracentral lobule and the right superior occipital cortex-, and the ability to respond after a short cue-target delay suggesting that the local connectedness of such nodes plays a central role when the source of temporal expectation is explicit. When the potential for functional segregation was tested, we found highly clustered structural connectivity across the right superior, the left middle inferior frontal gyrus and the left caudate nucleus as related to explicit temporal orienting. Conversely, when the interaction between

Segregation of Brain Structural Networks Supports Spatio-Temporal Predictive Processing.

Science.gov (United States)

Ciullo, Valentina; Vecchio, Daniela; Gili, Tommaso; Spalletta, Gianfranco; Piras, Federica

2018-01-01

The ability to generate probabilistic expectancies regarding when and where sensory stimuli will occur, is critical to derive timely and accurate inferences about updating contexts. However, the existence of specialized neural networks for inferring predictive relationships between events is still debated. Using graph theoretical analysis applied to structural connectivity data, we tested the extent of brain connectivity properties associated with spatio-temporal predictive performance across 29 healthy subjects. Participants detected visual targets appearing at one out of three locations after one out of three intervals; expectations about stimulus location (spatial condition) or onset (temporal condition) were induced by valid or invalid symbolic cues. Connectivity matrices and centrality/segregation measures, expressing the relative importance of, and the local interactions among specific cerebral areas respect to the behavior under investigation, were calculated from whole-brain tractography and cortico-subcortical parcellation. Results: Response preparedness to cued stimuli relied on different structural connectivity networks for the temporal and spatial domains. Significant covariance was observed between centrality measures of regions within a subcortical-fronto-parietal-occipital network -comprising the left putamen, the right caudate nucleus, the left frontal operculum, the right inferior parietal cortex, the right paracentral lobule and the right superior occipital cortex-, and the ability to respond after a short cue-target delay suggesting that the local connectedness of such nodes plays a central role when the source of temporal expectation is explicit. When the potential for functional segregation was tested, we found highly clustered structural connectivity across the right superior, the left middle inferior frontal gyrus and the left caudate nucleus as related to explicit temporal orienting. Conversely, when the interaction between explicit and

The potential of predictive analytics to provide clinical decision support in depression treatment planning.

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Kessler, Ronald C

2018-01-01

To review progress developing clinical decision support tools for personalized treatment of major depressive disorder (MDD). Over the years, a variety of individual indicators ranging from biomarkers to clinical observations and self-report scales have been used to predict various aspects of differential MDD treatment response. Most of this work focused on predicting remission either with antidepressant medications versus psychotherapy, some antidepressant medications versus others, some psychotherapies versus others, and combination therapies versus monotherapies. However, to date, none of the individual predictors in these studies has been strong enough to guide optimal treatment selection for most patients. Interest consequently turned to decision support tools made up of multiple predictors, but the development of such tools has been hampered by small study sample sizes. Design recommendations are made here for future studies to address this problem. Recommendations include using large prospective observational studies followed by pragmatic trials rather than smaller, expensive controlled treatment trials for preliminary development of decision support tools; basing these tools on comprehensive batteries of inexpensive self-report and clinical predictors (e.g., self-administered performance-based neurocognitive tests) versus expensive biomarkers; and reserving biomarker assessments for targeted studies of patients not well classified by inexpensive predictor batteries.

Theoretical expectations for σtot at the large hadron collider

Indian Academy of Sciences (India)

particular QCD based model of the energy dependence of the total cross-section, including the effect of soft ... Hence, a critical evaluation of the range of theoretical predictions, is absolutely ... fitted to explain the observed low energy data and the model predictions are then .... Note here that the experimentally measured.

Prediction on sunspot activity based on fuzzy information granulation and support vector machine

Science.gov (United States)

Peng, Lingling; Yan, Haisheng; Yang, Zhigang

2018-04-01

In order to analyze the range of sunspots, a combined prediction method of forecasting the fluctuation range of sunspots based on fuzzy information granulation (FIG) and support vector machine (SVM) was put forward. Firstly, employing the FIG to granulate sample data and extract va)alid information of each window, namely the minimum value, the general average value and the maximum value of each window. Secondly, forecasting model is built respectively with SVM and then cross method is used to optimize these parameters. Finally, the fluctuation range of sunspots is forecasted with the optimized SVM model. Case study demonstrates that the model have high accuracy and can effectively predict the fluctuation of sunspots.

Theoretical investigation of aspects of radioactive contamination

International Nuclear Information System (INIS)

Smith, A.H.; Chandratillake, M.R.; Taylor, J.B.

1998-01-01

The BNFL programme of work has investigated theoretical aspects of the mechanisms responsible for the deposition and adherence of contamination to metallic surfaces and the energetics of physical decontamination processes. The work has been conducted in two phases: The theoretical and laboratory study of deposition of species from aqueous media on to stainless steel; Theoretical assessment of the forces causing the attraction of PuO 2 and UO 2 particles to stainless steel in an air environment and comparison of these forces with the energies delivered by physical jetting processes. The first phase produced a model which was found to give good agreement with plant operational experience of the deposition of simple aqueous ions such as Cobalt. Due to the complexities, however, of surface / colloid and surface / particle interactions the model was found not to be successful at predicting deposition for more complex compounds, such as Ruthenium Nitrosyls. At this stage the model had fulfilled its original requirement of underpinning design work on pipework shielding systems and it was decided not to pursue the library of chemical speciation data that would be necessary to model the behaviour of a full spectrum of possible contaminants. The second phase predicts by theoretical analysis that the relation of the energy delivered by jetting techniques to the physical forces causing the adherence of PuO 2 and UO 2 particles will vary considerably with particle size. This is particularly notably for larger PuO 2 particles which are firmly held as a result of high levels of electrostatic charge due to their intense alpha activity. Small particles tend to be difficult to remove due to the low profile that they present to the jetting medium. Large and small PuO 2 particles and small UO 2 particle are thus predicted to be difficult to remove and will present an energy threshold which may not be crossed by all decontamination techniques. (author)

A Bayesian network model for predicting aquatic toxicity mode of action using two dimensional theoretical molecular descriptors

Energy Technology Data Exchange (ETDEWEB)

Carriger, John F. [U.S. Environmental Protection Agency, Office of Research and Development, Gulf Ecology Division, Gulf Breeze, FL, 32561 (United States); Martin, Todd M. [U.S. Environmental Protection Agency, Office of Research and Development, Sustainable Technology Division, Cincinnati, OH, 45220 (United States); Barron, Mace G., E-mail: barron.mace@epa.gov [U.S. Environmental Protection Agency, Office of Research and Development, Gulf Ecology Division, Gulf Breeze, FL, 32561 (United States)

2016-11-15

Highlights: • A Bayesian network was developed to classify chemical mode of action (MoA). • The network was based on the aquatic toxicity MoA for over 1000 chemicals. • A Markov blanket algorithm selected a subset of theoretical molecular descriptors. • Sensitivity analyses found influential descriptors for classifying the MoAs. • Overall precision of the Bayesian MoA classification model was 80%. - Abstract: The mode of toxic action (MoA) has been recognized as a key determinant of chemical toxicity, but development of predictive MoA classification models in aquatic toxicology has been limited. We developed a Bayesian network model to classify aquatic toxicity MoA using a recently published dataset containing over one thousand chemicals with MoA assignments for aquatic animal toxicity. Two dimensional theoretical chemical descriptors were generated for each chemical using the Toxicity Estimation Software Tool. The model was developed through augmented Markov blanket discovery from the dataset of 1098 chemicals with the MoA broad classifications as a target node. From cross validation, the overall precision for the model was 80.2%. The best precision was for the AChEI MoA (93.5%) where 257 chemicals out of 275 were correctly classified. Model precision was poorest for the reactivity MoA (48.5%) where 48 out of 99 reactive chemicals were correctly classified. Narcosis represented the largest class within the MoA dataset and had a precision and reliability of 80.0%, reflecting the global precision across all of the MoAs. False negatives for narcosis most often fell into electron transport inhibition, neurotoxicity or reactivity MoAs. False negatives for all other MoAs were most often narcosis. A probabilistic sensitivity analysis was undertaken for each MoA to examine the sensitivity to individual and multiple descriptor findings. The results show that the Markov blanket of a structurally complex dataset can simplify analysis and interpretation by

LMethyR-SVM: Predict Human Enhancers Using Low Methylated Regions based on Weighted Support Vector Machines.

Science.gov (United States)

Xu, Jingting; Hu, Hong; Dai, Yang

The identification of enhancers is a challenging task. Various types of epigenetic information including histone modification have been utilized in the construction of enhancer prediction models based on a diverse panel of machine learning schemes. However, DNA methylation profiles generated from the whole genome bisulfite sequencing (WGBS) have not been fully explored for their potential in enhancer prediction despite the fact that low methylated regions (LMRs) have been implied to be distal active regulatory regions. In this work, we propose a prediction framework, LMethyR-SVM, using LMRs identified from cell-type-specific WGBS DNA methylation profiles and a weighted support vector machine learning framework. In LMethyR-SVM, the set of cell-type-specific LMRs is further divided into three sets: reliable positive, like positive and likely negative, according to their resemblance to a small set of experimentally validated enhancers in the VISTA database based on an estimated non-parametric density distribution. Then, the prediction model is obtained by solving a weighted support vector machine. We demonstrate the performance of LMethyR-SVM by using the WGBS DNA methylation profiles derived from the human embryonic stem cell type (H1) and the fetal lung fibroblast cell type (IMR90). The predicted enhancers are highly conserved with a reasonable validation rate based on a set of commonly used positive markers including transcription factors, p300 binding and DNase-I hypersensitive sites. In addition, we show evidence that the large fraction of the LMethyR-SVM predicted enhancers are not predicted by ChromHMM in H1 cell type and they are more enriched for the FANTOM5 enhancers. Our work suggests that low methylated regions detected from the WGBS data are useful as complementary resources to histone modification marks in developing models for the prediction of cell-type-specific enhancers.

The predicting roles of reasons for living and social support on depression, anxiety and stress among young people in Malaysia.

Science.gov (United States)

Amit, N; Ibrahim, N; Aga Mohd Jaladin, R; Che Din, N

2017-10-01

This research examined the predicting roles of reasons for living and social support on depression, anxiety and stress in Malaysia. This research was carried out on a sample of 263 participants (age range 12-24 years old), from Klang Valley, Selangor. The survey package comprises demographic information, a measure of reasons for living, social support, depression, anxiety and stress. To analyse the data, correlation analysis and a series of linear multiple regression analysis were carried out. Findings showed that there were low negative relationships between all subdomains and the total score of reasons for living and depression. There were also low negative relationships between domain-specific of social support (family and friends) and total social support and depression. In terms of the family alliance, self-acceptance and total score of reasons for living, they were negatively associated with anxiety, whereas family social support was negatively associated with stress. The linear regression analysis showed that only future optimism and family social support found to be the significant predictors for depression. Family alliance and total reasons for living were significant in predicting anxiety, whereas family social support was significant in predicting stress. These findings have the potential to promote awareness related to depression, anxiety, and stress among youth in Malaysia.

The Weighted Support Vector Machine Based on Hybrid Swarm Intelligence Optimization for Icing Prediction of Transmission Line

Directory of Open Access Journals (Sweden)

Xiaomin Xu

2015-01-01

Full Text Available Not only can the icing coat on transmission line cause the electrical fault of gap discharge and icing flashover but also it will lead to the mechanical failure of tower, conductor, insulators, and others. It will bring great harm to the people’s daily life and work. Thus, accurate prediction of ice thickness has important significance for power department to control the ice disaster effectively. Based on the analysis of standard support vector machine, this paper presents a weighted support vector machine regression model based on the similarity (WSVR. According to the different importance of samples, this paper introduces the weighted support vector machine and optimizes its parameters by hybrid swarm intelligence optimization algorithm with the particle swarm and ant colony (PSO-ACO, which improves the generalization ability of the model. In the case study, the actual data of ice thickness and climate in a certain area of Hunan province have been used to predict the icing thickness of the area, which verifies the validity and applicability of this proposed method. The predicted results show that the intelligent model proposed in this paper has higher precision and stronger generalization ability.

Ductility prediction of substrate-supported metal layers based on rate-independent crystal plasticity theory

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Akpama Holanyo K.

2016-01-01

Full Text Available In this paper, both the bifurcation theory and the initial imperfection approach are used to predict localized necking in substrate-supported metal layers. The self-consistent scale-transition scheme is used to derive the mechanical behavior of a representative volume element of the metal layer from the behavior of its microscopic constituents (the single crystals. The mechanical behavior of the elastomer substrate follows the neo-Hookean hyperelastic model. The adherence between the two layers is assumed to be perfect. Through numerical results, it is shown that the limit strains predicted by the initial imperfection approach tend towards the bifurcation predictions when the size of the geometric imperfection in the metal layer vanishes. Also, it is shown that the addition of an elastomer layer to a metal layer enhances ductility.

Applying a multi-replication framework to support dynamic situation assessment and predictive capabilities

Science.gov (United States)

Lammers, Craig; McGraw, Robert M.; Steinman, Jeffrey S.

2005-05-01

Technological advances and emerging threats reduce the time between target detection and action to an order of a few minutes. To effectively assist with the decision-making process, C4I decision support tools must quickly and dynamically predict and assess alternative Courses Of Action (COAs) to assist Commanders in anticipating potential outcomes. These capabilities can be provided through the faster-than-real-time predictive simulation of plans that are continuously re-calibrating with the real-time picture. This capability allows decision-makers to assess the effects of re-tasking opportunities, providing the decision-maker with tremendous freedom to make time-critical, mid-course decisions. This paper presents an overview and demonstrates the use of a software infrastructure that supports DSAP capabilities. These DSAP capabilities are demonstrated through the use of a Multi-Replication Framework that supports (1) predictivie simulations using JSAF (Joint Semi-Automated Forces); (2) real-time simulation, also using JSAF, as a state estimation mechanism; and, (3) real-time C4I data updates through TBMCS (Theater Battle Management Core Systems). This infrastructure allows multiple replications of a simulation to be executed simultaneously over a grid faster-than-real-time, calibrated with live data feeds. A cost evaluator mechanism analyzes potential outcomes and prunes simulations that diverge from the real-time picture. In particular, this paper primarily serves to walk a user through the process for using the Multi-Replication Framework providing an enhanced decision aid.

Quantitative structure–activity relationship model for amino acids as corrosion inhibitors based on the support vector machine and molecular design

International Nuclear Information System (INIS)

Zhao, Hongxia; Zhang, Xiuhui; Ji, Lin; Hu, Haixiang; Li, Qianshu

2014-01-01

Highlights: • Nonlinear quantitative structure–activity relationship (QSAR) model was built by the support vector machine. • Descriptors for QSAR model were selected by principal component analysis. • Binding energy was taken as one of the descriptors for QSAR model. • Acidic solution and protonation of the inhibitor were considered. - Abstract: The inhibition performance of nineteen amino acids was studied by theoretical methods. The affection of acidic solution and protonation of inhibitor were considered in molecular dynamics simulation and the results indicated that the protonated amino-group was not adsorbed on Fe (1 1 0) surface. Additionally, a nonlinear quantitative structure–activity relationship (QSAR) model was built by the support vector machine. The correlation coefficient was 0.97 and the root mean square error, the differences between predicted and experimental inhibition efficiencies (%), was 1.48. Furthermore, five new amino acids were theoretically designed and their inhibition efficiencies were predicted by the built QSAR model

Decision support system in Predicting the Best teacher with Multi Atribute Decesion Making Weighted Product (MADMWP Method

Directory of Open Access Journals (Sweden)

Solikhun Solikhun

2017-06-01

Full Text Available Predicting of the best teacher in Indonesia aims to spur the development of the growth and improve the quality of the education. In this paper, the predicting  of the best teacher is implemented based on predefined criteria. To help the predicting process, a decision support system is needed. This paper employs Multi Atribute Decesion Making Weighted Product (MADMWP method. The result of this method is tested some teachers in  junior high school islamic boarding Al-Barokah school, Simalungun, North Sumatera, Indonesia. This system can be used to help in solving problems of the best teacher prediction.

A causal link between prediction errors, dopamine neurons and learning.

Science.gov (United States)

Steinberg, Elizabeth E; Keiflin, Ronald; Boivin, Josiah R; Witten, Ilana B; Deisseroth, Karl; Janak, Patricia H

2013-07-01

Situations in which rewards are unexpectedly obtained or withheld represent opportunities for new learning. Often, this learning includes identifying cues that predict reward availability. Unexpected rewards strongly activate midbrain dopamine neurons. This phasic signal is proposed to support learning about antecedent cues by signaling discrepancies between actual and expected outcomes, termed a reward prediction error. However, it is unknown whether dopamine neuron prediction error signaling and cue-reward learning are causally linked. To test this hypothesis, we manipulated dopamine neuron activity in rats in two behavioral procedures, associative blocking and extinction, that illustrate the essential function of prediction errors in learning. We observed that optogenetic activation of dopamine neurons concurrent with reward delivery, mimicking a prediction error, was sufficient to cause long-lasting increases in cue-elicited reward-seeking behavior. Our findings establish a causal role for temporally precise dopamine neuron signaling in cue-reward learning, bridging a critical gap between experimental evidence and influential theoretical frameworks.

Virtual-view PSNR prediction based on a depth distortion tolerance model and support vector machine.

Science.gov (United States)

Chen, Fen; Chen, Jiali; Peng, Zongju; Jiang, Gangyi; Yu, Mei; Chen, Hua; Jiao, Renzhi

2017-10-20

Quality prediction of virtual-views is important for free viewpoint video systems, and can be used as feedback to improve the performance of depth video coding and virtual-view rendering. In this paper, an efficient virtual-view peak signal to noise ratio (PSNR) prediction method is proposed. First, the effect of depth distortion on virtual-view quality is analyzed in detail, and a depth distortion tolerance (DDT) model that determines the DDT range is presented. Next, the DDT model is used to predict the virtual-view quality. Finally, a support vector machine (SVM) is utilized to train and obtain the virtual-view quality prediction model. Experimental results show that the Spearman's rank correlation coefficient and root mean square error between the actual PSNR and the predicted PSNR by DDT model are 0.8750 and 0.6137 on average, and by the SVM prediction model are 0.9109 and 0.5831. The computational complexity of the SVM method is lower than the DDT model and the state-of-the-art methods.

Reservoir rock permeability prediction using support vector regression in an Iranian oil field

International Nuclear Information System (INIS)

Saffarzadeh, Sadegh; Shadizadeh, Seyed Reza

2012-01-01

Reservoir permeability is a critical parameter for the evaluation of hydrocarbon reservoirs. It is often measured in the laboratory from reservoir core samples or evaluated from well test data. The prediction of reservoir rock permeability utilizing well log data is important because the core analysis and well test data are usually only available from a few wells in a field and have high coring and laboratory analysis costs. Since most wells are logged, the common practice is to estimate permeability from logs using correlation equations developed from limited core data; however, these correlation formulae are not universally applicable. Recently, support vector machines (SVMs) have been proposed as a new intelligence technique for both regression and classification tasks. The theory has a strong mathematical foundation for dependence estimation and predictive learning from finite data sets. The ultimate test for any technique that bears the claim of permeability prediction from well log data is the accurate and verifiable prediction of permeability for wells where only the well log data are available. The main goal of this paper is to develop the SVM method to obtain reservoir rock permeability based on well log data. (paper)

Theoretical study of ionization and one-electron oxidation potentials of N-heterocyclic compounds.

Science.gov (United States)

Sviatenko, Liudmyla K; Gorb, Leonid; Hill, Frances C; Leszczynski, Jerzy

2013-05-15

A number of density functionals was utilized to predict gas-phase adiabatic ionization potentials (IPs) for nitrogen-rich heterocyclic compounds. Various solvation models were applied to the calculation of difference in free energies of solvation of oxidized and reduced forms of heterocyclic compounds in acetonitrile (AN) for correct reproduction of their standard oxidation potentials. We developed generally applicable protocols that could successfully predict the gas-phase adiabatic ionization potentials of nitrogen-rich heterocyclic compounds and their standard oxidation potentials in AN. This approach is supported by a MPW1K/6-31+G(d) level of theory which uses SMD(UA0) approximation for estimation of solvation energy of neutral molecules and PCM(UA0) model for ionized ones. The mean absolute derivation (MAD) and root mean square error (RMSE) of the current theoretical models for IP are equal to 0.22 V and 0.26, respectively, and for oxidation potentials MAD = 0.13 V and RMSE = 0.17. Copyright © 2013 Wiley Periodicals, Inc.

Experimental and theoretical studies of a pyrazole-thiazolidin-2,4-di-one hybrid

Science.gov (United States)

Mushtaque, Md.; Avecilla, Fernando; Haque, Ashanul; Perwez, Ahmad; Khan, Md. Shahzad; Rizvi, M. Moshahid Alam

2017-08-01

The present work describes synthesis, characterization and biological evaluations of a hybrid compound 10 composed of two intriguing scaffolds pyrazole and thiazolidin-2,4-di-one. The title compound was obtained via multi-step reaction and characterized by a number of techniques (viz. IR, UV-Visible, 1H-NMR, 13C-NMR and MS) including X-ray crystallography. The structural and photophysical data of compound 10 were well supported by theoretical calculations performed at density functional (DFT) level. In-vitro anticancer studies on different human cancer cell lines indicated moderate to low activity of the compounds. The molecular target of the compound was predicted through in-silico studies. Finding of the studies are presented herein.

Do parents' support behaviours predict whether or not their children get sufficient sleep? A cross-sectional study.

Science.gov (United States)

Pyper, Evelyn; Harrington, Daniel; Manson, Heather

2017-05-24

Sleep is an essential component of healthy cognitive and physical development. Lack of sleep may put children at risk for a variety of mental and physical health outcomes, including overweight, obesity and related chronic diseases. Given that children's sleep duration has decreased in recent decades, there is a need to understand the determinants of child sleep, including the role of parental support behaviours. This study aims to determine the relative contribution of different types of parental support behaviours for predicting the likelihood that children meet recently established Canadian sleep guidelines. Data were collected using Computer Assisted Telephone Interviews (CATI) of parents or guardians with at least one child under the age of 18 living in Ontario, Canada. To align with sleep guidelines, parents included in this analysis had at least one child between 5 and 17 years of age (n = 1622). Two multivariable logistic regression models were built to predict whether or not parents reported their child was meeting sleep guidelines - one for weekday sleep and another for sleep on weekends. Independent variables included parent and child age and gender, motivational and regulatory parental support behaviours, and socio-demographic characteristics. On weekdays, enforcing rules about child bedtime was a significant positive predictor of children meeting sleep guidelines (OR: 1.59; 95% CI: 1.03-2.44); while encouraging the child to go to bed at a specific time was a significant negative predictor of child meeting sleep guidelines (OR: 0.29; 95% CI: 0.13-0.65). On weekends, none of the parental support behaviours contributed significantly to the predictions of child sleep. For both weekdays and weekends, the child's age group was an important predictor of children meeting sleep guidelines. The contribution of parental support behaviours to predictions of children meeting sleep guidelines varied with the type of support provided, and weekend versus weekday

Do parents’ support behaviours predict whether or not their children get sufficient sleep? A cross-sectional study

Directory of Open Access Journals (Sweden)

Evelyn Pyper

2017-05-01

Full Text Available Abstract Background Sleep is an essential component of healthy cognitive and physical development. Lack of sleep may put children at risk for a variety of mental and physical health outcomes, including overweight, obesity and related chronic diseases. Given that children’s sleep duration has decreased in recent decades, there is a need to understand the determinants of child sleep, including the role of parental support behaviours. This study aims to determine the relative contribution of different types of parental support behaviours for predicting the likelihood that children meet recently established Canadian sleep guidelines. Methods Data were collected using Computer Assisted Telephone Interviews (CATI of parents or guardians with at least one child under the age of 18 living in Ontario, Canada. To align with sleep guidelines, parents included in this analysis had at least one child between 5 and 17 years of age (n = 1622. Two multivariable logistic regression models were built to predict whether or not parents reported their child was meeting sleep guidelines – one for weekday sleep and another for sleep on weekends. Independent variables included parent and child age and gender, motivational and regulatory parental support behaviours, and socio-demographic characteristics. Results On weekdays, enforcing rules about child bedtime was a significant positive predictor of children meeting sleep guidelines (OR: 1.59; 95% CI: 1.03–2.44; while encouraging the child to go to bed at a specific time was a significant negative predictor of child meeting sleep guidelines (OR: 0.29; 95% CI: 0.13–0.65. On weekends, none of the parental support behaviours contributed significantly to the predictions of child sleep. For both weekdays and weekends, the child’s age group was an important predictor of children meeting sleep guidelines. Conclusions The contribution of parental support behaviours to predictions of children meeting sleep

Prediction of RNA secondary structure using generalized centroid estimators.

Science.gov (United States)

Hamada, Michiaki; Kiryu, Hisanori; Sato, Kengo; Mituyama, Toutai; Asai, Kiyoshi

2009-02-15

Recent studies have shown that the methods for predicting secondary structures of RNAs on the basis of posterior decoding of the base-pairing probabilities has an advantage with respect to prediction accuracy over the conventionally utilized minimum free energy methods. However, there is room for improvement in the objective functions presented in previous studies, which are maximized in the posterior decoding with respect to the accuracy measures for secondary structures. We propose novel estimators which improve the accuracy of secondary structure prediction of RNAs. The proposed estimators maximize an objective function which is the weighted sum of the expected number of the true positives and that of the true negatives of the base pairs. The proposed estimators are also improved versions of the ones used in previous works, namely CONTRAfold for secondary structure prediction from a single RNA sequence and McCaskill-MEA for common secondary structure prediction from multiple alignments of RNA sequences. We clarify the relations between the proposed estimators and the estimators presented in previous works, and theoretically show that the previous estimators include additional unnecessary terms in the evaluation measures with respect to the accuracy. Furthermore, computational experiments confirm the theoretical analysis by indicating improvement in the empirical accuracy. The proposed estimators represent extensions of the centroid estimators proposed in Ding et al. and Carvalho and Lawrence, and are applicable to a wide variety of problems in bioinformatics. Supporting information and the CentroidFold software are available online at: http://www.ncrna.org/software/centroidfold/.

MERGERS IN ΛCDM: UNCERTAINTIES IN THEORETICAL PREDICTIONS AND INTERPRETATIONS OF THE MERGER RATE

International Nuclear Information System (INIS)

Hopkins, Philip F.; Bundy, Kevin; Wetzel, Andrew; Ma, Chung-Pei; Croton, Darren; Khochfar, Sadegh; Hernquist, Lars; Genel, Shy; Van den Bosch, Frank; Somerville, Rachel S.; Keres, Dusan; Stewart, Kyle; Younger, Joshua D.

2010-01-01

Different theoretical methodologies lead to order-of-magnitude variations in predicted galaxy-galaxy merger rates. We examine how this arises and quantify the dominant uncertainties. Modeling of dark matter and galaxy inspiral/merger times contribute factor of ∼2 uncertainties. Different estimates of the halo-halo merger rate, the subhalo 'destruction' rate, and the halo merger rate with some dynamical friction time delay for galaxy-galaxy mergers, agree to within this factor of ∼2, provided proper care is taken to define mergers consistently. There are some caveats: if halo/subhalo masses are not appropriately defined the major-merger rate can be dramatically suppressed, and in models with 'orphan' galaxies and under-resolved subhalos the merger timescale can be severely over-estimated. The dominant differences in galaxy-galaxy merger rates between models owe to the treatment of the baryonic physics. Cosmological hydrodynamic simulations without strong feedback and some older semi-analytic models (SAMs), with known discrepancies in mass functions, can be biased by large factors (∼5) in predicted merger rates. However, provided that models yield a reasonable match to the total galaxy mass function, the differences in properties of central galaxies are sufficiently small to alone contribute small (factor of ∼1.5) additional systematics to merger rate predictions. But variations in the baryonic physics of satellite galaxies in models can also have a dramatic effect on merger rates. The well-known problem of satellite 'over-quenching' in most current SAMs-whereby SAM satellite populations are too efficiently stripped of their gas-could lead to order-of-magnitude under-estimates of merger rates for low-mass, gas-rich galaxies. Models in which the masses of satellites are fixed by observations (or SAMs adjusted to resolve this 'over-quenching') tend to predict higher merger rates, but with factor of ∼2 uncertainties stemming from the uncertainty in those

Predicting Career Adaptability through Self-Esteem and Social Support: A Research on Young Adults

Science.gov (United States)

Ataç, Lale Oral; Dirik, Deniz; Tetik, Hilmiye Türesin

2018-01-01

The purpose of this study is to investigate the relationship between career adaptability and self-esteem, and analyze the moderating role of social support in this relationship on a sample of 313 young adults. The results of the study confirm that career adaptability is significantly predicted by self-esteem. Moreover, findings suggest that (1)…

Internal rotation for predicting conformational population of 1,2-difluorethane and 1,2-dichloroethane

Energy Technology Data Exchange (ETDEWEB)

Venâncio, Mateus F. [Laboratório de Química Computacional e Modelagem Molecular, Departamento de Química, ICEx, Universidade Federal de Minas Gerais, Campus Universitário, 31.270-901 Belo Horizonte, MG (Brazil); Dos Santos, Hélio F. [Núcleo de Estudos em Química Computacional (NEQC), Departamento de Química, ICE, Universidade Federal de Juiz de Fora (UFJF), Campus Universitário, Martelos, Juiz de Fora, MG 36036-330 (Brazil); De Almeida, Wagner B., E-mail: wbdealmeida@gmail.com [Laboratório de Química Computacional (LQC), Departamento de Química Inorgânica, Instituto de Química, Universidade Federal Fluminense, Campus do Valonguinho, Centro, Niterói, RJ CEP: 24020-141 (Brazil)

2016-06-15

Highlights: • Contribution of internal rotation to Gibbs free energy estimated using the quantum pendulum model. • Theoretical prediction of conformational population of 1,2-difluorethane and 1,2-dichloroethane. • The predicted populations are in excellent agreement with experimental gas phase data available. • QPM model account for low vibrational frequency modes effect on thermodynamic calculation. • Caution is needed when the RR–HO approach has to be used in conformational analysis studies. - Abstract: The contribution of internal rotation to the thermal correction of Gibbs free energy (ΔG) is estimated using the quantum pendulum model (QPM) to solve the characteristic Schrödinger equation. The procedure is applied to theoretical prediction of conformational population of 1,2-difluorethane (1,2-DFE) and 1,2-dichloroethane (1,2-DCE) molecules. The predicted population for the anti form was 37% and 75%, for 1,2-DFE and 1,2-DCE respectively, in excellent agreement with experimental gas phase data available, 37 ± 5% and 78 ± 5%. These results provide great support to the use of the QPM model to account for the low vibrational frequency modes effect on the calculation of thermodynamic properties.

Towards Accurate Prediction of Unbalance Response, Oil Whirl and Oil Whip of Flexible Rotors Supported by Hydrodynamic Bearings

Directory of Open Access Journals (Sweden)

Rob Eling

2016-09-01

Full Text Available Journal bearings are used to support rotors in a wide range of applications. In order to ensure reliable operation, accurate analyses of these rotor-bearing systems are crucial. Coupled analysis of the rotor and the journal bearing is essential in the case that the rotor is flexible. The accuracy of prediction of the model at hand depends on its comprehensiveness. In this study, we construct three bearing models of increasing modeling comprehensiveness and use these to predict the response of two different rotor-bearing systems. The main goal is to evaluate the correlation with measurement data as a function of modeling comprehensiveness: 1D versus 2D pressure prediction, distributed versus lumped thermal model, Newtonian versus non-Newtonian fluid description and non-mass-conservative versus mass-conservative cavitation description. We conclude that all three models predict the existence of critical speeds and whirl for both rotor-bearing systems. However, the two more comprehensive models in general show better correlation with measurement data in terms of frequency and amplitude. Furthermore, we conclude that a thermal network model comprising temperature predictions of the bearing surroundings is essential to obtain accurate predictions. The results of this study aid in developing accurate and computationally-efficient models of flexible rotors supported by plain journal bearings.

A theoretical approach to artificial intelligence systems in medicine.

Science.gov (United States)

Spyropoulos, B; Papagounos, G

1995-10-01

The various theoretical models of disease, the nosology which is accepted by the medical community and the prevalent logic of diagnosis determine both the medical approach as well as the development of the relevant technology including the structure and function of the A.I. systems involved. A.I. systems in medicine, in addition to the specific parameters which enable them to reach a diagnostic and/or therapeutic proposal, entail implicitly theoretical assumptions and socio-cultural attitudes which prejudice the orientation and the final outcome of the procedure. The various models -causal, probabilistic, case-based etc. -are critically examined and their ethical and methodological limitations are brought to light. The lack of a self-consistent theoretical framework in medicine, the multi-faceted character of the human organism as well as the non-explicit nature of the theoretical assumptions involved in A.I. systems restrict them to the role of decision supporting "instruments" rather than regarding them as decision making "devices". This supporting role and, especially, the important function which A.I. systems should have in the structure, the methods and the content of medical education underscore the need of further research in the theoretical aspects and the actual development of such systems.

Recent theoretical, neural, and clinical advances in sustained attention research

Science.gov (United States)

Fortenbaugh, Francesca C.; DeGutis, Joseph; Esterman, Michael

2017-01-01

Models of attention often distinguish between attention subtypes, with classic models separating orienting, switching, and sustaining functions. Compared to other forms of attention, the neurophysiological basis of sustaining attention has received far less attention yet it is known that momentary failures of sustained attention can have far ranging negative impacts in healthy individuals and lasting sustained attention deficits are pervasive in clinical populations. In recent years, however, there has been increased interest in characterizing moment-to-moment fluctuations in sustained attention in addition to the overall vigilance decrement and understanding how these neurocognitive systems change over the lifespan and across various clinical populations. The use of novel neuroimaging paradigms and statistical approaches has allowed for better characterization of the neural networks supporting sustained attention, and highlighted dynamic interactions within and across multiple distributed networks that predict behavioral performance. These advances have also provided potential biomarkers to identify individuals with sustained attention deficits. These findings have led to new theoretical models of why sustaining focused attention is a challenge for individuals and form the basis for the next generation of sustained attention research, which seeks to accurately diagnose and develop theoretically-driven treatments for sustained attention deficits that affect a variety of clinical populations. PMID:28260249

Recent theoretical, neural, and clinical advances in sustained attention research.

Science.gov (United States)

Fortenbaugh, Francesca C; DeGutis, Joseph; Esterman, Michael

2017-05-01

Models of attention often distinguish among attention subtypes, with classic models separating orienting, switching, and sustaining functions. Compared with other forms of attention, the neurophysiological basis of sustaining attention has received far less notice, yet it is known that momentary failures of sustained attention can have far-ranging negative effects in healthy individuals, and lasting sustained attention deficits are pervasive in clinical populations. In recent years, however, there has been increased interest in characterizing moment-to-moment fluctuations in sustained attention, in addition to the overall vigilance decrement, and understanding how these neurocognitive systems change over the life span and across various clinical populations. The use of novel neuroimaging paradigms and statistical approaches has allowed for better characterization of the neural networks supporting sustained attention and has highlighted dynamic interactions within and across multiple distributed networks that predict behavioral performance. These advances have also provided potential biomarkers to identify individuals with sustained attention deficits. These findings have led to new theoretical models explaining why sustaining focused attention is a challenge for individuals and form the basis for the next generation of sustained attention research, which seeks to accurately diagnose and develop theoretically driven treatments for sustained attention deficits that affect a variety of clinical populations. © 2017 New York Academy of Sciences.

Analysis of two-phase flow induced vibrations in perpendiculary supported U-type piping systems

International Nuclear Information System (INIS)

Hiramatsu, Tsutomu; Komura, Yoshiaki; Ito, Atsushi.

1984-01-01

The perpose of this analysis is to predict the vibration level of a pipe conveying a two-phase flowing fluid. Experiments were carried out with a perpendiculary supported U-type piping system, conveying an air-water two-phase flow in a steady state condition. Fluctuation signals are observed by a void signal sensor, and power spectral densities and probability density functions are obtained from the void signals. Theoretical studies using FEM and an estimation of the exciting forces from the PSD of void signals, provided a good predictional estimation of vibration responses of the piping system. (author)

A theoretical model investigation of peptide bond formation involving two water molecules in ribosome supports the two-step and eight membered ring mechanism

International Nuclear Information System (INIS)

Wang, Qiang; Gao, Jun; Zhang, Dongju; Liu, Chengbu

2015-01-01

Highlights: • We theoretical studied peptide bond formation reaction mechanism with two water molecules. • The first water molecule can decrease the reaction barriers by forming hydrogen bonds. • The water molecule mediated three-proton transfer mechanism is the favorable mechanism. • Our calculation supports the two-step and eight membered ring mechanism. - Abstract: The ribosome is the macromolecular machine that catalyzes protein synthesis. The kinetic isotope effect analysis reported by Strobel group supports the two-step mechanism. However, the destination of the proton originating from the nucleophilic amine is uncertain. A computational simulation of different mechanisms including water molecules is carried out using the same reaction model and theoretical level. Formation the tetrahedral intermediate with proton transfer from nucleophilic nitrogen, is the rate-limiting step when two water molecules participate in peptide bond formation. The first water molecule forming hydrogen bonds with O9′ and H15′ in the A site can decrease the reaction barriers. Combined with results of the solvent isotope effects analysis, we conclude that the three-proton transfer mechanism in which water molecule mediate the proton shuttle between amino and carbon oxygen in rate-limiting step is the favorable mechanism. Our results will shield light on a better understand the reaction mechanism of ribosome

Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model

Science.gov (United States)

Yeganeh, B.; Motlagh, M. Shafie Pour; Rashidi, Y.; Kamalan, H.

2012-08-01

Due to the health impacts caused by exposures to air pollutants in urban areas, monitoring and forecasting of air quality parameters have become popular as an important topic in atmospheric and environmental research today. The knowledge on the dynamics and complexity of air pollutants behavior has made artificial intelligence models as a useful tool for a more accurate pollutant concentration prediction. This paper focuses on an innovative method of daily air pollution prediction using combination of Support Vector Machine (SVM) as predictor and Partial Least Square (PLS) as a data selection tool based on the measured values of CO concentrations. The CO concentrations of Rey monitoring station in the south of Tehran, from Jan. 2007 to Feb. 2011, have been used to test the effectiveness of this method. The hourly CO concentrations have been predicted using the SVM and the hybrid PLS-SVM models. Similarly, daily CO concentrations have been predicted based on the aforementioned four years measured data. Results demonstrated that both models have good prediction ability; however the hybrid PLS-SVM has better accuracy. In the analysis presented in this paper, statistic estimators including relative mean errors, root mean squared errors and the mean absolute relative error have been employed to compare performances of the models. It has been concluded that the errors decrease after size reduction and coefficients of determination increase from 56 to 81% for SVM model to 65-85% for hybrid PLS-SVM model respectively. Also it was found that the hybrid PLS-SVM model required lower computational time than SVM model as expected, hence supporting the more accurate and faster prediction ability of hybrid PLS-SVM model.

Theoretical prediction and validation of cell recovery rates in preparing platelet-rich plasma through a centrifugation.

Science.gov (United States)

Piao, Linfeng; Park, Hyungmin; Jo, Chris Hyunchul

2017-01-01

In the present study, we propose a theoretical framework to predict the recovery rates of platelets and white blood cells in the process of centrifugal separation of whole blood contained in a tube for the preparation of platelet-rich plasma. Compared to previous efforts to optimize or standardize the protocols of centrifugation, we try to further the physical background (i.e., based on the multiphase flow phenomena) of analysis to develop a universal approach that can be applied to widely different conditions. That is, one-dimensional quasi-linear partial differential equation to describe the centrifugal sedimentation of dispersed phase (red and white blood cells) in continuous phase (plasma) is derived based on the kinematic-wave theory. With the information of whole blood volume and tube geometry considered, it is possible to determine the positions of interfaces between supernatant/suspension and suspension/sediment, i.e., the particle concentration gradient in a tube, for a wide range of centrifugation parameters (time and acceleration). While establishing a theory to predict the recovery rates of the platelet and white blood cell from the pre-determined interface positions, we also propose a new correlation model between the recovery rates of plasma and platelets, which is found to be a function of the whole blood volume, centrifugal time and acceleration, and tube geometry. The present predictions for optimal condition show good agreements with available human clinical data, obtained from different conditions, indicating the universal applicability of our method. Furthermore, the dependence of recovery rates on centrifugal conditions reveals that there exist a different critical acceleration and time for the maximum recovery rate of platelets and white blood cells, respectively. The other parameters such as hematocrit, whole blood volume and tube geometry are also found to strongly affect the maximum recovery rates of blood cells, and finally, as a strategy

Operational Precipitation prediction in Support of Real-Time Flash Flood Prediction and Reservoir Management

Science.gov (United States)

Georgakakos, K. P.

2006-05-01

The presentation will outline the implementation and performance evaluation of a number of national and international projects pertaining to operational precipitation estimation and prediction in the context of hydrologic warning systems and reservoir management support. In all cases, uncertainty measures of the estimates and predictions are an integral part of the precipitation models. Outstanding research issues whose resolution is likely to lead to improvements in the operational environment are presented. The presentation draws from the experience of the Hydrologic Research Center (http://www.hrc-lab.org) prototype implementation projects at the Panama Canal, Central America, Northern California, and South-Central US. References: Carpenter, T.M, and K.P. Georgakakos, "Discretization Scale Dependencies of the Ensemble Flow Range versus Catchment Area Relationship in Distributed Hydrologic Modeling," Journal of Hydrology, 2006, in press. Carpenter, T.M., and K.P. Georgakakos, "Impacts of Parametric and Radar Rainfall Uncertainty on the Ensemble Streamflow Simulations of a Distributed Hydrologic Model," Journal of Hydrology, 298, 202-221, 2004. Georgakakos, K.P., Graham, N.E., Carpenter, T.M., Georgakakos, A.P., and H. Yao, "Integrating Climate- Hydrology Forecasts and Multi-Objective Reservoir Management in Northern California," EOS, 86(12), 122,127, 2005. Georgakakos, K.P., and J.A. Sperfslage, "Operational Rainfall and Flow Forecasting for the Panama Canal Watershed," in The Rio Chagres: A Multidisciplinary Profile of a Tropical Watershed, R.S. Harmon, ed., Kluwer Academic Publishers, The Netherlands, Chapter 16, 323-334, 2005. Georgakakos, K. P., "Analytical results for operational flash flood guidance," Journal of Hydrology, doi:10.1016/j.jhydrol.2005.05.009, 2005.

Emotions predictably modify response times in the initiation of human motor actions: A meta-analytic review.

Science.gov (United States)

Beatty, Garrett F; Cranley, Nicole M; Carnaby, Giselle; Janelle, Christopher M

2016-03-01

Emotions motivate individuals to attain appetitive goals and avoid aversive consequences. Empirical investigations have detailed how broad approach and avoidance orientations are reflected in fundamental movement attributes such as the speed, accuracy, and variability of motor actions. Several theoretical perspectives propose explanations for how emotional states influence the speed with which goal directed movements are initiated. These perspectives include biological predisposition, muscle activation, distance regulation, cognitive evaluation, and evaluative response coding accounts. A comprehensive review of literature and meta-analysis were undertaken to quantify empirical support for these theoretical perspectives. The systematic review yielded 34 studies that contained 53 independent experiments producing 128 effect sizes used to evaluate the predictions of existing theories. The central tenets of the biological predisposition (Hedges' g = -0.356), distance regulation (g = -0.293; g = 0.243), and cognitive evaluation (g = -0.249; g = -0.405; g = -0.174) accounts were supported. Partial support was also identified for the evaluative response coding (g = -0.255) framework. Our findings provide quantitative evidence that substantiate existing theoretical perspectives, and provide potential direction for conceptual integration of these independent perspectives. Recommendations for future empirical work in this area are discussed. (c) 2016 APA, all rights reserved).

Short-term traffic flow prediction model using particle swarm optimization–based combined kernel function-least squares support vector machine combined with chaos theory

Directory of Open Access Journals (Sweden)

Qiang Shang

2016-08-01

Full Text Available Short-term traffic flow prediction is an important part of intelligent transportation systems research and applications. For further improving the accuracy of short-time traffic flow prediction, a novel hybrid prediction model (multivariate phase space reconstruction–combined kernel function-least squares support vector machine based on multivariate phase space reconstruction and combined kernel function-least squares support vector machine is proposed. The C-C method is used to determine the optimal time delay and the optimal embedding dimension of traffic variables’ (flow, speed, and occupancy time series for phase space reconstruction. The G-P method is selected to calculate the correlation dimension of attractor which is an important index for judging chaotic characteristics of the traffic variables’ series. The optimal input form of combined kernel function-least squares support vector machine model is determined by multivariate phase space reconstruction, and the model’s parameters are optimized by particle swarm optimization algorithm. Finally, case validation is carried out using the measured data of an expressway in Xiamen, China. The experimental results suggest that the new proposed model yields better predictions compared with similar models (combined kernel function-least squares support vector machine, multivariate phase space reconstruction–generalized kernel function-least squares support vector machine, and phase space reconstruction–combined kernel function-least squares support vector machine, which indicates that the new proposed model exhibits stronger prediction ability and robustness.

A Detection-Theoretic Analysis of Auditory Streaming and Its Relation to Auditory Masking

Directory of Open Access Journals (Sweden)

An-Chieh Chang

2016-09-01

Full Text Available Research on hearing has long been challenged with understanding our exceptional ability to hear out individual sounds in a mixture (the so-called cocktail party problem. Two general approaches to the problem have been taken using sequences of tones as stimuli. The first has focused on our tendency to hear sequences, sufficiently separated in frequency, split into separate cohesive streams (auditory streaming. The second has focused on our ability to detect a change in one sequence, ignoring all others (auditory masking. The two phenomena are clearly related, but that relation has never been evaluated analytically. This article offers a detection-theoretic analysis of the relation between multitone streaming and masking that underscores the expected similarities and differences between these phenomena and the predicted outcome of experiments in each case. The key to establishing this relation is the function linking performance to the information divergence of the tone sequences, DKL (a measure of the statistical separation of their parameters. A strong prediction is that streaming and masking of tones will be a common function of DKL provided that the statistical properties of sequences are symmetric. Results of experiments are reported supporting this prediction.

Maternal Support of Children's Early Numerical Concept Learning Predicts Preschool and First-Grade Math Achievement.

Science.gov (United States)

Casey, Beth M; Lombardi, Caitlin M; Thomson, Dana; Nguyen, Hoa Nha; Paz, Melissa; Theriault, Cote A; Dearing, Eric

2018-01-01

The primary goal in this study was to examine maternal support of numerical concepts at 36 months as predictors of math achievement at 4½ and 6-7 years. Observational measures of mother-child interactions (n = 140) were used to examine type of support for numerical concepts. Maternal support that involved labeling the quantities of sets of objects was predictive of later child math achievement. This association was significant for preschool (d = .45) and first-grade math (d = .49), controlling for other forms of numerical support (identifying numerals, one-to-one counting) as well as potential confounding factors. The importance of maternal support of labeling set sizes at 36 months is discussed as a precursor to children's eventual understanding of the cardinal principle. © 2016 The Authors. Child Development © 2016 Society for Research in Child Development, Inc.

Theoretical study on new bias factor methods to effectively use critical experiments for improvement of prediction accuracy of neutronic characteristics

International Nuclear Information System (INIS)

Kugo, Teruhiko; Mori, Takamasa; Takeda, Toshikazu

2007-01-01

Extended bias factor methods are proposed with two new concepts, the LC method and the PE method, in order to effectively use critical experiments and to enhance the applicability of the bias factor method for the improvement of the prediction accuracy of neutronic characteristics of a target core. Both methods utilize a number of critical experimental results and produce a semifictitious experimental value with them. The LC and PE methods define the semifictitious experimental values by a linear combination of experimental values and the product of exponentiated experimental values, respectively, and the corresponding semifictitious calculation values by those of calculation values. A bias factor is defined by the ratio of the semifictitious experimental value to the semifictitious calculation value in both methods. We formulate how to determine weights for the LC method and exponents for the PE method in order to minimize the variance of the design prediction value obtained by multiplying the design calculation value by the bias factor. From a theoretical comparison of these new methods with the conventional method which utilizes a single experimental result and the generalized bias factor method which was previously proposed to utilize a number of experimental results, it is concluded that the PE method is the most useful method for improving the prediction accuracy. The main advantages of the PE method are summarized as follows. The prediction accuracy is necessarily improved compared with the design calculation value even when experimental results include large experimental errors. This is a special feature that the other methods do not have. The prediction accuracy is most effectively improved by utilizing all the experimental results. From these facts, it can be said that the PE method effectively utilizes all the experimental results and has a possibility to make a full-scale-mockup experiment unnecessary with the use of existing and future benchmark

DEVELOPING MEASURES TO IMPROVE STRENGTH INDICES OF SUPPORTING STRUCTURES FOR HEAD CARS OF DIESEL TRAINS DR1A ON THE BASIS OF EXPERIMENTAL-AND-THEORETICAL WORKS

Directory of Open Access Journals (Sweden)

O. M. Bondarev

2014-11-01

Full Text Available Purpose. The objective is to determine the stress-strain state of supporting structures of the head car body and the traction transmission unit, which can be created in the operation of emergency situations, and to develop the measures aimed at improving the stress-strain state of these elements. Methodology. In order to achieve this objective, in performing the work an experimental determination of efforts and stress levels in the most loaded elements of supporting structures as well as the traction transmission units was conducted; design models for the theoretical determination of stress and effort levels were developed. Findings. Based on the analysis of the calculation results the best options for the upgrades, which have been put into the basis of proposals aimed at improving the strength indices, were revealed. Originality. Based on the experimental and theoretical studies, scientific monitoring of development works on modernization and improvement of strength indices of supporting structures of head cars of diesel trains DR1A was performed. Practical value. The technical solution to the measures, which are to be carried out beyond the limits of assigned operation lifetime for diesel train of the series specified was developed and transferred to the Ukrzaliznytsia experts to introduce the proposed measures on improving the strength indices.

Review of Nearshore Morphologic Prediction

Science.gov (United States)

Plant, N. G.; Dalyander, S.; Long, J.

2014-12-01

The evolution of the world's erodible coastlines will determine the balance between the benefits and costs associated with human and ecological utilization of shores, beaches, dunes, barrier islands, wetlands, and estuaries. So, we would like to predict coastal evolution to guide management and planning of human and ecological response to coastal changes. After decades of research investment in data collection, theoretical and statistical analysis, and model development we have a number of empirical, statistical, and deterministic models that can predict the evolution of the shoreline, beaches, dunes, and wetlands over time scales of hours to decades, and even predict the evolution of geologic strata over the course of millennia. Comparisons of predictions to data have demonstrated that these models can have meaningful predictive skill. But these comparisons also highlight the deficiencies in fundamental understanding, formulations, or data that are responsible for prediction errors and uncertainty. Here, we review a subset of predictive models of the nearshore to illustrate tradeoffs in complexity, predictive skill, and sensitivity to input data and parameterization errors. We identify where future improvement in prediction skill will result from improved theoretical understanding, and data collection, and model-data assimilation.

In Pursuit of Theoretical Ground in Behavior Change Support Systems: Analysis of Peer-to-Peer Communication in a Health-Related Online Community.

Science.gov (United States)

Myneni, Sahiti; Cobb, Nathan; Cohen, Trevor

2016-02-02

Research studies involving health-related online communities have focused on examining network structure to understand mechanisms underlying behavior change. Content analysis of the messages exchanged in these communities has been limited to the "social support" perspective. However, existing behavior change theories suggest that message content plays a prominent role reflecting several sociocognitive factors that affect an individual's efforts to make a lifestyle change. An understanding of these factors is imperative to identify and harness the mechanisms of behavior change in the Health 2.0 era. The objective of this work is two-fold: (1) to harness digital communication data to capture essential meaning of communication and factors affecting a desired behavior change, and (2) to understand the applicability of existing behavior change theories to characterize peer-to-peer communication in online platforms. In this paper, we describe grounded theory-based qualitative analysis of digital communication in QuitNet, an online community promoting smoking cessation. A database of 16,492 de-identified public messages from 1456 users from March 1-April 30, 2007, was used in our study. We analyzed 795 messages using grounded theory techniques to ensure thematic saturation. This analysis enabled identification of key concepts contained in the messages exchanged by QuitNet members, allowing us to understand the sociobehavioral intricacies underlying an individual's efforts to cease smoking in a group setting. We further ascertained the relevance of the identified themes to theoretical constructs in existing behavior change theories (eg, Health Belief Model) and theoretically linked techniques of behavior change taxonomy. We identified 43 different concepts, which were then grouped under 12 themes based on analysis of 795 messages. Examples of concepts include "sleepiness," "pledge," "patch," "spouse," and "slip." Examples of themes include "traditions," "social support

Actual and theoretical gas consumption in Dutch dwellings: What causes the differences?

International Nuclear Information System (INIS)

Majcen, Daša; Itard, Laure; Visscher, Henk

2013-01-01

Energy labels in buildings are awarded based on theoretical gas and electricity consumption based on dwelling's physical characteristics. Prior to this research, a large-scale study was conducted in The Netherlands comparing theoretical energy use with data on actual energy use revealing substantial discrepancies (Majcen et al., 2013). This study uses identical energy label data, supplemented with additional data sources in order to reveal how different parameters influence theoretical and actual consumptions gas and electricity. Analysis is conducted through descriptive statistics and regression analysis. Regression analysis explained far less of the variation in the actual consumption than in the theoretical and has shown that variables such as floor area, ownership type, salary and the value of the house, which predicted a high degree of change in actual gas consumption, were insignificant (ownership, salary, value) or had a minor impact on theoretical consumption (floor area). Since some possibly fundamental variables were unavailable for regression analysis, we also conducted a sensitivity study of theoretical gas consumption. It showed that average indoor temperature, ventilation rate and accuracy of U-value have a large influence on the theoretical gas consumption; whereas the number of occupants and internal heat load have a rather limited impact. - Highlights: • Floor area, ownership, salary and value predict the change in actual gas use well. • Mentioned variables are insignificant or have small impact on theoretical use. • Energy consumption of less energy efficient systems is overestimated. • Accurate model assumptions and inspections would reduce the discrepancies. • Big discrepancies stem from misassumption of temperature, heated floor area, U values

Predicting metabolic syndrome using decision tree and support vector machine methods

Directory of Open Access Journals (Sweden)

Farzaneh Karimi-Alavijeh

2016-06-01

Full Text Available BACKGROUND: Metabolic syndrome which underlies the increased prevalence of cardiovascular disease and Type 2 diabetes is considered as a group of metabolic abnormalities including central obesity, hypertriglyceridemia, glucose intolerance, hypertension, and dyslipidemia. Recently, artificial intelligence based health-care systems are highly regarded because of its success in diagnosis, prediction, and choice of treatment. This study employs machine learning technics for predict the metabolic syndrome. METHODS: This study aims to employ decision tree and support vector machine (SVM to predict the 7-year incidence of metabolic syndrome. This research is a practical one in which data from 2107 participants of Isfahan Cohort Study has been utilized. The subjects without metabolic syndrome according to the ATPIII criteria were selected. The features that have been used in this data set include: gender, age, weight, body mass index, waist circumference, waist-to-hip ratio, hip circumference, physical activity, smoking, hypertension, antihypertensive medication use, systolic blood pressure (BP, diastolic BP, fasting blood sugar, 2-hour blood glucose, triglycerides (TGs, total cholesterol, low-density lipoprotein, high density lipoprotein-cholesterol, mean corpuscular volume, and mean corpuscular hemoglobin. Metabolic syndrome was diagnosed based on ATPIII criteria and two methods of decision tree and SVM were selected to predict the metabolic syndrome. The criteria of sensitivity, specificity and accuracy were used for validation. RESULTS: SVM and decision tree methods were examined according to the criteria of sensitivity, specificity and accuracy. Sensitivity, specificity and accuracy were 0.774 (0.758, 0.74 (0.72 and 0.757 (0.739 in SVM (decision tree method. CONCLUSION: The results show that SVM method sensitivity, specificity and accuracy is more efficient than decision tree. The results of decision tree method show that the TG is the most

Predicting metabolic syndrome using decision tree and support vector machine methods.

Science.gov (United States)

Karimi-Alavijeh, Farzaneh; Jalili, Saeed; Sadeghi, Masoumeh

2016-05-01

Metabolic syndrome which underlies the increased prevalence of cardiovascular disease and Type 2 diabetes is considered as a group of metabolic abnormalities including central obesity, hypertriglyceridemia, glucose intolerance, hypertension, and dyslipidemia. Recently, artificial intelligence based health-care systems are highly regarded because of its success in diagnosis, prediction, and choice of treatment. This study employs machine learning technics for predict the metabolic syndrome. This study aims to employ decision tree and support vector machine (SVM) to predict the 7-year incidence of metabolic syndrome. This research is a practical one in which data from 2107 participants of Isfahan Cohort Study has been utilized. The subjects without metabolic syndrome according to the ATPIII criteria were selected. The features that have been used in this data set include: gender, age, weight, body mass index, waist circumference, waist-to-hip ratio, hip circumference, physical activity, smoking, hypertension, antihypertensive medication use, systolic blood pressure (BP), diastolic BP, fasting blood sugar, 2-hour blood glucose, triglycerides (TGs), total cholesterol, low-density lipoprotein, high density lipoprotein-cholesterol, mean corpuscular volume, and mean corpuscular hemoglobin. Metabolic syndrome was diagnosed based on ATPIII criteria and two methods of decision tree and SVM were selected to predict the metabolic syndrome. The criteria of sensitivity, specificity and accuracy were used for validation. SVM and decision tree methods were examined according to the criteria of sensitivity, specificity and accuracy. Sensitivity, specificity and accuracy were 0.774 (0.758), 0.74 (0.72) and 0.757 (0.739) in SVM (decision tree) method. The results show that SVM method sensitivity, specificity and accuracy is more efficient than decision tree. The results of decision tree method show that the TG is the most important feature in predicting metabolic syndrome. According

Decision-theoretic troubleshooting: Hardness of approximation

Czech Academy of Sciences Publication Activity Database

Lín, Václav

2014-01-01

Roč. 55, č. 4 (2014), s. 977-988 ISSN 0888-613X R&D Projects: GA ČR GA13-20012S Institutional support: RVO:67985556 Keywords : Decision-theoretic troubleshooting * Hardness of approximation * NP-completeness Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 2.451, year: 2014

Theoretical Predictions of Springing and Their Comparison with Full Scale Measurements

DEFF Research Database (Denmark)

Gu, X.; Storhaug, G.; Vidic-Perunovic, Jelena

2003-01-01

The present paper considers a large ocean going ship with significant springing responses, which have made a large contribution to the fatigue cracking for certain structural details. Four different theories for predicting ship responses and associated computer programs for predictions of springing...

Social support for healthy behaviors: Scale psychometrics and prediction of weight loss among women in a behavioral program

Science.gov (United States)

Kiernan, Michaela; Moore, Susan D.; Schoffman, Danielle E.; Lee, Katherine; King, Abby C.; Taylor, C. Barr; Kiernan, Nancy Ellen; Perri, Michael G.

2015-01-01

Social support could be a powerful weight-loss treatment moderator or mediator but is rarely assessed. We assessed the psychometric properties, initial levels, and predictive validity of a measure of perceived social support and sabotage from friends and family for healthy eating and physical activity (eight subscales). Overweight/obese women randomized to one of two 6-month, group-based behavioral weight-loss programs (N=267; mean BMI 32.1±3.5; 66.3% White) completed subscales at baseline, and weight loss was assessed at 6 months. Internal consistency, discriminant validity, and content validity were excellent for support subscales and adequate for sabotage subscales; qualitative responses revealed novel deliberate instances not reflected in current sabotage items. Most women (>75%) “never” or “rarely” experienced support from friends or family. Using non-parametric classification methods, we identified two subscales—support from friends for healthy eating and support from family for physical activity—that predicted three clinically meaningful subgroups who ranged in likelihood of losing ≥5% of initial weight at 6 months. Women who “never” experienced family support were least likely to lose weight (45.7% lost weight) whereas women who experienced both frequent friend and family support were more likely to lose weight (71.6% lost weight). Paradoxically, women who “never” experienced friend support were most likely to lose weight (80.0% lost weight), perhaps because the group-based programs provided support lacking from friendships. Psychometrics for support subscales were excellent; initial support was rare; and the differential roles of friend versus family support could inform future targeted weight-loss interventions to subgroups at risk. PMID:21996661

BacHbpred: Support Vector Machine Methods for the Prediction of Bacterial Hemoglobin-Like Proteins

Directory of Open Access Journals (Sweden)

MuthuKrishnan Selvaraj

2016-01-01

Full Text Available The recent upsurge in microbial genome data has revealed that hemoglobin-like (HbL proteins may be widely distributed among bacteria and that some organisms may carry more than one HbL encoding gene. However, the discovery of HbL proteins has been limited to a small number of bacteria only. This study describes the prediction of HbL proteins and their domain classification using a machine learning approach. Support vector machine (SVM models were developed for predicting HbL proteins based upon amino acid composition (AC, dipeptide composition (DC, hybrid method (AC + DC, and position specific scoring matrix (PSSM. In addition, we introduce for the first time a new prediction method based on max to min amino acid residue (MM profiles. The average accuracy, standard deviation (SD, false positive rate (FPR, confusion matrix, and receiver operating characteristic (ROC were analyzed. We also compared the performance of our proposed models in homology detection databases. The performance of the different approaches was estimated using fivefold cross-validation techniques. Prediction accuracy was further investigated through confusion matrix and ROC curve analysis. All experimental results indicate that the proposed BacHbpred can be a perspective predictor for determination of HbL related proteins. BacHbpred, a web tool, has been developed for HbL prediction.

Final Report. Research in Theoretical High Energy Physics

Energy Technology Data Exchange (ETDEWEB)

Greensite, Jeffrey P. [San Francisco State Univ., CA (United States); Golterman, Maarten F.L. [San Francisco State Univ., CA (United States)

2015-04-30

Grant-supported research in theoretical high-energy physics, conducted in the period 1992-2015 is briefly described, and a full listing of published articles result from those research activities is supplied.

Theoretical description and predictions of the properties of superheavy nuclei

Energy Technology Data Exchange (ETDEWEB)

Sobiczewski, A [Department of Theoretical Physics, Andrzej Soltan Institute for Nuclear Studies (Poland)

2009-12-31

Theoretical descriptions of superheavy atomic nuclei are shortly reviewed and illustrated by their results. Such properties of these nuclei as their shapes, masses, fission barriers, decay modes, decay energies, half-lives, are discussed. Special attention is given to the shell structure of the nuclei, due to which they exist. The role of the physical studies of the superheavy nuclei for the chemical research on the superheavy elements and, more generally, the relationship between these two kinds of investigation is underlined. This stresses the importance of close cooperation between physicists and chemists, experimentalists and theoreticians, in these studies.

Intrasubject Predictions of Vocational Preference: Convergent Validation via the Decision Theoretic Paradigm.

Science.gov (United States)

Monahan, Carlyn J.; Muchinsky, Paul M.

1985-01-01

The degree of convergent validity among four methods of identifying vocational preferences is assessed via the decision theoretic paradigm. Vocational preferences identified by Holland's Vocational Preference Inventory (VPI), a rating procedure, and ranking were compared with preferences identified from a policy-capturing model developed from an…

Watershed Management Optimization Support Tool (WMOST) v3: Theoretical Documentation

Science.gov (United States)

The Watershed Management Optimization Support Tool (WMOST) is a decision support tool that facilitates integrated water management at the local or small watershed scale. WMOST models the environmental effects and costs of management decisions in a watershed context, accounting fo...

A Theoretical Framework for Ecological Interface Design

DEFF Research Database (Denmark)

Vicente, Kim J.; Rasmussen, Jens

1988-01-01

A theoretical framework for designing interfaces for complex systems is de-scribed. The framework, called ecological interface design (EID), suggests a set of principles for designing interfaces in a way that supports the funda-mental properties of human cognition. The basis of EID is the skills...... of the task require. The EID approach extends the concept of direct manipulation inter-faces by taking into account the added complications introduced by complex systems. In this paper, we describe the development of the framework, its theoretical foundations, and examples of its application to various work...

Theoretical models to predict the transient heat transfer performance of HIFAR fuel elements under non-forced convective conditions

International Nuclear Information System (INIS)

Green, W.J.

1987-04-01

Simple theoretical models have been developed which are suitable for predicting the thermal responses of irradiated research fuel elements of markedly different geometries when they are subjected to loss-of-coolant accident conditions. These models have been used to calculate temperature responses corresponding to various non-forced convective conditions. Comparisons between experimentally observed temperatures and calculated values have shown that a suitable value for surface thermal emissivity is 0.35; modelling of the fuel element beyond the region of the fuel plate needs to be included since these areas account for approximately 25 per cent of the thermal power dissipated; general agreement between calculated and experimental temperatures for both transient and steady-state conditions is good - the maximum discrepancy between calculated and experimental temperatures for a HIFAR Mark IV/V fuel element is ∼ 70 deg C, and for an Oak Ridge Reactor (ORR) box-type fuel element ∼ 30 deg C; and axial power distribution does not significantly affect thermal responses for the conditions investigated. Overall, the comparisons have shown that the models evolved can reproduce experimental data to a level of accuracy that provides confidence in the modelling technique and the postulated heat dissipation mechanisms, and that these models can be used to predict thermal responses of fuel elements in accident conditions that are not easily investigated experimentally

CO oxidation on gold nanoparticles: Theoretical studies

DEFF Research Database (Denmark)

Remediakis, Ioannis; Lopez, Nuria; Nørskov, Jens Kehlet

2005-01-01

We present a summary of our theoretical results regarding CO oxidation on both oxide-supported and isolated gold nanoparticles. Using Density Functional Theory we have studied the adsorption of molecules and the oxidation reaction of CO on gold clusters. Low-coordinated sites on the gold...... nanoparticles can adsorb small inorganic molecules such as O2 and CO, and the presence of these sites is the key factor for the catalytic properties of supported gold nanoclusters. Other contributions, induced by the presence of the support, can provide parallel channels for the reaction and modulate the final...

Predicting Teacher Retention Using Stress and Support Variables

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Sass, Daniel A.; Seal, Andrea K.; Martin, Nancy K.

2011-01-01

Purpose: Teacher attrition is a significant international concern facing administrators. Although a considerable amount of literature exists related to the causes of job dissatisfaction and teachers leaving the profession, relatively few theoretical models test the complex interrelationships between these variables. The goal of this paper is to…

Theoretical frameworks for the learning of geometrical reasoning

OpenAIRE

Jones, Keith

1998-01-01

With the growth in interest in geometrical ideas it is important to be clear about the nature of geometrical reasoning and how it develops. This paper provides an overview of three theoretical frameworks for the learning of geometrical reasoning: the van Hiele model of thinking in geometry, Fischbein’s theory of figural concepts, and Duval’s cognitive model of geometrical reasoning. Each of these frameworks provides theoretical resources to support research into the development of geometrical...

Home Away Home: Better Understanding of the Role of Social Support in Predicting Cross-Cultural Adjustment among International Students

Science.gov (United States)

Baba, Yoko; Hosoda, Megumi

2014-01-01

Numerous studies have examined international students' adjustment problems, yet, these studies have not explored the mechanisms through which social support operates in the context of stressful events in predicting cross-cultural adjustment among international students. Using Barrera's (1988) models of social support, the present study…

Theoretical model for the mechanical behavior of prestressed beams under torsion

Directory of Open Access Journals (Sweden)

Sérgio M.R. Lopes

2014-12-01

Full Text Available In this article, a global theoretical model previously developed and validated by the authors for reinforced concrete beams under torsion is reviewed and corrected in order to predict the global behavior of beams under torsion with uniform longitudinal prestress. These corrections are based on the introduction of prestress factors and on the modification of the equilibrium equations in order to incorporate the contribution of the prestressing reinforcement. The theoretical results obtained with the new model are compared with some available results of prestressed concrete (PC beams under torsion found in the literature. The results obtained in this study validate the proposed computing procedure to predict the overall behavior of PC beams under torsion.

Predicting nonstationary flood frequencies: Evidence supports an updated stationarity thesis in the United States

Science.gov (United States)

Luke, Adam; Vrugt, Jasper A.; AghaKouchak, Amir; Matthew, Richard; Sanders, Brett F.

2017-07-01

Nonstationary extreme value analysis (NEVA) can improve the statistical representation of observed flood peak distributions compared to stationary (ST) analysis, but management of flood risk relies on predictions of out-of-sample distributions for which NEVA has not been comprehensively evaluated. In this study, we apply split-sample testing to 1250 annual maximum discharge records in the United States and compare the predictive capabilities of NEVA relative to ST extreme value analysis using a log-Pearson Type III (LPIII) distribution. The parameters of the LPIII distribution in the ST and nonstationary (NS) models are estimated from the first half of each record using Bayesian inference. The second half of each record is reserved to evaluate the predictions under the ST and NS models. The NS model is applied for prediction by (1) extrapolating the trend of the NS model parameters throughout the evaluation period and (2) using the NS model parameter values at the end of the fitting period to predict with an updated ST model (uST). Our analysis shows that the ST predictions are preferred, overall. NS model parameter extrapolation is rarely preferred. However, if fitting period discharges are influenced by physical changes in the watershed, for example from anthropogenic activity, the uST model is strongly preferred relative to ST and NS predictions. The uST model is therefore recommended for evaluation of current flood risk in watersheds that have undergone physical changes. Supporting information includes a MATLAB® program that estimates the (ST/NS/uST) LPIII parameters from annual peak discharge data through Bayesian inference.

Droplet size in flow: Theoretical model and application to polymer blends

Science.gov (United States)

Fortelný, Ivan; Jůza, Josef

2017-05-01

The paper is focused on prediction of the average droplet radius, R, in flowing polymer blends where the droplet size is determined by dynamic equilibrium between the droplet breakup and coalescence. Expressions for the droplet breakup frequency in systems with low and high contents of the dispersed phase are derived using available theoretical and experimental results for model blends. Dependences of the coalescence probability, Pc, on system parameters, following from recent theories, is considered and approximate equation for Pc in a system with a low polydispersity in the droplet size is proposed. Equations for R in systems with low and high contents of the dispersed phase are derived. Combination of these equations predicts realistic dependence of R on the volume fraction of dispersed droplets, φ. Theoretical prediction of the ratio of R to the critical droplet radius at breakup agrees fairly well with experimental values for steadily mixed polymer blends.

A theoretical model for the effects of reduced hemoglobin-oxygen affinity on tumor oxygenation

International Nuclear Information System (INIS)

Kavanagh, Brian D.; Secomb, Timothy W.; Hsu, Richard; Lin, P.-S.; Venitz, Jurgen; Dewhirst, Mark W.

2002-01-01

Purpose: To develop a theoretical model for oxygen delivery to tumors, and to use the model to simulate the effects of changing the affinity of hemoglobin for oxygen on tumor oxygenation. Methods and Materials: Hemoglobin affinity is expressed in terms of P 50 , the partial pressure of oxygen (Po 2 ) at half saturation. Effects of changing P 50 on arterial Po 2 are predicted using an effective vessel approach to describe diffusive oxygen transport in the lungs, assuming fixed systemic oxygen demand and fixed blood flow rate. The decline in oxygen content of blood as it flows through normal tissue before entering the tumor region is assumed fixed. The hypoxic fraction of the tumor region is predicted using a three-dimensional simulation of diffusion from a network of vessels whose geometry is derived from observations of tumor microvasculature in the rat. Results: In air-breathing rats, predicted hypoxic fraction decreases with moderate increases in P 50 , but increases with further increases of P 50 , in agreement with previous experimental results. In rats breathing hyperoxic gases, and in humans breathing either normoxic or hyperoxic gases, increased P 50 is predicted to improve tumor oxygenation. Conclusions: The results support the administration of synthetic agents to increase P 50 during radiation treatment of tumors

Psychotherapy Integration via Theoretical Unification

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Warren W. Tryon

2017-01-01

Full Text Available Meaningful psychotherapy integration requires theoretical unification because psychotherapists can only be expected to treat patients with the same diagnoses similarly if they understand these disorders similarly and if they agree on the mechanisms by which effective treatments work. Tryon (in press has proposed a transtheoretic transdiagnostic psychotherapy based on an Applied Psychological Science (APS clinical orientation, founded on a BioPsychology Network explanatory system that provides sufficient theoretical unification to support meaningful psychotherapy integration. That proposal focused mainly on making a neuroscience argument. This article makes a different argument for theoretical unification and consequently psychotherapy integration. The strength of theories of psychotherapy, like all theory, is to focus on certain topics, goals, and methods. But this strength is also a weakness because it can blind one to alternative perspectives and thereby promote unnecessary competition among therapies. This article provides a broader perspective based on learning and memory that is consistent with the behavioral, cognitive, cognitive-behavioral, psychodynamic, pharmacologic, and Existential/Humanistic/Experiential clinical orientations. It thereby provides a basis for meaningful psychotherapy integration.

Theoretical prediction of thermodynamic properties of tritiated beryllium molecules and application to ITER source term

Energy Technology Data Exchange (ETDEWEB)

Virot, F., E-mail: francois.virot@irsn.fr; Barrachin, M.; Souvi, S.; Cantrel, L.

2014-10-15

Highlights: • Standard enthalpies of formation of BeH, BeH{sub 2}, BeOH, Be(OH){sub 2} have been calculated. • The impact of hydrogen isotopy on thermodynamic properties has been shown. • Speciation in the vacuum vessel shows that the main tritiated species is tritiated steam. • Beryllium hydroxide and hydride could exist during an accidental event. - Abstract: By quantum chemistry calculations, we have evaluated the standard enthalpies of formation of some gaseous species of the Be-O-H chemical system: BeH, BeH{sub 2}, BeOH, Be(OH){sub 2} for which the values in the referenced thermodynamic databases (NIST-JANAF [1] or COACH [2]) were, due to the lack of experimental data, estimated or reported with a large uncertainty. Comparison between post-HF, DFT approaches and available experimental data allows validation of the ability of an accurate exchange-correlation functional, VSXC, to predict the thermo-chemical properties of the beryllium species of interest. Deviation of enthalpy of formation induced by changes in hydrogen isotopy has been also calculated. From these new theoretically determinated data, we have calculated the chemical speciation in conditions simulating an accident of water ingress in the vacuum vessel of ITER.

Patients’ Acceptance of Smartphone Health Technology for Chronic Disease Management: A Theoretical Model and Empirical Test

Science.gov (United States)

Dou, Kaili; Yu, Ping; Liu, Fang; Guan, YingPing; Li, Zhenye; Ji, Yumeng; Du, Ningkai; Lu, Xudong; Duan, Huilong

2017-01-01

confirmed the positive relationship between intention to use and actual use of smartphone health apps for chronic disease management. Conclusions This study developed a theoretical model to predict patients’ acceptance of smartphone health technology for chronic disease management. Although resistance to change is a significant barrier to technology acceptance, careful management of doctor-patient relationship, and raising patients’ awareness of the negative effect of chronic disease can negate the effect of resistance and encourage acceptance and use of smartphone health technology to support chronic disease management for patients in the community. PMID:29212629

Negative feedback, beliefs and personal goals in prediction of dysfunctional emotions

Directory of Open Access Journals (Sweden)

Popov Boris

2007-01-01

Full Text Available Rational emotive behavior therapy (REBT demonstrates good results in evaluation therapy researches. However, some of its basic concepts, as well as theory as a whole itself, did not receive satisfactory empirical support so far, in comparison to other cognitive models (Beck, Lazarus etc.. Quasiexperimental study was designed to test the role that (1 negative feedback (A and (2 irrational beliefs (B both play in formation of dysfunctional negative emotions, in the context of significant personal goals (in our case value of potential award - G. ABC theoretical model received limited support: statistically significant three-times interaction A x B x G was found in predicting general negative emotional state, as well as anger. In contrast with that, ANOVA showed only main effect of irrational beliefs (as continuous variable to be significant in predicting emotions of anxiety and depression. Findings are discussed in the context of REBT theory of emotions, as well as their possible practical applications. Limitations of the study were also mentioned. .

Theoretical prediction of a rotating magnon wave packet in ferromagnets.

Science.gov (United States)

Matsumoto, Ryo; Murakami, Shuichi

2011-05-13

We theoretically show that the magnon wave packet has a rotational motion in two ways: a self-rotation and a motion along the boundary of the sample (edge current). They are similar to the cyclotron motion of electrons, but unlike electrons the magnons have no charge and the rotation is not due to the Lorentz force. These rotational motions are caused by the Berry phase in momentum space from the magnon band structure. Furthermore, the rotational motion of the magnon gives an additional correction term to the magnon Hall effect. We also discuss the Berry curvature effect in the classical limit of long-wavelength magnetostatic spin waves having macroscopic coherence length.

The Roles of Perceived Social Support, Coping, and Loneliness in Predicting Internet Addiction in Adolescents

Science.gov (United States)

Çevik, Gülsen Büyüksahin; Yildiz, Mehmet Ali

2017-01-01

The current research aims to examine the roles of perceived social support, coping, and loneliness when predicting the Internet addiction in adolescents. The research participants included 300 high school students, with an average age of 16.49 and SD = 1.27, attending schools in a city in Southeastern Anatolian Region during 2015-2016 academic…

Production of electroweak bosons at hadron colliders: theoretical aspects

CERN Document Server

Mangano, Michelangelo L.

2016-01-01

Since the W and Z discovery, hadron colliders have provided a fertile ground, in which continuously improving measurements and theoretical predictions allow to precisely determine the gauge boson properties, and to probe the dynamics of electroweak and strong interactions. This article will review, from a theoretical perspective, the role played by the study, at hadron colliders, of electroweak boson production properties, from the better understanding of the proton structure, to the discovery and studies of the top quark and of the Higgs, to the searches for new phenomena beyond the Standard Model.

Slow dynamics at critical points: the field-theoretical perspective

International Nuclear Information System (INIS)

Gambassi, Andrea

2006-01-01

The dynamics at a critical point provides a simple instance of slow collective evolution, characterised by aging phenomena and by a violation of the fluctuation-dissipation relation even for long times. By virtue of the universality in critical phenomena it is possible to provide quantitative predictions for some aspects of these behaviours by field-theoretical methods. We review some of the theoretical results that have been obtained in recent years for the relevant (universal) quantities, such as the fluctuation-dissipation ratio, associated with the non-equilibrium critical dynamics

Predicting quality of life and self-management from dyadic support and overprotection after myocardial infarction.

Science.gov (United States)

Joekes, Katherine; Maes, Stan; Warrens, Matthijs

2007-11-01

Using a self-regulatory framework, this study aims to identify how couples perceive a partner's support style after myocardial infarction (MI), and whether this predicts the patient's health-related quality of life (HR-QoL) and self-management (S-M) 9 months later. This longitudinal dyadic study includes 73 couples (86% of patients were men), recruited from two cardiac rehabilitation programmes in the Netherlands. Mean age of patients was 54.8 (SD=9.6) and of partners 52.5 (SD=9.8). Participants were interviewed and completed questionnaires at baseline (T1). Repeat questionnaires were returned by 69 and 67 couples after 3 (T2) and 9 months (T3), respectively. Support by partners is conceptualized in this study as 'active engagement' (AE), which involves the extent to which a partner engages the patient in conversations which focus on emotional support and problem solving. Levels of AE do not change over time, nor do they differ between members of the dyad. Levels of overprotection (OP) diminish with time, whilst patients consistently perceive more OP than partners report providing. Patients' experience of goal hindrance (at T3) due to the MI is associated with a decreased HR-QoL at T3 (controlling for baseline measures). The perception of having a supportive (AE) partner at T1 contributes to enhanced patient HR-QoL at each subsequent time point, although not to physical functioning. Perceiving a partner as overprotective (at T1) predicts worsened physical functioning in patients (at T3). Improvements in S-M at T3 (controlling for baseline measures) are reported by patients whose partner displays active engagement at T1. Cardiac rehabilitation should aim to redress the experience of goal disturbance and advise partners on how to provide support.

A theoretical framework for a virtual diabetes self-management community intervention.

Science.gov (United States)

Vorderstrasse, Allison; Shaw, Ryan J; Blascovich, Jim; Johnson, Constance M

2014-10-01

Due to its high prevalence, chronic nature, potential complications, and self-management challenges for patients, diabetes presents significant health education and support issues. We developed and pilot-tested a virtual community for adults with type 2 diabetes to promote self-management education and provide social support. Although digital-based programs such as virtual environments can address significant barriers to reaching patients (i.e., child care, transportation, location), they must be strongly grounded in a theoretical basis to be well-developed and effective. In this article, we discuss how we synthesized behavioral and virtual environment theoretical frameworks to guide the development of SLIDES (Second Life Impacts Diabetes Education and Support). © The Author(s) 2014.

[Predictive values of different critical scoring systems for mortality in patients with severe acute respiratory failure supported by extracorporeal membrane oxygenation].

Science.gov (United States)

Wang, R; Sun, B; Li, X Y; He, H Y; Tang, X; Zhan, Q Y; Tong, Z H

2016-09-01

To investigate the predictive values of different critical scoring systems for mortality in patients with severe acute respiratory failure (ARF) supported by venovenous extracorporeal membrane oxygenation (VV-ECMO). Forty-two patients with severe ARF supported by VV-ECMO were enrolled from November 2009 to July 2015.There were 25 males and 17 females. The mean age was (44±18) years (rang 18-69 years). Acute Physiology and Chronic Health Evaluation (APACHE) Ⅱ, Ⅲ, Ⅳ, Simplified Acute Physiology Score Ⅱ (SAPS) Ⅱ, Sequential Organ Failure Assessment (SOFA), ECMO net, PRedicting dEath for SEvere ARDS on VVECMO (PRESERVE), and Respiratory ECMO Survival Prediction (RESP) scores were collected within 6 hours before VV-ECMO support. The patients were divided into the survivors group (n=17) and the nonsurvivors group (n=25) by survival at 180 d after receiving VV-ECMO. The patient clinical characteristics and aforementioned scoring systems were compared between groups. Scoring systems for predicting prognosis were assessed using the area under the receiver-operating characteristic (ROC) curve. The Kaplan-Meier method was used to draw the surviving curve, and the survival of the patients was analyzed by the Log-rank test. The risk factors were assessed for prognosis by multiple logistic regression analysis. (1) Positive end expiratory pressure (PEEP) 6 hours prior to VV-ECMO support in the survivors group [(9.7±5.0)cmH2O, (1 cmH2O=0.098 kPa)] was lower than that in the nonsurvivors group [(13.2±5.4)cmH2O, t=-2.134, P=0.039]. VV-ECMO combination with continuous renal replacement therapy(CRRT) in the nonsurvivors group (32%) was used more than in the survivors group (6%, χ(2)=4.100, P=0.043). Duration of VV-ECMO support in the nonsurvivors group [(15±13) d] was longer than that in the survivors group [(12±11)d, t=-2.123, P=0.041]. APACHE Ⅱ, APACHE Ⅲ, APACHE Ⅳ, ECMO net, PRESERVE, and RESP scores in the survivors group were superior to the nonsurvivors

Improving the theoretical prediction for the Bs - B̅s width difference: matrix elements of next-to-leading order ΔB = 2 operators

Science.gov (United States)

Davies, Christine; Harrison, Judd; Lepage, G. Peter; Monahan, Christopher; Shigemitsu, Junko; Wingate, Matthew

2018-03-01

We present lattice QCD results for the matrix elements of R2 and other dimension-7, ΔB = 2 operators relevant for calculations of Δs, the Bs - B̅s width difference. We have computed correlation functions using 5 ensembles of the MILC Collaboration's 2+1 + 1-flavour gauge field configurations, spanning 3 lattice spacings and light sea quarks masses down to the physical point. The HISQ action is used for the valence strange quarks, and the NRQCD action is used for the bottom quarks. Once our analysis is complete, the theoretical uncertainty in the Standard Model prediction for ΔΓs will be substantially reduced.

Developing a theoretical framework for complex community-based interventions.

Science.gov (United States)

Angeles, Ricardo N; Dolovich, Lisa; Kaczorowski, Janusz; Thabane, Lehana

2014-01-01

Applying existing theories to research, in the form of a theoretical framework, is necessary to advance knowledge from what is already known toward the next steps to be taken. This article proposes a guide on how to develop a theoretical framework for complex community-based interventions using the Cardiovascular Health Awareness Program as an example. Developing a theoretical framework starts with identifying the intervention's essential elements. Subsequent steps include the following: (a) identifying and defining the different variables (independent, dependent, mediating/intervening, moderating, and control); (b) postulating mechanisms how the independent variables will lead to the dependent variables; (c) identifying existing theoretical models supporting the theoretical framework under development; (d) scripting the theoretical framework into a figure or sets of statements as a series of hypotheses, if/then logic statements, or a visual model; (e) content and face validation of the theoretical framework; and (f) revising the theoretical framework. In our example, we combined the "diffusion of innovation theory" and the "health belief model" to develop our framework. Using the Cardiovascular Health Awareness Program as the model, we demonstrated a stepwise process of developing a theoretical framework. The challenges encountered are described, and an overview of the strategies employed to overcome these challenges is presented.

Theoretical estimation of Z´ boson mass

International Nuclear Information System (INIS)

Maji, Priya; Banerjee, Debika; Sahoo, Sukadev

2016-01-01

The discovery of Higgs boson at the LHC brings a renewed perspective in particle physics. With the help of Higgs mechanism, standard model (SM) allows the generation of particle mass. The ATLAS and CMS experiments at the LHC have predicted the mass of Higgs boson as m_H=125-126 GeV. Recently, it is claimed that the Higgs boson might interact with dark matter and there exists relation between the Higgs boson and dark matter (DM). Hertzberg has predicted a correlation between the Higgs mass and the abundance of dark matter. His theoretical result is in good agreement with current data. He has predicted the mass of Higgs boson as GeV. The Higgs boson could be coupled to the particle that constitutes all or part of the dark matter in the universe. Light Z´ boson could have important implications in dark matter phenomenology

Theoretical issues in Spheromak research

International Nuclear Information System (INIS)

Cohen, R. H.; Hooper, E.B.; LoDestro, L.L.; Mattor, N.; Pearlstein, L.D.; Ryutov, D.D.

1997-01-01

This report summarizes the state of theoretical knowledge of several physics issues important to the spheromak. It was prepared as part of the preparation for the Sustained Spheromak Physics Experiment (SSPX), which addresses these goals: energy confinement and the physics which determines it; the physics of transition from a short-pulsed experiment, in which the equilibrium and stability are determined by a conducting wall (''''flux conserver'''') to one in which the equilibrium is supported by external coils. Physics is examined in this report in four important areas. The status of present theoretical understanding is reviewed, physics which needs to be addressed more fully is identified, and tools which are available or require more development are described. Specifically, the topics include: MHD equilibrium and design, review of MHD stability, spheromak dynamo, and edge plasma in spheromaks

Prediction of biochar yield from cattle manure pyrolysis via least squares support vector machine intelligent approach.

Science.gov (United States)

Cao, Hongliang; Xin, Ya; Yuan, Qiaoxia

2016-02-01

To predict conveniently the biochar yield from cattle manure pyrolysis, intelligent modeling approach was introduced in this research. A traditional artificial neural networks (ANN) model and a novel least squares support vector machine (LS-SVM) model were developed. For the identification and prediction evaluation of the models, a data set with 33 experimental data was used, which were obtained using a laboratory-scale fixed bed reaction system. The results demonstrated that the intelligent modeling approach is greatly convenient and effective for the prediction of the biochar yield. In particular, the novel LS-SVM model has a more satisfying predicting performance and its robustness is better than the traditional ANN model. The introduction and application of the LS-SVM modeling method gives a successful example, which is a good reference for the modeling study of cattle manure pyrolysis process, even other similar processes. Copyright © 2015 Elsevier Ltd. All rights reserved.

Prediction of Quality of Life of Non–Insulin-Dependent Diabetic Patients Based on Perceived Social Support

Directory of Open Access Journals (Sweden)

Hossein Shareh

2012-04-01

Full Text Available Background: The objective of this study was to predic quality of life based on perceived social support components in non–insulin-dependent diabetic patients.Materials and Method: Fifty patients with non–insulin-dependent diabetes mellitus from Al-Zahra diabetic center in Shiraz participated in a cross-sectional study via survey instrument. All subjects completed multidimensional scale of perceived social support (MSPSS and world health organization quality of life- brief (WHOQOL-BREF questionnaires. Results: On the basis of stepwise multiple regression analysis friends and family dimensions of perceived social support were the best predictors of the quality of life and its dimensions (p<0.01.Conclusion: Friends and family dimensions of perceived social support have significant contributions in predicting quality of life of patients with non–insulin-dependent diabetes mellitus.

A course in theoretical physics

CERN Document Server

Shepherd, P J

2013-01-01

This book is a comprehensive account of five extended modules covering the key branches of twentieth-century theoretical physics, taught by the author over a period of three decades to students on bachelor and master university degree courses in both physics and theoretical physics. The modules cover nonrelativistic quantum mechanics, thermal and statistical physics, many-body theory, classical field theory (including special relativity and electromagnetism), and, finally, relativistic quantum mechanics and gauge theories of quark and lepton interactions, all presented in a single, self-contained volume. In a number of universities, much of the material covered (for example, on Einstein’s general theory of relativity, on the BCS theory of superconductivity, and on the Standard Model, including the theory underlying the prediction of the Higgs boson) is taught in postgraduate courses to beginning PhD students. A distinctive feature of the book is that full, step-by-step mathematical proofs of all essentia...

Probabilistic source term predictions for use with decision support systems

International Nuclear Information System (INIS)

Grindon, E.; Kinniburgh, C.G.

2003-01-01

Full text: Decision Support Systems for use in off-site emergency management, following an incident at a Nuclear Power Plant (NPP) within Europe, are becoming accepted as a useful and appropriate tool to aid decision makers. An area which is not so well developed is the 'upstream' prediction of the source term released into the environment. Rapid prediction of this source term is crucial to the appropriate early management of a nuclear emergency. The initial source term prediction would today be typically based on simple tabulations taking little, or no, account of plant status. It is the interface between the inward looking plant control room team and the outward looking off-site emergency management team that needs to be addressed. This is not an easy proposition as these two distinct disciplines have little common basis from which to communicate their immediate findings and concerns. Within the Euratom Fifth Framework Programme (FP5), complementary approaches are being developed to the pre-release stage; each based on software tools to help bridge this gap. Traditionally source terms (or releases into the environment) provided for use with Decision Support Systems are estimated on a deterministic basis. These approaches use a single, deterministic assumption about plant status. The associated source term represents the 'best estimate' based an available information. No information is provided an the potential for uncertainty in the source term estimate. Using probabilistic methods the outcome is typically a number of possible plant states each with an associated source term and probability. These represent both the best estimate and the spread of the likely source term. However, this is a novel approach and the usefulness of such source term prediction tools is yet to be tested on a wide scale. The benefits of probabilistic source term estimation are presented here; using, as an example, the SPRINT tool developed within the FP5 STERPS project. System for the

Role of social support in lifestyle-focused weight management interventions

NARCIS (Netherlands)

Verheijden, M.W.; Bakx, J.C.; Weel, van C.; Koelen, M.A.; Staveren, van W.A.

2005-01-01

Social support is important to achieve beneficial changes in risk factors for disease, such as overweight and obesity. This paper presents the theoretical and practical framework for social support, and the mechanisms by which social support affects body weight. The theoretical and practical

Role of social support in lifestyle-focused weight management interventions.

NARCIS (Netherlands)

Verheijden, M.W.; Bakx, J.C.; Weel, C. van; Koelen, M.A.; Staveren, W.A. van

2005-01-01

Social support is important to achieve beneficial changes in risk factors for disease, such as overweight and obesity. This paper presents the theoretical and practical framework for social support, and the mechanisms by which social support affects body weight. The theoretical and practical

Transport and stability analyses supporting disruption prediction in high beta KSTAR plasmas

Science.gov (United States)

Ahn, J.-H.; Sabbagh, S. A.; Park, Y. S.; Berkery, J. W.; Jiang, Y.; Riquezes, J.; Lee, H. H.; Terzolo, L.; Scott, S. D.; Wang, Z.; Glasser, A. H.

2017-10-01

KSTAR plasmas have reached high stability parameters in dedicated experiments, with normalized beta βN exceeding 4.3 at relatively low plasma internal inductance li (βN/li>6). Transport and stability analyses have begun on these plasmas to best understand a disruption-free path toward the design target of βN = 5 while aiming to maximize the non-inductive fraction of these plasmas. Initial analysis using the TRANSP code indicates that the non-inductive current fraction in these plasmas has exceeded 50 percent. The advent of KSTAR kinetic equilibrium reconstructions now allows more accurate computation of the MHD stability of these plasmas. Attention is placed on code validation of mode stability using the PEST-3 and resistive DCON codes. Initial evaluation of these analyses for disruption prediction is made using the disruption event characterization and forecasting (DECAF) code. The present global mode kinetic stability model in DECAF developed for low aspect ratio plasmas is evaluated to determine modifications required for successful disruption prediction of KSTAR plasmas. Work supported by U.S. DoE under contract DE-SC0016614.

Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks

International Nuclear Information System (INIS)

Li Qiong; Meng Qinglin; Cai Jiejin; Yoshino, Hiroshi; Mochida, Akashi

2009-01-01

This study presents four modeling techniques for the prediction of hourly cooling load in the building. In addition to the traditional back propagation neural network (BPNN), the radial basis function neural network (RBFNN), general regression neural network (GRNN) and support vector machine (SVM) are considered. All the prediction models have been applied to an office building in Guangzhou, China. Evaluation of the prediction accuracy of the four models is based on the root mean square error (RMSE) and mean relative error (MRE). The simulation results demonstrate that the four discussed models can be effective for building cooling load prediction. The SVM and GRNN methods can achieve better accuracy and generalization than the BPNN and RBFNN methods

Development of a Decision Support System to Predict Physicians' Rehabilitation Protocols for Patients with Knee Osteoarthritis

Science.gov (United States)

Hawamdeh, Ziad M.; Alshraideh, Mohammad A.; Al-Ajlouni, Jihad M.; Salah, Imad K.; Holm, Margo B.; Otom, Ali H.

2012-01-01

To design a medical decision support system (MDSS) that would accurately predict the rehabilitation protocols prescribed by the physicians for patients with knee osteoarthritis (OA) using only their demographic and clinical characteristics. The demographic and clinical variables for 170 patients receiving one of three treatment protocols for knee…

Opportune maintenance and predictive maintenance decision support

OpenAIRE

Thomas , Edouard; Levrat , Eric; Iung , Benoît; Cocheteux , Pierre

2009-01-01

International audience; Conventional maintenance strategies on a single component are being phased out in favour of more predictive maintenance actions. These new kinds of actions are performed in order to control the global performances of the whole industrial system. They are anticipative in nature, which allows a maintenance expert to consider non-already-planned maintenance actions. Two questions naturally emerge: when to perform a predictive maintenance action; how a maintenance expert c...

Experimental and theoretical studies of the effects of nonuniformities in equilibrium MHD generators

International Nuclear Information System (INIS)

Rosenbaum, M.; Shamma, S.E.; Louis, J.F.

1980-01-01

An experimental study of the effects of thermal and velocity nonuniformities is performed in an equilibrium plasma for a range of Hall parameters. An electrodeless MHD disk generator with radial flow is chosen as the ideal geometry for these experiments. By introducing equally spaced cold blades in the flow, it is possible to create well defined two-dimensional wake nonuniformities with strong variations of the plasma properties in the direction normal to the magnetic field and the flow. This type of nonuniformity is predicted to provide the strongest reduction of Hall coefficient and effective conductivity for high values of Hall parameter. This degradation is controlled by both the level of nonuniformities and the value of the ideal Hall parameter. The former is dependent upon the number of blades (root mean square deviation of the conductivity), and the latter is dependent upon the values of the magnetic field intensities. The results provide basic quantitative information about the effects of conductivity and velocity nonuniformities on the performance of equilibrium MHD generators over a wide range of Hall coefficients, between 2 and 7. Reduction formulae are established between the effective and ideal Hall parameters for different levels of nonuniformities intensities. Theoretical predictions are derived from a detailed two-dimensional electrodynamic analysis and a simplified engineering model based on a generalization of Rosa's layer model. These experiments validate the analytical studies and support the use of the theoretical layer models in describing the effect of boundary layers on the performance of linear generators

The role of family expressed emotion and perceived social support in predicting addiction relapse.

Science.gov (United States)

Atadokht, Akbar; Hajloo, Nader; Karimi, Masoud; Narimani, Mohammad

2015-03-01

Emotional conditions governing the family and patients' perceived social support play important roles in the treatment or relapse process of the chronic disease. The current study aimed to investigate the role of family expressed emotion and perceived social support in prediction of addiction relapse. The descriptive-correlation method was used in the current study. The study population consisted of the individuals referred to the addiction treatment centers in Ardabil from October 2013 to January 2014. The subjects (n = 80) were randomly selected using cluster sampling method. To collect data, expressed emotion test by Cole and Kazaryan, and Multidimensional Scale of Perceived Social Support (MSPSS) were used, and the obtained data was analyzed using the Pearson's correlation coefficient and multiple regression analyses. Results showed a positive relationship between family expressed emotions and the frequency of relapse (r = 0.26, P = 0.011) and a significant negative relationship between perceived social support and the frequency of relapse (r = -0.34, P = 0.001). Multiple regression analysis also showed that perceived social support from family and the family expressed emotions significantly explained 12% of the total variance of relapse frequency. These results have implications for addicted people, their families and professionals working in addiction centers to use the emotional potential of families especially their expressed emotions and the perceived social support of addicts to increase the success rate of addiction treatment.

Theoretical Studies Of Nucleation Kinetics And Nanodroplet Microstructure

International Nuclear Information System (INIS)

Wilemski, Gerald

2009-01-01

The goals of this project were to (1) explore ways of bridging the gap between fundamental molecular nucleation theories and phenomenological approaches based on thermodynamic reasoning, (2) test and improve binary nucleation theory, and (3) provide the theoretical underpinning for a powerful new experimental technique, small angle neutron scattering (SANS) from nanodroplet aerosols, that can probe the compositional structure of nanodroplets. This report summarizes the accomplishments of this project in realizing these goals. Publications supported by this project fall into three general categories: (1) theoretical work on nucleation theory (2) experiments and modeling of nucleation and condensation in supersonic nozzles, and (3) experimental and theoretical work on nanodroplet structure and neutron scattering. These publications are listed and briefly summarized in this report.

A framework for multiple kernel support vector regression and its applications to siRNA efficacy prediction.

Science.gov (United States)

Qiu, Shibin; Lane, Terran

2009-01-01

The cell defense mechanism of RNA interference has applications in gene function analysis and promising potentials in human disease therapy. To effectively silence a target gene, it is desirable to select appropriate initiator siRNA molecules having satisfactory silencing capabilities. Computational prediction for silencing efficacy of siRNAs can assist this screening process before using them in biological experiments. String kernel functions, which operate directly on the string objects representing siRNAs and target mRNAs, have been applied to support vector regression for the prediction and improved accuracy over numerical kernels in multidimensional vector spaces constructed from descriptors of siRNA design rules. To fully utilize information provided by string and numerical data, we propose to unify the two in a kernel feature space by devising a multiple kernel regression framework where a linear combination of the kernels is used. We formulate the multiple kernel learning into a quadratically constrained quadratic programming (QCQP) problem, which although yields global optimal solution, is computationally demanding and requires a commercial solver package. We further propose three heuristics based on the principle of kernel-target alignment and predictive accuracy. Empirical results demonstrate that multiple kernel regression can improve accuracy, decrease model complexity by reducing the number of support vectors, and speed up computational performance dramatically. In addition, multiple kernel regression evaluates the importance of constituent kernels, which for the siRNA efficacy prediction problem, compares the relative significance of the design rules. Finally, we give insights into the multiple kernel regression mechanism and point out possible extensions.

PREDICTED SIZES OF PRESSURE-SUPPORTED HI CLOUDS IN THE OUTSKIRTS OF THE VIRGO CLUSTER

Energy Technology Data Exchange (ETDEWEB)

Burkhart, Blakesley; Loeb, Abraham [Harvard-Smithsonian Center for Astrophysics, 60 Garden St. Cambridge, MA (United States)

2016-06-10

Using data from the ALFALFA AGES Arecibo HI survey of galaxies and the Virgo cluster X-ray pressure profiles from XMM-Newton , we investigate the possibility that starless dark HI clumps, also known as “dark galaxies,” are supported by external pressure in the surrounding intercluster medium. We find that the starless HI clump masses, velocity dispersions, and positions allow these clumps to be in pressure equilibrium with the X-ray gas near the virial radius of the Virgo cluster. We predict the sizes of these clumps to range from 1 to 10 kpc, in agreement with the range of sizes found for spatially resolved HI starless clumps outside of Virgo. Based on the predicted HI surface density of the Virgo sources, as well as a sample of other similar resolved ALFALFA HI dark clumps with follow-up optical/radio observations, we predict that most of the HI dark clumps are on the cusp of forming stars. These HI sources therefore mark the transition between starless HI clouds and dwarf galaxies with stars.

Comparative Study on Theoretical and Machine Learning Methods for Acquiring Compressed Liquid Densities of 1,1,1,2,3,3,3-Heptafluoropropane (R227ea via Song and Mason Equation, Support Vector Machine, and Artificial Neural Networks

Directory of Open Access Journals (Sweden)

Hao Li

2016-01-01

Full Text Available 1,1,1,2,3,3,3-Heptafluoropropane (R227ea is a good refrigerant that reduces greenhouse effects and ozone depletion. In practical applications, we usually have to know the compressed liquid densities at different temperatures and pressures. However, the measurement requires a series of complex apparatus and operations, wasting too much manpower and resources. To solve these problems, here, Song and Mason equation, support vector machine (SVM, and artificial neural networks (ANNs were used to develop theoretical and machine learning models, respectively, in order to predict the compressed liquid densities of R227ea with only the inputs of temperatures and pressures. Results show that compared with the Song and Mason equation, appropriate machine learning models trained with precise experimental samples have better predicted results, with lower root mean square errors (RMSEs (e.g., the RMSE of the SVM trained with data provided by Fedele et al. [1] is 0.11, while the RMSE of the Song and Mason equation is 196.26. Compared to advanced conventional measurements, knowledge-based machine learning models are proved to be more time-saving and user-friendly.

Prediction of endoplasmic reticulum resident proteins using fragmented amino acid composition and support vector machine

Directory of Open Access Journals (Sweden)

Ravindra Kumar

2017-09-01

Full Text Available Background The endoplasmic reticulum plays an important role in many cellular processes, which includes protein synthesis, folding and post-translational processing of newly synthesized proteins. It is also the site for quality control of misfolded proteins and entry point of extracellular proteins to the secretory pathway. Hence at any given point of time, endoplasmic reticulum contains two different cohorts of proteins, (i proteins involved in endoplasmic reticulum-specific function, which reside in the lumen of the endoplasmic reticulum, called as endoplasmic reticulum resident proteins and (ii proteins which are in process of moving to the extracellular space. Thus, endoplasmic reticulum resident proteins must somehow be distinguished from newly synthesized secretory proteins, which pass through the endoplasmic reticulum on their way out of the cell. Approximately only 50% of the proteins used in this study as training data had endoplasmic reticulum retention signal, which shows that these signals are not essentially present in all endoplasmic reticulum resident proteins. This also strongly indicates the role of additional factors in retention of endoplasmic reticulum-specific proteins inside the endoplasmic reticulum. Methods This is a support vector machine based method, where we had used different forms of protein features as inputs for support vector machine to develop the prediction models. During training leave-one-out approach of cross-validation was used. Maximum performance was obtained with a combination of amino acid compositions of different part of proteins. Results In this study, we have reported a novel support vector machine based method for predicting endoplasmic reticulum resident proteins, named as ERPred. During training we achieved a maximum accuracy of 81.42% with leave-one-out approach of cross-validation. When evaluated on independent dataset, ERPred did prediction with sensitivity of 72.31% and specificity of 83

Coping Styles, Social Support, Relational Self-Construal, and Resilience in Predicting Students' Adjustment to University Life

Science.gov (United States)

Rahat, Enes; Ilhan, Tahsin

2016-01-01

The purpose of the present study is to investigate how well coping styles, social support, relational self-construal, and resilience characteristics predict first year university students' ability to adjust to university life. Participants consisted of 527 at-risk students attending a state university in Turkey. The Personal Information Form, Risk…

Termination of Resuscitation Rules to Predict Neurological Outcomes in Out-of-Hospital Cardiac Arrest for an Intermediate Life Support Prehospital System.

Science.gov (United States)

Cheong, Randy Wang Long; Li, Huihua; Doctor, Nausheen Edwin; Ng, Yih Yng; Goh, E Shaun; Leong, Benjamin Sieu-Hon; Gan, Han Nee; Foo, David; Tham, Lai Peng; Charles, Rabind; Ong, Marcus Eng Hock

2016-01-01

Futile resuscitation can lead to unnecessary transports for out-of-hospital cardiac arrest (OHCA). The Basic Life Support (BLS) and Advanced Life Support (ALS) termination of resuscitation (TOR) guidelines have been validated with good results in North America. This study aims to evaluate the performance of these two rules in predicting neurological outcomes of OHCA patients in Singapore, which has an intermediate life support Emergency Medical Services (EMS) system. A retrospective cohort study was carried out on Singapore OHCA data collected from April 2010 to May 2012 for the Pan-Asian Resuscitation Outcomes Study (PAROS). The outcomes of each rule were compared to the actual neurological outcomes of the patients. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and predicted transport rates of each test were evaluated. A total of 2,193 patients had cardiac arrest of presumed cardiac etiology. TOR was recommended for 1,411 patients with the BLS-TOR rule, with a specificity of 100% (91.9, 100.0) for predicting poor neurological outcomes, PPV 100% (99.7, 100.0), sensitivity 65.7% (63.6, 67.7), NPV 5.6% (4.1, 7.5), and transportation rate 35.6%. Using the ALS-TOR rule, TOR was recommended for 587 patients, specificity 100% (91.9, 100.0) for predicting poor neurological outcomes, PPV 100% (99.4, 100.0), sensitivity 27.3% (25.4, 29.3), NPV 2.7% (2.0, 3.7), and transportation rate 73.2%. BLS-TOR predicted survival (any neurological outcome) with specificity 93.4% (95% CI 85.3, 97.8) versus ALS-TOR 98.7% (95% CI 92.9, 99.8). Both the BLS and ALS-TOR rules had high specificities and PPV values in predicting neurological outcomes, the BLS-TOR rule had a lower predicted transport rate while the ALS-TOR rule was more accurate in predicting futility of resuscitation. Further research into unique local cultural issues would be useful to evaluate the feasibility of any system-wide implementation of TOR.

Prediction of Five Softwood Paper Properties from its Density using Support Vector Machine Regression Techniques

Directory of Open Access Journals (Sweden)

Esperanza García-Gonzalo

2016-01-01

Full Text Available Predicting paper properties based on a limited number of measured variables can be an important tool for the industry. Mathematical models were developed to predict mechanical and optical properties from the corresponding paper density for some softwood papers using support vector machine regression with the Radial Basis Function Kernel. A dataset of different properties of paper handsheets produced from pulps of pine (Pinus pinaster and P. sylvestris and cypress species (Cupressus lusitanica, C. sempervirens, and C. arizonica beaten at 1000, 4000, and 7000 revolutions was used. The results show that it is possible to obtain good models (with high coefficient of determination with two variables: the numerical variable density and the categorical variable species.

Predicting Child Physical Activity and Screen Time: Parental Support for Physical Activity and General Parenting Styles

Science.gov (United States)

Crain, A. Lauren; Senso, Meghan M.; Levy, Rona L.; Sherwood, Nancy E.

2014-01-01

Objective: To examine relationships between parenting styles and practices and child moderate-to-vigorous physical activity (MVPA) and screen time. Methods: Participants were children (6.9 ± 1.8 years) with a body mass index in the 70–95th percentile and their parents (421 dyads). Parent-completed questionnaires assessed parental support for child physical activity (PA), parenting styles and child screen time. Children wore accelerometers to assess MVPA. Results: Parenting style did not predict MVPA, but support for PA did (positive association). The association between support and MVPA, moreover, varied as a function of permissive parenting. For parents high in permissiveness, the association was positive (greater support was related to greater MVPA and therefore protective). For parents low in permissiveness, the association was neutral; support did not matter. Authoritarian and permissive parenting styles were both associated with greater screen time. Conclusions: Parenting practices and styles should be considered jointly, offering implications for tailored interventions. PMID:24812256

Landslide susceptibility mapping & prediction using Support Vector Machine for Mandakini River Basin, Garhwal Himalaya, India

Science.gov (United States)

Kumar, Deepak; Thakur, Manoj; Dubey, Chandra S.; Shukla, Dericks P.

2017-10-01

In recent years, various machine learning techniques have been applied for landslide susceptibility mapping. In this study, three different variants of support vector machine viz., SVM, Proximal Support Vector Machine (PSVM) and L2-Support Vector Machine - Modified Finite Newton (L2-SVM-MFN) have been applied on the Mandakini River Basin in Uttarakhand, India to carry out the landslide susceptibility mapping. Eight thematic layers such as elevation, slope, aspect, drainages, geology/lithology, buffer of thrusts/faults, buffer of streams and soil along with the past landslide data were mapped in GIS environment and used for landslide susceptibility mapping in MATLAB. The study area covering 1625 km2 has merely 0.11% of area under landslides. There are 2009 pixels for past landslides out of which 50% (1000) landslides were considered as training set while remaining 50% as testing set. The performance of these techniques has been evaluated and the computational results show that L2-SVM-MFN obtains higher prediction values (0.829) of receiver operating characteristic curve (AUC-area under the curve) as compared to 0.807 for PSVM model and 0.79 for SVM. The results obtained from L2-SVM-MFN model are found to be superior than other SVM prediction models and suggest the usefulness of this technique to problem of landslide susceptibility mapping where training data is very less. However, these techniques can be used for satisfactory determination of susceptible zones with these inputs.

Effects of subordinate feedback to the supervisor and participation in decision-making in the prediction of organizational support.

Science.gov (United States)

1992-03-01

The present study tested the hypothesis that participation in decision-making (PDM) and perceived effectiveness of subordinate feedback to the supervisor would contribute unique variance in the prediction of perceptions of organizational support. In ...

Individualized prediction of illness course at the first psychotic episode: a support vector machine MRI study.

LENUS (Irish Health Repository)

Mourao-Miranda, J

2012-05-01

To date, magnetic resonance imaging (MRI) has made little impact on the diagnosis and monitoring of psychoses in individual patients. In this study, we used a support vector machine (SVM) whole-brain classification approach to predict future illness course at the individual level from MRI data obtained at the first psychotic episode.

Mentoring Support and Power: A Three Year Predictive Field Study on Protege Networking and Career Success

Science.gov (United States)

Blickle, Gerhard; Witzki, Alexander H.; Schneider, Paula B.

2009-01-01

Career success of early employees was analyzed from a power perspective and a developmental network perspective. In a predictive field study with 112 employees mentoring support and mentors' power were assessed in the first wave, employees' networking was assessed after two years, and career success (i.e. income and hierarchical position) and…

Theoretical models of neutron emission in fission

International Nuclear Information System (INIS)

Madland, D.G.

1992-01-01

A brief survey of theoretical representations of two of the observables in neutron emission in fission is given, namely, the prompt fission neutron spectrum N(E) and the average prompt neutron multiplicity bar v p . Early representations of the two observables are presented and their deficiencies are discussed. This is followed by summaries and examples of recent theoretical models for the calculation of these quantities. Emphasis is placed upon the predictability and accuracy of the new models. In particular, the dependencies of N(E) and bar v p upon the fissioning nucleus and its excitation energy are treated. Recent work in the calculation of the prompt fission neutron spectrum matrix N(E,E n ), where E n is the energy of the neutron inducing fission, is then discussed. Concluding remarks address the current status of our ability to calculate these observables with confidence, the direction of future theoretical efforts, and limititations to current and future calculations. Finally, recommendations are presented as to which model should be used currently and which model should be pursued in future efforts

Center for Theoretical Underground Physics and Related Areas - CETUP*2013 Summer Program

Energy Technology Data Exchange (ETDEWEB)

Szczerbinska, Barbara [Dakota State Univ., Madison, SD (United States)

2014-06-01

theoretical end experimental aspects. PPC was initiated at Texas A&M University in 2007 and travelled to many places which include Geneva, Turin, Seoul (S. Korea) etc. during the last 5 years before coming back to USA. The objectives of CETUP* and PPC were to analyze the connection between dark matter and particle physics models, discuss the connections among dark matter, grand unification models and recent neutrino results and predictions for possible experiments, develop a theoretical understanding of the three-neutrino oscillation parameters, provide a stimulating venue for exchange of scientific ideas among experts in neutrino physics and unification, connect with venues for public education outreach to communicate the importance of dark matter, neutrino research, and support of investment in science education, support mission of the Snowmass meeting and allow for extensive discussions of the ideas crucial for the future of high energy physics. The selected subjects represented the forefront of research topics in particle and nuclear physics, for example: recent precise measurements of all the neutrino mixing angles (that necessitate a theoretical roadmap for future experiments) or understanding of the nature of dark matter (that allows us to comprehend the composition of the cosmos better). All the covered topics are considered as a base for new physics beyond the Standard Model of particle physics.

Theoretical Simulations of Materials for Nuclear Energy Applications

International Nuclear Information System (INIS)

Abrikosov, A.; Ponomareva, A.V.; Nikonov, A.Y.; Barannikova, S.A.; Dmitriev, A.I.

2014-01-01

We have demonstrated that state-of-the art theoretical calculations have a capability to predict thermodynamic and mechanical properties of materials with very high accuracy, comparable to the experimental accuracy. Considering Fe-Cr alloys, we have investigated the effect of multicomponent alloying on their phase stability, and we have shown that alloying elements Ni, Mn, and Mo, present in RPV steels, reduce the stability of low-Cr steels against binodal, as well as spinodal decomposition. Considering Zr-Nb alloys, we have demonstrated a possibility of obtaining their elastic moduli from ab initio electronic structure calculations. We argue that theoretical simulations represent valuable tool for a design of new materials for nuclear energy applications

Experimental and theoretical study of magnetohydrodynamic ship models.

Science.gov (United States)

Cébron, David; Viroulet, Sylvain; Vidal, Jérémie; Masson, Jean-Paul; Viroulet, Philippe

2017-01-01

Magnetohydrodynamic (MHD) ships represent a clear demonstration of the Lorentz force in fluids, which explains the number of students practicals or exercises described on the web. However, the related literature is rather specific and no complete comparison between theory and typical small scale experiments is currently available. This work provides, in a self-consistent framework, a detailed presentation of the relevant theoretical equations for small MHD ships and experimental measurements for future benchmarks. Theoretical results of the literature are adapted to these simple battery/magnets powered ships moving on salt water. Comparison between theory and experiments are performed to validate each theoretical step such as the Tafel and the Kohlrausch laws, or the predicted ship speed. A successful agreement is obtained without any adjustable parameter. Finally, based on these results, an optimal design is then deduced from the theory. Therefore this work provides a solid theoretical and experimental ground for small scale MHD ships, by presenting in detail several approximations and how they affect the boat efficiency. Moreover, the theory is general enough to be adapted to other contexts, such as large scale ships or industrial flow measurement techniques.

Experimental and theoretical study of magnetohydrodynamic ship models.

Directory of Open Access Journals (Sweden)

David Cébron

Full Text Available Magnetohydrodynamic (MHD ships represent a clear demonstration of the Lorentz force in fluids, which explains the number of students practicals or exercises described on the web. However, the related literature is rather specific and no complete comparison between theory and typical small scale experiments is currently available. This work provides, in a self-consistent framework, a detailed presentation of the relevant theoretical equations for small MHD ships and experimental measurements for future benchmarks. Theoretical results of the literature are adapted to these simple battery/magnets powered ships moving on salt water. Comparison between theory and experiments are performed to validate each theoretical step such as the Tafel and the Kohlrausch laws, or the predicted ship speed. A successful agreement is obtained without any adjustable parameter. Finally, based on these results, an optimal design is then deduced from the theory. Therefore this work provides a solid theoretical and experimental ground for small scale MHD ships, by presenting in detail several approximations and how they affect the boat efficiency. Moreover, the theory is general enough to be adapted to other contexts, such as large scale ships or industrial flow measurement techniques.

Mesoscopic structure prediction of nanoparticle assembly and coassembly: Theoretical foundation

KAUST Repository

Hur, Kahyun; Hennig, Richard G.; Escobedo, Fernando A.; Wiesner, Ulrich

2010-01-01

structures and interactions. We validate our approach by comparing its predictions with previous simulation results for model systems. We illustrate the flexibility of our approach by applying it to hybrid systems composed of block copolymers and ligand

Harnessing Facebook for Smoking Reduction and Cessation Interventions: Facebook User Engagement and Social Support Predict Smoking Reduction

Science.gov (United States)

Marsch, Lisa A; Brunette, Mary F; Dallery, Jesse

2017-01-01

Background Social media technologies offer a novel opportunity for scalable health interventions that can facilitate user engagement and social support, which in turn may reinforce positive processes for behavior change. Objective By using principles from health communication and social support literature, we implemented a Facebook group–based intervention that targeted smoking reduction and cessation. This study hypothesized that participants’ engagement with and perceived social support from our Facebook group intervention would predict smoking reduction. Methods We recruited 16 regular smokers who live in the United States and who were motivated in quitting smoking at screening. We promoted message exposure as well as engagement and social support systems throughout the intervention. For message exposure, we posted prevalidated, antismoking messages (such as national antismoking campaigns) on our smoking reduction and cessation Facebook group. For engagement and social support systems, we delivered a high degree of engagement and social support systems during the second and third week of the intervention and a low degree of engagement and social support systems during the first and fourth week. A total of six surveys were conducted via Amazon Mechanical Turk (MTurk) at baseline on a weekly basis and at a 2-week follow-up. Results Of the total 16 participants, most were female (n=13, 81%), white (n=15, 94%), and between 25 and 50 years of age (mean 34.75, SD 8.15). There was no study attrition throughout the 6-time-point baseline, weekly, and follow-up surveys. We generated Facebook engagement and social support composite scores (mean 19.19, SD 24.35) by combining the number of likes each participant received and the number of comments or wall posts each participant posted on our smoking reduction and cessation Facebook group during the intervention period. The primary outcome was smoking reduction in the past 7 days measured at baseline and at the two

Theoretical predictions of the lateral spreading of implanted ions

International Nuclear Information System (INIS)

Ashworth, D.G.; Oven, R.

1986-01-01

The theoretical model and computer program (AAMPITS-3D) of Ashworth and co-workers for the calculation of three-dimensional distributions of implanted ions in multi-element amorphous targets are extended to show that the lateral rest distribution is gaussian in a form with a lateral standard deviation (lateral-spread function) which is a function of depth beneath the target surface. A method is given whereby this function may be accurately determined from a knowledge of the projected range and chord range rest distribution functions. Examples of the lateral-spread function are given for boron, phosphorus and arsenic ions implanted into silicon and a detailed description is given of how the lateral-spread function may be used in conjunction with the projected range rest distribution function to provide a fully three-dimensional rest distribution of ions implanted into amorphous targets. Examples of normalised single ion isodensity contours computed from AMPITS-3D are compared with those obtained using the previous assumption of a lateral standard deviation which was independent of distance beneath the target surface. (author)

Combine experimental and theoretical investigation on an alkaloid-Dimethylisoborreverine

Science.gov (United States)

Singh, Swapnil; Singh, Harshita; Karthick, T.; Agarwal, Parag; Erande, Rohan D.; Dethe, Dattatraya H.; Tandon, Poonam

2016-01-01

A combined experimental (FT-IR, 1H and 13C NMR) and theoretical approach is used to study the structure and properties of antimalarial drug dimethylisoborreverine (DMIB). Conformational analysis, has been performed by plotting one dimensional potential energy curve that was computed using density functional theory (DFT) with B3LYP/6-31G method and predicted conformer A1 as the most stable conformer. After full geometry optimization, harmonic wavenumbers were computed for conformer A1 at the DFT/B3LYP/6-311++G(d,P) level. A complete vibrational assignment of all the vibrational modes have been performed on the bases of the potential energy distribution (PED) and theoretical results were found to be in good agreement with the observed data. To predict the solvent effect, the UV-Vis spectra were calculated in different solvents by polarizable continuum model using TD-DFT method. Molecular docking studies were performed to test the biological activity of the sample using SWISSDOCK web server and Hex 8.0.0 software. The molecular electrostatic potential (MESP) was plotted to identify the reactive sites of the molecule. Natural bond orbital (NBO) analysis was performed to get a deep insight of intramolecular charge transfer. Thermodynamical parameters were calculated to predict the direction of chemical reaction.

Daily work-family conflict and alcohol use: testing the cross-level moderation effects of peer drinking norms and social support.

Science.gov (United States)

Wang, Mo; Liu, Songqi; Zhan, Yujie; Shi, Junqi

2010-03-01

In the current study, we conducted daily telephone interviews with a sample of Chinese workers (N = 57) for 5 weeks to examine relationships between daily work-family conflict and alcohol use. Drawn from the tension reduction theory and the stressor-vulnerability model, daily work-family conflict variables were hypothesized to predict employees' daily alcohol use. Further, social variables (i.e., peer drinking norms, family support, and coworker support) were hypothesized to moderate the relationship between work-family conflict and alcohol use. Results showed that daily work-to-family conflict but not family-to-work conflict had a significant within-subject main effect on daily alcohol use. In addition, there was significant between-subject variation in the relationship between work-to-family conflict and alcohol use, which was predicted by peer drinking norms, coworker support, and family support. The current findings shed light on the daily health behavior consequences of work-family conflict and provide important theoretical and practical implications. 2010 APA, all rights reserved

Theoretical and simulation studies of seeding methods

Energy Technology Data Exchange (ETDEWEB)

Pellegrini, Claudio [Univ. of California, Los Angeles, CA (United States)

2017-12-11

We report the theoretical and experimental studies done with the support of DOE-Grant DE-SC0009983 to increase an X-ray FEL peak power from the present level of 20 to 40 GW to one or more TW by seeding, undulator tapering and using the new concept of the Double Bunch FEL.

Scheduling results applicable to decision-theoretic troubleshooting

Czech Academy of Sciences Publication Activity Database

Lín, Václav

2015-01-01

Roč. 57, č. 1 (2015), s. 87-107 ISSN 0888-613X R&D Projects: GA ČR GA13-20012S Institutional support: RVO:67985556 Keywords : Decision-theoretic troubleshooting * Single machine scheduling with weighted flowtime * Algorithms * Computational complexity Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 2.696, year: 2015

Implementing Lumberjacks and Black Swans Into Model-Based Tools to Support Human-Automation Interaction.

Science.gov (United States)

Sebok, Angelia; Wickens, Christopher D

2017-03-01

The objectives were to (a) implement theoretical perspectives regarding human-automation interaction (HAI) into model-based tools to assist designers in developing systems that support effective performance and (b) conduct validations to assess the ability of the models to predict operator performance. Two key concepts in HAI, the lumberjack analogy and black swan events, have been studied extensively. The lumberjack analogy describes the effects of imperfect automation on operator performance. In routine operations, an increased degree of automation supports performance, but in failure conditions, increased automation results in more significantly impaired performance. Black swans are the rare and unexpected failures of imperfect automation. The lumberjack analogy and black swan concepts have been implemented into three model-based tools that predict operator performance in different systems. These tools include a flight management system, a remotely controlled robotic arm, and an environmental process control system. Each modeling effort included a corresponding validation. In one validation, the software tool was used to compare three flight management system designs, which were ranked in the same order as predicted by subject matter experts. The second validation compared model-predicted operator complacency with empirical performance in the same conditions. The third validation compared model-predicted and empirically determined time to detect and repair faults in four automation conditions. The three model-based tools offer useful ways to predict operator performance in complex systems. The three tools offer ways to predict the effects of different automation designs on operator performance.

Experimental and theoretical assessment of flexural properties of hybrid natural fibre composites

DEFF Research Database (Denmark)

Raghavalu Thirumalai, Durai Prabhakaran; Toftegaard, Helmuth Langmaack; Markussen, Christen Malte

2014-01-01

The concept of hybridization of natural fibre composites with synthetic fibres is attracting increasing scientific attention. The present study addresses the flexural properties of hybrid flax/glass/epoxy composites to demonstrate the potential benefits of hybridization. The study covers both...... experimental and theoretical assessments. Composite laminates with different hybrid fibre mixing ratios and different layer configurations were manufactured, and their volumetric composition and flexural properties were measured. The relationship between volume fractions in the composites is shown to be well...... predicted as a function of the hybrid fibre mixing ratio. The flexural modulus of the composites is theoretically assessed by using micromechanical models and laminate theory. The model predictions are compared with the experimentally determined flexural properties. Both approaches show that the flexural...

Experimental and Theoretical Investigations of a Mechanical Lever System Driven by a DC Motor

Science.gov (United States)

Nana, B.; Fautso Kuiate, G.; Yamgoué, S. B.

This paper presents theoretical and experimental results on the investigation of the dynamics of a nonlinear electromechanical system made of a lever arm actuated by a DC motor and controlled through a repulsive magnetic force. We use the method of harmonic balance to derive oscillatory solutions. Theoretical tools such as, bifurcation diagrams, Lyapunov exponents, phase portraits, are used to unveil the rich nonlinear behavior of the system including chaos and hysteresis. The experimental results are in close accordance with the theoretical predictions.

Graph-theoretic measures of multivariate association and prediction

International Nuclear Information System (INIS)

Friedman, J.H.; Rafsky, L.C.

1983-01-01

Interpoint-distance-based graphs can be used to define measures of association that extend Kendall's notion of a generalized correlation coefficient. The authors present particular statistics that provide distribution-free tests of independence sensitive to alternatives involving non-monotonic relationships. Moreover, since ordering plays no essential role, the ideas that fully applicable in a multivariate setting. The authors also define an asymmetric coefficient measuring the extent to which (a vector) X can be used to make single-valued predictions of (a vector) Y. The authors discuss various techniques for proving that such statistics are asymptotically normal. As an example of the effectiveness of their approach, the authors present an application to the examination of residuals from multiple regression. 18 references, 2 figures, 1 table

Perceived parenting and social support: can they predict academic achievement in Argentinean college students?

OpenAIRE

de la Iglesia, Guadalupe; Freiberg Hoffmann, Agustin; Fernández Liporace, Mercedes

2014-01-01

Guadalupe de la Iglesia,1,2 Agustin Freiberg Hoffmann,2 Mercedes Fernández Liporace1,2 1National Council of Scientific and Technical Research (CONICET), 2University of Buenos Aires, Buenos Aires, Argentina Abstract: The aim of this study was to test the ability to predict academic achievement through the perception of parenting and social support in a sample of 354 Argentinean college students. Their mean age was 23.50 years (standard deviation =2.62 years) and most of them (83.3%...

Performance Comparison Between Support Vector Regression and Artificial Neural Network for Prediction of Oil Palm Production

Directory of Open Access Journals (Sweden)

Mustakim Mustakim

2016-02-01

Full Text Available The largest region that produces oil palm in Indonesia has an important role in improving the welfare of society and economy. Oil palm has increased significantly in Riau Province in every period, to determine the production development for the next few years with the functions and benefits of oil palm carried prediction production results that were seen from time series data last 8 years (2005-2013. In its prediction implementation, it was done by comparing the performance of Support Vector Regression (SVR method and Artificial Neural Network (ANN. From the experiment, SVR produced the best model compared with ANN. It is indicated by the correlation coefficient of 95% and 6% for MSE in the kernel Radial Basis Function (RBF, whereas ANN produced only 74% for R2 and 9% for MSE on the 8th experiment with hiden neuron 20 and learning rate 0,1. SVR model generates predictions for next 3 years which increased between 3% - 6% from actual data and RBF model predictions.

Ultrafast Gain Dynamics in Quantum Dot Amplifiers: Theoretical Analysis and Experimental Investigations

DEFF Research Database (Denmark)

Poel, Mike van der; Gehrig, Edeltraud; Hess, Ortwin

2005-01-01

Ultrafast gain dynamics in an optical amplifier with an active layer of self-organized quantum dots (QDs) emitting near 1.3$muhbox m$is characterized experimentally in a pump-probe experiment and modeled theoretically on the basis of QD Maxwell–Bloch equations. Experiment and theory are in good......$factor) is theoretically predicted and demonstrated in the experiments. The fundamental analysis reveals the underlying physical processes and indicates limitations to QD-based devices....

Failure and reliability prediction by support vector machines regression of time series data

International Nuclear Information System (INIS)

Chagas Moura, Marcio das; Zio, Enrico; Lins, Isis Didier; Droguett, Enrique

2011-01-01

Support Vector Machines (SVMs) are kernel-based learning methods, which have been successfully adopted for regression problems. However, their use in reliability applications has not been widely explored. In this paper, a comparative analysis is presented in order to evaluate the SVM effectiveness in forecasting time-to-failure and reliability of engineered components based on time series data. The performance on literature case studies of SVM regression is measured against other advanced learning methods such as the Radial Basis Function, the traditional MultiLayer Perceptron model, Box-Jenkins autoregressive-integrated-moving average and the Infinite Impulse Response Locally Recurrent Neural Networks. The comparison shows that in the analyzed cases, SVM outperforms or is comparable to other techniques. - Highlights: → Realistic modeling of reliability demands complex mathematical formulations. → SVM is proper when the relation input/output is unknown or very costly to be obtained. → Results indicate the potential of SVM for reliability time series prediction. → Reliability estimates support the establishment of adequate maintenance strategies.

Theoretical prediction of low-density hexagonal ZnO hollow structures

Energy Technology Data Exchange (ETDEWEB)

Tuoc, Vu Ngoc, E-mail: tuoc.vungoc@hust.edu.vn [Institute of Engineering Physics, Hanoi University of Science and Technology, 1 Dai Co Viet Road, Hanoi (Viet Nam); Huan, Tran Doan [Institute of Materials Science, University of Connecticut, Storrs, Connecticut 06269-3136 (United States); Thao, Nguyen Thi [Institute of Engineering Physics, Hanoi University of Science and Technology, 1 Dai Co Viet Road, Hanoi (Viet Nam); Hong Duc University, 307 Le Lai, Thanh Hoa City (Viet Nam); Tuan, Le Manh [Hong Duc University, 307 Le Lai, Thanh Hoa City (Viet Nam)

2016-10-14

Along with wurtzite and zinc blende, zinc oxide (ZnO) has been found in a large number of polymorphs with substantially different properties and, hence, applications. Therefore, predicting and synthesizing new classes of ZnO polymorphs are of great significance and have been gaining considerable interest. Herein, we perform a density functional theory based tight-binding study, predicting several new series of ZnO hollow structures using the bottom-up approach. The geometry of the building blocks allows for obtaining a variety of hexagonal, low-density nanoporous, and flexible ZnO hollow structures. Their stability is discussed by means of the free energy computed within the lattice-dynamics approach. Our calculations also indicate that all the reported hollow structures are wide band gap semiconductors in the same fashion with bulk ZnO. The electronic band structures of the ZnO hollow structures are finally examined in detail.

Supportive Accountability: A model for providing human support for internet and ehealth interventions

NARCIS (Netherlands)

Mohr, D.C.; Cuijpers, P.; Lehman, K.A.

2011-01-01

The effectiveness of and adherence to eHealth interventions is enhanced by human support. However, human support has largely not been manualized and has usually not been guided by clear models. The objective of this paper is to develop a clear theoretical model, based on relevant empirical

Patients' Acceptance of Smartphone Health Technology for Chronic Disease Management: A Theoretical Model and Empirical Test.

Science.gov (United States)

Dou, Kaili; Yu, Ping; Deng, Ning; Liu, Fang; Guan, YingPing; Li, Zhenye; Ji, Yumeng; Du, Ningkai; Lu, Xudong; Duan, Huilong

2017-12-06

and actual use of smartphone health apps for chronic disease management. This study developed a theoretical model to predict patients' acceptance of smartphone health technology for chronic disease management. Although resistance to change is a significant barrier to technology acceptance, careful management of doctor-patient relationship, and raising patients' awareness of the negative effect of chronic disease can negate the effect of resistance and encourage acceptance and use of smartphone health technology to support chronic disease management for patients in the community. ©Kaili Dou, Ping Yu, Ning Deng, Fang Liu, YingPing Guan, Zhenye Li, Yumeng Ji, Ningkai Du, Xudong Lu, Huilong Duan. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 06.12.2017.

Predictability and Prediction for an Experimental Cultural Market

Science.gov (United States)

Colbaugh, Richard; Glass, Kristin; Ormerod, Paul

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

Satisfaction with human resource management practices and turnover intention in a five-star hotel: The mediating role of perceived organizational support

Directory of Open Access Journals (Sweden)

A.P. Duarte

2015-05-01

Full Text Available Employees’ satisfaction with human resources management (HRM practices has been envisaged as a solid predictor of turnover; nonetheless, how these practices may influence employee’s behavior is still an unclear process. There are suggestions that HRM practices may be distal determinants of turnover, as their effects may be mediated by more proximal variables such as perceived organizational support. This study empirically tested a model of research arising from these theoretical suggestions in a five-star hotel. The data collected from the survey of 152 workers were subjected to structural equation analyses. The results showed that the theoretical model had a strong fit to the data, giving empirical support to the prediction that satisfaction with HRM practices reduces turnover intentions, by increasing perceived organizational support. The importance of these results is discussed and interpreted from the point of view of strategic gains associated to the quality of HRM practices for the management of voluntary turnover.

Theoretical models for the muon spectrum at sea level

International Nuclear Information System (INIS)

Abdel-Monem, M.S.; Benbrook, J.R.; Osborne, A.R.; Sheldon, W.R.

1975-01-01

The absolute vertical cosmic ray muon spectrum is investigated theoretically. Models of high energy interactions (namely, Maeda-Cantrell (MC), Constant Energy (CE), Cocconi-Koester-Perkins (CKP) and Scaling Models) are used to calculate the spectrum of cosmic ray muons at sea level. A comparison is made between the measured spectrum and that predicted from each of the four theoretical models. It is concluded that the recently available measured muon differential intensities agree with the scaling model for energies less than 100 GeV and with the CKP model for energies greater than 200 GeV. The measured differential intensities (Abdel-Monem et al.) agree with scaling. (orig.) [de

Theoretical Assessment of 178m2Hf De-Excitation

Energy Technology Data Exchange (ETDEWEB)

Hartouni, E P; Chen, M; Descalle, M A; Escher, J E; Loshak, A; Navratil, P; Ormand, W E; Pruet, J; Thompson, I J; Wang, T F

2008-10-06

This document contains a comprehensive literature review in support of the theoretical assessment of the {sup 178m2}Hf de-excitation, as well as a rigorous description of controlled energy release from an isomeric nuclear state.

Predicting child physical activity and screen time: parental support for physical activity and general parenting styles.

Science.gov (United States)

Langer, Shelby L; Crain, A Lauren; Senso, Meghan M; Levy, Rona L; Sherwood, Nancy E

2014-07-01

To examine relationships between parenting styles and practices and child moderate-to-vigorous physical activity (MVPA) and screen time. Participants were children (6.9 ± 1.8 years) with a body mass index in the 70-95th percentile and their parents (421 dyads). Parent-completed questionnaires assessed parental support for child physical activity (PA), parenting styles and child screen time. Children wore accelerometers to assess MVPA. Parenting style did not predict MVPA, but support for PA did (positive association). The association between support and MVPA, moreover, varied as a function of permissive parenting. For parents high in permissiveness, the association was positive (greater support was related to greater MVPA and therefore protective). For parents low in permissiveness, the association was neutral; support did not matter. Authoritarian and permissive parenting styles were both associated with greater screen time. Parenting practices and styles should be considered jointly, offering implications for tailored interventions. © The Author 2014. Published by Oxford University Press on behalf of the Society of Pediatric Psychology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Optimally matching support and perceived spousal sensitivity.

Science.gov (United States)

Cutrona, Carolyn E; Shaffer, Philip A; Wesner, Kristin A; Gardner, Kelli A

2007-12-01

Partner sensitivity is an important antecedent of both intimacy (H. T. Reis & P. Shaver, 1988) and attachment (M. D. S. Ainsworth, 1989). On the basis of the optimal matching model of social support (C. E. Cutrona & D. Russell, 1990), support behaviors that "matched" the support goals of the stressed individual were predicted to lead to the perception of partner sensitivity. Predictions were tested with 59 married couples, who engaged in a videotaped self-disclosure task. Matching support was defined as the disclosure of emotions followed by emotional support or a request for information followed by informational support. Partial evidence was found for the predictions. Matching support following the disclosure of emotions was predictive of perceived partner sensitivity. Mismatched support following the disclosure of emotions predicted lower marital satisfaction, through the mediation of partner sensitivity. Matching support following a request for information was not predictive of perceived partner sensitivity, but negative partner responses (e.g., criticism or sarcasm) following a request for information negatively predicted perceptions of partner sensitivity. The importance of considering the context of support transactions is discussed.

The predictive factors for perceived social support among cancer patients and caregiver burden of their family caregivers in Turkish population.

Science.gov (United States)

Oven Ustaalioglu, Basak; Acar, Ezgi; Caliskan, Mecit

2018-03-01

We aimed to identify the predictive factors for the perceived family social support among cancer patients and caregiver burden of their family caregivers. Participants were 302 cancer patients and their family caregivers. Family social support scale was used for cancer patients, burden interview was used for family caregivers.All subjects also completed Beck depression invantery. The related socio-demographical factors with perceived social support (PSS) and caregiver burden were evaluated by correlation analysis. To find independent factors predicting caregiver burden and PSS, logistic regression analysis were conducted. Depression scores was higher among patients than their family caregivers (12.5 vs. 8). PSS was lower in depressed patients (p Family caregiver burden were also higher in depressive groups (p family caregiver role was negatively correlated (p caregiver burden. Presence of depression was the independent predictor for both, lower PSS for patients and higher burden for caregivers. The results of this study is noteworthy because it may help for planning any supportive care program not only for patients but together with their caregiver at the same time during chemotherapy period in Turkish population.

Theoretical Investigation of the Structural Stabilities of Ceria Surfaces and Supported Metal Nanocluster in Vapor and Aqueous Phases

Energy Technology Data Exchange (ETDEWEB)

Ren, Zhibo [State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing 100029, China; Institute for Integrated Catalysis, Pacific Northwest National Laboratory, Richland, Washington 99352, United States; Liu, Ning [State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing 100029, China; Institute for Integrated Catalysis, Pacific Northwest National Laboratory, Richland, Washington 99352, United States; Chen, Biaohua [State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing 100029, China; Li, Jianwei [State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing 100029, China; Mei, Donghai [Institute for Integrated Catalysis, Pacific Northwest National Laboratory, Richland, Washington 99352, United States

2018-01-25

Understanding the structural stability and dynamics at the interface between the solid metal oxide and aqueous phase is significant in a variety of industrial applications including heterogeneous catalysis and environmental remediation. In the present work, the stabilities of three low-index ceria (CeO2) surfaces, i.e., (111), (110) and (100) in vapor and aqueous phases were studied using ab initio molecular dynamics simulations and density functional theory (DFT) calculations. Gibbs surface free energies as a function of temperature, water partial pressure, and water coverages were calculated using DFT based atomistic thermodynamic approach. On the basis of surface free energies, the morphology and exposed surface structures of the CeO2 nanoparticle were predicted using Wulff construction principle. It is found that the partially hydroxylated (111) and (100) are two major surface structures of CeO2 nanoparticles in vapor phase at ambient temperature (300 K). As the temperature increases, the fully dehydrated (111) surface gradually becomes the most dominant surface structure. While in aqueous phase, the exposed surface of the CeO2 nanoparticle is dominated by the hydroxylated (110) structure at 393 K. Finally, the morphology and stability of a cuboctahedron Pt13 nanocluster supported on CeO2 surfaces in both gas and aqueous phases were investigated. In gas phase, the supported Pt13 nanocluster has the tendency to wetting the CeO2 surface due to the strong metal-support interaction. The calculated interaction energies suggest the CeO2(110) surface provides the best stability for the Pt13 nanocluster. The CeO2 supported Pt13 nanoclusters are oxidized. Compared to the gas phase, the morphology of the CeO2 supported Pt13 nanocluster is less distorted due to the solvation effect provided by surrounding water molecules in aqueous phase. More electrons are transferred from the Pt13 nanocluster to the CeO2 support, implying the supported Pt13 nanocluster is further

Ultra-large bandwidth hollow-core guiding in all-silica bragg fibers with nano-supports

DEFF Research Database (Denmark)

Vienne, Guillaume; Xu, Yong; Jakobsen, Christian

2004-01-01

We demonstrate a new class of hollow-core Bragg fibers that are composed of concentric cylindrical silica rings separated by nanoscale support bridges. We theoretically predict and experimentally observe hollow-core confinement over an octave frequency range. The bandwidth of bandgap guiding in t...... in this new class of Bragg fibers exceeds that of other hollow-core fibers reported in the literature. With only three rings of silica cladding layers, these Bragg fibers achieve propagation loss of the order of 1 dB/m....

Theoretical Studies of Elementary Hydrocarbon Species and Their Reactions

Energy Technology Data Exchange (ETDEWEB)

Allen, Wesley D. [Univ. of Georgia, Athens, GA (United States). Dept. of Chemistry. Center for Computational Quantum Chemistry; Schaefer, III, Henry F. [Univ. of Georgia, Athens, GA (United States). Dept. of Chemistry. Center for Computational Quantum Chemistry

2015-11-14

This is the final report of the theoretical studies of elementary hydrocarbon species and their reactions. Part A has a bibliography of publications supported by DOE from 2010 to 2016 and Part B goes into recent research highlights.

Application of Artificial Neural Network and Support Vector Machines in Predicting Metabolizable Energy in Compound Feeds for Pigs.

Science.gov (United States)

Ahmadi, Hamed; Rodehutscord, Markus

2017-01-01

In the nutrition literature, there are several reports on the use of artificial neural network (ANN) and multiple linear regression (MLR) approaches for predicting feed composition and nutritive value, while the use of support vector machines (SVM) method as a new alternative approach to MLR and ANN models is still not fully investigated. The MLR, ANN, and SVM models were developed to predict metabolizable energy (ME) content of compound feeds for pigs based on the German energy evaluation system from analyzed contents of crude protein (CP), ether extract (EE), crude fiber (CF), and starch. A total of 290 datasets from standardized digestibility studies with compound feeds was provided from several institutions and published papers, and ME was calculated thereon. Accuracy and precision of developed models were evaluated, given their produced prediction values. The results revealed that the developed ANN [ R 2  = 0.95; root mean square error (RMSE) = 0.19 MJ/kg of dry matter] and SVM ( R 2  = 0.95; RMSE = 0.21 MJ/kg of dry matter) models produced better prediction values in estimating ME in compound feed than those produced by conventional MLR ( R 2  = 0.89; RMSE = 0.27 MJ/kg of dry matter). The developed ANN and SVM models produced better prediction values in estimating ME in compound feed than those produced by conventional MLR; however, there were not obvious differences between performance of ANN and SVM models. Thus, SVM model may also be considered as a promising tool for modeling the relationship between chemical composition and ME of compound feeds for pigs. To provide the readers and nutritionist with the easy and rapid tool, an Excel ® calculator, namely, SVM_ME_pig, was created to predict the metabolizable energy values in compound feeds for pigs using developed support vector machine model.

Exploratory experimental and theoretical studies of cyclone gasification of wood powder

Energy Technology Data Exchange (ETDEWEB)

Fredriksson, Christian

1999-11-01

This thesis describes an exploratory experimental and theoretical study of gasification of wood powder in a cyclone gasifier. The generated gas could be used to operate a gas turbine in a combined cycle power plant. The objective has been to develop the understanding of cyclone gasification by experimental studies of the performance of a cyclone designed in principle as a separation cyclone and by comparisons between the experimental results and theoretical predictions. The experiments were carried out with commercial Swedish wood powder fuels, injected with air or steam/air mixture through two diametrically opposite tangential inlets and gasified at atmospheric pressure in cyclones of two different configurations with a volume of about 0.034 m{sup 3}. The studies show that stable gasification of this fuel can be obtained for a specific fuel feeding rate of about 5 MW/m{sup 3} cyclone volume for equivalence ratios above 0.15 and that the equivalence ratio had to be kept below about 0.4 in order to avoid material temperatures above 950 deg C. A cyclone with a short outlet pipe, designed as a conventional separation cyclone was found to give lower char conversion than a modified cyclone with a long outlet pipe. The heating value of the gas was found to be approximately 4.5 MJ/kg. The dust load in the product gas was measured to between 1000 and 2500 mg/Nm{sup 3}. It was possible to separate at least 40-60% of the potassium and 60-90% of the sodium supplied with the wood. The alkali that left the cyclone with the product gas appear to be in solid or melted phase in the unseparated char particles and consequently not vaporised during gasification. As the K and Na were assumed to remain within the particles during gasification, it was concluded that to reduce the amount of alkali metals in the product gas it would be necessary to improve the particle separation efficiency. The results of the theoretical modelling, using the existing models in the commercial software CFX

3rd Joint Dutch-Brazil School on Theoretical Physics

CERN Document Server

2015-01-01

The Joint Dutch-Brazil School on Theoretical Physics is now in its third edition with previous schools in 2007 and 2011. This edition of the school will feature minicourses by Nima Arkani-Hamed (IAS Princeton), Jan de Boer (University of Amsterdam) and Cumrun Vafa (Harvard University), as well as student presentations. The school is jointly organized with the Dutch Research School of Theoretical Physics (DRSTP) and is intended for graduate students and researchers in the field of high-energy theoretical physics. There is no registration fee and limited funds are available for local and travel support of participants. This school in São Paulo will be preceded by the XVIII J. A. Swieca School in Campos de Jordão.

Theoretical optical spectroscopy of complex systems

Energy Technology Data Exchange (ETDEWEB)

Conte, A. Mosca, E-mail: adriano.mosca.conte@roma2.infn.it [MIFP, NAST, ETSF,CNR INFM-SMC, Universitá di Roma Tor Vergata, Via della Ricerca Scientifica 1, Roma (Italy); Violante, C., E-mail: claudia.violante@roma2.infn.it [MIFP, NAST, ETSF,CNR INFM-SMC, Universitá di Roma Tor Vergata, Via della Ricerca Scientifica 1, Roma (Italy); Missori, M., E-mail: mauro.missori@isc.cnr.it [Istituto dei Sistemi Complessi, Consiglio Nazionale delle Ricerche, Via Salaria Km 29.300, 00016 Monterotondo Scalo (Rome) (Italy); Bechstedt, F., E-mail: bech@ifto.physik.uni-jena.de [Institut fur Festkorpertheorie und -optik, Friedrich-Schiller-Universitat, Max-Wien-Platz 1, 07743 Jena (Germany); Teodonio, L. [MIFP, NAST, ETSF,CNR INFM-SMC, Universitá di Roma Tor Vergata, Via della Ricerca Scientifica 1, Roma (Italy); Istituto centrale per il restauro e la conservazione del patrimonio archivistico e librario (IC-RCPAL), Italian Minister for Cultural Heritage, Via Milano 76, 00184 Rome (Italy); Ippoliti, E.; Carloni, P. [German Research School for Simulation Sciences, Julich (Germany); Guidoni, L., E-mail: leonardo.guidoni@univaq.it [Università degli Studi di L’Aquila, Dipartimento di Chimica e Materiali, Via Campo di Pile, 67100 L’Aquila (Italy); Pulci, O., E-mail: olivia.pulci@roma2.infn.it [MIFP, NAST, ETSF,CNR INFM-SMC, Universitá di Roma Tor Vergata, Via della Ricerca Scientifica 1, Roma (Italy)

2013-08-15

Highlights: ► We review some theoretical condensed matter ab initio spectroscopic computational techniques. ► We show several applications ranging from 0 to 3 dimensional systems. ► For each system studied, we show which kind of information it is possible to obtain by performing these calculations. -- Abstract: We review here some of the most reliable and efficient computational theoretical ab initio techniques for the prediction of optical and electronic spectroscopic properties and show some important applications to molecules, surfaces, and solids. We investigate the role of the solvent in the optical absorption spectrum of indole molecule. We study the excited-state properties of a photo-active minimal model molecule for the retinal of rhodopsin, responsible for vision mechanism in animals. We then show a study about spectroscopic properties of Si(1 1 1) surface. Finally we simulate a bulk system: paper, that is mainly made of cellulose, a pseudo-crystalline material representing 40% of annual biomass production in the Earth.

Theoretical optical spectroscopy of complex systems

International Nuclear Information System (INIS)

Conte, A. Mosca; Violante, C.; Missori, M.; Bechstedt, F.; Teodonio, L.; Ippoliti, E.; Carloni, P.; Guidoni, L.; Pulci, O.

2013-01-01

Highlights: ► We review some theoretical condensed matter ab initio spectroscopic computational techniques. ► We show several applications ranging from 0 to 3 dimensional systems. ► For each system studied, we show which kind of information it is possible to obtain by performing these calculations. -- Abstract: We review here some of the most reliable and efficient computational theoretical ab initio techniques for the prediction of optical and electronic spectroscopic properties and show some important applications to molecules, surfaces, and solids. We investigate the role of the solvent in the optical absorption spectrum of indole molecule. We study the excited-state properties of a photo-active minimal model molecule for the retinal of rhodopsin, responsible for vision mechanism in animals. We then show a study about spectroscopic properties of Si(1 1 1) surface. Finally we simulate a bulk system: paper, that is mainly made of cellulose, a pseudo-crystalline material representing 40% of annual biomass production in the Earth

41st Vietnam National Conference on Theoretical Physics

International Nuclear Information System (INIS)

2017-01-01

Preface The 41 st Vietnam National Conference on Theoretical Physics (NCTP-41) was held during 1-4 August 2016 in Nha Trang, Vietnam. The NCTP-41 was organized by the Institute of Physics, Vietnam Academy of Science and Technology (IOP-VAST) under the support of the Vietnamese Theoretical Physics Society (VTPS). This meeting belongs to a series of annual theoretical physics conferences that started in 1976. The conference has covered a wide range of theoretical physics topics from 4 major fields: • Particle, nuclear and astro- physics, • Molecular physics, quantum optics and quantum computation, • Condensed matter physics, • Soft matter, biological and interdisciplinary physics. 115 participants have participated in the conference. 2 invited talks, 22 oral and 75 poster contributions were presented. This volume contains selected papers contributed by the participants. Editors of the NCTP-41 Proceedings Trinh Xuan Hoang, Hoang Anh Tuan and Vu Ngoc Tuoc Information about Organizer, Sponsor, Honorary Chair and Chair and also lists of committees and participants are available in the PDF (paper)

Harnessing Facebook for Smoking Reduction and Cessation Interventions: Facebook User Engagement and Social Support Predict Smoking Reduction.

Science.gov (United States)

Kim, Sunny Jung; Marsch, Lisa A; Brunette, Mary F; Dallery, Jesse

2017-05-23

Social media technologies offer a novel opportunity for scalable health interventions that can facilitate user engagement and social support, which in turn may reinforce positive processes for behavior change. By using principles from health communication and social support literature, we implemented a Facebook group-based intervention that targeted smoking reduction and cessation. This study hypothesized that participants' engagement with and perceived social support from our Facebook group intervention would predict smoking reduction. We recruited 16 regular smokers who live in the United States and who were motivated in quitting smoking at screening. We promoted message exposure as well as engagement and social support systems throughout the intervention. For message exposure, we posted prevalidated, antismoking messages (such as national antismoking campaigns) on our smoking reduction and cessation Facebook group. For engagement and social support systems, we delivered a high degree of engagement and social support systems during the second and third week of the intervention and a low degree of engagement and social support systems during the first and fourth week. A total of six surveys were conducted via Amazon Mechanical Turk (MTurk) at baseline on a weekly basis and at a 2-week follow-up. Of the total 16 participants, most were female (n=13, 81%), white (n=15, 94%), and between 25 and 50 years of age (mean 34.75, SD 8.15). There was no study attrition throughout the 6-time-point baseline, weekly, and follow-up surveys. We generated Facebook engagement and social support composite scores (mean 19.19, SD 24.35) by combining the number of likes each participant received and the number of comments or wall posts each participant posted on our smoking reduction and cessation Facebook group during the intervention period. The primary outcome was smoking reduction in the past 7 days measured at baseline and at the two-week follow-up. Compared with the baseline

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

Directory of Open Access Journals (Sweden)

Ivan Marović

2017-01-01

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

A System Theoretical Inspired Approach to Knowledge Construction

DEFF Research Database (Denmark)

Mathiasen, Helle

2008-01-01

student's knowledge construction, in the light of operative constructivism, inspired by the German sociologist N. Luhmann's system theoretical approach to epistemology. Taking observations as operations based on distinction and indication (selection) contingency becomes a fundamental condition in learning......  Abstract The aim of this paper is to discuss the relation between teaching and learning. The point of departure is that teaching environments (communication forums) is a potential facilitator for learning processes and knowledge construction. The paper present a theoretical frame work, to discuss...... processes, and a condition which teaching must address as far as teaching strives to stimulate non-random learning outcomes. Thus learning outcomes understood as the individual learner's knowledge construction cannot be directly predicted from events and characteristics in the environment. This has...

Support vector regression for porosity prediction in a heterogeneous reservoir: A comparative study

Science.gov (United States)

Al-Anazi, A. F.; Gates, I. D.

2010-12-01

In wells with limited log and core data, porosity, a fundamental and essential property to characterize reservoirs, is challenging to estimate by conventional statistical methods from offset well log and core data in heterogeneous formations. Beyond simple regression, neural networks have been used to develop more accurate porosity correlations. Unfortunately, neural network-based correlations have limited generalization ability and global correlations for a field are usually less accurate compared to local correlations for a sub-region of the reservoir. In this paper, support vector machines are explored as an intelligent technique to correlate porosity to well log data. Recently, support vector regression (SVR), based on the statistical learning theory, have been proposed as a new intelligence technique for both prediction and classification tasks. The underlying formulation of support vector machines embodies the structural risk minimization (SRM) principle which has been shown to be superior to the traditional empirical risk minimization (ERM) principle employed by conventional neural networks and classical statistical methods. This new formulation uses margin-based loss functions to control model complexity independently of the dimensionality of the input space, and kernel functions to project the estimation problem to a higher dimensional space, which enables the solution of more complex nonlinear problem optimization methods to exist for a globally optimal solution. SRM minimizes an upper bound on the expected risk using a margin-based loss function ( ɛ-insensitivity loss function for regression) in contrast to ERM which minimizes the error on the training data. Unlike classical learning methods, SRM, indexed by margin-based loss function, can also control model complexity independent of dimensionality. The SRM inductive principle is designed for statistical estimation with finite data where the ERM inductive principle provides the optimal solution (the

Theoretical vs. actual energy consumption of labelled dwellings in the Netherlands: Discrepancies and policy implications

International Nuclear Information System (INIS)

Majcen, D.; Itard, L.C.M.; Visscher, H.

2013-01-01

In Europe, the Energy Performance of Buildings Directive (EPBD) provides for compulsory energy performance certification (labelling) for all existing dwellings. In the Netherlands, a labelling scheme was introduced in 2008. Certificates contain the energy label of the dwelling and corresponding theoretical gas and electricity consumption, calculated based on the dwellings physical characteristics, its heating, ventilation and cooling systems and standard use characteristics. This paper reports on a large-scale study of around 200,000 dwellings comparing labels and theoretical energy use with data on actual energy use. The study shows that dwellings with a low energy label actually consume much less energy than predicted by the label, but on the other hand, energy-efficient dwellings consume more than predicted. In practice, policy targets are set according to the theoretical rather than the actual consumptions of the building stock. In line with identified discrepancies, the study shows that whereas most energy reduction targets can be met according to the theoretical energy consumption of the dwelling stock, the future actual energy reduction potential is much lower and fails to meet most of the current energy reduction targets. - Highlights: ► Actual gas consumption in Dutch dwellings is lower than the theoretical. ► In the dwellings with label A–B, theoretical gas consumption is lower than actual gas consumption. ► In less efficient dwellings, theoretical gas consumption is much higher than the actual. ► Most current energy reduction targets are unachievable if modelled with actual instead of theoretical energy consumption

[Theoretical modeling and experimental research on direct compaction characteristics of multi-component pharmaceutical powders based on the Kawakita equation].

Science.gov (United States)

Si, Guo-Ning; Chen, Lan; Li, Bao-Guo

2014-04-01

Base on the Kawakita powder compression equation, a general theoretical model for predicting the compression characteristics of multi-components pharmaceutical powders with different mass ratios was developed. The uniaxial flat-face compression tests of powder lactose, starch and microcrystalline cellulose were carried out, separately. Therefore, the Kawakita equation parameters of the powder materials were obtained. The uniaxial flat-face compression tests of the powder mixtures of lactose, starch, microcrystalline cellulose and sodium stearyl fumarate with five mass ratios were conducted, through which, the correlation between mixture density and loading pressure and the Kawakita equation curves were obtained. Finally, the theoretical prediction values were compared with experimental results. The analysis showed that the errors in predicting mixture densities were less than 5.0% and the errors of Kawakita vertical coordinate were within 4.6%, which indicated that the theoretical model could be used to predict the direct compaction characteristics of multi-component pharmaceutical powders.

A geostatistical approach to the change-of-support problem and variable-support data fusion in spatial analysis

Science.gov (United States)

Wang, Jun; Wang, Yang; Zeng, Hui

2016-01-01

A key issue to address in synthesizing spatial data with variable-support in spatial analysis and modeling is the change-of-support problem. We present an approach for solving the change-of-support and variable-support data fusion problems. This approach is based on geostatistical inverse modeling that explicitly accounts for differences in spatial support. The inverse model is applied here to produce both the best predictions of a target support and prediction uncertainties, based on one or more measurements, while honoring measurements. Spatial data covering large geographic areas often exhibit spatial nonstationarity and can lead to computational challenge due to the large data size. We developed a local-window geostatistical inverse modeling approach to accommodate these issues of spatial nonstationarity and alleviate computational burden. We conducted experiments using synthetic and real-world raster data. Synthetic data were generated and aggregated to multiple supports and downscaled back to the original support to analyze the accuracy of spatial predictions and the correctness of prediction uncertainties. Similar experiments were conducted for real-world raster data. Real-world data with variable-support were statistically fused to produce single-support predictions and associated uncertainties. The modeling results demonstrate that geostatistical inverse modeling can produce accurate predictions and associated prediction uncertainties. It is shown that the local-window geostatistical inverse modeling approach suggested offers a practical way to solve the well-known change-of-support problem and variable-support data fusion problem in spatial analysis and modeling.

Theoretical prediction of pullout strengths for dental and orthopaedic screws with conical profile and buttress threads.

Science.gov (United States)

Shih, Kao-Shang; Hou, Sheng-Mou; Lin, Shang-Chih

2017-12-01

The pullout strength of a screw is an indicator of how secure bone fragments are being held in place. Such bone-purchasing ability is sensitive to bone quality, thread design, and the pilot hole, and is often evaluated by experimental and numerical methods. Historically, there are some mathematical formulae to simulate the screw withdrawal from the synthetic bone. There are great variations in screw specifications. However, extensive investigation of the correlation between experimental and analytical results has not been reported in literature. Referring to the literature formulae, this study aims to evaluate the differences in the calculated pullout strengths. The pullout tests of the surgical screws are measured and the sawbone is used as the testing block. The absolute errors and correlation coefficients of the experimental and analytical results are calculated as the comparison baselines of the formulae. The absolute error of the dental, traumatic, and spinal groups are 21.7%, 95.5%, and 37.0%, respectively. For the screws with a conical profile and/or tiny threads, the calculated and measured results are not well correlated. The formulae are not accurate indicators of the pullout strengths of the screws where the design parameters are slightly varied. However, the experimental and numerical results are highly correlated for the cylindrical screws. The pullout strength of a conical screw is higher than that of its counterpart, but all formulae consistently predict the opposite results. In general, the bony purchase of the buttress threads is securer than that of the symmetric thread. An absolute error of up to 51.4% indicates the theoretical results cannot predict the actual value of the pullout strength. Only thread diameter, pitch, and depth are considered in the investigated formulae. The thread profile and shape should be formulated to modify the slippage mechanism at the bone-screw interfaces and simulate the strength change in the squeezed bones

BLProt: Prediction of bioluminescent proteins based on support vector machine and relieff feature selection

KAUST Repository

Kandaswamy, Krishna Kumar

2011-08-17

Background: Bioluminescence is a process in which light is emitted by a living organism. Most creatures that emit light are sea creatures, but some insects, plants, fungi etc, also emit light. The biotechnological application of bioluminescence has become routine and is considered essential for many medical and general technological advances. Identification of bioluminescent proteins is more challenging due to their poor similarity in sequence. So far, no specific method has been reported to identify bioluminescent proteins from primary sequence.Results: In this paper, we propose a novel predictive method that uses a Support Vector Machine (SVM) and physicochemical properties to predict bioluminescent proteins. BLProt was trained using a dataset consisting of 300 bioluminescent proteins and 300 non-bioluminescent proteins, and evaluated by an independent set of 141 bioluminescent proteins and 18202 non-bioluminescent proteins. To identify the most prominent features, we carried out feature selection with three different filter approaches, ReliefF, infogain, and mRMR. We selected five different feature subsets by decreasing the number of features, and the performance of each feature subset was evaluated.Conclusion: BLProt achieves 80% accuracy from training (5 fold cross-validations) and 80.06% accuracy from testing. The performance of BLProt was compared with BLAST and HMM. High prediction accuracy and successful prediction of hypothetical proteins suggests that BLProt can be a useful approach to identify bioluminescent proteins from sequence information, irrespective of their sequence similarity. 2011 Kandaswamy et al; licensee BioMed Central Ltd.

BLProt: Prediction of bioluminescent proteins based on support vector machine and relieff feature selection

KAUST Repository

Kandaswamy, Krishna Kumar; Pugalenthi, Ganesan; Hazrati, Mehrnaz Khodam; Kalies, Kai-Uwe; Martinetz, Thomas

2011-01-01

Background: Bioluminescence is a process in which light is emitted by a living organism. Most creatures that emit light are sea creatures, but some insects, plants, fungi etc, also emit light. The biotechnological application of bioluminescence has become routine and is considered essential for many medical and general technological advances. Identification of bioluminescent proteins is more challenging due to their poor similarity in sequence. So far, no specific method has been reported to identify bioluminescent proteins from primary sequence.Results: In this paper, we propose a novel predictive method that uses a Support Vector Machine (SVM) and physicochemical properties to predict bioluminescent proteins. BLProt was trained using a dataset consisting of 300 bioluminescent proteins and 300 non-bioluminescent proteins, and evaluated by an independent set of 141 bioluminescent proteins and 18202 non-bioluminescent proteins. To identify the most prominent features, we carried out feature selection with three different filter approaches, ReliefF, infogain, and mRMR. We selected five different feature subsets by decreasing the number of features, and the performance of each feature subset was evaluated.Conclusion: BLProt achieves 80% accuracy from training (5 fold cross-validations) and 80.06% accuracy from testing. The performance of BLProt was compared with BLAST and HMM. High prediction accuracy and successful prediction of hypothetical proteins suggests that BLProt can be a useful approach to identify bioluminescent proteins from sequence information, irrespective of their sequence similarity. 2011 Kandaswamy et al; licensee BioMed Central Ltd.

Do similarities or differences between CEO leadership and organizational culture have a more positive effect on firm performance? A test of competing predictions.

Science.gov (United States)

Hartnell, Chad A; Kinicki, Angelo J; Lambert, Lisa Schurer; Fugate, Mel; Doyle Corner, Patricia

2016-06-01

This study examines the nature of the interaction between CEO leadership and organizational culture using 2 common metathemes (task and relationship) in leadership and culture research. Two perspectives, similarity and dissimilarity, offer competing predictions about the fit, or interaction, between leadership and culture and its predicted effect on firm performance. Predictions for the similarity perspective draw upon attribution theory and social identity theory of leadership, whereas predictions for the dissimilarity perspective are developed based upon insights from leadership contingency theories and the notion of substitutability. Hierarchical regression results from 114 CEOs and 324 top management team (TMT) members failed to support the similarity hypotheses but revealed broad support for the dissimilarity predictions. Findings suggest that culture can serve as a substitute for leadership when leadership behaviors are redundant with cultural values (i.e., they both share a task- or relationship-oriented focus). Findings also support leadership contingency theories indicating that CEO leadership is effective when it provides psychological and motivational resources lacking in the organization's culture. We discuss theoretical and practical implications and delineate directions for future research. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Basic Modelling principles and Validation of Software for Prediction of Collision Damage

DEFF Research Database (Denmark)

Simonsen, Bo Cerup

2000-01-01

This report describes basic modelling principles, the theoretical background and validation examples for the collision damage prediction module in the ISESO stand-alone software.......This report describes basic modelling principles, the theoretical background and validation examples for the collision damage prediction module in the ISESO stand-alone software....

K. Sridhar Moorthy's Theoretical Modelling in Marketing - A Review

African Journals Online (AJOL)

Toshiba

experimental design for theoretical modelling of sales force compensation is vivid and ... different from the concept of a model in decision support systems and behavioural .... ―refers to the fact that people may not optimize.‖ This, of course, is.

Prediction of the dollar to the ruble rate. A system-theoretic approach

Science.gov (United States)

Borodachev, Sergey M.

2017-07-01

Proposed a simple state-space model of dollar rate formation based on changes in oil prices and some mechanisms of money transfer between monetary and stock markets. Comparison of predictions by means of input-output model and state-space model is made. It concludes that with proper use of statistical data (Kalman filter) the second approach provides more adequate predictions of the dollar rate.

A Modified Method Combined with a Support Vector Machine and Bayesian Algorithms in Biological Information

Directory of Open Access Journals (Sweden)

Wen-Gang Zhou

2015-06-01

Full Text Available With the deep research of genomics and proteomics, the number of new protein sequences has expanded rapidly. With the obvious shortcomings of high cost and low efficiency of the traditional experimental method, the calculation method for protein localization prediction has attracted a lot of attention due to its convenience and low cost. In the machine learning techniques, neural network and support vector machine (SVM are often used as learning tools. Due to its complete theoretical framework, SVM has been widely applied. In this paper, we make an improvement on the existing machine learning algorithm of the support vector machine algorithm, and a new improved algorithm has been developed, combined with Bayesian algorithms. The proposed algorithm can improve calculation efficiency, and defects of the original algorithm are eliminated. According to the verification, the method has proved to be valid. At the same time, it can reduce calculation time and improve prediction efficiency.

Data warehouse based decision support system in nuclear power plants

International Nuclear Information System (INIS)

Nadinic, B.

2004-01-01

Safety is an important element in business decision making processes in nuclear power plants. Information about component reliability, structures and systems, data recorded during the nuclear power plant's operation and outage periods, as well as experiences from other power plants are located in different database systems throughout the power plant. It would be possible to create a decision support system which would collect data, transform it into a standardized form and store it in a single location in a format more suitable for analyses and knowledge discovery. This single location where the data would be stored would be a data warehouse. Such data warehouse based decision support system could help make decision making processes more efficient by providing more information about business processes and predicting possible consequences of different decisions. Two main functionalities in this decision support system would be an OLAP (On Line Analytical Processing) and a data mining system. An OLAP system would enable the users to perform fast, simple and efficient multidimensional analysis of existing data and identify trends. Data mining techniques and algorithms would help discover new, previously unknown information from the data as well as hidden dependencies between various parameters. Data mining would also enable analysts to create relevant prediction models that could predict behaviour of different systems during operation and inspection results during outages. The basic characteristics and theoretical foundations of such decision support system are described and the reasons for choosing a data warehouse as the underlying structure are explained. The article analyzes obvious business benefits of such system as well as potential uses of OLAP and data mining technologies. Possible implementation methodologies and problems that may arise, especially in the field of data integration, are discussed and analyzed.(author)

Pseudoracemic amino acid complexes: blind predictions for flexible two-component crystals.

Science.gov (United States)

Görbitz, Carl Henrik; Dalhus, Bjørn; Day, Graeme M

2010-08-14

Ab initio prediction of the crystal packing in complexes between two flexible molecules is a particularly challenging computational chemistry problem. In this work we present results of single crystal structure determinations as well as theoretical predictions for three 1 ratio 1 complexes between hydrophobic l- and d-amino acids (pseudoracemates), known from previous crystallographic work to form structures with one of two alternative hydrogen bonding arrangements. These are accurately reproduced in the theoretical predictions together with a series of patterns that have never been observed experimentally. In this bewildering forest of potential polymorphs, hydrogen bonding arrangements and molecular conformations, the theoretical predictions succeeded, for all three complexes, in finding the correct hydrogen bonding pattern. For two of the complexes, the calculations also reproduce the exact space group and side chain orientations in the best ranked predicted structure. This includes one complex for which the observed crystal packing clearly contradicted previous experience based on experimental data for a substantial number of related amino acid complexes. The results highlight the significant recent advances that have been made in computational methods for crystal structure prediction.

Combined prediction model for supply risk in nuclear power equipment manufacturing industry based on support vector machine and decision tree

International Nuclear Information System (INIS)

Shi Chunsheng; Meng Dapeng

2011-01-01

The prediction index for supply risk is developed based on the factor identifying of nuclear equipment manufacturing industry. The supply risk prediction model is established with the method of support vector machine and decision tree, based on the investigation on 3 important nuclear power equipment manufacturing enterprises and 60 suppliers. Final case study demonstrates that the combination model is better than the single prediction model, and demonstrates the feasibility and reliability of this model, which provides a method to evaluate the suppliers and measure the supply risk. (authors)

The organization of irrational beliefs in posttraumatic stress symptomology: testing the predictions of REBT theory using structural equation modelling.

Science.gov (United States)

Hyland, Philip; Shevlin, Mark; Adamson, Gary; Boduszek, Daniel

2014-01-01

This study directly tests a central prediction of rational emotive behaviour therapy (REBT) that has received little empirical attention regarding the core and intermediate beliefs in the development of posttraumatic stress symptoms. A theoretically consistent REBT model of posttraumatic stress disorder (PTSD) was examined using structural equation modelling techniques among a sample of 313 trauma-exposed military and law enforcement personnel. The REBT model of PTSD provided a good fit of the data, χ(2) = 599.173, df = 356, p depreciation beliefs. Results were consistent with the predictions of REBT theory and provides strong empirical support that the cognitive variables described by REBT theory are critical cognitive constructs in the prediction of PTSD symptomology. © 2013 Wiley Periodicals, Inc.

Short-Term Prediction of Air Pollution in Macau Using Support Vector Machines

Directory of Open Access Journals (Sweden)

Chi-Man Vong

2012-01-01

Full Text Available Forecasting of air pollution is a popular and important topic in recent years due to the health impact caused by air pollution. It is necessary to build an early warning system, which provides forecast and also alerts health alarm to local inhabitants by medical practitioners and the local government. Meteorological and pollutions data collected daily at monitoring stations of Macau can be used in this study to build a forecasting system. Support vector machines (SVMs, a novel type of machine learning technique based on statistical learning theory, can be used for regression and time series prediction. SVM is capable of good generalization while the performance of the SVM model is often hinged on the appropriate choice of the kernel.

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

Science.gov (United States)

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

2014-08-01

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

Why and when social support predicts older adults' pain-related disability: a longitudinal study.

Science.gov (United States)

Matos, Marta; Bernardes, Sónia F; Goubert, Liesbet

2017-10-01

Pain-related social support has been shown to be directly associated with pain-related disability, depending on whether it promotes functional autonomy or dependence. However, previous studies mostly relied on cross-sectional methods, precluding conclusions on the temporal relationship between pain-related social support and disability. Also, research on the behavioral and psychological processes that account for such a relationship is scarce. Therefore, this study aimed at investigating the following longitudinally: (1) direct effects of social support for functional autonomy/dependence on pain-related disability, (2) mediating role of physical functioning, pain-related self-efficacy, and fear, and (3) whether pain duration and pain intensity moderate such mediating processes. A total of 168 older adults (Mage = 78.3; SDage = 8.7) participated in a 3-month prospective design, with 3 moments of measurement, with a 6-week lag between them. Participants completed the Formal Social Support for Autonomy and Dependence in Pain Inventory, the Brief Pain Inventory, the 36-SF Health Survey, behavioral tasks from the Senior Fitness Test, the Pain Self-Efficacy Questionnaire, and the Tampa Scale for Kinesiophobia. Moderated mediation analyses showed that formal social support for functional dependence (T1) predicted an increase in pain-related disability (T3), that was mediated by self-reported physical functioning (T2) and by pain-related self-efficacy (T2) at short to moderate pain duration and at low to moderate pain intensity, but not at higher levels. Findings emphasized that social support for functional dependence is a risk factor for pain-related disability and uncovered the "why" and "when" of this relationship. Implications for the design of social support interventions aiming at promoting older adults' healthy aging despite chronic pain are drawn.

A method for predicting monthly rainfall patterns

International Nuclear Information System (INIS)

Njau, E.C.

1987-11-01

A brief survey is made of previous methods that have been used to predict rainfall trends or drought spells in different parts of the earth. The basic methodologies or theoretical strategies used in these methods are compared with contents of a recent theory of Sun-Weather/Climate links (Njau, 1985a; 1985b; 1986; 1987a; 1987b; 1987c) which point towards the possibility of practical climatic predictions. It is shown that not only is the theoretical basis of each of these methodologies or strategies fully incorporated into the above-named theory, but also this theory may be used to develop a technique by which future monthly rainfall patterns can be predicted in further and finer details. We describe the latter technique and then illustrate its workability by means of predictions made on monthly rainfall patterns in some East African meteorological stations. (author). 43 refs, 11 figs, 2 tabs

Predicting Efficient Antenna Ligands for Tb(III) Emission

Energy Technology Data Exchange (ETDEWEB)

Samuel, Amanda P.S.; Xu, Jide; Raymond, Kenneth

2008-10-06

A series of highly luminescent Tb(III) complexes of para-substituted 2-hydroxyisophthalamide ligands (5LI-IAM-X) has been prepared (X = H, CH{sub 3}, (C=O)NHCH{sub 3}, SO{sub 3}{sup -}, NO{sub 2}, OCH{sub 3}, F, Cl, Br) to probe the effect of substituting the isophthalamide ring on ligand and Tb(III) emission in order to establish a method for predicting the effects of chromophore modification on Tb(III) luminescence. The energies of the ligand singlet and triplet excited states are found to increase linearly with the {pi}-withdrawing ability of the substituent. The experimental results are supported by time-dependent density functional theory (TD-DFT) calculations performed on model systems, which predict ligand singlet and triplet energies within {approx}5% of the experimental values. The quantum yield ({Phi}) values of the Tb(III) complex increases with the triplet energy of the ligand, which is in part due to the decreased non-radiative deactivation caused by thermal repopulation of the triplet. Together, the experimental and theoretical results serve as a predictive tool that can be used to guide the synthesis of ligands used to sensitize lanthanide luminescence.

Adaptive Encoding of Outcome Prediction by Prefrontal Cortex Ensembles Supports Behavioral Flexibility.

Science.gov (United States)

Del Arco, Alberto; Park, Junchol; Wood, Jesse; Kim, Yunbok; Moghaddam, Bita

2017-08-30

The prefrontal cortex (PFC) is thought to play a critical role in behavioral flexibility by monitoring action-outcome contingencies. How PFC ensembles represent shifts in behavior in response to changes in these contingencies remains unclear. We recorded single-unit activity and local field potentials in the dorsomedial PFC (dmPFC) of male rats during a set-shifting task that required them to update their behavior, among competing options, in response to changes in action-outcome contingencies. As behavior was updated, a subset of PFC ensembles encoded the current trial outcome before the outcome was presented. This novel outcome-prediction encoding was absent in a control task, in which actions were rewarded pseudorandomly, indicating that PFC neurons are not merely providing an expectancy signal. In both control and set-shifting tasks, dmPFC neurons displayed postoutcome discrimination activity, indicating that these neurons also monitor whether a behavior is successful in generating rewards. Gamma-power oscillatory activity increased before the outcome in both tasks but did not differentiate between expected outcomes, suggesting that this measure is not related to set-shifting behavior but reflects expectation of an outcome after action execution. These results demonstrate that PFC neurons support flexible rule-based action selection by predicting outcomes that follow a particular action. SIGNIFICANCE STATEMENT Tracking action-outcome contingencies and modifying behavior when those contingencies change is critical to behavioral flexibility. We find that ensembles of dorsomedial prefrontal cortex neurons differentiate between expected outcomes when action-outcome contingencies change. This predictive mode of signaling may be used to promote a new response strategy at the service of behavioral flexibility. Copyright © 2017 the authors 0270-6474/17/378363-11$15.00/0.

Comparisons Between Experimental and Semi-theoretical Cutting Forces of CCS Disc Cutters

Science.gov (United States)

Xia, Yimin; Guo, Ben; Tan, Qing; Zhang, Xuhui; Lan, Hao; Ji, Zhiyong

2018-05-01

This paper focuses on comparisons between the experimental and semi-theoretical forces of CCS disc cutters acting on different rocks. The experimental forces obtained from LCM tests were used to evaluate the prediction accuracy of a semi-theoretical CSM model. The results show that the CSM model reliably predicts the normal forces acting on red sandstone and granite, but underestimates the normal forces acting on marble. Some additional LCM test data from the literature were collected to further explore the ability of the CSM model to predict the normal forces acting on rocks of different strengths. The CSM model underestimates the normal forces acting on soft rocks, semi-hard rocks and hard rocks by approximately 38, 38 and 10%, respectively, but very accurately predicts those acting on very hard and extremely hard rocks. A calibration factor is introduced to modify the normal forces estimated by the CSM model. The overall trend of the calibration factor is characterized by an exponential decrease with increasing rock uniaxial compressive strength. The mean fitting ratios between the normal forces estimated by the modified CSM model and the experimental normal forces acting on soft rocks, semi-hard rocks and hard rocks are 1.076, 0.879 and 1.013, respectively. The results indicate that the prediction accuracy and the reliability of the CSM model have been improved.

Some theoretical aspects of the design of stereoscopic television systems

International Nuclear Information System (INIS)

Jones, A.

1980-03-01

Several parameters which together specify the performance of a stereoscopic television system which has been demonstrated in reactors are investigated theoretically. These are: (1) the minimum resolvable depth interval in object space, (2) the region of space which can be displayed in three dimensions without causing undue eyestrain to the observer, (3) distortions which may arise in the display. The resulting equations form a basis from which operational stereocameras can be designed and a particular example is given, which also illustrates the relationships between the parameters. It is argued that the extent of the stereo region (parameter (2) above) predicted by previously published work is probably too large for closed circuit television inspection. This arises because the criterion used to determine the maximum tolerable screen parallax is too generous. An alternative, based upon the size of Panum's fusional area (a property of the observer's eye) is proposed. Preliminary experimental support for the proposal is given by measurements of the extent of the stereoscopic region using a number of observers. (author)

Neurocognitive mechanisms of perception-action coordination: a review and theoretical integration.

Science.gov (United States)

Ridderinkhof, K Richard

2014-10-01

The present analysis aims at a theoretical integration of, and a systems-neuroscience perspective on, a variety of historical and contemporary views on perception-action coordination (PAC). We set out to determine the common principles or lawful linkages between sensory and motor systems that explain how perception is action-oriented and how action is perceptually guided. To this end, we analyze the key ingredients to such an integrated framework, examine the architecture of dual-system conjectures of PAC, and endeavor in an historical analysis of the key characteristics, mechanisms, and phenomena of PACs. This analysis will reveal that dual-systems views are in need of fundamental re-thinking, and its elements will be amalgamated with current views on action-oriented predictive processing into a novel integrative theoretical framework (IMPPACT: Impetus, Motivation, and Prediction in Perception-Action Coordination theory). From this framework and its neurocognitive architecture we derive a number of non-trivial predictions regarding conative, motive-driven PAC. We end by presenting a brief outlook on how IMPPACT might present novel insights into certain pathologies and into action expertise. Copyright © 2014 Elsevier Ltd. All rights reserved.

Predicting Job Satisfaction.

Science.gov (United States)

Blai, Boris, Jr.

Psychological theories about human motivation and accommodation to environment can be used to achieve a better understanding of the human factors that function in the work environment. Maslow's theory of human motivational behavior provided a theoretical framework for an empirically-derived method to predict job satisfaction and explore the…

Experimental, computational and theoretical studies of δ′ phase coarsening in Al–Li alloys

International Nuclear Information System (INIS)

Pletcher, B.A.; Wang, K.G.; Glicksman, M.E.

2012-01-01

Experimental characterization of microstructure evolution in three binary Al–Li alloys provides critical tests of both diffusion screening theory and multiparticle diffusion simulations, which predict late-stage phase-coarsening kinetics. Particle size distributions, growth kinetics and maximum particle sizes obtained using quantitative, centered dark-field transmission electron microscopy are compared quantitatively with theoretical and computational predictions. We also demonstrate the dependence on δ′ precipitate volume fraction of the rate constant for coarsening and the microstructure’s maximum particle size, both of which remained undetermined for this alloy system for nearly a half century. Our experiments show quantitatively that the diffusion-screening theoretical description of phase coarsening yields reasonable kinetic predictions, and that useful simulations of microstructure evolution are obtained via multiparticle diffusion. The tested theory and simulation method will provide useful tools for future design of two-phase alloys for elevated temperature applications.

Depression and anxiety mediate perceived social support to predict health-related quality of life in pregnant women living with HIV.

Science.gov (United States)

Xiaowen, Wang; Guangping, Guo; Ling, Zhou; Jiarui, Zheng; Xiumin, Liang; Zhaoqin, Li; Hongzhuan, Luo; Yuyan, Yang; Liyuan, Yang; Lin, Lu

2018-04-01

Pregnant women living with HIV represent one of the most high-priority groups for HIV treatment and health assessment. Although social support has been shown to be a protective factor for improved health-related quality of life (HRQoL), and depression and anxiety have been identified as two major causes of psychological distress among people living with HIV, it is still unclear how social support, anxiety, and depression interact to influence HRQoL. The objective of our study was to demonstrate the nature of predictors, direct effects and mediator effects among social support, anxiety, depression symptoms and HRQoL in pregnant women living with HIV. We investigated a total of 101 pregnant women living with HIV in Yunnan province in China from April 2016 to June 2016. All participants completed the Social Support Rating Scale (SSRS), the Chinese version of the Hospital Anxiety and Depression Scales (HADS) and Quality of Life instruments (EuroQoL Five Dimensions Questionnaire, EQ-5D). The relationships between the variables were examined by Pearson's or Spearman's correlation analysis. Predictor effects were tested using separate multiple regressions, controlling for demographic variables and HIV diagnosis variables. Direct and mediation effects of social support on HRQoL were tested using a structural equation model (SEM). Anxiety and depression symptoms were negatively correlated with subjective social support, support utilization, social support and HRQoL. Social support significantly predicted better HRQoL, and anxiety and depression symptoms significantly predicted poorer HRQoL. Anxiety and depression symptoms partially mediated the associations between social support and HRQoL. Anxiety and depression symptoms completely mediated the associations of objective support and support utilization with HRQoL. Interventions to improve HRQoL in pregnant women living with HIV must consider the mediation effect of anxiety and depression symptoms on the association between

What predicts depression in cardiac patients: Sociodemographic factors, disease severity or theoretical vulnerabilities?

OpenAIRE

Doyle, Frank; McGee, Hannah; Conroy, Ronán; Delaney, Mary

2011-01-01

Depression is associated with increased cardiovascular risk in patients with acute coronary syndrome (ACS), but some argue that elevated depression is actually a marker of cardiovascular disease severity. Therefore, disease indices should be better predictors of depression than established theoretical causes of depression (interpersonal life events, reinforcing events, cognitive distortions, type D personality). However, little theory-based research has been conducted in this area. In a cross...

Theoretical Predictions of the thermodynamic Properties of Solid Sorbents Capture CO2 Applications

Energy Technology Data Exchange (ETDEWEB)

Duan, Yuhua; Sorescu, Dan; Luebke David; Pennline, Henry

2012-05-02

We are establishing a theoretical procedure to identify most potential candidates of CO{sub 2} solid sorbents from a large solid material databank to meet the DOE programmatic goal for energy conversion; and to explore the optimal working conditions for the promising CO{sub 2} solid sorbents, especially from room to warm T ranges with optimal energy usage, used for both pre- and post-combustion capture technologies.

Theoretical proposal of a low-loss wide-bandwidth silicon photonic crystal fiber for supporting 30 orbital angular momentum modes.

Directory of Open Access Journals (Sweden)

Xun Xu

Full Text Available We propose a novel four-ring hollow-core silicon photonic crystal fiber (PCF, and we systematically and theoretically investigate the properties of their vector modes. Our PCF can stably support 30 OAM states from the wavelength of 1.5 μm to 2.4 μm, with a large effective refractive index separation of above 1×10-4. The confinement loss is less than 1×10-9 dB/m at the wavelength of 1.55 μm, and the average confinement loss is less than 1×10-8 dB/m from the wavelength of 1.2 μm to 2.4 μm. Moreover, the curve of the dispersion tends to flatten as the wavelength increases. In addition, we comparably investigate PCFs with different hole spacing. This kind of fiber structure will be a potential candidate for high-capacity optical fiber communications and OAM sensing applications using fibers.

Allostatic load: A theoretical model for understanding the relationship between maternal posttraumatic stress disorder and adverse birth outcomes.

Science.gov (United States)

Li, Yang; Rosemberg, Marie-Anne Sanon; Seng, Julia S

2018-07-01

Adverse birth outcomes such as preterm birth and low birth weight are significant public health concerns and contribute to neonatal morbidity and mortality. Studies have increasingly been exploring the predictive effects of maternal posttraumatic stress disorder (PTSD) on adverse birth outcomes. However, the biological mechanisms by which maternal PTSD affects birth outcomes are not well understood. Allostatic load refers to the cumulative dysregulations of the multiple physiological systems as a response to multiple social-ecological levels of chronic stress. Allostatic load has been well documented in relation to both chronic stress and adverse health outcomes in non-pregnant populations. However, the mediating role of allostatic load is less understood when it comes to maternal PTSD and adverse birth outcomes. To propose a theoretical model that depicts how allostatic load could mediate the impact of maternal PTSD on birth outcomes. We followed the procedures for theory synthesis approach described by Walker and Avant (2011), including specifying focal concepts, identifying related factors and relationships, and constructing an integrated representation. We first present a theoretical overview of the allostatic load theory and the other 4 relevant theoretical models. Then we provide a brief narrative review of literature that empirically supports the propositions of the integrated model. Finally, we describe our theoretical model. The theoretical model synthesized has the potential to advance perinatal research by delineating multiple biomarkers to be used in future. After it is well validated, it could be utilized as the theoretical basis for health care professionals to identify high-risk women by evaluating their experiences of psychosocial and traumatic stress and to develop and evaluate service delivery and clinical interventions that might modify maternal perceptions or experiences of stress and eliminate their impacts on adverse birth outcomes. Copyright

Agricultural drought prediction using climate indices based on Support Vector Regression in Xiangjiang River basin.

Science.gov (United States)

Tian, Ye; Xu, Yue-Ping; Wang, Guoqing

2018-05-01

Drought can have a substantial impact on the ecosystem and agriculture of the affected region and does harm to local economy. This study aims to analyze the relation between soil moisture and drought and predict agricultural drought in Xiangjiang River basin. The agriculture droughts are presented with the Precipitation-Evapotranspiration Index (SPEI). The Support Vector Regression (SVR) model incorporating climate indices is developed to predict the agricultural droughts. Analysis of climate forcing including El Niño Southern Oscillation and western Pacific subtropical high (WPSH) are carried out to select climate indices. The results show that SPEI of six months time scales (SPEI-6) represents the soil moisture better than that of three and one month time scale on drought duration, severity and peaks. The key factor that influences the agriculture drought is the Ridge Point of WPSH, which mainly controls regional temperature. The SVR model incorporating climate indices, especially ridge point of WPSH, could improve the prediction accuracy compared to that solely using drought index by 4.4% in training and 5.1% in testing measured by Nash Sutcliffe efficiency coefficient (NSE) for three month lead time. The improvement is more significant for the prediction with one month lead (15.8% in training and 27.0% in testing) than that with three months lead time. However, it needs to be cautious in selection of the input parameters, since adding redundant information could have a counter effect in attaining a better prediction. Copyright © 2017 Elsevier B.V. All rights reserved.

Prediction of ttt curves of cold working tool steels using support vector machine model

Science.gov (United States)

Pillai, Nandakumar; Karthikeyan, R., Dr.

2018-04-01

The cold working tool steels are of high carbon steels with metallic alloy additions which impart higher hardenability, abrasion resistance and less distortion in quenching. The microstructure changes occurring in tool steel during heat treatment is of very much importance as the final properties of the steel depends upon these changes occurred during the process. In order to obtain the desired performance the alloy constituents and its ratio plays a vital role as the steel transformation itself is complex in nature and depends very much upon the time and temperature. The proper treatment can deliver satisfactory results, at the same time process deviation can completely spoil the results. So knowing time temperature transformation (TTT) of phases is very critical which varies for each type depending upon its constituents and proportion range. To obtain adequate post heat treatment properties the percentage of retained austenite should be lower and metallic carbides obtained should be fine in nature. Support vector machine is a computational model which can learn from the observed data and use these to predict or solve using mathematical model. Back propagation feedback network will be created and trained for further solutions. The points on the TTT curve for the known transformations curves are used to plot the curves for different materials. These data will be trained to predict TTT curves for other steels having similar alloying constituents but with different proportion range. The proposed methodology can be used for prediction of TTT curves for cold working steels and can be used for prediction of phases for different heat treatment methods.

Residual Strength Prediction of Debond Damaged Sandwich Panels

DEFF Research Database (Denmark)

Berggreen, Carl Christian

followed by debond growth. The developed theoretical procedure is an extension of the Crack Surface Displacement method, here denoted the Crack Surface Displacement Extrapolation method. The method is first developed in 2D and then extended to 3D by use of a number of realistic assumptions...... for the considered configurations. Comparison of the theoretical predictions to two series of large-scale experiments with loadings (uniform and non-uniform in-plane compression) comparable with real life loading scenarios for sandwich ships shows that the model is indeed able to predict the failure modes...

Tapping generalized essentialism to predict outgroup prejudices.

Science.gov (United States)

Hodson, Gordon; Skorska, Malvina N

2015-06-01

Psychological essentialism, the perception that groups possess inherent properties binding them and differentiating them from others, is theoretically relevant to predicting prejudice. Recent developments isolate two key dimensions: essentialistic entitativity (EE; groups as unitary, whole, entity-like) and essentialistic naturalness (EN; groups as fixed and immutable). We introduce a novel question: does tapping the covariance between EE and EN, rather than pitting them against each other, boost prejudice prediction? In Study 1 (re-analysis of Roets & Van Hiel, 2011b, Samples 1-3, in Belgium) and Study 2 (new Canadian data) their common/shared variance, modelled as generalized essentialism, doubles the predictive power relative to regression-based approaches with regard to racism (but not anti-gay or -schizophrenic prejudices). Theoretical implications are discussed. © 2014 The British Psychological Society.

Accurate predictions for the LHC made easy

CERN Multimedia

CERN. Geneva

2014-01-01

The data recorded by the LHC experiments is of a very high quality. To get the most out of the data, precise theory predictions, including uncertainty estimates, are needed to reduce as much as possible theoretical bias in the experimental analyses. Recently, significant progress has been made in computing Next-to-Leading Order (NLO) computations, including matching to the parton shower, that allow for these accurate, hadron-level predictions. I shall discuss one of these efforts, the MadGraph5_aMC@NLO program, that aims at the complete automation of predictions at the NLO accuracy within the SM as well as New Physics theories. I’ll illustrate some of the theoretical ideas behind this program, show some selected applications to LHC physics, as well as describe the future plans.

The Predictiveness of Achievement Goals

Directory of Open Access Journals (Sweden)

Huy P. Phan