Kasatkina, T. I.; Dushkin, A. V.; Pavlov, V. A.; Shatovkin, R. R.
In the development of information, systems and programming to predict the series of dynamics, neural network methods have recently been applied. They are more flexible, in comparison with existing analogues and are capable of taking into account the nonlinearities of the series. In this paper, we propose a modified algorithm for predicting the series of dynamics, which includes a method for training neural networks, an approach to describing and presenting input data, based on the prediction by the multilayer perceptron method. To construct a neural network, the values of a series of dynamics at the extremum points and time values corresponding to them, formed based on the sliding window method, are used as input data. The proposed algorithm can act as an independent approach to predicting the series of dynamics, and be one of the parts of the forecasting system. The efficiency of predicting the evolution of the dynamics series for a short-term one-step and long-term multi-step forecast by the classical multilayer perceptron method and a modified algorithm using synthetic and real data is compared. The result of this modification was the minimization of the magnitude of the iterative error that arises from the previously predicted inputs to the inputs to the neural network, as well as the increase in the accuracy of the iterative prediction of the neural network.
Flinn, J.E.; Reimers, R.S.
The report describes the results of a program to identify, rank and project short- and intermediate-term future pollution problems. Identification was accomplished using three independent search approaches based on industrial production, environmental, and societal trends and activity. Primary emphasis was placed on the environmental trends as gleaned from EPA, Battelle, Literature, and other sources. An initial list of problems was compiled with specific stressors identified with each. Nine ranking factors were devised to select ten most serious problems from the initial list. The factors included: persistence; mobility/pervasiveness; environmental, technological, social, and political complexity; physiological risk; research needs; and bulk or volume of the pollutant. The ten problems selected by this method were further ranked in order of relative importance. The ten selected problems in rank order are as follows: (1) Impacts of new energy initiatives; (2) Geophysical modifications of the earth; (3) Trace element (metal) contaminants; (4) Proliferating hazardous and toxic chemicals; (5) Emissions from new automobile fuels, additives, and control devices; (6) Disposal of waste sludges, liquids, and solid residues; (7) Critical radiation problems; (8) Fine particulates; (9) Expanding drinking water contamination; (10) Irrigation (impoundment) practices. Five to ten year projections were made of the ten problems which resulted
Farmer, J.D.; Sidorowich, J.J.
We present a forecasting technique for chaotic data. After embedding a time series in a state space using delay coordinates, we ''learn'' the induced nonlinear mapping using local approximation. This allows us to make short-term predictions of the future behavior of a time series, using information based only on past values. We present an error estimate for this technique, and demonstrate its effectiveness by applying it to several examples, including data from the Mackey-Glass delay differential equation, Rayleigh-Benard convection, and Taylor-Couette flow
This book aims to dispel the mystery and fear experienced by students surrounding sequences, series, convergence, and their applications. The author, an accomplished female mathematician, achieves this by taking a problem solving approach, starting with fascinating problems and solving them step by step with clear explanations and illuminating diagrams. The reader will find the problems interesting, unusual, and fun, yet solved with the rigor expected in a competition. Some problems are taken directly from mathematics competitions, with the name and year of the exam provided for reference. Proof techniques are emphasized, with a variety of methods presented. The text aims to expand the mind of the reader by often presenting multiple ways to attack the same problem, as well as drawing connections with different fields of mathematics. Intuitive and visual arguments are presented alongside technical proofs to provide a well-rounded methodology. With nearly 300 problems including hints, answers, and solutions,Met...
Zahirniak, Daniel R.; DeSimio, Martin P.
In this paper we compare the performance of nonlinear neural network techniques to those of linear filtering techniques in the prediction of time series. Specifically, we compare the results of using the nonlinear systems, known as multilayer perceptron and radial basis function neural networks, with the results obtained using the conventional linear Wiener filter, Kalman filter and Widrow-Hoff adaptive filter in predicting future values of stationary and non- stationary time series. Our results indicate the performance of each type of system is heavily dependent upon the form of the time series being predicted and the size of the system used. In particular, the linear filters perform adequately for linear or near linear processes while the nonlinear systems perform better for nonlinear processes. Since the linear systems take much less time to be developed, they should be tried prior to using the nonlinear systems when the linearity properties of the time series process are unknown.
Full Text Available In the present paper I wish to discuss three texts published by two mathematical authors, Oronce Fine and his student Jean Borrel. These three texts are typical of the time and are examples of the transformation occurring in the genres of mathematical books. This had direct consequences on the ways and the purpose of presenting series of problems followed by further mathematical authors. Oronce Fine's contribution has been to present commercial problems as Euclidean problems on proportions, as well as to replace the university quadrivium by four practical disciplines: practical arithmetic, practical geometry, cosmography, sundials. The two books by his disciple Borrel reflect the view on mathematics promoted by Oronce Fine: we can recognize mathematics in every aspect of the world. Borrel is also very concerned with distinctions in the human world: variety in the Opera geometrica and in the Logistica corresponds to redefinition of professional roles according to Fine's program. In particular, Borrel wants to stress the role of a new category of mathematicians, specialized in the practical disciplines Fine taught at the Collège Royal, the geometers. They dealt with practical problems by using classical humanistic education and practical mathematics, exactly what jurists did: Through the crucial rhetorical notion of varietas he is able to illustrate, in the Opera geometrica, the multifarious mathematical competence required for jurists. Among geometers, Borrel distinguishes a group of people, the logisticians, dealing in particular with the computational side. The readership of his Logisticawas also, by and large, constituted by jurists. In fact many jurists were logisticians or needed competence in this art, and many logisticians had a training in law. The texts by the two authors examined here show a use of series of problems as varieties at two levels: the level of the presentation of examples for a rule, mostly in the main part of the text
Moradabadi, Behnaz; Meybodi, Mohammad Reza
Link prediction is a main social network challenge that uses the network structure to predict future links. The common link prediction approaches to predict hidden links use a static graph representation where a snapshot of the network is analyzed to find hidden or future links. For example, similarity metric based link predictions are a common traditional approach that calculates the similarity metric for each non-connected link and sort the links based on their similarity metrics and label the links with higher similarity scores as the future links. Because people activities in social networks are dynamic and uncertainty, and the structure of the networks changes over time, using deterministic graphs for modeling and analysis of the social network may not be appropriate. In the time-series link prediction problem, the time series link occurrences are used to predict the future links In this paper, we propose a new time series link prediction based on learning automata. In the proposed algorithm for each link that must be predicted there is one learning automaton and each learning automaton tries to predict the existence or non-existence of the corresponding link. To predict the link occurrence in time T, there is a chain consists of stages 1 through T - 1 and the learning automaton passes from these stages to learn the existence or non-existence of the corresponding link. Our preliminary link prediction experiments with co-authorship and email networks have provided satisfactory results when time series link occurrences are considered.
Simmhan, Yogesh; Noor, Muhammad Usman
Time series datasets are a canonical form of high velocity Big Data, and often generated by pervasive sensors, such as found in smart infrastructure. Performing predictive analytics on time series data can be computationally complex, and requires approximation techniques. In this paper, we motivate this problem using a real application from the smart grid domain. We propose an incremental clustering technique, along with a novel affinity score for determining cluster similarity, which help reduce the prediction error for cumulative time series within a cluster. We evaluate this technique, along with optimizations, using real datasets from smart meters, totaling ~700,000 data points, and show the efficacy of our techniques in improving the prediction error of time series data within polynomial time.
De Silva, Anthony Mihirana
This book proposes a novel approach for time-series prediction using machine learning techniques with automatic feature generation. Application of machine learning techniques to predict time-series continues to attract considerable attention due to the difficulty of the prediction problems compounded by the non-linear and non-stationary nature of the real world time-series. The performance of machine learning techniques, among other things, depends on suitable engineering of features. This book proposes a systematic way for generating suitable features using context-free grammar. A number of feature selection criteria are investigated and a hybrid feature generation and selection algorithm using grammatical evolution is proposed. The book contains graphical illustrations to explain the feature generation process. The proposed approaches are demonstrated by predicting the closing price of major stock market indices, peak electricity load and net hourly foreign exchange client trade volume. The proposed method ...
A hybrid of the two called Elman-Jordan (or Multi-recurrent) neural network is also being used. In this study, we evaluated the performance of these neural networks on three established bench mark time series prediction problems. Results from the experiments showed that Jordan neural network performed significantly ...
Lerche, I; Tautz, R C
In discussing radiation from multiple point charges or magnetic dipoles, moving in circles or ellipses, a variety of Kapteyn series of the second kind arises. Some of the series have been known in closed form for a hundred years or more, others appear not to be available to analytic persuasion. This paper shows how 12 such generic series can be developed to produce either closed analytic expressions or integrals that are not analytically tractable. In addition, the method presented here may be of benefit when one has other Kapteyn series of the second kind to consider, thereby providing an additional reason to consider such series anew
Full Text Available In order to detect outliers in hydrological time series data for improving data quality and decision-making quality related to design, operation, and management of water resources, this research develops a time series outlier detection method for hydrologic data that can be used to identify data that deviate from historical patterns. The method first built a forecasting model on the history data and then used it to predict future values. Anomalies are assumed to take place if the observed values fall outside a given prediction confidence interval (PCI, which can be calculated by the predicted value and confidence coefficient. The use of PCI as threshold is mainly on the fact that it considers the uncertainty in the data series parameters in the forecasting model to address the suitable threshold selection problem. The method performs fast, incremental evaluation of data as it becomes available, scales to large quantities of data, and requires no preclassification of anomalies. Experiments with different hydrologic real-world time series showed that the proposed methods are fast and correctly identify abnormal data and can be used for hydrologic time series analysis.
Rasmussen, Peter Funder; Rosbjerg, Dan
In order to obtain a good description of the exceedances in a partial duration series it is often necessary to divide the year into a number (2-4) of seasons. Hereby a stationary exceedance distribution can be maintained within each season. This type of seasonal models may, however, not be suitable...... for prediction purposes due to the large number of parameters required. In the particular case with exponentially distributed exceedances and Poissonian occurrence times the precision of the T year event estimator has been thoroughly examined considering both seasonal and nonseasonal models. The two......-seasonal probability density function of the T year event estimator has been deduced and used in the assessment of the precision of approximate moments. The nonseasonal approach covered both a total omission of seasonality by pooling data from different flood seasons and a discarding of nonsignificant season(s) before...
This thesis examines the topic of chaotic time series. An overview of chaos, dynamical systems, and traditional approaches to time series analysis is provided, followed by an examination of state space reconstruction...
Various social problems are created by migration of low-income rural people into urban areas. These people are classified "low income" because their material level-of-living is often less than that found in urban areas. The dominant national values for material well-being are based upon urban middle class standards, thus creating a social problem…
Yalaoui, Alice; Chu, Chengbin; Chatelet, Eric
In order to improve system reliability, designers may introduce in a system different technologies in parallel. When each technology is composed of components in series, the configuration belongs to the series-parallel systems. This type of system has not been studied as much as the parallel-series architecture. There exist no methods dedicated to the reliability allocation in series-parallel systems with different technologies. We propose in this paper theoretical and practical results for the allocation problem in a series-parallel system. Two resolution approaches are developed. Firstly, a one stage problem is studied and the results are exploited for the multi-stages problem. A theoretical condition for obtaining the optimal allocation is developed. Since this condition is too restrictive, we secondly propose an alternative approach based on an approximated function and the results of the one-stage study. This second approach is applied to numerical examples
Roddy, Robert F; Hess, David E; Faller, Will
.... This report describes the development of feedforward neural network (FFNN) predictions of four-quadrant thrust and torque behavior for the Wageningen B-Screw Series of propellers and for two Wageningen ducted propeller series...
A new prediction model for the early warning of apple scab is proposed in this study. The method is based on artificial intelligence and time series prediction. The infection period of apple scab was evaluated as the time series prediction model instead of summation of wetness duration. Also, the relations of different ...
Full Text Available Forecasting naturally occurring phenomena is a common problem in many domains of science, and this has been addressed and investigated by many scientists. The importance of time series prediction stems from the fact that it has wide range of applications, including control systems, engineering processes, environmental systems and economics. From the knowledge of some aspects of the previous behaviour of the system, the aim of the prediction process is to determine or predict its future behaviour. In this paper, we consider a novel application of a higher order polynomial neural network architecture called Dynamic Ridge Polynomial Neural Network that combines the properties of higher order and recurrent neural networks for the prediction of physical time series. In this study, four types of signals have been used, which are; The Lorenz attractor, mean value of the AE index, sunspot number, and heat wave temperature. The simulation results showed good improvements in terms of the signal to noise ratio in comparison to a number of higher order and feedforward neural networks in comparison to the benchmarked techniques.
This book presents machine learning and type-2 fuzzy sets for the prediction of time-series with a particular focus on business forecasting applications. It also proposes new uncertainty management techniques in an economic time-series using type-2 fuzzy sets for prediction of the time-series at a given time point from its preceding value in fluctuating business environments. It employs machine learning to determine repetitively occurring similar structural patterns in the time-series and uses stochastic automaton to predict the most probabilistic structure at a given partition of the time-series. Such predictions help in determining probabilistic moves in a stock index time-series Primarily written for graduate students and researchers in computer science, the book is equally useful for researchers/professionals in business intelligence and stock index prediction. A background of undergraduate level mathematics is presumed, although not mandatory, for most of the sections. Exercises with tips are provided at...
Runge, Jakob; Donner, Reik V.; Kurths, Jürgen
Forecasting a time series from multivariate predictors constitutes a challenging problem, especially using model-free approaches. Most techniques, such as nearest-neighbor prediction, quickly suffer from the curse of dimensionality and overfitting for more than a few predictors which has limited their application mostly to the univariate case. Therefore, selection strategies are needed that harness the available information as efficiently as possible. Since often the right combination of predictors matters, ideally all subsets of possible predictors should be tested for their predictive power, but the exponentially growing number of combinations makes such an approach computationally prohibitive. Here a prediction scheme that overcomes this strong limitation is introduced utilizing a causal preselection step which drastically reduces the number of possible predictors to the most predictive set of causal drivers making a globally optimal search scheme tractable. The information-theoretic optimality is derived and practical selection criteria are discussed. As demonstrated for multivariate nonlinear stochastic delay processes, the optimal scheme can even be less computationally expensive than commonly used suboptimal schemes like forward selection. The method suggests a general framework to apply the optimal model-free approach to select variables and subsequently fit a model to further improve a prediction or learn statistical dependencies. The performance of this framework is illustrated on a climatological index of El Niño Southern Oscillation.
Zhao Pengfei; Xing Lei; Yu Jun
In this Letter, a new local linear prediction model is proposed to predict a chaotic time series of a component x(t) by using the chaotic time series of another component y(t) in the same system with x(t). Our approach is based on the phase space reconstruction coming from the Takens embedding theorem. To illustrate our results, we present an example of Lorenz system and compare with the performance of the original local linear prediction model.
This paper reviews briefly the origin of Fourier Series Method. The paper then gives a vivid description of how the method can be applied to solve a diffusion problem, subject to some boundary conditions. The result obtained is quite appealing as it can be used to solve similar examples of diffusion equations. JONAMP Vol.
de Gooijer, J.G.; Zerom Godefay, D.
Several nonparametric predictors based on the Nadaraya-Watson kernel regression estimator have been proposed in the literature. They include the conditional mean, the conditional median, and the conditional mode. In this paper, we consider three types of predictive regions for these predictors — the
Núñez, Alfredo A; Cortés, Cristián E
Hybrid Predictive Control for Dynamic Transport Problems develops methods for the design of predictive control strategies for nonlinear-dynamic hybrid discrete-/continuous-variable systems. The methodology is designed for real-time applications, particularly the study of dynamic transport systems. Operational and service policies are considered, as well as cost reduction. The control structure is based on a sound definition of the key variables and their evolution. A flexible objective function able to capture the predictive behaviour of the system variables is described. Coupled with efficient algorithms, mainly drawn from the area of computational intelligence, this is shown to optimize performance indices for real-time applications. The framework of the proposed predictive control methodology is generic and, being able to solve nonlinear mixed-integer optimization problems dynamically, is readily extendable to other industrial processes. The main topics of this book are: ●hybrid predictive control (HPC) ...
Costa, Crist H.
The purpose of this study was to combine knowledge of teacher demographic data with time-series forecasting methods to predict teacher turnover. Moving averages and exponential smoothing were used to forecast discrete time series. The study used data collected from the 22 largest school districts in Iowa, designated as FACT schools. Predictions…
Liu, Zitao; Hauskrecht, Milos
Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. We tested our framework by first learning the time series model from data for the patients in the training set, and then using it to predict future time series values for the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive performance. Copyright © 2014 Elsevier B.V. All rights reserved.
Liu, Zitao; Hauskrecht, Milos
Objective Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Materials and methods Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. Results We tested our framework by first learning the time series model from data for the patient in the training set, and then applying the model in order to predict future time series values on the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. Conclusion A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive
Full Text Available This paper is the second in a series of two, and describes the current state of the art in modeling and prediction of chaotic time series. Sample data from deterministic non-linear systems may look stochastic when analysed with linear methods. However, the deterministic structure may be uncovered and non-linear models constructed that allow improved prediction. We give the background for such methods from a geometrical point of view, and briefly describe the following types of methods: global polynomials, local polynomials, multilayer perceptrons and semi-local methods including radial basis functions. Some illustrative examples from known chaotic systems are presented, emphasising the increase in prediction error with time. We compare some of the algorithms with respect to prediction accuracy and storage requirements, and list applications of these methods to real data from widely different areas.
(ERNN, the empirical results show that the proposed neural network displays the best performance among these neural networks in financial time series forecasting. Further, the empirical research is performed in testing the predictive effects of SSE, TWSE, KOSPI, and Nikkei225 with the established model, and the corresponding statistical comparisons of the above market indices are also exhibited. The experimental results show that this approach gives good performance in predicting the values from the stock market indices.
Full Text Available BackgroundStroke is one of the major diseases with human mortality. Recent clinical research has indicated that early changes in common physiological variables represent a potential therapeutic target, thus the manipulation of these variables may eventually yield an effective way to optimise stroke recovery.AimsWe examined correlations between physiological parameters of patients during the first 48 hours after a stroke, and their stroke outcomes after 3 months. We wanted to discover physiological determinants that could be used to improve health outcomes by supporting the medical decisions that need to be made early on a patient’s stroke experience.Method We applied regression-based machine learning techniques to build a prediction algorithm that can forecast 3-month outcomes from initial physiological time series data during the first 48 hours after stroke. In our method, not only did we use statistical characteristics as traditional prediction features, but also we adopted trend patterns of time series data as new key features.ResultsWe tested our prediction method on a real physiological data set of stroke patients. The experiment results revealed an average high precision rate: 90%. We also tested prediction methods only considering statistical characteristics of physiological data, and concluded an average precision rate: 71%.ConclusionWe demonstrated that using trend pattern features in prediction methods improved the accuracy of stroke outcome prediction. Therefore, trend patterns of physiological time series data have an important role in the early treatment of patients with acute ischaemic stroke.
Dabo-Niang, Sophie; Laksaci, Ali
We consider $\\alpha$-mixing observations and deal with the estimation of the conditional mode of a scalar response variable $Y$ given a random variable $X$ taking values in a semi-metric space. We provide a convergence rate in $L^p$ norm of the estimator. A useful and typical application to functional times series prediction is given.
Gualandi, A.; Serpelloni, E.; Belardinelli, M. E.
A critical point in the analysis of ground displacement time series, as those recorded by space geodetic techniques, is the development of data-driven methods that allow the different sources of deformation to be discerned and characterized in the space and time domains. Multivariate statistic includes several approaches that can be considered as a part of data-driven methods. A widely used technique is the principal component analysis (PCA), which allows us to reduce the dimensionality of the data space while maintaining most of the variance of the dataset explained. However, PCA does not perform well in finding the solution to the so-called blind source separation (BSS) problem, i.e., in recovering and separating the original sources that generate the observed data. This is mainly due to the fact that PCA minimizes the misfit calculated using an L2 norm (χ 2), looking for a new Euclidean space where the projected data are uncorrelated. The independent component analysis (ICA) is a popular technique adopted to approach the BSS problem. However, the independence condition is not easy to impose, and it is often necessary to introduce some approximations. To work around this problem, we test the use of a modified variational Bayesian ICA (vbICA) method to recover the multiple sources of ground deformation even in the presence of missing data. The vbICA method models the probability density function (pdf) of each source signal using a mix of Gaussian distributions, allowing for more flexibility in the description of the pdf of the sources with respect to standard ICA, and giving a more reliable estimate of them. Here we present its application to synthetic global positioning system (GPS) position time series, generated by simulating deformation near an active fault, including inter-seismic, co-seismic, and post-seismic signals, plus seasonal signals and noise, and an additional time-dependent volcanic source. We evaluate the ability of the PCA and ICA decomposition
Troncoso Lora, Alicia; Riquelme Santos, Jesús Manuel; Riquelme Santos, José Cristóbal; Gómez Expósito, Antonio; Martínez Ramos, José Luis
This paper describes a time-series prediction method based on the kNN technique. The proposed methodology is applied to the 24-hour load forecasting problem. Also, based on recorded data, an alternative model is developed by means of a conventional dynamic regression technique, where the parameters are estimated by solving a least squares problem. Finally, results obtained from the application of both techniques to the Spanish transmission system are compared in terms of maximum, average and ...
Liu, Zitao; Hauskrecht, Milos
Building of an accurate predictive model of clinical time series for a patient is critical for understanding of the patient condition, its dynamics, and optimal patient management. Unfortunately, this process is not straightforward. First, patient-specific variations are typically large and population-based models derived or learned from many different patients are often unable to support accurate predictions for each individual patient. Moreover, time series observed for one patient at any point in time may be too short and insufficient to learn a high-quality patient-specific model just from the patient's own data. To address these problems we propose, develop and experiment with a new adaptive forecasting framework for building multivariate clinical time series models for a patient and for supporting patient-specific predictions. The framework relies on the adaptive model switching approach that at any point in time selects the most promising time series model out of the pool of many possible models, and consequently, combines advantages of the population, patient-specific and short-term individualized predictive models. We demonstrate that the adaptive model switching framework is very promising approach to support personalized time series prediction, and that it is able to outperform predictions based on pure population and patient-specific models, as well as, other patient-specific model adaptation strategies.
Mann, Jack; McFadden, John P; White, Jonathan M L; White, Ian R; Banerjee, Piu
Negative patch test results with fragrance allergy markers in the European baseline series do not always predict a negative reaction to individual fragrance substances. To determine the frequencies of positive test reactions to the 26 fragrance substances for which labelling is mandatory in the EU, and how effectively reactions to fragrance markers in the baseline series predict positive reactions to the fragrance substances that are labelled. The records of 1951 eczema patients, routinely tested with the labelled fragrance substances and with an extended European baseline series in 2011 and 2012, were retrospectively reviewed. Two hundred and eighty-one (14.4%) (71.2% females) reacted to one or more allergens from the labelled-fragrance substance series and/or a fragrance marker from the European baseline series. The allergens that were positive with the greatest frequencies were cinnamyl alcohol (48; 2.46%), Evernia furfuracea (44; 2.26%), and isoeugenol (40; 2.05%). Of the 203 patients who reacted to any of the 26 fragrances in the labelled-fragrance substance series, only 117 (57.6%) also reacted to a fragrance marker in the baseline series. One hundred and seven (52.7%) reacted to either fragrance mix I or fragrance mix II, 28 (13.8%) reacted to Myroxylon pereirae, and 13 (6.4%) reacted to hydroxyisohexyl 3-cyclohexene carboxaldehyde. These findings confirm that the standard fragrance markers fail to identify patients with contact allergies to the 26 fragrances. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Jain, Shilpa; Bisht, Dinesh C. S.; Singh, Phool; Mathpal, Prakash C.
Genetic Algorithm (GA) forms a subset of evolutionary computing, rapidly growing area of Artificial Intelligence (A.I.). Some variants of GA are binary GA, real GA, messy GA, micro GA, saw tooth GA, differential evolution GA. This research article presents a real coded GA for predicting enrollments of University of Alabama. Data of Alabama University is a fuzzy time series. Here, fuzzy logic is used to predict enrollments of Alabama University and genetic algorithm optimizes fuzzy intervals. Results are compared to other eminent author works and found satisfactory, and states that real coded GA are fast and accurate.
Full Text Available Autoregressive moving average (ARMA process and dynamic neural networks namely the nonlinear autoregressive moving average with exogenous inputs (NARX are compared by evaluating their ability to predict financial time series; for instance the S&P500 returns. Two classes of ARMA are considered. The first one is the standard ARMA model which is a linear static system. The second one uses Kalman filter (KF to estimate and predict ARMA coefficients. This model is a linear dynamic system. The forecasting ability of each system is evaluated by means of mean absolute error (MAE and mean absolute deviation (MAD statistics. Simulation results indicate that the ARMA-KF system performs better than the standard ARMA alone. Thus, introducing dynamics into the ARMA process improves the forecasting accuracy. In addition, the ARMA-KF outperformed the NARX. This result may suggest that the linear component found in the S&P500 return series is more dominant than the nonlinear part. In sum, we conclude that introducing dynamics into the ARMA process provides an effective system for S&P500 time series prediction.
Reid, David; Hussain, Abir Jaafar; Tawfik, Hissam
In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction. It is argued that the inherent temporal capabilities of this type of network are suited to non-stationary data such as this. The performance of the spiking neural network was benchmarked against three systems: two "traditional", rate-encoded, neural networks; a Multi-Layer Perceptron neural network and a Dynamic Ridge Polynomial neural network, and a standard Linear Predictor Coefficients model. For this comparison three non-stationary and noisy time series were used: IBM stock data; US/Euro exchange rate data, and the price of Brent crude oil. The experiments demonstrated favourable prediction results for the Spiking Neural Network in terms of Annualised Return and prediction error for 5-Step ahead predictions. These results were also supported by other relevant metrics such as Maximum Drawdown and Signal-To-Noise ratio. This work demonstrated the applicability of the Polychronous Spiking Network to financial data forecasting and this in turn indicates the potential of using such networks over traditional systems in difficult to manage non-stationary environments.
Alparslan, A. K.; Sayar, M.; Atilgan, A. R.
A simple method for predicting the continuation of scalar chaotic time series ahead in time is proposed. The false nearest neighbors technique in connection with the time-delayed embedding is employed so as to reconstruct the state space. A local forecasting model based upon the time evolution of the topological neighboring in the reconstructed phase space is suggested. A moving root-mean-square error is utilized in order to monitor the error along the prediction horizon. The model is tested for the convection amplitude of the Lorenz model. The results indicate that for approximately 100 cycles of the training data, the prediction follows the actual continuation very closely about six cycles. The proposed model, like other state-space forecasting models, captures the long-term behavior of the system due to the use of spatial neighbors in the state space.
Full Text Available Shellfish farms are closed for harvest when microbial pollutants are present. Such pollutants are typically present in rainfall runoff from various land uses in catchments. Experts currently use a number of observable parameters (river flow, rainfall, salinity as proxies to determine when to close farms. We have proposed using the short term historical rainfall data as a time-series prediction problem where we aim to predict the closure of shellfish farms based only on rainfall. Time-series event prediction consists of two steps: (i feature extraction, and (ii prediction. A number of data mining challenges exist for these scenarios: (i which feature extraction method best captures the rainfall pattern over successive days that leads to opening or closure of the farms?, (ii The farm closure events occur infrequently and this leads to a class imbalance problem; the question is what is the best way to deal with this problem? In this paper we have analysed and compared different combinations of balancing methods (under-sampling and over-sampling, feature extraction methods (cluster profile, curve fitting, Fourier Transform, Piecewise Aggregate Approximation, and Wavelet Transform and learning algorithms (neural network, support vector machine, k-nearest neighbour, decision tree, and Bayesian Network to predict closure events accurately considering the above data mining challenges. We have identified the best combination of techniques to accurately predict shellfish farm closure from rainfall, given the above data mining challenges.
Rosen-Zvi, Michal; Kanter, Ido; Kinzel, Wolfgang
The difficulties that a neural network faces when trying to learn from a quasi-periodic time series are studied analytically using a teacher-student scenario where the random input is divided into two macroscopic regions with different variances, 1 and 1/γ 2 (γ >> 1). The generalization error is found to decrease as ε g ∝ exp(-α/γ 2 ), where α is the number of examples per input dimension. In contradiction to this very slow vanishing generalization error, the next output prediction is found to be almost free of mistakes. This picture is consistent with learning quasi-periodic time series produced by feedforward neural networks, which is dominated by enhanced components of the Fourier spectrum of the input. Simulation results are in good agreement with the analytical results
Tran, V.D.; Moreaud, M.; Thiebaut, E.; Denis, L.; Becker, J.M.
In the refining industry, morphological measurements of particles have become an essential part in the characterization catalyst supports. Through these parameters, one can infer the specific physico-chemical properties of the studied materials. One of the main acquisition techniques is electron tomography (or nano-tomography). 3D volumes are reconstructed from sets of projections from different angles made by a Transmission Electron Microscope (TEM). This technique provides a real three-dimensional information at the nano-metric scale. A major issue in this method is the misalignment of the projections that contributes to the reconstruction. The current alignment techniques usually employ fiducial markers such as gold particles for a correct alignment of the images. When the use of markers is not possible, the correlation between adjacent projections is used to align them. However, this method sometimes fails. In this paper, we propose a new method based on the inverse problem approach where a certain criterion is minimized using a variant of the Nelder and Mead simplex algorithm. The proposed approach is composed of two steps. The first step consists of an initial alignment process, which relies on the minimization of a cost function based on robust statistics measuring the similarity of a projection to its previous projections in the series. It reduces strong shifts resulting from the acquisition between successive projections. In the second step, the pre-registered projections are used to initialize an iterative alignment-refinement process which alternates between (i) volume reconstructions and (ii) registrations of measured projections onto simulated projections computed from the volume reconstructed in (i). At the end of this process, we have a correct reconstruction of the volume, the projections being correctly aligned. Our method is tested on simulated data and shown to estimate accurately the translation, rotation and scale of arbitrary transforms. We
Beal, Carole R.; Galan, Federico Cirett
In the present study, the authors focused on the use of electroencephalography (EEG) data about cognitive workload and sustained attention to predict math problem solving outcomes. EEG data were recorded as students solved a series of easy and difficult math problems. Sequences of attention and cognitive workload estimates derived from the EEG…
Full Text Available This paper puts forward a prediction model based on membrane computing optimization algorithm for chaos time series; the model optimizes simultaneously the parameters of phase space reconstruction (τ,m and least squares support vector machine (LS-SVM (γ,σ by using membrane computing optimization algorithm. It is an important basis for spectrum management to predict accurately the change trend of parameters in the electromagnetic environment, which can help decision makers to adopt an optimal action. Then, the model presented in this paper is used to forecast band occupancy rate of frequency modulation (FM broadcasting band and interphone band. To show the applicability and superiority of the proposed model, this paper will compare the forecast model presented in it with conventional similar models. The experimental results show that whether single-step prediction or multistep prediction, the proposed model performs best based on three error measures, namely, normalized mean square error (NMSE, root mean square error (RMSE, and mean absolute percentage error (MAPE.
Nazaripouya, Hamidreza; Wang, Yubo; Chu, Chi-Cheng; Pota, Hemanshu; Gadh, Rajit
This paper proposes a new hybrid method for super short-term solar power prediction. Solar output power usually has a complex, nonstationary, and nonlinear characteristic due to intermittent and time varying behavior of solar radiance. In addition, solar power dynamics is fast and is inertia less. An accurate super short-time prediction is required to compensate for the fluctuations and reduce the impact of solar power penetration on the power system. The objective is to predict one step-ahead solar power generation based only on historical solar power time series data. The proposed method incorporates discrete wavelet transform (DWT), Auto-Regressive Moving Average (ARMA) models, and Recurrent Neural Networks (RNN), while the RNN architecture is based on Nonlinear Auto-Regressive models with eXogenous inputs (NARX). The wavelet transform is utilized to decompose the solar power time series into a set of richer-behaved forming series for prediction. ARMA model is employed as a linear predictor while NARX is used as a nonlinear pattern recognition tool to estimate and compensate the error of wavelet-ARMA prediction. The proposed method is applied to the data captured from UCLA solar PV panels and the results are compared with some of the common and most recent solar power prediction methods. The results validate the effectiveness of the proposed approach and show a considerable improvement in the prediction precision.
Khalid, M.; Sultana, M.; Zaidi, F.
Predicting the behavior of solar activity has become very significant. It is due to its influence on Earth and the surrounding environment. Apt predictions of the amplitude and timing of the next solar cycle will aid in the estimation of the several results of Space Weather. In the past, many prediction procedures have been used and have been successful to various degrees in the field of solar activity forecast. In this study, Solar cycle 24 is forecasted by the Fourier series method. Comparative analysis has been made by auto regressive integrated moving averages method. From sources, January 2008 was the minimum preceding solar cycle 24, the amplitude and shape of solar cycle 24 is approximate on monthly number of sunspots. This forecast framework approximates a mean solar cycle 24, with the maximum appearing during May 2014 (+- 8 months), with most sunspot of 98 +- 10. Solar cycle 24 will be ending in June 2020 (+- 7 months). The difference between two consecutive peak values of solar cycles (i.e. solar cycle 23 and 24 ) is 165 months(+- 6 months). (author)
Full Text Available Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used.
Eisenstein, E.; Kanter, I.; Kessler, D.A.; Kinzel, W.
Generation and prediction of time series are analyzed for the case of a bit generator: a perceptron where in each time step the input units are shifted one bit to the right with the state of the leftmost input unit set equal to the output unit in the previous time step. The long-time dynamical behavior of the bit generator consists of cycles whose typical period scales polynomially with the size of the network and whose spatial structure is periodic with a typical finite wavelength. The generalization error on a cycle is zero for a finite training set, and global dynamical behaviors can also be learned in a finite time. Hence, a projection of a rule can be learned in a finite time
Dion, Peter; Ho, Anthony
This article describes what is often referred to as the dog, beetle, mice, ant, or turtle problem. Solutions to this problem exist, some being variations of each other, which involve mathematics of a wide range of complexity. Herein, the authors describe the intuitive solution and the calculus solution and then offer a completely new solution…
Full Text Available Predicting student failure is an important task that can empower educators to counteract the factors that affect student performance. In this paper, a part of the bigger problem of predicting student failure is addressed: predicting the students that do not complete their assignment tasks. For solving this problem, real data collected by our university’s educational platform was used. Because the problem consisted of predicting one of three possible classes (multi-class classification, the appropriate algorithms and methods were selected. Several experiments were carried out to find the best approach for this prediction problem and the used data set. An approach of time segmentation is proposed in order to facilitate the prediction from early on. Methods that address the problems of high dimensionality and imbalanced data were also evaluated. The outcome of each approach is shown and compared in order to select the best performing classification algorithm for the problem at hand.
Valent, Peter; Howden, Nicholas J. K.; Szolgay, Jan; Komornikova, Magda
After the Second World War the nitrate concentrations in European water bodies changed significantly as the result of increased nitrogen fertilizer use and changes in land use. However, in the last decades, as a consequence of the implementation of nitrate-reducing measures in Europe, the nitrate concentrations in water bodies slowly decrease. This causes that the mean and variance of the observed time series also changes with time (nonstationarity and heteroscedascity). In order to detect changes and properly describe the behaviour of such time series by time series analysis, linear models (such as autoregressive (AR), moving average (MA) and autoregressive moving average models (ARMA)), are no more suitable. Time series with sudden changes in statistical characteristics can cause various problems in the calibration of traditional water quality models and thus give biased predictions. Proper statistical analysis of these non-stationary and heteroscedastic time series with the aim of detecting and subsequently explaining the variations in their statistical characteristics requires the use of nonlinear time series models. This information can be then used to improve the model building and calibration of conceptual water quality model or to select right calibration periods in order to produce reliable predictions. The objective of this contribution is to analyze two long time series of nitrate concentrations of the rivers Ouse and Stour with advanced nonlinear statistical modelling techniques and compare their performance with traditional linear models of the ARMA class in order to identify changes in the time series characteristics. The time series were analysed with nonlinear models with multiple regimes represented by self-exciting threshold autoregressive (SETAR) and Markov-switching models (MSW). The analysis showed that, based on the value of residual sum of squares (RSS) in both datasets, SETAR and MSW models described the time-series better than models of the
Zhongda, Tian; Shujiang, Li; Yanhong, Wang; Yi, Sha
In order to improve the prediction accuracy of chaotic time series, a prediction method based on wavelet transform and multiple models fusion is proposed. The chaotic time series is decomposed and reconstructed by wavelet transform, and approximate components and detail components are obtained. According to different characteristics of each component, least squares support vector machine (LSSVM) is used as predictive model for approximation components. At the same time, an improved free search algorithm is utilized for predictive model parameters optimization. Auto regressive integrated moving average model (ARIMA) is used as predictive model for detail components. The multiple prediction model predictive values are fusion by Gauss–Markov algorithm, the error variance of predicted results after fusion is less than the single model, the prediction accuracy is improved. The simulation results are compared through two typical chaotic time series include Lorenz time series and Mackey–Glass time series. The simulation results show that the prediction method in this paper has a better prediction.
Full Text Available This study applied a model predictive control (MPC framework to solve the cruising control problem of a series hydraulic hybrid vehicle (SHHV. The controller not only regulates vehicle velocity, but also engine torque, engine speed, and accumulator pressure to their corresponding reference values. At each time step, a quadratic programming problem is solved within a predictive horizon to obtain the optimal control inputs. The objective is to minimize the output error. This approach ensures that the components operate at high efficiency thereby improving the total efficiency of the system. The proposed SHHV control system was evaluated under urban and highway driving conditions. By handling constraints and input-output interactions, the MPC-based control system ensures that the system operates safely and efficiently. The fuel economy of the proposed control scheme shows a noticeable improvement in comparison with the PID-based system, in which three Proportional-Integral-Derivative (PID controllers are used for cruising control.
National Oceanic and Atmospheric Administration, Department of Commerce — The global temperature time series provides time series charts using station based observations of daily temperature. These charts provide information about the...
National Oceanic and Atmospheric Administration, Department of Commerce — The global precipitation time series provides time series charts showing observations of daily precipitation as well as accumulated precipitation compared to normal...
Francisco Javier Duque-Pintor
Full Text Available The occurrence of outliers in real-world phenomena is quite usual. If these anomalous data are not properly treated, unreliable models can be generated. Many approaches in the literature are focused on a posteriori detection of outliers. However, a new methodology to a priori predict the occurrence of such data is proposed here. Thus, the main goal of this work is to predict the occurrence of outliers in time series, by using, for the first time, imbalanced classification techniques. In this sense, the problem of forecasting outlying data has been transformed into a binary classification problem, in which the positive class represents the occurrence of outliers. Given that the number of outliers is much lower than the number of common values, the resultant classification problem is imbalanced. To create training and test sets, robust statistical methods have been used to detect outliers in both sets. Once the outliers have been detected, the instances of the dataset are labeled accordingly. Namely, if any of the samples composing the next instance are detected as an outlier, the label is set to one. As a study case, the methodology has been tested on electricity demand time series in the Spanish electricity market, in which most of the outliers were properly forecast.
Scott, James R.; Martini, Michael C.
Taylor series integration is implemented in a spacecraft trajectory analysis code-the Spacecraft N-body Analysis Program (SNAP) - and compared with the code s existing eighth-order Runge-Kutta Fehlberg time integration scheme. Nine trajectory problems, including near Earth, lunar, Mars and Europa missions, are analyzed. Head-to-head comparison at five different error tolerances shows that, on average, Taylor series is faster than Runge-Kutta Fehlberg by a factor of 15.8. Results further show that Taylor series has superior convergence properties. Taylor series integration proves that it can provide rapid, highly accurate solutions to spacecraft trajectory problems.
Roddy, Robert F; Hess, David E; Faller, Will
.... This report describes the development of feedforward neural network (FFNN) predictions of four-quadrant thrust and torque behavior for the Wageningen B-Screw Series of propellers and for two Wageningen ducted propeller series...
Cabrera-Palmer, Belkis [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Predicting the performance of radiation detection systems at field sites based on measured performance acquired under controlled conditions at test locations, e.g., the Nevada National Security Site (NNSS), remains an unsolved and standing issue within DNDO’s testing methodology. Detector performance can be defined in terms of the system’s ability to detect and/or identify a given source or set of sources, and depends on the signal generated by the detector for the given measurement configuration (i.e., source strength, distance, time, surrounding materials, etc.) and on the quality of the detection algorithm. Detector performance is usually evaluated in the performance and operational testing phases, where the measurement configurations are selected to represent radiation source and background configurations of interest to security applications.
Full Text Available In this article, the Sumudu transform series expansion method is used to handle the local fractional Laplace equation arising in the steady fractal heat-transfer problem via local fractional calculus.
Ritchey, Kristen D.; Silverman, Rebecca D.; Schatschneider, Christopher; Speece, Deborah L.
The longitudinal prediction of reading problems from fourth grade to sixth grade was investigated with a sample of 173 students. Reading problems at the end of sixth grade were defined by significantly below average performance (= 15th percentile) on reading factors defining word reading, fluency, and reading comprehension. Sixth grade poor reader…
Trench, William F
Answers to Selected Problems in Multivariable Calculus with Linear Algebra and Series contains the answers to selected problems in linear algebra, the calculus of several variables, and series. Topics covered range from vectors and vector spaces to linear matrices and analytic geometry, as well as differential calculus of real-valued functions. Theorems and definitions are included, most of which are followed by worked-out illustrative examples.The problems and corresponding solutions deal with linear equations and matrices, including determinants; vector spaces and linear transformations; eig
Norbert A. Agana
Full Text Available Drought is a stochastic natural feature that arises due to intense and persistent shortage of precipitation. Its impact is mostly manifested as agricultural and hydrological droughts following an initial meteorological phenomenon. Drought prediction is essential because it can aid in the preparedness and impact-related management of its effects. This study considers the drought forecasting problem by developing a hybrid predictive model using a denoised empirical mode decomposition (EMD and a deep belief network (DBN. The proposed method first decomposes the data into several intrinsic mode functions (IMFs using EMD, and a reconstruction of the original data is obtained by considering only relevant IMFs. Detrended fluctuation analysis (DFA was applied to each IMF to determine the threshold for robust denoising performance. Based on their scaling exponents, irrelevant intrinsic mode functions are identified and suppressed. The proposed method was applied to predict different time scale drought indices across the Colorado River basin using a standardized streamflow index (SSI as the drought index. The results obtained using the proposed method was compared with standard methods such as multilayer perceptron (MLP and support vector regression (SVR. The proposed hybrid model showed improvement in prediction accuracy, especially for multi-step ahead predictions.
Xiu, Chunbo; Wang, Tiantian; Tian, Meng; Li, Yanqing; Cheng, Yi
Highlights: • An improved fractal interpolation prediction method is proposed. • The chaos optimization algorithm is used to obtain the iterated function system. • The fractal extrapolate interpolation prediction of wind speed series is performed. - Abstract: In order to improve the prediction performance of the wind speed series, the rescaled range analysis is used to analyze the fractal characteristics of the wind speed series. An improved fractal interpolation prediction method is proposed to predict the wind speed series whose Hurst exponents are close to 1. An optimization function which is composed of the interpolation error and the constraint items of the vertical scaling factors in the fractal interpolation iterated function system is designed. The chaos optimization algorithm is used to optimize the function to resolve the optimal vertical scaling factors. According to the self-similarity characteristic and the scale invariance, the fractal extrapolate interpolation prediction can be performed by extending the fractal characteristic from internal interval to external interval. Simulation results show that the fractal interpolation prediction method can get better prediction result than others for the wind speed series with the fractal characteristic, and the prediction performance of the proposed method can be improved further because the fractal characteristic of its iterated function system is similar to that of the predicted wind speed series
Md. Rabiul Islam
Full Text Available This paper presents a subband approach to financial time series prediction. Multivariate empirical mode decomposition (MEMD is employed here for multiband representation of multichannel financial time series together. Autoregressive moving average (ARMA model is used in prediction of individual subband of any time series data. Then all the predicted subband signals are summed up to obtain the overall prediction. The ARMA model works better for stationary signal. With multiband representation, each subband becomes a band-limited (narrow band signal and hence better prediction is achieved. The performance of the proposed MEMD-ARMA model is compared with classical EMD, discrete wavelet transform (DWT, and with full band ARMA model in terms of signal-to-noise ratio (SNR and mean square error (MSE between the original and predicted time series. The simulation results show that the MEMD-ARMA-based method performs better than the other methods.
Steimer, Andreas; Müller, Michael; Schindler, Kaspar
During the last 20 years, predictive modeling in epilepsy research has largely been concerned with the prediction of seizure events, whereas the inference of effective brain targets for resective surgery has received surprisingly little attention. In this exploratory pilot study, we describe a distributional clustering framework for the modeling of multivariate time series and use it to predict the effects of brain surgery in epilepsy patients. By analyzing the intracranial EEG, we demonstrate how patients who became seizure free after surgery are clearly distinguished from those who did not. More specifically, for 5 out of 7 patients who obtained seizure freedom (= Engel class I) our method predicts the specific collection of brain areas that got actually resected during surgery to yield a markedly lower posterior probability for the seizure related clusters, when compared to the resection of random or empty collections. Conversely, for 4 out of 5 Engel class III/IV patients who still suffer from postsurgical seizures, performance of the actually resected collection is not significantly better than performances displayed by random or empty collections. As the number of possible collections ranges into billions and more, this is a substantial contribution to a problem that today is still solved by visual EEG inspection. Apart from epilepsy research, our clustering methodology is also of general interest for the analysis of multivariate time series and as a generative model for temporally evolving functional networks in the neurosciences and beyond. Hum Brain Mapp 38:2509-2531, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.
Catania, Leopoldo; Grassi, Stefano; Ravazzolo, Francesco
Cryptocurrencies have recently gained a lot of interest from investors, central banks and governments worldwide. The lack of any form of political regu- lation and their market far from being “efficient”, require new forms of regulation in the near future. From an econometric viewpoint, the process underlying the evo- lution of the cryptocurrencies’ volatility has been found to exhibit at the same time differences and similarities with other financial time–series, e.g. foreign exchanges retur...
Ranjbar, Mansour; Bayani, Ali Asghar; Bayani, Ali
Background : The main objective of this study was predicting student′s mental health using social problem solving- ability . Methods : In this correlational- descriptive study, 369 (208 female and 161 male) from, Mazandaran University of Medical Science were selected through stratified random sampling method. In order to collect the data, the social problem solving inventory-revised and general health questionnaire were used. Data were analyzed through SPSS-19, Pearson′s correlation, t tes...
Full Text Available Battery electric vehicles (BEVs reduce energy consumption and air pollution as compared with conventional vehicles. However, the limited driving range and potential long charging time of BEVs create new problems. Accurate charging time prediction of BEVs helps drivers determine travel plans and alleviate their range anxiety during trips. This study proposed a combined model for charging time prediction based on regression and time-series methods according to the actual data from BEVs operating in Beijing, China. After data analysis, a regression model was established by considering the charged amount for charging time prediction. Furthermore, a time-series method was adopted to calibrate the regression model, which significantly improved the fitting accuracy of the model. The parameters of the model were determined by using the actual data. Verification results confirmed the accuracy of the model and showed that the model errors were small. The proposed model can accurately depict the charging time characteristics of BEVs in Beijing.
Li, Ke-Ping; Chen, Tian-Lun
A nonlinear feedback term is introduced into the evaluation equation of weights of the backpropagation algorithm for neural network, the network becomes a chaotic one. For the purpose of that we can investigate how the different feedback terms affect the process of learning and forecasting, we use the model to forecast the nonlinear time series which is produced by Makey-Glass equation. By selecting the suitable feedback term, the system can escape from the local minima and converge to the global minimum or its approximate solutions, and the forecasting results are better than those of backpropagation algorithm. The project supported by National Basic Research Project "Nonlinear Science" and National Natural Science Foundation of China under Grant No. 60074020
Full Text Available The aim of this analysis is to forecast a mini-market sales volume for the period of twelve months starting August 2015 to August 2016. The study is based on the monthly sales in Iraqi Dinar for a private local mini-market for the month of April 2014 to July 2015. As revealed on the graph and of course if the stagnant economic condition continues, the trend of future sales is down-warding. Based on time series analysis, the business may continue to operate and generate small revenues until August 2016. However, due to low sales volume, low profit margin and operating expenses, the revenues may not be adequate enough to produce positive net income and the business may not be able to operate afterward. The principal question rose from this is the forecasting sales in the region will be difficult where the business cycle so dynamic and revolutionary due to systematic risks and unforeseeable future.
Ranjbar, Mansour; Bayani, Ali Asghar; Bayani, Ali
The main objective of this study was predicting student's mental health using social problem solving- ability. In this correlational. descriptive study, 369 (208 female and 161 male) from, Mazandaran University of Medical Science were selected through stratified random sampling method. In order to collect the data, the social problem solving inventory-revised and general health questionnaire were used. Data were analyzed through SPSS-19, Pearson's correlation, t test, and stepwise regression analysis. Data analysis showed significant relationship between social problem solving ability and mental health (P Social problem solving ability was significantly associated with the somatic symptoms, anxiety and insomnia, social dysfunction and severe depression (P social problem solving ability and mental health.
Cao, Dingzhou; Murat, Alper; Chinnam, Ratna Babu
This paper proposes a decomposition-based approach to exactly solve the multi-objective Redundancy Allocation Problem for series-parallel systems. Redundancy allocation problem is a form of reliability optimization and has been the subject of many prior studies. The majority of these earlier studies treat redundancy allocation problem as a single objective problem maximizing the system reliability or minimizing the cost given certain constraints. The few studies that treated redundancy allocation problem as a multi-objective optimization problem relied on meta-heuristic solution approaches. However, meta-heuristic approaches have significant limitations: they do not guarantee that Pareto points are optimal and, more importantly, they may not identify all the Pareto-optimal points. In this paper, we treat redundancy allocation problem as a multi-objective problem, as is typical in practice. We decompose the original problem into several multi-objective sub-problems, efficiently and exactly solve sub-problems, and then systematically combine the solutions. The decomposition-based approach can efficiently generate all the Pareto-optimal solutions for redundancy allocation problems. Experimental results demonstrate the effectiveness and efficiency of the proposed method over meta-heuristic methods on a numerical example taken from the literature.
Ma Qianli; Zheng Qilun; Peng Hong; Qin Jiangwei; Zhong Tanwei
This paper proposes a co-evolutionary recurrent neural network (CERNN) for the multi-step-prediction of chaotic time series, it estimates the proper parameters of phase space reconstruction and optimizes the structure of recurrent neural networks by co-evolutionary strategy. The searching space was separated into two subspaces and the individuals are trained in a parallel computational procedure. It can dynamically combine the embedding method with the capability of recurrent neural network to incorporate past experience due to internal recurrence. The effectiveness of CERNN is evaluated by using three benchmark chaotic time series data sets: the Lorenz series, Mackey-Glass series and real-world sun spot series. The simulation results show that CERNN improves the performances of multi-step-prediction of chaotic time series
Liang, Peipeng; Jia, Xiuqin; Taatgen, Niels A; Zhong, Ning; Li, Kuncheng
Neural correlate of human inductive reasoning process is still unclear. Number series and letter series completion are two typical inductive reasoning tasks, and with a common core component of rule induction. Previous studies have demonstrated that different strategies are adopted in number series and letter series completion tasks; even the underlying rules are identical. In the present study, we examined cortical activation as a function of two different reasoning strategies for solving series completion tasks. The retrieval strategy, used in number series completion tasks, involves direct retrieving of arithmetic knowledge to get the relations between items. The procedural strategy, used in letter series completion tasks, requires counting a certain number of times to detect the relations linking two items. The two strategies require essentially the equivalent cognitive processes, but have different working memory demands (the procedural strategy incurs greater demands). The procedural strategy produced significant greater activity in areas involved in memory retrieval (dorsolateral prefrontal cortex, DLPFC) and mental representation/maintenance (posterior parietal cortex, PPC). An ACT-R model of the tasks successfully predicted behavioral performance and BOLD responses. The present findings support a general-purpose dual-process theory of inductive reasoning regarding the cognitive architecture. Copyright © 2014 Elsevier B.V. All rights reserved.
Xu Ruirui; Chen Tianlun; Gao Chengfeng
Nonlinear time series prediction is studied by using an improved least squares support vector machine (LS-SVM) regression based on chaotic mutation evolutionary programming (CMEP) approach for parameter optimization. We analyze how the prediction error varies with different parameters (σ, γ) in LS-SVM. In order to select appropriate parameters for the prediction model, we employ CMEP algorithm. Finally, Nasdaq stock data are predicted by using this LS-SVM regression based on CMEP, and satisfactory results are obtained.
Chagas Moura, Marcio das; Zio, Enrico; Lins, Isis Didier; Droguett, Enrique
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.
Huang, Jia; Tan, Shu-ping; Walsh, Sarah C; Spriggens, Lauren K; Neumann, David L; Shum, David H K; Chan, Raymond C K
The current study aimed to examine the contribution of neurocognition and social cognition to components of social problem solving. Sixty-seven inpatients with schizophrenia and 31 healthy controls were administrated batteries of neurocognitive tests, emotion perception tests, and the Chinese Assessment of Interpersonal Problem Solving Skills (CAIPSS). MANOVAs were conducted to investigate the domains in which patients with schizophrenia showed impairments. Correlations were used to determine which impaired domains were associated with social problem solving, and multiple regression analyses were conducted to compare the relative contribution of neurocognitive and social cognitive functioning to components of social problem solving. Compared with healthy controls, patients with schizophrenia performed significantly worse in sustained attention, working memory, negative emotion, intention identification and all components of the CAIPSS. Specifically, sustained attention, working memory and negative emotion identification were found to correlate with social problem solving and 1-back accuracy significantly predicted the poor performance in social problem solving. Among the dysfunctions in schizophrenia, working memory contributed most to deficits in social problem solving in patients with schizophrenia. This finding provides support for targeting working memory in the development of future social problem solving rehabilitation interventions. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
The Taylor series or differential operator perturbation method, implemented in MCNP and invoked using the PERT card, can be used for efficient parameter studies in shielding problems. This paper shows how only two PERT cards are needed to generate an entire parameter study, including statistical uncertainty estimates (an additional three PERT cards can be used to give exact statistical uncertainties). One realistic example problem involves a detailed helium-3 neutron detector model and its efficiency as a function of the density of its high-density polyethylene moderator. The MCNP differential operator perturbation capability is extremely accurate for this problem. A second problem involves the density of the polyethylene reflector of the BeRP ball and is an example of first-order sensitivity analysis using the PERT capability. A third problem is an analytic verification of the PERT capability.
Lyashko, A. D.
A new analytical presentation of the solution for steady-state oscillations of orthotopic rectangular prism is found. The corresponding infinite system of linear algebraic equations has been deduced by the superposition method. A countable set of precise eigenfrequencies and elementary eigenforms is found. The identities are found which make it possible to improve the convergence of all the infinite series in the solution of the problem. All the infinite series in presentation of solution are analytically summed up. Numerical calculations of stresses in the rectangular orthotropic prism with a uniform along the border and harmonic in time load on two opposite faces have been performed.
Torres Junior, Rubiao G.; Machado, Maria Augusta S. [Instituto Brasileiro de Mercado de Capitais (IBMEC), Rio de Janeiro, RJ (Brazil); Souza, Reinaldo C. [Pontificia Univ. Catolica do Rio de Janeiro, RJ (Brazil)
The objective of this work is the application of two failure prediction models in industrial maintenance with the use of Artificial Neural Networks (ANN). A characteristic of the modern industrial environment is a strong competition which leads companies to search for costs minimization methods. Thus, dada gathering and maintenance dada treatment becomes extremely important in this scenario for it aims the equipment and plant systems real repair necessity. Therefore, the objective becomes the widening of the system's full activity in a continuous manner, in the required period, without problems in their integrating parts. A daily time series is modeled based on maintenance interventions pauses dada from a five years period derived form many productive systems in the finalization areas of PETROFLEX Ind. and Com. S.A. Thus, the purpose is to introduce models based on neural networks and verify its system's pauses prediction capacity, so as to intervene with adequate timing before the system fails, extend the operational period and consequently increase its availability. The results obtained in this work demonstrate the employment of Neural Networks in the prediction of pauses in PETROFLEX industrial area maintenance. The ANN's prediction capacity in a group of dada with strong non-linear component where other statistical techniques have shown little efficient has also been confirmed. Discover neural models to predict failure systems time series has enable a breakthrough in the research field, especially due to the market demand. It's no doubt a technique that will evolve in the industrial maintenance area financing important managing decision. Prediction techniques, such as the ones illustrated in this study, work side by side maintenance planning and if carefully implemented and followed up can in the medium run supply a substantial increase in the available operational hours. (author)
In this paper we address the problem of forecasting the target events of a time series given the distribution ξ of time gaps between target events. Strong earthquakes and stock market crashes are the two types of such events that we are focusing on. In the series of earthquakes, as McCann et al. show [W.R. Mc Cann, S.P. Nishenko, L.R. Sykes, J. Krause, Seismic gaps and plate tectonics: seismic potential for major boundaries, Pure and Applied Geophysics 117 (1979) 1082-1147], there are well-defined gaps (called seismic gaps) between strong earthquakes. On the other hand, usually there are no regular gaps in the series of stock market crashes [M. Raberto, E. Scalas, F. Mainardi, Waiting-times and returns in high-frequency financial data: an empirical study, Physica A 314 (2002) 749-755]. For the case of seismic gaps, we analytically derive an upper bound of prediction efficiency given the coefficient of variation of the distribution ξ. For the case of stock market crashes, we develop an algorithm that predicts the next crash within a certain time interval after the previous one. We show that this algorithm outperforms random prediction. The efficiency of our algorithm sets up a lower bound of efficiency for effective prediction of stock market crashes.
A new method of predicting chaotic time series is presented based on a local Lyapunov exponent, by quantitatively measuring the exponential rate of separation or attraction of two infinitely close trajectories in state space. After reconstructing state space from one-dimensional chaotic time series, neighboring multiple-state vectors of the predicting point are selected to deduce the prediction formula by using the definition of the local Lyapunov exponent. Numerical simulations are carried out to test its effectiveness and verify its higher precision over two older methods. The effects of the number of referential state vectors and added noise on forecasting accuracy are also studied numerically. (general)
The geometric optical series approach of Bremmer is generalized for multisurface nonadiabatic scattering problems. This method yields the formal solution of the Schroedinger equation as an infinite series of multiple integrals. The zeroth order term corresponds to WKB propagation on a single adiabatic surface, while the general Nth order term involves N reflections and/or transitions between surfaces accompanied by ''free,'' single surface semiclassical propagation between the points of reflection and transition. Each term is integrated over all possible transition and reflection points. The adiabatic and diabatic limits of this expression are discussed. Numerical results, in which all reflections are ignored, are presented for curve crossing and noncrossing problems. These results are compared to exact quantum results and are shown to be highly accurate
Blennow, Mattias; Ohlsson, Tommy
In this paper, we present a real nonlinear differential equation for the two flavor neutrino oscillation problem in matter with an arbitrary density profile. We also present an exact series solution to this nonlinear differential equation. In addition, we investigate numerically the convergence of this solution for different matter density profiles such as constant and linear profiles as well as the Preliminary Reference Earth Model describing the Earth's matter density profile. Finally, we discuss other methods used for solving the neutrino flavor evolution problem
A boundary value problem for a nonlinear second order differential equation has been considered. A numerical method has been proposed to solve this problem using power series. Results of numerical experiments have been presented in the paper [ru
Yuan Can; Cai Qi; Guo Li; Yan Feng
The paper have proposed a chaotic time series prediction model, which combined phase space reconstruction with support vector machines. The model has been used to predict the coolant volume flow, in which a synchronous parameter optimization method was brought up based on particle swarm optimization algorithm, since the numerical value selection of related parameter was a key factor for the prediction precision. The average relative error of prediction values and actual observation values was l,5% and relative precision was 0.9879. The result indicated that the model could apply for the natural circulation coolant volume flow prediction under rolling motion condition with high accuracy and robustness. (authors)
deAndrés-Galiana, Enrique J; Fernández-Martínez, Juan Luis; Sonis, Stephen T
Genomics has been used with varying degrees of success in the context of drug discovery and in defining mechanisms of action for diseases like cancer and neurodegenerative and rare diseases in the quest for orphan drugs. To improve its utility, accuracy, and cost-effectiveness optimization of analytical methods, especially those that translate to clinically relevant outcomes, is critical. Here we define a novel tool for genomic analysis termed a biomedical robot in order to improve phenotype prediction, identifying disease pathogenesis and significantly defining therapeutic targets. Biomedical robot analytics differ from historical methods in that they are based on melding feature selection methods and ensemble learning techniques. The biomedical robot mathematically exploits the structure of the uncertainty space of any classification problem conceived as an ill-posed optimization problem. Given a classifier, there exist different equivalent small-scale genetic signatures that provide similar predictive accuracies. We perform the sensitivity analysis to noise of the biomedical robot concept using synthetic microarrays perturbed by different kinds of noises in expression and class assignment. Finally, we show the application of this concept to the analysis of different diseases, inferring the pathways and the correlation networks. The final aim of a biomedical robot is to improve knowledge discovery and provide decision systems to optimize diagnosis, treatment, and prognosis. This analysis shows that the biomedical robots are robust against different kinds of noises and particularly to a wrong class assignment of the samples. Assessing the uncertainty that is inherent to any phenotype prediction problem is the right way to address this kind of problem.
Febrian Umbara, Rian; Tarwidi, Dede; Budi Setiawan, Erwin
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.
Full Text Available The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations. Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. The proposed model has several advantages including the following: (i the proposed model is different from the previous models lacking the concept of time series; (ii the proposed integrated attribute selection method can find the core attributes and reduce high dimensional data; and (iii the proposed model can generate the rules and mathematical formulas of financial distress for providing references to the investors and decision makers. The result shows that the proposed method is better than the listing classifiers under three criteria; hence, the proposed model has competitive advantages in predicting the financial distress of companies.
The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations. Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. The proposed model has several advantages including the following: (i) the proposed model is different from the previous models lacking the concept of time series; (ii) the proposed integrated attribute selection method can find the core attributes and reduce high dimensional data; and (iii) the proposed model can generate the rules and mathematical formulas of financial distress for providing references to the investors and decision makers. The result shows that the proposed method is better than the listing classifiers under three criteria; hence, the proposed model has competitive advantages in predicting the financial distress of companies. PMID:29765399
Cheng, Ching-Hsue; Chan, Chia-Pang; Yang, Jun-He
The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations. Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. The proposed model has several advantages including the following: (i) the proposed model is different from the previous models lacking the concept of time series; (ii) the proposed integrated attribute selection method can find the core attributes and reduce high dimensional data; and (iii) the proposed model can generate the rules and mathematical formulas of financial distress for providing references to the investors and decision makers. The result shows that the proposed method is better than the listing classifiers under three criteria; hence, the proposed model has competitive advantages in predicting the financial distress of companies.
Full Text Available Online time series prediction is the mainstream method in a wide range of fields, ranging from speech analysis and noise cancelation to stock market analysis. However, the data often contains many outliers with the increasing length of time series in real world. These outliers can mislead the learned model if treated as normal points in the process of prediction. To address this issue, in this paper, we propose a robust and adaptive online gradient learning method, RoAdam (Robust Adam, for long short-term memory (LSTM to predict time series with outliers. This method tunes the learning rate of the stochastic gradient algorithm adaptively in the process of prediction, which reduces the adverse effect of outliers. It tracks the relative prediction error of the loss function with a weighted average through modifying Adam, a popular stochastic gradient method algorithm for training deep neural networks. In our algorithm, the large value of the relative prediction error corresponds to a small learning rate, and vice versa. The experiments on both synthetic data and real time series show that our method achieves better performance compared to the existing methods based on LSTM.
Yang, Haimin; Pan, Zhisong; Tao, Qing
Online time series prediction is the mainstream method in a wide range of fields, ranging from speech analysis and noise cancelation to stock market analysis. However, the data often contains many outliers with the increasing length of time series in real world. These outliers can mislead the learned model if treated as normal points in the process of prediction. To address this issue, in this paper, we propose a robust and adaptive online gradient learning method, RoAdam (Robust Adam), for long short-term memory (LSTM) to predict time series with outliers. This method tunes the learning rate of the stochastic gradient algorithm adaptively in the process of prediction, which reduces the adverse effect of outliers. It tracks the relative prediction error of the loss function with a weighted average through modifying Adam, a popular stochastic gradient method algorithm for training deep neural networks. In our algorithm, the large value of the relative prediction error corresponds to a small learning rate, and vice versa. The experiments on both synthetic data and real time series show that our method achieves better performance compared to the existing methods based on LSTM.
Moeeni, Hamid; Bonakdari, Hossein; Fatemi, Seyed Ehsan
Because time series stationarization has a key role in stochastic modeling results, three methods are analyzed in this study. The methods are seasonal differencing, seasonal standardization and spectral analysis to eliminate the periodic effect on time series stationarity. First, six time series including 4 streamflow series and 2 water temperature series are stationarized. The stochastic term for these series obtained with ARIMA is subsequently modeled. For the analysis, 9228 models are introduced. It is observed that seasonal standardization and spectral analysis eliminate the periodic term completely, while seasonal differencing maintains seasonal correlation structures. The obtained results indicate that all three methods present acceptable performance overall. However, model accuracy in monthly streamflow prediction is higher with seasonal differencing than with the other two methods. Another advantage of seasonal differencing over the other methods is that the monthly streamflow is never estimated as negative. Standardization is the best method for predicting monthly water temperature although it is quite similar to seasonal differencing, while spectral analysis performed the weakest in all cases. It is concluded that for each monthly seasonal series, seasonal differencing is the best stationarization method in terms of periodic effect elimination. Moreover, the monthly water temperature is predicted with more accuracy than monthly streamflow. The criteria of the average stochastic term divided by the amplitude of the periodic term obtained for monthly streamflow and monthly water temperature were 0.19 and 0.30, 0.21 and 0.13, and 0.07 and 0.04 respectively. As a result, the periodic term is more dominant than the stochastic term for water temperature in the monthly water temperature series compared to streamflow series.
Jade, A M; Jayaraman, V K; Kulkarni, B D
A new method for model selection in prediction of time series is proposed. Apart from the conventional criterion of minimizing RMS error, the method also minimizes the error on the distribution of singularities, evaluated through the local Hoelder estimates and its probability density spectrum. Predictions of two simulated and one real time series have been done using kernel principal component regression (KPCR) and model parameters of KPCR have been selected employing the proposed as well as the conventional method. Results obtained demonstrate that the proposed method takes into account the sharp changes in a time series and improves the generalization capability of the KPCR model for better prediction of the unseen test data. (letter to the editor)
Knop, Joachim; Penick, Elizabeth C; Jensen, Per
records and a series of structured interviews and psychometric tests at ages 19-20 and 30 years. The present analysis focuses on the degree to which premorbid differences between the high- and low-risk groups later predicted lifetime drinking problems at age 30 (n = 241). RESULTS: As expected lifetime...... alcohol abuse/dependence by age 30 was reported significantly more often in the high-risk group. Of the 394 premorbid variables tested, 68 were found to distinguish the high- from the low-risk group before any subjects had developed a drinking problem. Of these 68 variables, 28 (41%) were also associated...... with DSM-III-R alcohol abuse/dependence at age 30. These 28 putative markers were reduced to 12 that were entered into a multiple regression analysis to search for the most powerful unique predictors of alcoholism. Four of the 28 putative markers were independently associated with problem drinking at age...
Full Text Available The aim of the article is to present a very important phenomenon affecting human integrity and homeostasis that is Threat Prediction Process. This process can be defined as “experiencing apprehension concerning results of potential/ actual dangers,” (Mamcarz, 2015 oscillating in terminological area of anxiety, fear, stress, restlessness. Moreover, it highlights a cognitive process distinctive for listed phenomenon’s. The process accompanied with technological and organization changes increases number of health problems affecting many populations. Hard work conditions; changing life style; or many social and political threats have influence on people’s quality of life that are even greater and more dangerous than physical and psychological factors, which, in turn, have much more consequences for human normal functioning. The present article is based on chosen case studies of a qualitative analysis of threat prediction process
Ramirez-Marquez, Jose E.; Coit, David W.
The redundancy allocation problem is formulated with the objective of minimizing design cost, when the system exhibits a multi-state reliability behavior, given system-level performance constraints. When the multi-state nature of the system is considered, traditional solution methodologies are no longer valid. This study considers a multi-state series-parallel system (MSPS) with capacitated binary components that can provide different multi-state system performance levels. The different demand levels, which must be supplied during the system-operating period, result in the multi-state nature of the system. The new solution methodology offers several distinct benefits compared to traditional formulations of the MSPS redundancy allocation problem. For some systems, recognizing that different component versions yield different system performance is critical so that the overall system reliability estimation and associated design models the true system reliability behavior more realistically. The MSPS design problem, solved in this study, has been previously analyzed using genetic algorithms (GAs) and the universal generating function. The specific problem being addressed is one where there are multiple component choices, but once a component selection is made, only the same component type can be used to provide redundancy. This is the first time that the MSPS design problem has been addressed without using GAs. The heuristic offers more efficient and straightforward analyses. Solutions to three different problem types are obtained illustrating the simplicity and ease of application of the heuristic without compromising the intended optimization needs
Kennedy, Curtis E; Turley, James P
Thousands of children experience cardiac arrest events every year in pediatric intensive care units. Most of these children die. Cardiac arrest prediction tools are used as part of medical emergency team evaluations to identify patients in standard hospital beds that are at high risk for cardiac arrest. There are no models to predict cardiac arrest in pediatric intensive care units though, where the risk of an arrest is 10 times higher than for standard hospital beds. Current tools are based on a multivariable approach that does not characterize deterioration, which often precedes cardiac arrests. Characterizing deterioration requires a time series approach. The purpose of this study is to propose a method that will allow for time series data to be used in clinical prediction models. Successful implementation of these methods has the potential to bring arrest prediction to the pediatric intensive care environment, possibly allowing for interventions that can save lives and prevent disabilities. We reviewed prediction models from nonclinical domains that employ time series data, and identified the steps that are necessary for building predictive models using time series clinical data. We illustrate the method by applying it to the specific case of building a predictive model for cardiac arrest in a pediatric intensive care unit. Time course analysis studies from genomic analysis provided a modeling template that was compatible with the steps required to develop a model from clinical time series data. The steps include: 1) selecting candidate variables; 2) specifying measurement parameters; 3) defining data format; 4) defining time window duration and resolution; 5) calculating latent variables for candidate variables not directly measured; 6) calculating time series features as latent variables; 7) creating data subsets to measure model performance effects attributable to various classes of candidate variables; 8) reducing the number of candidate features; 9
Peluso, E.; Gelfusa, M.; Lungaroni, M.; Talebzadeh, S.; Gaudio, P.; Murari, A.; Contributors, JET
Prediction is one of the main objectives of scientific analysis and it refers to both modelling and forecasting. The determination of the limits of predictability is an important issue of both theoretical and practical relevance. In the case of modelling time series, reached a certain level in performance in either modelling or prediction, it is often important to assess whether all the information available in the data has been exploited or whether there are still margins for improvement of the tools being developed. In this paper, an information theoretic approach is proposed to address this issue and quantify the quality of the models and/or predictions. The excellent properties of the proposed indicator have been proved with the help of a systematic series of numerical tests and a concrete example of extreme relevance for nuclear fusion.
Xu Ruirui; Bian Guoxing; Gao Chenfeng; Chen Tianlun
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.
Full Text Available Shallow vestibule, gingival recession, inadequate width of attached gingiva (AG and aberrant frenum pull are an array of mucogingival problems for which several independent and effective surgical solutions are reported in the literature. This case series reports the effectiveness of the bridge flap technique as a single-step surgical entity for increasing the depth of the vestibule, root coverage, increasing the width of the AG and solving the problem of abnormal frenum pull. Eight patients with 18 teeth altogether having Millers class I, II or III recession along with problems of shallow vestibule, inadequate width of AG and with or without frenum pull underwent this surgical procedure and were followed-up till 9 months post-operatively. The mean root coverage obtained was 55% and the mean average gain in width of the AG was 3.5 mm. The mean percentage gain in clinical attachment level was 41%. The bridge flap technique can be an effective single-step solution for the aforementioned mucogingival problems if present simultaneously in any case, and offers considerable advantages over other mucogingival surgical techniques in terms of simplicity, limited chair-time for the patient and the operator, single surgical intervention for manifold mucogingival problems and low morbidity because of the absence of palatal donor tissue.
Bao, Yukun; Xiong, Tao; Hu, Zhongyi
Multistep-ahead time series prediction is one of the most challenging research topics in the field of time series modeling and prediction, and is continually under research. Recently, the multiple-input several multiple-outputs (MISMO) modeling strategy has been proposed as a promising alternative for multistep-ahead time series prediction, exhibiting advantages compared with the two currently dominating strategies, the iterated and the direct strategies. Built on the established MISMO strategy, this paper proposes a particle swarm optimization (PSO)-based MISMO modeling strategy, which is capable of determining the number of sub-models in a self-adaptive mode, with varying prediction horizons. Rather than deriving crisp divides with equal-size s prediction horizons from the established MISMO, the proposed PSO-MISMO strategy, implemented with neural networks, employs a heuristic to create flexible divides with varying sizes of prediction horizons and to generate corresponding sub-models, providing considerable flexibility in model construction, which has been validated with simulated and real datasets.
Antonik, Piotr; Hermans, Michiel; Duport, François; Haelterman, Marc; Massar, Serge
Reservoir Computing is a bio-inspired computing paradigm for processing time dependent signals that is particularly well suited for analog implementations. Our team has demonstrated several photonic reservoir computers with performance comparable to digital algorithms on a series of benchmark tasks such as channel equalisation and speech recognition. Recently, we showed that our opto-electronic reservoir computer could be trained online with a simple gradient descent algorithm programmed on an FPGA chip. This setup makes it in principle possible to feed the output signal back into the reservoir, and thus highly enrich the dynamics of the system. This will allow to tackle complex prediction tasks in hardware, such as pattern generation and chaotic and financial series prediction, which have so far only been studied in digital implementations. Here we report simulation results of our opto-electronic setup with an FPGA chip and output feedback applied to pattern generation and Mackey-Glass chaotic series prediction. The simulations take into account the major aspects of our experimental setup. We find that pattern generation can be easily implemented on the current setup with very good results. The Mackey-Glass series prediction task is more complex and requires a large reservoir and more elaborate training algorithm. With these adjustments promising result are obtained, and we now know what improvements are needed to match previously reported numerical results. These simulation results will serve as basis of comparison for experiments we will carry out in the coming months.
Kaufman, M.; Pasynok, S.
Russian State Time, Frequency and Earth Rotation Service provides the official EOP data and time for use in scientific, technical and metrological works in Russia. The observations of GLONASS and GPS on 30 stations in Russia, and also the Russian and worldwide observations data of VLBI (35 stations) and SLR (20 stations) are used now. To these three series of EOP the data calculated in two other Russian analysis centers are added: IAA (VLBI, GPS and SLR series) and MCC (SLR). Joint processing of these 7 series is carried out every day (the operational EOP data for the last day and the predicted values for 50 days). The EOP values are weekly refined and systematic errors of every individual series are corrected. The combined results become accessible on the VNIIFTRI server (ftp.imvp.ru) approximately at 6h UT daily.
Kusev, Petko; van Schaik, Paul; Tsaneva-Atanasova, Krasimira; Juliusson, Asgeir; Chater, Nick
When attempting to predict future events, people commonly rely on historical data. One psychological characteristic of judgmental forecasting of time series, established by research, is that when people make forecasts from series, they tend to underestimate future values for upward trends and overestimate them for downward ones, so-called trend-damping (modeled by anchoring on, and insufficient adjustment from, the average of recent time series values). Events in a time series can be experienced sequentially (dynamic mode), or they can also be retrospectively viewed simultaneously (static mode), not experienced individually in real time. In one experiment, we studied the influence of presentation mode (dynamic and static) on two sorts of judgment: (a) predictions of the next event (forecast) and (b) estimation of the average value of all the events in the presented series (average estimation). Participants' responses in dynamic mode were anchored on more recent events than in static mode for all types of judgment but with different consequences; hence, dynamic presentation improved prediction accuracy, but not estimation. These results are not anticipated by existing theoretical accounts; we develop and present an agent-based model-the adaptive anchoring model (ADAM)-to account for the difference between processing sequences of dynamically and statically presented stimuli (visually presented data). ADAM captures how variation in presentation mode produces variation in responses (and the accuracy of these responses) in both forecasting and judgment tasks. ADAM's model predictions for the forecasting and judgment tasks fit better with the response data than a linear-regression time series model. Moreover, ADAM outperformed autoregressive-integrated-moving-average (ARIMA) and exponential-smoothing models, while neither of these models accounts for people's responses on the average estimation task. Copyright © 2017 The Authors. Cognitive Science published by Wiley
Scott, James R.; Martini, Michael C.
It has been known for some time that Taylor series (TS) integration is among the most efficient and accurate numerical methods in solving differential equations. However, the full benefit of the method has yet to be realized in calculating spacecraft trajectories, for two main reasons. First, most applications of Taylor series to trajectory propagation have focused on relatively simple problems of orbital motion or on specific problems and have not provided general applicability. Second, applications that have been more general have required use of a preprocessor, which inevitably imposes constraints on computational efficiency. The latter approach includes the work of Berryman et al., who solved the planetary n-body problem with relativistic effects. Their work specifically noted the computational inefficiencies arising from use of a preprocessor and pointed out the potential benefit of manually coding derivative routines. In this Engineering Note, we report on a systematic effort to directly implement Taylor series integration in an operational trajectory propagation code: the Spacecraft N-Body Analysis Program (SNAP). The present Taylor series implementation is unique in that it applies to spacecraft virtually anywhere in the solar system and can be used interchangeably with another integration method. SNAP is a high-fidelity trajectory propagator that includes force models for central body gravitation with N X N harmonics, other body gravitation with N X N harmonics, solar radiation pressure, atmospheric drag (for Earth orbits), and spacecraft thrusting (including shadowing). The governing equations are solved using an eighth-order Runge-Kutta Fehlberg (RKF) single-step method with variable step size control. In the present effort, TS is implemented by way of highly integrated subroutines that can be used interchangeably with RKF. This makes it possible to turn TS on or off during various phases of a mission. Current TS force models include central body
Full Text Available Reasonable prediction makes significant practical sense to stochastic and unstable time series analysis with small or limited sample size. Motivated by the rolling idea in grey theory and the practical relevance of very short-term forecasting or 1-step-ahead prediction, a novel autoregressive (AR prediction approach with rolling mechanism is proposed. In the modeling procedure, a new developed AR equation, which can be used to model nonstationary time series, is constructed in each prediction step. Meanwhile, the data window, for the next step ahead forecasting, rolls on by adding the most recent derived prediction result while deleting the first value of the former used sample data set. This rolling mechanism is an efficient technique for its advantages of improved forecasting accuracy, applicability in the case of limited and unstable data situations, and requirement of little computational effort. The general performance, influence of sample size, nonlinearity dynamic mechanism, and significance of the observed trends, as well as innovation variance, are illustrated and verified with Monte Carlo simulations. The proposed methodology is then applied to several practical data sets, including multiple building settlement sequences and two economic series.
Wu Xuedong; Song Zhihuan
On the assumption that random interruptions in the observation process are modelled by a sequence of independent Bernoulli random variables, this paper generalize the extended Kalman filtering (EKF), the unscented Kalman filtering (UKF) and the Gaussian particle filtering (GPF) to the case in which there is a positive probability that the observation in each time consists of noise alone and does not contain the chaotic signal (These generalized novel algorithms are referred to as GEKF, GUKF and GGPF correspondingly in this paper). Using weights and network output of neural networks to constitute state equation and observation equation for chaotic time-series prediction to obtain the linear system state transition equation with continuous update scheme in an online fashion, and the prediction results of chaotic time series represented by the predicted observation value, these proposed novel algorithms are applied to the prediction of Mackey–Glass time-series with additive and multiplicative noises. Simulation results prove that the GGPF provides a relatively better prediction performance in comparison with GEKF and GUKF. (general)
Ouzineb, Mohamed; Nourelfath, Mustapha; Gendreau, Michel
This paper develops an efficient tabu search (TS) heuristic to solve the redundancy allocation problem for multi-state series-parallel systems. The system has a range of performance levels from perfect functioning to complete failure. Identical redundant elements are included in order to achieve a desirable level of availability. The elements of the system are characterized by their cost, performance and availability. These elements are chosen from a list of products available in the market. System availability is defined as the ability to satisfy consumer demand, which is represented as a piecewise cumulative load curve. A universal generating function technique is applied to evaluate system availability. The proposed TS heuristic determines the minimal cost system configuration under availability constraints. An originality of our approach is that it proceeds by dividing the search space into a set of disjoint subsets, and then by applying TS to each subset. The design problem, solved in this study, has been previously analyzed using genetic algorithms (GAs). Numerical results for the test problems from previous research are reported, and larger test problems are randomly generated. Comparisons show that the proposed TS out-performs GA solutions, in terms of both the solution quality and the execution time
Safety of unmanned aerial systems (UAS) is paramount, but the large number of dynamically changing controller parameters makes it hard to determine if the system is currently stable, and the time before loss of control if not. We propose a hierarchical statistical model using Treed Gaussian Processes to predict (i) whether a flight will be stable (success) or become unstable (failure), (ii) the time-to-failure if unstable, and (iii) time series outputs for flight variables. We first classify the current flight input into success or failure types, and then use separate models for each class to predict the time-to-failure and time series outputs. As different inputs may cause failures at different times, we have to model variable length output curves. We use a basis representation for curves and learn the mappings from input to basis coefficients. We demonstrate the effectiveness of our prediction methods on a NASA neuro-adaptive flight control system.
Ak, Ronay; Fink, Olga; Zio, Enrico
The increasing liberalization of European electricity markets, the growing proportion of intermittent renewable energy being fed into the energy grids, and also new challenges in the patterns of energy consumption (such as electric mobility) require flexible and intelligent power grids capable of providing efficient, reliable, economical, and sustainable energy production and distribution. From the supplier side, particularly, the integration of renewable energy sources (e.g., wind and solar) into the grid imposes an engineering and economic challenge because of the limited ability to control and dispatch these energy sources due to their intermittent characteristics. Time-series prediction of wind speed for wind power production is a particularly important and challenging task, wherein prediction intervals (PIs) are preferable results of the prediction, rather than point estimates, because they provide information on the confidence in the prediction. In this paper, two different machine learning approaches to assess PIs of time-series predictions are considered and compared: 1) multilayer perceptron neural networks trained with a multiobjective genetic algorithm and 2) extreme learning machines combined with the nearest neighbors approach. The proposed approaches are applied for short-term wind speed prediction from a real data set of hourly wind speed measurements for the region of Regina in Saskatchewan, Canada. Both approaches demonstrate good prediction precision and provide complementary advantages with respect to different evaluation criteria.
In the situation that world's population, especially in developing countries, is rapidly growing, humankind is facing to global problems that they cannot steadily live unless they find individual places to live, obtain foods, and peacefully get energy necessary for living for centuries. For this purpose, humankind has to think what behavior they should take in the finite environment, talk, agree and execute. Though energy has been long respected as a symbol for improving living, demanded and used, they have come to limit the use making the global environment more serious. If there is sufficient energy not loading cost to the environment. If nuclear energy regarded as such one sustain the resource for long and has market competitiveness. What situation of realization of compensating new energy is now in the case the use of nuclear energy is restricted by the society fearing radioactivity. If there are promising ones for the future. One concerning with the study of energy cannot go without knowing these. The statistical materials compiled here are thought to be useful for that purpose, and are collected mainly from ones viewing future prediction based on past practices. Studies on the prediction is so important to have future measures that these data bases are expected to be improved for better accuracy. (author)
Abdul Hamid Hazrul
Full Text Available Carbon monoxide (CO is a poisonous, colorless, odourless and tasteless gas. The main source of carbon monoxide is from motor vehicles and carbon monoxide levels in residential areas closely reflect the traffic density. Prediction of carbon monoxide is important to give an early warning to sufferer of respiratory problems and also can help the related authorities to be more prepared to prevent and take suitable action to overcome the problem. This research was carried out using secondary data from Department of Environment Malaysia from 2013 to 2014. The main objectives of this research is to understand the characteristic of CO concentration and also to find the most suitable time series model to predict the CO concentration in Bachang, Melaka and Kuala Terengganu. Based on the lowest AIC value and several error measure, the results show that ARMA (1,1 is the most appropriate model to predict CO concentration level in Bachang, Melaka while ARMA (1,2 is the most suitable model with smallest error to predict the CO concentration level for residential area in Kuala Terengganu.
Mohammad Esmail Ahmad
Full Text Available The instability in the world and OPEC oil process results from many factors through a long time. The problems can be summarized as that the oil exports dont constitute a large share of N.I. only but it also makes up most of the saving of the oil states. The oil prices affect their market through the interaction of supply and demand forces of oil. The research hypothesis states that the movement of oil prices caused shocks crises and economic problems. These shocks happen due to changes in oil prices need to make a prediction within the framework of economic planning in a short run period in order to avoid shocks through using computer techniques by time series models.
Full Text Available Due to the sustainable and pollution-free characteristics, wind energy has been one of the fastest growing renewable energy sources. However, the intermittent and random fluctuation of wind speed presents many challenges for reliable wind power integration and normal operation of wind farm. Accurate wind speed prediction is the key to ensure the safe operation of power system and to develop wind energy resources. Therefore, this paper has presented a wavelet time series wind speed prediction model based on Lorenz disturbance. Therefore, in this paper, combined with the atmospheric dynamical system, a wavelet-time series improved wind speed prediction model based on Lorenz disturbance is proposed and the wind turbines of different climate types in Spain and China are used to simulate the disturbances of Lorenz equations with different initial values. The prediction results show that the improved model can effectively correct the preliminary prediction of wind speed, improving the prediction. In a word, the research work in this paper will be helpful to arrange the electric power dispatching plan and ensure the normal operation of the wind farm.
Hamid Hazrul Abdul
Full Text Available Air pollution modelling is one of an important tool that usually used to make short term and long term prediction. Since air pollution gives a big impact especially to human health, prediction of air pollutants concentration is needed to help the local authorities to give an early warning to people who are in risk of acute and chronic health effects from air pollution. Finding the best time series model would allow prediction to be made accurately. This research was carried out to find the best time series model to predict the PM10 concentrations in Nilai, Negeri Sembilan, Malaysia. By considering two seasons which is wet season (north east monsoon and dry season (south west monsoon, seasonal autoregressive integrated moving average model were used to find the most suitable model to predict the PM10 concentrations in Nilai, Negeri Sembilan by using three error measures. Based on AIC statistics, results show that ARIMA (1, 1, 1 × (1, 0, 012 is the most suitable model to predict PM10 concentrations in Nilai, Negeri Sembilan.
Kappen, Teus H.; Peelen, Linda M.
PURPOSE OF REVIEW: Perioperative prediction models can help to improve personalized patient care by providing individual risk predictions to both patients and providers. However, the scientific literature on prediction model development and validation can be quite technical and challenging to
Rakityansky, S A; Elander, N
For a two-dimensional quantum-mechanical problem, we obtain a generalized power series expansion of the S-matrix that can be done near an arbitrary point on the Riemann surface of the energy, similar to the standard effective-range expansion. In order to do this, we consider the Jost function and analytically factorize its momentum dependence that causes the Jost function to be a multi-valued function. The remaining single-valued function of the energy is then expanded in the power series near an arbitrary point in the complex energy plane. A systematic and accurate procedure has been developed for calculating the expansion coefficients. This makes it possible to obtain a semi-analytic expression for the Jost function (and therefore for the S-matrix) near an arbitrary point on the Riemann surface and use it, for example, to locate the spectral points (bound and resonant states) as the S-matrix poles. The method is applied to a model similar to those used in the theory of quantum dots. (paper)
Ye Meiying; Wang Xiaodong
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.
Li, Qiongge; Chan, Maria F
Over half of cancer patients receive radiotherapy (RT) as partial or full cancer treatment. Daily quality assurance (QA) of RT in cancer treatment closely monitors the performance of the medical linear accelerator (Linac) and is critical for continuous improvement of patient safety and quality of care. Cumulative longitudinal QA measurements are valuable for understanding the behavior of the Linac and allow physicists to identify trends in the output and take preventive actions. In this study, artificial neural networks (ANNs) and autoregressive moving average (ARMA) time-series prediction modeling techniques were both applied to 5-year daily Linac QA data. Verification tests and other evaluations were then performed for all models. Preliminary results showed that ANN time-series predictive modeling has more advantages over ARMA techniques for accurate and effective applicability in the dosimetry and QA field. © 2016 New York Academy of Sciences.
Kwon, H.; Khalil, A.; Brown, C.; Lall, U.; Ahn, H.; Moon, Y.
Traditionally forecasting and characterizations of hydrologic systems is performed utilizing many techniques. Stochastic linear methods such as AR and ARIMA and nonlinear ones such as statistical learning theory based tools have been extensively used. The common difficulty to all methods is the determination of sufficient and necessary information and predictors for a successful prediction. Relationships between hydrologic variables are often highly nonlinear and interrelated across the temporal scale. A new hybrid approach is proposed for the simulation of hydrologic time series combining both the wavelet transform and the nonlinear model. The present model employs some merits of wavelet transform and nonlinear time series model. The Wavelet Transform is adopted to decompose a hydrologic nonlinear process into a set of mono-component signals, which are simulated by nonlinear model. The hybrid methodology is formulated in a manner to improve the accuracy of a long term forecasting. The proposed hybrid model yields much better results in terms of capturing and reproducing the time-frequency properties of the system at hand. Prediction results are promising when compared to traditional univariate time series models. An application of the plausibility of the proposed methodology is provided and the results conclude that wavelet based time series model can be utilized for simulating and forecasting of hydrologic variable reasonably well. This will ultimately serve the purpose of integrated water resources planning and management.
Richard Pincak; Marian Repasan
In this paper we apply a new approach of the string theory to the real financial market. It is direct extension and application of the work  into prediction of prices. The models are constructed with an idea of prediction models based on the string invariants (PMBSI). The performance of PMBSI is compared to support vector machines (SVM) and artificial neural networks (ANN) on an artificial and a financial time series. Brief overview of the results and analysis is given. The first model is ...
A time series analysis method for predicting the probability density of a dynamical system is proposed. A nonstationary parametric model of the probability density is estimated from data within a maximum likelihood framework and then extrapolated to forecast the future probability density and explore the system for critical transitions or tipping points. A full systematic account of parameter uncertainty is taken. The technique is generic, independent of the underlying dynamics of the system. The method is verified on simulated data and then applied to prediction of Arctic sea-ice extent.
Full Text Available Effective anomaly detection of sensing data is essential for identifying potential system failures. Because they require no prior knowledge or accumulated labels, and provide uncertainty presentation, the probability prediction methods (e.g., Gaussian process regression (GPR and relevance vector machine (RVM are especially adaptable to perform anomaly detection for sensing series. Generally, one key parameter of prediction models is coverage probability (CP, which controls the judging threshold of the testing sample and is generally set to a default value (e.g., 90% or 95%. There are few criteria to determine the optimal CP for anomaly detection. Therefore, this paper designs a graphic indicator of the receiver operating characteristic curve of prediction interval (ROC-PI based on the definition of the ROC curve which can depict the trade-off between the PI width and PI coverage probability across a series of cut-off points. Furthermore, the Youden index is modified to assess the performance of different CPs, by the minimization of which the optimal CP is derived by the simulated annealing (SA algorithm. Experiments conducted on two simulation datasets demonstrate the validity of the proposed method. Especially, an actual case study on sensing series from an on-orbit satellite illustrates its significant performance in practical application.
Bergsma, Michiel; Mooij, E.
Taylor-series integration is a numerical integration technique that computes the Taylor series of state variables using recurrence relations and uses this series to propagate the state in time. A Taylor-series integration reentry integrator is developed and compared with the fifth-order
Full Text Available Due to the important role of figures of speech in prose, the present research tried to investigate the figures of speech in the novel, Harry Potter Series, and their Persian translations. The main goal of this research was to investigate the translators’ problems in translating figures of speech from English into Persian. To achieve this goal, the collected data were analyzed and compared with their Persian equivalents. Then, the theories of Newmark (1988 & 2001, Larson (1998, and Nolan (2005 were used in order to find the applied strategies for rendering the figures of speech by the translators. After identifying the applied translation strategies, the descriptive and inferential analyses were applied to answer the research question and test its related hypothesis. The results confirmed that the most common pitfalls in translating figures of speech from English into Persian based on Nolan (2005 were, not identifying of figures of speech, their related meanings and translating them literally. Overall, the research findings rejected the null hypothesis. The findings of present research can be useful for translators, especially beginners. They can be aware of the existing problems in translating figures of speech, so they can avoid committing the same mistakes in their works.
Full Text Available To improve the CNC machine tools precision, a thermal error modeling for the motorized spindle was proposed based on time series analysis, considering the length of cutting tools and thermal declined angles, and the real-time error compensation was implemented. A five-point method was applied to measure radial thermal declinations and axial expansion of the spindle with eddy current sensors, solving the problem that the three-point measurement cannot obtain the radial thermal angle errors. Then the stationarity of the thermal error sequences was determined by the Augmented Dickey-Fuller Test Algorithm, and the autocorrelation/partial autocorrelation function was applied to identify the model pattern. By combining both Yule-Walker equations and information criteria, the order and parameters of the models were solved effectively, which improved the prediction accuracy and generalization ability. The results indicated that the prediction accuracy of the time series model could reach up to 90%. In addition, the axial maximum error decreased from 39.6 μm to 7 μm after error compensation, and the machining accuracy was improved by 89.7%. Moreover, the X/Y-direction accuracy can reach up to 77.4% and 86%, respectively, which demonstrated that the proposed methods of measurement, modeling, and compensation were effective.
Full Text Available Increasing maize cultivation and changed cropping practices promote the selection of typical maize weeds that may also profit strongly from climate change. Predicting potential weed problems is of high interest for plant production. Within the project KLIFF, experiments were combined with species distribution modelling for this task in the region of Lower Saxony, Germany. For our study, we modelled ecological and damage niches of nine weed species that are significant and wide spread in maize cropping in a number of European countries. Species distribution models describe the ecological niche of a species, these are the environmental conditions under which a species can maintain a vital population. It is also possible to estimate a damage niche, i.e. the conditions under which a species causes damage in agricultural crops. For this, we combined occurrence data of European national data bases with high resolution climate, soil and land use data. Models were also projected to simulated climate conditions for the time horizon 2070 - 2100 in order to estimate climate change effects. Modelling results indicate favourable conditions for typical maize weed occurrence virtually all over the study region, but only a few species are important in maize cropping. This is in good accordance with the findings of an earlier maize weed monitoring. Reaction to changing climate conditions is species-specific, for some species neutral (E. crus-galli, other species may gain (Polygonum persicaria or loose (Viola arvensis large areas of suitable habitats. All species with damage potential under present conditions will remain important in maize cropping, some more species will gain regional importance (Calystegia sepium, Setara viridis.
Yin, Song Nan; Kim, Woo Gon; Kim, Yong Wan; Park, Jae Young; Kim, Soen Jin
In this study, a Taylor series (T-S) model based on the Arrhenius, McVetty, and Monkman-Grant equations was developed using a mathematical analysis. In order to reduce fitting errors, the McVetty equation was transformed by considering the first three terms of the Taylor series equation. The model parameters were accurately determined by a statistical technique of maximum likelihood estimation, and this model was applied to the creep data of alloy 617. The T-S model results showed better agreement with the experimental data than other models such as the Eno, exponential, and L-M models. In particular, the T-S model was converted into an isothermal Taylor series (IT-S) model that can predict the creep strength at a given temperature. It was identified that the estimations obtained using the converted ITS model was better than that obtained using the T-S model for predicting the long-term creep life of alloy 617
Naro, Daniel; Rummel, Christian; Schindler, Kaspar; Andrzejak, Ralph G
The rank-based nonlinear predictability score was recently introduced as a test for determinism in point processes. We here adapt this measure to time series sampled from time-continuous flows. We use noisy Lorenz signals to compare this approach against a classical amplitude-based nonlinear prediction error. Both measures show an almost identical robustness against Gaussian white noise. In contrast, when the amplitude distribution of the noise has a narrower central peak and heavier tails than the normal distribution, the rank-based nonlinear predictability score outperforms the amplitude-based nonlinear prediction error. For this type of noise, the nonlinear predictability score has a higher sensitivity for deterministic structure in noisy signals. It also yields a higher statistical power in a surrogate test of the null hypothesis of linear stochastic correlated signals. We show the high relevance of this improved performance in an application to electroencephalographic (EEG) recordings from epilepsy patients. Here the nonlinear predictability score again appears of higher sensitivity to nonrandomness. Importantly, it yields an improved contrast between signals recorded from brain areas where the first ictal EEG signal changes were detected (focal EEG signals) versus signals recorded from brain areas that were not involved at seizure onset (nonfocal EEG signals).
Jafri, Y.Z.; Kamal, L.
Various statistical techniques was used on five-year data from 1998-2002 of average humidity, rainfall, maximum and minimum temperatures, respectively. The relationships to regression analysis time series (RATS) were developed for determining the overall trend of these climate parameters on the basis of which forecast models can be corrected and modified. We computed the coefficient of determination as a measure of goodness of fit, to our polynomial regression analysis time series (PRATS). The correlation to multiple linear regression (MLR) and multiple linear regression analysis time series (MLRATS) were also developed for deciphering the interdependence of weather parameters. Spearman's rand correlation and Goldfeld-Quandt test were used to check the uniformity or non-uniformity of variances in our fit to polynomial regression (PR). The Breusch-Pagan test was applied to MLR and MLRATS, respectively which yielded homoscedasticity. We also employed Bartlett's test for homogeneity of variances on a five-year data of rainfall and humidity, respectively which showed that the variances in rainfall data were not homogenous while in case of humidity, were homogenous. Our results on regression and regression analysis time series show the best fit to prediction modeling on climatic data of Quetta, Pakistan. (author)
Full Text Available The Combined Helmholtz Integral Equation – Fourier series Formulation (CHIEFF) is based on representation of a velocity potential in terms of Fourier series and finding the Fourier coefficients of this expansion. The solution could be substantially...
Liang, Peipeng; Jia, Xiuqin; Taatgen, Niels A.; Zhong, Ning; Li, Kuncheng
Neural correlate of human inductive reasoning process is still unclear. Number series and letter series completion are two typical inductive reasoning tasks, and with a common core component of rule induction. Previous studies have demonstrated that different strategies are adopted in number series
Full Text Available A series of direct smelting reduction experiment has been carried out with high phosphorous iron ore of the different bases by thermogravimetric analyzer. The derivative thermogravimetric (DTG data have been obtained from the experiments. One-step forward local weighted linear (LWL method , one of the most suitable ways of predicting chaotic time-series methods which focus on the errors, is used to predict DTG. In the meanwhile, empirical mode decomposition-autoregressive (EMD-AR, a data mining technique in signal processing, is also used to predict DTG. The results show that (1 EMD-AR(4 is the most appropriate and its error is smaller than the former; (2 root mean square error (RMSE has decreased about two-thirds; (3 standardized root mean square error (NMSE has decreased in an order of magnitude. Finally in this paper, EMD-AR method has been improved by golden section weighting; its error would be smaller than before. Therefore, the improved EMD-AR model is a promising alternative for apparent reaction rate (DTG. The analytical results have been an important reference in the field of industrial control.
Swanson, David J.
The electromagnetic interference prediction problem is characteristically ill-defined and complicated. Severe EMI problems are prevalent throughout the U.S. Navy, causing both expected and unexpected impacts on the operational performance of electronic combat systems onboard ships. This paper focuses on applying artificial intelligence (AI) technology to the prediction of ship related electromagnetic interference (EMI) problems.
Wang, Zhilong; Zhang, Min; Wang, Danshi; Song, Chuang; Liu, Min; Li, Jin; Lou, Liqi; Liu, Zhuo
In this paper, we propose a performance monitoring and failure prediction method in optical networks based on machine learning. The primary algorithms of this method are the support vector machine (SVM) and double exponential smoothing (DES). With a focus on risk-aware models in optical networks, the proposed protection plan primarily investigates how to predict the risk of an equipment failure. To the best of our knowledge, this important problem has not yet been fully considered. Experimental results showed that the average prediction accuracy of our method was 95% when predicting the optical equipment failure state. This finding means that our method can forecast an equipment failure risk with high accuracy. Therefore, our proposed DES-SVM method can effectively improve traditional risk-aware models to protect services from possible failures and enhance the optical network stability.
Barrios Andrés, José Luis; López-Soria, Leyre Mónica; Alastruey Izquierdo, Ana; Echevarría Ecenarro, Jaime; Feijoó Lera, Raquel; Garrido Fierro, Jesus; Cabrerizo Nuñez, Francisco Javier; Canut Blasco, Andrés
Although fortunately very rare in countries with a temperate climate, certain factors, such as clinical or pharmacological immunosuppression, may cause Fusarium-related fungal infections to become an emerging problem. Moreover, Fusarium is one of the most important etiological agents in exogenous endophthalmitis, which is often favored by the disruption of the epithelial barriers. The aim of this series of clinical cases is to identify characteristic clinical findings that may allow an early diagnosis and more efficient management of this ophthalmologic emergency. Three cases of endophthalmitis due to Fusarium solani and Fusarium oxysporum, diagnosed in 2009, 2010, and 2014 in patients from two different health regions belonging to the same health system and separated by around 43 miles, are presented. The Fusarium isolates were initially identified microscopically and the species subsequently confirmed by sequencing the elongation factor alpha (EFα) and internal transcribed spacers (ITS). Susceptibility to antifungal agents was determined using the EUCAST broth dilution method. Evolution was poor as two of the three patients progressed to phthisis bulbi despite surgical measures and broad-spectrum antifungal antibiotic therapy. It is essential to rapidly instigate multidisciplinary measures to combat suspected endophthalmitis due to Fusarium given the poor prognosis of this type of infection. Copyright © 2018 Asociación Española de Micología. Publicado por Elsevier España, S.L.U. All rights reserved.
Full Text Available In order to improve the accuracy and real-time of all kinds of information in the cash business, and solve the problem which accuracy and stability is not high of the data linkage between cash inventory forecasting and cash management information in the commercial bank, a hybrid learning algorithm is proposed based on adaptive population activity particle swarm optimization (APAPSO algorithm combined with the least squares method (LMS to optimize the adaptive network-based fuzzy inference system (ANFIS model parameters. Through the introduction of metric function of population diversity to ensure the diversity of population and adaptive changes in inertia weight and learning factors, the optimization ability of the particle swarm optimization (PSO algorithm is improved, which avoids the premature convergence problem of the PSO algorithm. The simulation comparison experiments are carried out with BP-LMS algorithm and standard PSO-LMS by adopting real commercial banks’ cash flow data to verify the effectiveness of the proposed time series prediction of bank cash flow based on improved PSO-ANFIS optimization method. Simulation results show that the optimization speed is faster and the prediction accuracy is higher.
Kane, Michael J; Price, Natalie; Scotch, Matthew; Rabinowitz, Peter
Time series models can play an important role in disease prediction. Incidence data can be used to predict the future occurrence of disease events. Developments in modeling approaches provide an opportunity to compare different time series models for predictive power. We applied ARIMA and Random Forest time series models to incidence data of outbreaks of highly pathogenic avian influenza (H5N1) in Egypt, available through the online EMPRES-I system. We found that the Random Forest model outperformed the ARIMA model in predictive ability. Furthermore, we found that the Random Forest model is effective for predicting outbreaks of H5N1 in Egypt. Random Forest time series modeling provides enhanced predictive ability over existing time series models for the prediction of infectious disease outbreaks. This result, along with those showing the concordance between bird and human outbreaks (Rabinowitz et al. 2012), provides a new approach to predicting these dangerous outbreaks in bird populations based on existing, freely available data. Our analysis uncovers the time-series structure of outbreak severity for highly pathogenic avain influenza (H5N1) in Egypt.
As Corsica is a non-interconnected island, its energy supply is very special case. Indeed, as all islands, a large part of the electricity production must be generated locally. Often, renewable energies are considered as a good solution to overcome the isolation problem. However, because of their intermittent nature, they are included in a limited way in power systems. Thus, it is necessary to use in addition other energy productions, with main problem the management of the dispatch between these two energy types. This study is related to the solar and PV prediction in order to quantify available energy and to allow the optimal transition between intermittent and conventional energies sources. Throughout this work, we tested different techniques of prediction concerning four horizons interesting the power manager: d+1; h+24, h+1 and m+5. After all these manipulations, we can conclude that according the considered horizon, the prioritization of the different predictors varies. Note that for the d+1 horizon, it is interesting to use an approach based on neural network being careful to make stationary the time series, and to use exogenous variables. For the h+1 horizon, a hybrid methodology combining the robustness of the autoregressive models and the non-linearity of the connectionist models provides satisfactory results. For the h+24 case, neural networks with multiple outputs give very good results. About the m+5 horizon, our conclusions are different. Thus, even if neural networks are the most effective, the simplicity and the relatively good results shown by the persistence-based approach, lead us to recommend it. All the proposed methodologies and results are complementary to the prediction studies available in the literature. In conclusion, we can say that methodologies developed could eventually be included as prediction tools in the global command - control systems of energy sources. (author) [fr
Full Text Available The rapid industrial development has led to the intermittent outbreak of pm2.5 or haze in developing countries, which has brought about great environmental issues, especially in big cities such as Beijing and New Delhi. We investigated the factors and mechanisms of haze change and present a long-term prediction model of Beijing haze episodes using time series analysis. We construct a dynamic structural measurement model of daily haze increment and reduce the model to a vector autoregressive model. Typical case studies on 886 continuous days indicate that our model performs very well on next day’s Air Quality Index (AQI prediction, and in severely polluted cases (AQI ≥ 300 the accuracy rate of AQI prediction even reaches up to 87.8%. The experiment of one-week prediction shows that our model has excellent sensitivity when a sudden haze burst or dissipation happens, which results in good long-term stability on the accuracy of the next 3–7 days’ AQI prediction.
Shahdoust, Maryam; Sadeghifar, Majid; Poorolajal, Jalal; Javanrooh, Niloofar; Amini, Payam
Hepatitis B (HB) is a major global mortality. Accurately predicting the trend of the disease can provide an appropriate view to make health policy disease prevention. This paper aimed to apply three different to predict monthly incidence rates of HB. This historical cohort study was conducted on the HB incidence data of Hamadan Province, the west of Iran, from 2004 to 2012. Weighted Markov Chain (WMC) method based on Markov chain theory and two time series models including Holt Exponential Smoothing (HES) and SARIMA were applied on the data. The results of different applied methods were compared to correct percentages of predicted incidence rates. The monthly incidence rates were clustered into two clusters as state of Markov chain. The correct predicted percentage of the first and second clusters for WMC, HES and SARIMA methods was (100, 0), (84, 67) and (79, 47) respectively. The overall incidence rate of HBV is estimated to decrease over time. The comparison of results of the three models indicated that in respect to existing seasonality trend and non-stationarity, the HES had the most accurate prediction of the incidence rates.
Wang, Jun; Li, ShengTing; Zhang, Yong
To find unknown protein-coding genes, annotation pipelines use a combination of ab initio gene prediction and similarity to experimentally confirmed genes or proteins. Here, we show that although the ab initio predictions have an intrinsically high false-positive rate, they also have a consistent...
Rashmi Ranjan Dhal
Full Text Available In any production environment, processing is centered on the manufacture of products. It is important to get adequate volumes of orders for those products. However, merely getting orders is not enough for the long-term sustainability of multinationals. They need to know the demand for their products well in advance in order to compete and win in a highly competitive market. To assess the demand of a product we need to track its order behavior and predict the future response of customers depending on the present dataset as well as historical dataset. In this paper we propose a systematic, time-series based scheme to perform this task using the Hadoop framework and Holt-Winter prediction function in the R environment to show the sales forecast for forthcoming years.
The Levenberg-Marquardt learning algorithm is applied for training a multilayer perception with three hidden layer each with ten neurons in order to carefully map the structure of chaotic time series such as Mackey-Glass time series. First the MLP network is trained with 1000 data, and then it is tested with next 500 data. After that the trained and tested network is applied for long-term prediction of next 120 data which come after test data. The prediction is such a way that, the first inputs to network for prediction are the four last data of test data, then the predicted value is shifted to the regression vector which is the input to the network, then after first four-step of prediction, the input regression vector to network is fully predicted values and in continue, each predicted data is shifted to input vector for subsequent prediction.
Lawson, Anneka Ruth; Ghosh, Bidisha; Broderick, Brian
Ambient air quality monitoring, modeling and compliance to the standards set by European Union (EU) directives and World Health Organization (WHO) guidelines are required to ensure the protection of human and environmental health. Congested urban areas are most susceptible to traffic-related air pollution which is the most problematic source of air pollution in Ireland. Long-term continuous real-time monitoring of ambient air quality at such urban centers is essential but often not realistic due to financial and operational constraints. Hence, the development of a resource-conservative ambient air quality monitoring technique is essential to ensure compliance with the threshold values set by the standards. As an intelligent and advanced statistical methodology, a Structural Time Series (STS) based approach has been introduced in this paper to develop a parsimonious and computationally simple air quality model. In STS methodology, the different components of a time-series dataset such as the trend, seasonal, cyclical and calendar variations can be modeled separately. To test the effectiveness of the proposed modeling strategy, average hourly concentrations of nitrogen dioxide and nitrogen oxides from a congested urban arterial in Dublin city center were modeled using STS methodology. The prediction error estimates from the developed air quality model indicate that the STS model can be a useful tool in predicting nitrogen dioxide and nitrogen oxides concentrations in urban areas and will be particularly useful in situations where the information on external variables such as meteorology or traffic volume is not available.
Park, Soojin; Megjhani, Murad; Frey, Hans-Peter; Grave, Edouard; Wiggins, Chris; Terilli, Kalijah L; Roh, David J; Velazquez, Angela; Agarwal, Sachin; Connolly, E Sander; Schmidt, J Michael; Claassen, Jan; Elhadad, Noemie
To develop and validate a prediction model for delayed cerebral ischemia (DCI) after subarachnoid hemorrhage (SAH) using a temporal unsupervised feature engineering approach, demonstrating improved precision over standard features. 488 consecutive SAH admissions from 2006 to 2014 to a tertiary care hospital were included. Models were trained on 80%, while 20% were set aside for validation testing. Baseline information and standard grading scales were evaluated: age, sex, Hunt Hess grade, modified Fisher Scale (mFS), and Glasgow Coma Scale (GCS). An unsupervised approach applying random kernels was used to extract features from physiological time series (systolic and diastolic blood pressure, heart rate, respiratory rate, and oxygen saturation). Classifiers (Partial Least Squares, linear and kernel Support Vector Machines) were trained on feature subsets of the derivation dataset. Models were applied to the validation dataset. The performances of the best classifiers on the validation dataset are reported by feature subset. Standard grading scale (mFS): AUC 0.58. Combined demographics and grading scales: AUC 0.60. Random kernel derived physiologic features: AUC 0.74. Combined baseline and physiologic features with redundant feature reduction: AUC 0.77. Current DCI prediction tools rely on admission imaging and are advantageously simple to employ. However, using an agnostic and computationally inexpensive learning approach for high-frequency physiologic time series data, we demonstrated that our models achieve higher classification accuracy.
Methods : In this correlational- descriptive study, 369 (208 female and 161 male from, Mazandaran University of Medical Science were selected through stratified random sampling method. In order to collect the data, the social problem solving inventory-revised and general health questionnaire were used. Data were analyzed through SPSS-19, Pearson′s correlation, t test, and stepwise regression analysis. Results : Data analysis showed significant relationship between social problem solving ability and mental health (P < 0.01. Social problem solving ability was significantly associated with the somatic symptoms, anxiety and insomnia, social dysfunction and severe depression (P < 0.01. Conclusions: The results of our study demonstrated that there is a significant correlation between social problem solving ability and mental health.
Dolson, Eugenia P.; Conklin, Heather M.; Li, Chenghong; Xiong, Xiaoping; Merchant, Thomas E.
Background Although radiation therapy is a primary treatment for craniopharyngioma, it can exacerbate existing problems related to the tumor and pre-irradiation management. Survival is often marked by neurologic deficits, panhypopituitarism, diabetes insipidus, cognitive deficiencies and behavioral and social problems. Procedure The Achenbach Child Behavior Checklist (CBCL) was used to evaluate behavioral and social problems during the first five years of follow-up in 27 patients with craniopharyngioma treated with conformal radiation therapy. Results All group averages for the CBCL scales were within the age-typical range at pre-irradiation baseline. Extent of surgical resection was implicated in baseline differences for the Internalizing, Externalizing, Behavior Problem and Social scores. Significant longitudinal changes were found in Internalizing, Externalizing, Behavior Problem and School scores that correlated with tumor and treatment related factors. Conclusions The most common variables implicated in post-irradiation behavioral and social problems were CSF shunting, presence of an Ommaya reservoir, diabetes insipidus, and low pre-irradiation growth hormone levels. PMID:19191345
Castro-Schilo, Laura; Ferrer, Emilio
We illustrate the idiographic/nomothetic debate by comparing 3 approaches to using daily self-report data on affect for predicting relationship quality and breakup. The 3 approaches included (a) the first day in the series of daily data; (b) the mean and variability of the daily series; and (c) parameters from dynamic factor analysis, a…
Papacharalampous, Georgia; Tyralis, Hristos; Koutsoyiannis, Demetris
We investigate the predictability of monthly temperature and precipitation by applying automatic univariate time series forecasting methods to a sample of 985 40-year-long monthly temperature and 1552 40-year-long monthly precipitation time series. The methods include a naïve one based on the monthly values of the last year, as well as the random walk (with drift), AutoRegressive Fractionally Integrated Moving Average (ARFIMA), exponential smoothing state-space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components (BATS), simple exponential smoothing, Theta and Prophet methods. Prophet is a recently introduced model inspired by the nature of time series forecasted at Facebook and has not been applied to hydrometeorological time series before, while the use of random walk, BATS, simple exponential smoothing and Theta is rare in hydrology. The methods are tested in performing multi-step ahead forecasts for the last 48 months of the data. We further investigate how different choices of handling the seasonality and non-normality affect the performance of the models. The results indicate that: (a) all the examined methods apart from the naïve and random walk ones are accurate enough to be used in long-term applications; (b) monthly temperature and precipitation can be forecasted to a level of accuracy which can barely be improved using other methods; (c) the externally applied classical seasonal decomposition results mostly in better forecasts compared to the automatic seasonal decomposition used by the BATS and Prophet methods; and (d) Prophet is competitive, especially when it is combined with externally applied classical seasonal decomposition.
Zinn-Justin, J.; Freie Univ. Berlin
In this review I present a method to estimate the large order behavior of perturbation theory in quantum mechanics and field theory. The basic idea, due to Lipatov, is to relate the large order behavior to (in general complex) instanton contributions to the path integral representation of Green's functions. I explain the method first in the case of a simple integral and of the anharmonic oscillator and recover the results of Bender and Wu. I apply it then to the PHI 4 field theory. I study general potentials and boson field theories. I show, following Parisi, how the method can be generalized to theories with fermions. Finally I outline the implications of these results for the summability of the series. In particular I explain a method to sum divergent series based on a Borel transformation. In a last section I compare the larger order behavior predictions to actual series calculation. I present also some numerical examples of series summation. (orig.)
CSR, Inc., Washington, DC.
This handbook is for administrators of programs in higher education settings which deal with alcohol and other drug (AOD) related problems. Chapter 1, "Defining the Problem, Issues, and Trends" examines the problem from various perspectives and presents the latest statistics on the extent of AOD use on campuses, specific problems affecting…
Bohlin, Gunilla; Eninger, Lilianne; Brocki, Karin Cecilia; Thorell, Lisa B.
The aim of the present study was to investigate whether attachment insecurity, focusing on disorganized attachment, and the executive function (EF) component of inhibition, assessed at age 5, were longitudinally related to general externalizing problem behaviors as well as to specific symptoms of ADHD and Autism spectrum disorder (ASD), and…
Verduijn, M.; Peek, N.; Voorbraak, F.; de Jonge, E.; de Mol, B. A. J. M.
Objectives. To develop a predictive model for the outcome length of stay at the Intensive Care Unit (ICU LOS), including the choice of an optimal dichotomization threshold for this outcome. Reduction of prediction problems of this type of outcome to a two-doss problem is a common strategy to
Turkman, Kamil Feridun; Zea Bermudez, Patrícia
This book offers a useful combination of probabilistic and statistical tools for analyzing nonlinear time series. Key features of the book include a study of the extremal behavior of nonlinear time series and a comprehensive list of nonlinear models that address different aspects of nonlinearity. Several inferential methods, including quasi likelihood methods, sequential Markov Chain Monte Carlo Methods and particle filters, are also included so as to provide an overall view of the available tools for parameter estimation for nonlinear models. A chapter on integer time series models based on several thinning operations, which brings together all recent advances made in this area, is also included. Readers should have attended a prior course on linear time series, and a good grasp of simulation-based inferential methods is recommended. This book offers a valuable resource for second-year graduate students and researchers in statistics and other scientific areas who need a basic understanding of nonlinear time ...
Hubert, Maxime; Dubertrand, Remy
The scattering of a wave obeying Helmholtz equation by an elliptic obstacle can be described exactly using series of Mathieu functions. This situation is relevant in optics, quantum mechanics and fluid dynamics. We focus on the case when the wavelength is comparable to the obstacle size, when the most standard approximations fail. The approximations of the radial (or modified) Mathieu functions using WKB method are shown to be especially efficient, in order to precisely evaluate series of suc...
Recent experiences in earthquake prediction are recalled. Precursor information seems to be available from geodetic measurements, hydrological and geochemical measurements, electric and magnetic measurements, purely seismic phenomena, and zoological phenomena; some new methods are proposed. A list of possible earthquake triggers is given. The dilatancy model is contrasted with a dry model; they seem to be equally successful. In conclusion, the space and time range of the precursors is discussed in relation to the magnitude of earthquakes. (RWR)
Byck, Gayle R.; Swann, Greg; Schalet, Benjamin; Bolland, John; Mustanski, Brian
There is limited literature on the relationship between sensation seeking and adolescent risk behaviors, particularly among African Americans. We tested the association between psychometrically-derived subscales of the Zuckerman Sensation Seeking Scale and the intercepts and slopes of individual growth curves of conduct problems, sexual risk taking, and substance use from ages 13-18 years by sex. Boys and girls had different associations between sensation seeking and baseline levels and growth of risk behaviors. The Pleasure Seeking scale was associated with baseline levels of conduct problems in boys and girls, baseline substance use in boys, and growth in sexual risk taking and substance use by girls. Girls had the same pattern of associations with the Danger/Novelty scale as the Pleasure Seeking scale. Knowledge about the relationships between adolescent risk taking and sensation seeking can help in the targeted design of prevention and intervention programs for the understudied population of very low-income, African American adolescents. PMID:25112599
local conformations . Moreover, all these models have the same theme in trying to define the properties a real protein has when folding. Today , it...attempted to solve the PSP problem with a real valued GA and found better results than a competitor (Scheraga, et al) ; however, today we know that...ACM Symposium on Applied computing (SAC01) (March 11-14 2001). Las Vegas, Nevada.  Derrida , B. “Random Energy Model: Limit of a Family of
Wood, P K; Sher, K J; Erickson, D J; DeBord, K A
The present article examines the relation of problematic alcohol use to collegiate academic problems based on a systematic assessment of problematic alcohol use and college transcript data. The degree to which this prospective association can be explained by reference to third variables is also explored. These third variables include: students' high school academic achievement and aptitude, concurrent drug use, participation in deviant behaviors and students' investment or participation in the college experience. A sample of 444 (240 female) college freshman recruited for a longitudinal study of alcohol use was followed for 6 years. Alcohol and drug involvement, general deviance, academic investment, campus involvement and several background variables were assessed during the freshman year. Additional measures of high school aptitude and achievement as well as collegiate performance were calculated based on college transcript data from all institutions attended. A latent variable structural equation model revealed that problematic alcohol use during the freshman year correlated +.32 with collegiate academic problems. No evidence was found for a unique association between the two constructs when additional constructs were included in the model. Specifically, the association was substantially reduced when preexisting student differences traditionally associated with academic failure in college were taken into account. The inclusion of concurrent drug use and deviance also resulted in a significant reduction in the magnitude of the association. Although a substantial bivariate association exists between problematic alcohol use and academic problems during college, much of this association appears attributable to preexisting student differences on admission to college.
Stavros-Richard G. Christopoulos
Full Text Available Recently, the study of the coherent noise model has led to a simple (binary prediction algorithm for the forthcoming earthquake magnitude in aftershock sequences. This algorithm is based on the concept of natural time and exploits the complexity exhibited by the coherent noise model. Here, using the relocated catalogue from Southern California Seismic Network for 1981 to June 2011, we evaluate the application of this algorithm for the aftershocks of strong earthquakes of magnitude M≥6. The study is also extended by using the Global Centroid Moment Tensor Project catalogue to the case of the six strongest earthquakes in the Earth during the last almost forty years. The predictor time series exhibits the ubiquitous 1/f noise behavior.
Wang, L.; Mukundan, R.; Zion, M.; Pierson, D. C.
The New York City Department of Environmental Protection (DEP) manages New York City's water supply, which is comprised of over 20 reservoirs and supplies over 1 billion gallons of water per day to more than 9 million customers. DEP's "West of Hudson" reservoirs located in the Catskill Mountains are unfiltered per a renewable filtration avoidance determination granted by the EPA. While water quality is usually pristine, high volume storm events occasionally cause the reservoirs to become highly turbid. A logical strategy for turbidity control is to temporarily remove the turbid reservoirs from service. While effective in limiting delivery of turbid water and reducing the need for in-reservoir alum flocculation, this strategy runs the risk of negatively impacting water supply reliability. Thus, it is advantageous for DEP to understand how long a particular turbidity event will affect their system. In order to understand the duration, intensity and total load of a turbidity event, predictions of future in-stream turbidity values are important. Traditionally, turbidity predictions have been carried out by applying streamflow observations/forecasts to a flow-turbidity rating curve. However, predictions from rating curves are often inaccurate due to inter- and intra-event variability in flow-turbidity relationships. Predictions can be improved by applying an autoregressive moving average (ARMA) time series model in combination with a traditional rating curve. Since 2003, DEP and the Upstate Freshwater Institute have compiled a relatively consistent set of 15-minute turbidity observations at various locations on Esopus Creek above Ashokan Reservoir. Using daily averages of this data and streamflow observations at nearby USGS gauges, flow-turbidity rating curves were developed via linear regression. Time series analysis revealed that the linear regression residuals may be represented using an ARMA(1,2) process. Based on this information, flow-turbidity regressions with
Full Text Available Aimed at resolving the issues of the imbalance of resources and workloads at data centers and the overhead together with the high cost of virtual machine (VM migrations, this paper proposes a new VM migration strategy which is based on the cloud model time series workload prediction algorithm. By setting the upper and lower workload bounds for host machines, forecasting the tendency of their subsequent workloads by creating a workload time series using the cloud model, and stipulating a general VM migration criterion workload-aware migration (WAM, the proposed strategy selects a source host machine, a destination host machine, and a VM on the source host machine carrying out the task of the VM migration. Experimental results and analyses show, through comparison with other peer research works, that the proposed method can effectively avoid VM migrations caused by momentary peak workload values, significantly lower the number of VM migrations, and dynamically reach and maintain a resource and workload balance for virtual machines promoting an improved utilization of resources in the entire data center.
Byerly, William Elwood
Originally published over a century ago, this work remains among the most useful and practical expositions of Fourier's series, and spherical, cylindrical, and ellipsoidal harmonics. The subsequent growth of science into a diverse range of specialties has enhanced the value of this classic, whose thorough, basic treatment presents material that is assumed in many other studies but seldom available in such concise form. The development of functions, series, and their differential equations receives detailed explanations, and throughout the text, theory is applied to practical problems, with the
Yuste, S Bravo; Borrego, R; Abad, E
We consider various anomalous d -dimensional diffusion problems in the presence of an absorbing boundary with radial symmetry. The motion of particles is described by a fractional diffusion equation. Their mean-square displacement is given by r(2) proportional, variant t(gamma)(0divergent series appear when the concentration or survival probabilities are evaluated via the method of separation of variables. While the solution for normal diffusion problems is, at most, divergent as t-->0 , the emergence of such series in the long-time domain is a specific feature of subdiffusion problems. We present a method to regularize such series, and, in some cases, validate the procedure by using alternative techniques (Laplace transform method and numerical simulations). In the normal diffusion case, we find that the signature of the initial condition on the approach to the steady state rapidly fades away and the solution approaches a single (the main) decay mode in the long-time regime. In remarkable contrast, long-time memory of the initial condition is present in the subdiffusive case as the spatial part Psi1(r) describing the long-time decay of the solution to the steady state is determined by a weighted superposition of all spatial modes characteristic of the normal diffusion problem, the weight being dependent on the initial condition. Interestingly, Psi1(r) turns out to be independent of the anomalous diffusion exponent gamma .
In this paper we apply a new approach of string theory to the real financial market. The models are constructed with an idea of prediction models based on the string invariants (PMBSI). The performance of PMBSI is compared to support vector machines (SVM) and artificial neural networks (ANN) on an artificial and a financial time series. A brief overview of the results and analysis is given. The first model is based on the correlation function as invariant and the second one is an application based on the deviations from the closed string/pattern form (PMBCS). We found the difference between these two approaches. The first model cannot predict the behavior of the forex market with good efficiency in comparison with the second one which is, in addition, able to make relevant profit per year. The presented string models could be useful for portfolio creation and financial risk management in the banking sector as well as for a nonlinear statistical approach to data optimization.
Lonigan, Christopher J; Spiegel, Jamie A; Goodrich, J Marc; Morris, Brittany M; Osborne, Colleen M; Lerner, Matthew D; Phillips, Beth M
Findings from prior research have consistently indicated significant associations between self-regulation and externalizing behaviors. Significant associations have also been reported between children's language skills and both externalizing behaviors and self-regulation. Few studies to date, however, have examined these relations longitudinally, simultaneously, or with respect to unique clusters of externalizing problems. The current study examined the influence of preschool self-regulation on general and specific externalizing behavior problems in early elementary school and whether these relations were independent of associations between language, self-regulation, and externalizing behaviors in a sample of 815 children (44% female). Additionally, given a general pattern of sex differences in the presentations of externalizing behavior problems, self-regulation, and language skills, sex differences for these associations were examined. Results indicated unique relations of preschool self-regulation and language with both general externalizing behavior problems and specific problems of inattention. In general, self-regulation was a stronger longitudinal correlate of externalizing behavior for boys than it was for girls, and language was a stronger longitudinal predictor of hyperactive/impulsive behavior for girls than it was for boys.
McKisson, Micki; MacRae-Campbell, Linda
Both humanity and nature have suffered greatly from human insensitivity. Not only are the natural resources of the earth being depleted and its air, land and water polluted, the financial resources of humanity are being wasted on destructive expenditures. The "Our Only Earth" series is an integrated science, language arts, and social…
McKisson, Micki; MacRae-Campbell, Linda
Both humanity and nature have suffered greatly from human insensitivity. Not only are the natural resources of the earth being depleted and its air, land and water polluted, the financial resources of humanity are being wasted on destructive expenditures. The "Our Only Earth" series is an integrated science, language arts, and social studies…
Kanao, M; Yamashita, K; Kuwajima, M
We introduced an on-line system of biochemical, hematological, serological, urinary, bacteriological, and emergency examinations and associated office work using a client server system NEC PC-LACS based on a system consisting of concentration of outpatient blood collection, concentration of outpatient reception, and outpatient examination by reservation. Using this on-line system, results of 71 items in chemical serological, hematological, and urinary examinations are rapidly reported within 1 hour. Since the ordering system at our hospital has not been completed yet, we constructed a rapid time series reporting system in which time series data obtained on 5 serial occasions are printed on 2 sheets of A4 paper at the time of the final report. In each consultation room of the medical outpatient clinic, at the neuromedical outpatient clinic, and at the kidney center where examinations are frequently performed, terminal equipment and a printer for inquiry were established for real-time output of time series reports. Results are reported by FAX to the other outpatient clinics and wards, and subsequently, time series reports are output at the clinical laboratory department. This system allowed rapid examination, especially preconsultation examination. This system was also useful for reducing office work and effectively utilize examination data.
This well-written, advanced-level text introduces students to Fourier analysis and some of its applications. The self-contained treatment covers Fourier series, orthogonal systems, Fourier and Laplace transforms, Bessel functions, and partial differential equations of the first and second orders. Over 260 exercises with solutions reinforce students' grasp of the material. 1970 edition.
Full Text Available The voluminous Treatise of the four mathematical sciences of Georgios Pachymeres is the most renowned quadrivium produced in Byzantium. Among its specific features, historians of mathematics have pointed out, is the inclusion of Diophantus, besides Nicomachus and Euclid, in the sources for the arithmetical section and, accordingly, the incorporation of series of problems and problem-solving in its contents. The present paper investigates the “Diophantine portion” of Pachymeres' treatise and it shows that it is structured according to two criteria intrinsically characterized by seriality: on one hand, the arrangement in which the problems are presented in book I of Diophantus' Arithmetica; on the other hand, for those problems of which the enunciation involves ratio, the order in which Nicomachus discusses the kinds of ratios in his Arithmetical introduction. Furthermore, it analyses the solutions that Pachymeres offers and argues that Nicomachus' Arithmetical introduction provides the necessary tools for pursuing them.
Full Text Available Contradictions in spatial resolution and temporal coverage emerge from earth observation remote sensing images due to limitations in technology and cost. Therefore, how to combine remote sensing images with low spatial yet high temporal resolution as well as those with high spatial yet low temporal resolution to construct images with both high spatial resolution and high temporal coverage has become an important problem called spatio-temporal fusion problem in both research and practice. A Multi-Dictionary Bayesian Spatio-Temporal Reflectance Fusion Model (MDBFM has been proposed in this paper. First, multiple dictionaries from regions of different classes are trained. Second, a Bayesian framework is constructed to solve the dictionary selection problem. A pixel-dictionary likehood function and a dictionary-dictionary prior function are constructed under the Bayesian framework. Third, remote sensing images before and after the middle moment are combined to predict images at the middle moment. Diverse shapes and textures information is learned from different landscapes in multi-dictionary learning to help dictionaries capture the distinctions between regions. The Bayesian framework makes full use of the priori information while the input image is classified. The experiments with one simulated dataset and two satellite datasets validate that the MDBFM is highly effective in both subjective and objective evaluation indexes. The results of MDBFM show more precise details and have a higher similarity with real images when dealing with both type changes and phenology changes.
This monograph discusses policies designed to deal with food and nutrition problems in Tanzania. Available information on food supplies and nutritional conditions in Tanzania clearly shows that the country faces nutritional problems; protein energy malnutrition is the most serious and requires priority action. Iron deficiency anemia, goiter, and…
Pelykh, S.N.; Maksimov, M.V.; Ryabchikov, S.D.
Highlights: • Fuel cladding failure forecasting is based on the fuel load history and the damage distribution. • The limit damage parameter is exceeded, though limit stresses are not reached. • The damage parameter plays a significant role in predicting the cladding failure. • The proposed failure probability criterion can be used to control the cladding tightness. - Abstract: A method for forecasting of VVER fuel element (FE) cladding failure due to accumulation of deformation damage parameter, taking into account the fuel assembly (FA) loading history and the damage parameter distribution among FEs included in the FA, has been developed. Using the concept of conservative FE groups, it is shown that the safety limit for damage parameter is exceeded for some FA rearrangement, though the limits for circumferential and equivalent stresses are not reached. This new result contradicts the wide-spread idea that the damage parameter value plays a minor role when estimating the limiting state of cladding. The necessary condition of rearrangement algorithm admissibility and the criterion for minimization of the probability of cladding failure due to damage parameter accumulation have been derived, for using in automated systems controlling the cladding tightness.
Luo, Yi; Zhang, Tao; Li, Xiao-song
To explore the application of fuzzy time series model based on fuzzy c-means clustering in forecasting monthly incidence of Hepatitis E in mainland China. Apredictive model (fuzzy time series method based on fuzzy c-means clustering) was developed using Hepatitis E incidence data in mainland China between January 2004 and July 2014. The incidence datafrom August 2014 to November 2014 were used to test the fitness of the predictive model. The forecasting results were compared with those resulted from traditional fuzzy time series models. The fuzzy time series model based on fuzzy c-means clustering had 0.001 1 mean squared error (MSE) of fitting and 6.977 5 x 10⁻⁴ MSE of forecasting, compared with 0.0017 and 0.0014 from the traditional forecasting model. The results indicate that the fuzzy time series model based on fuzzy c-means clustering has a better performance in forecasting incidence of Hepatitis E.
Parvareh, Maryam; Karimi, Asrin; Rezaei, Satar; Woldemichael, Abraha; Nili, Sairan; Nouri, Bijan; Nasab, Nader Esmail
Road traffic accidents are commonly encountered incidents that can cause high-intensity injuries to the victims and have direct impacts on the members of the society. Iran has one of the highest incident rates of road traffic accidents. The objective of this study was to model the patterns of road traffic accidents leading to injury in Kurdistan province, Iran. A time-series analysis was conducted to characterize and predict the frequency of road traffic accidents that lead to injury in Kurdistan province. The injuries were categorized into three separate groups which were related to the car occupants, motorcyclists and pedestrian road traffic accident injuries. The Box-Jenkins time-series analysis was used to model the injury observations applying autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) from March 2009 to February 2015 and to predict the accidents up to 24 months later (February 2017). The analysis was carried out using R-3.4.2 statistical software package. A total of 5199 pedestrians, 9015 motorcyclists, and 28,906 car occupants' accidents were observed. The mean (SD) number of car occupant, motorcyclist and pedestrian accident injuries observed were 401.01 (SD 32.78), 123.70 (SD 30.18) and 71.19 (SD 17.92) per year, respectively. The best models for the pattern of car occupant, motorcyclist, and pedestrian injuries were the ARIMA (1, 0, 0), SARIMA (1, 0, 2) (1, 0, 0) 12 , and SARIMA (1, 1, 1) (0, 0, 1) 12 , respectively. The motorcyclist and pedestrian injuries showed a seasonal pattern and the peak was during summer (August). The minimum frequency for the motorcyclist and pedestrian injuries were observed during the late autumn and early winter (December and January). Our findings revealed that the observed motorcyclist and pedestrian injuries had a seasonal pattern that was explained by air temperature changes overtime. These findings call the need for close monitoring of the
Sadeh, Avi; De Marcas, Gali; Guri, Yael; Berger, Andrea; Tikotzky, Liat; Bar-Haim, Yair
This longitudinal study assessed the role of early sleep patterns in predicting attention regulation and behavior problems. Sleep of 43 infants was assessed using actigraphy at 12 months of age and then reassessed when the children were 3-4 years old. During this follow-up, their attention regulation and behavior problems were also assessed using a computerized test and parental reports. Lower quality of sleep in infancy significantly predicted compromised attention regulation and behavior problems. These findings underscore the need to identify and treat early sleep problems.
Staib, A.; Domcke, W.; Sobolewski, A.L.
Two simple limiting cases of Jahn-Teller (JT) coupling in Rydberg states of polyatomic molecules are considered, namely (i) JT coupling in Rydberg orbitals as well as in the ionization continuum (nondegenerate ion core, degenerate Rydberg series) and (ii) JT coupling in the ion core (degenerate ion core, nondegenerate Rydberg series). For both models simple and efficient algorithms for the computation of spectra (dynamical JT effect) are developed. The orbital JT effect is shown to represent a novel type of multi-state vibronic coupling, giving rise to interesting spectroscopic phenomena, among them resonant inter-Rydberg perturbations and JT induced autoionization. Particular attention is paid to the demonstration of the characteristic spectroscopic signatures of the two types of JT coupling in Rydberg states. (orig.)
Full Text Available Although bus passenger demand prediction has attracted increased attention during recent years, limited research has been conducted in the context of short-term passenger demand forecasting. This paper proposes an interactive multiple model (IMM filter algorithm-based model to predict short-term passenger demand. After aggregated in 15 min interval, passenger demand data collected from a busy bus route over four months were used to generate time series. Considering that passenger demand exhibits various characteristics in different time scales, three time series were developed, named weekly, daily, and 15 min time series. After the correlation, periodicity, and stationarity analyses, time series models were constructed. Particularly, the heteroscedasticity of time series was explored to achieve better prediction performance. Finally, IMM filter algorithm was applied to combine individual forecasting models with dynamically predicted passenger demand for next interval. Different error indices were adopted for the analyses of individual and hybrid models. The performance comparison indicates that hybrid model forecasts are superior to individual ones in accuracy. Findings of this study are of theoretical and practical significance in bus scheduling.
Full Text Available The time dynamics of geoelectrical precursory time series has been investigated and a method to discriminate chaotic behaviour in geoelectrical precursory time series is proposed. It allows us to detect low-dimensional chaos when the only information about the time series comes from the time series themselves. The short-term predictability of these time series is evaluated using two possible forecasting approaches: global autoregressive approximation and local autoregressive approximation. The first views the data as a realization of a linear stochastic process, whereas the second considers the data points as a realization of a deterministic process, supposedly non-linear. The comparison of the predictive skill of the two techniques is a test to discriminate between low-dimensional chaos and random dynamics. The analyzed time series are geoelectrical measurements recorded by an automatic station located in Tito (Southern Italy in one of the most seismic areas of the Mediterranean region. Our findings are that the global (linear approach is superior to the local one and the physical system governing the phenomena of electrical nature is characterized by a large number of degrees of freedom. Power spectra of the filtered time series follow a P(f = F-a scaling law: they exhibit the typical behaviour of a broad class of fractal stochastic processes and they are a signature of the self-organized systems.
Full Text Available This article addresses the study related to forecasting with an actual high-speed decision making under careful modelling of time series data. The study uses data-mining modelling for algorithmic optimization of transport goals. Our finding brings to the future adequate techniques for the fitting of a prediction model. This model is going to be used for analyses of the future transaction costs in the frontiers of the Czech Republic. Time series prediction methods for the performance of prediction models in the package of Statistics are Exponential, ARIMA and Neural Network approaches. The primary target for a predictive scenario in the data mining workspace is to provide modelling data faster and with more versatility than the other management techniques.
Ganesan, Nandhini; Basu, Suman; Hariharan, Krishnan S.; Kolake, Subramanya Mayya; Song, Taewon; Yeo, Taejung; Sohn, Dong Kee; Doo, Seokgwang
Lithium-Ion batteries used for electric vehicle applications are subject to large currents and various operation conditions, making battery pack design and life extension a challenging problem. With increase in complexity, modeling and simulation can lead to insights that ensure optimal performance and life extension. In this manuscript, an electrochemical-thermal (ECT) coupled model for a 6 series × 5 parallel pack is developed for Li ion cells with NCA/C electrodes and validated against experimental data. Contribution of the cathode to overall degradation at various operating conditions is assessed. Pack asymmetry is analyzed from a design and an operational perspective. Design based asymmetry leads to a new approach of obtaining the individual cell responses of the pack from an average ECT output. Operational asymmetry is demonstrated in terms of effects of thermal gradients on cycle life, and an efficient model predictive control technique is developed. Concept of reconfigurable battery pack is studied using detailed simulations that can be used for effective monitoring and extension of battery pack life.
Disruption prediction of tokamak plasma has been studied by neural network. The disruption prediction performances by neural network are estimated by the prediction success rate, false alarm rate, and time prior to disruption. The current driving type disruption is predicted by time series data, and plasma lifetime, risk of disruption and plasma stability. Some disruptions generated by density limit, impurity mixture, error magnetic field can be predicted 100 % of prediction success rate by the premonitory symptoms. The pressure driving type disruption phenomena generate some hundred micro seconds before, so that the operation limits such as β N limit of DIII-D and density limit of ADITYA were investigated. The false alarm rate was decreased by β N limit training under stable discharge. The pressure driving disruption generated with increasing plasma pressure can be predicted about 90 % by evaluating plasma stability. (S.Y.)
Sivakumar, Seshadri; Sivakumar, Shyamala
This paper introduces a discrete-time recurrent neural network architecture using triangular feedback weight matrices that allows a simplified approach to ensuring network and training stability. The triangular structure of the weight matrices is exploited to readily ensure that the eigenvalues of the feedback weight matrix represented by the block diagonal elements lie on the unit circle in the complex z-plane by updating these weights based on the differential of the angular error variable. Such placement of the eigenvalues together with the extended close interaction between state variables facilitated by the nondiagonal triangular elements, enhances the learning ability of the proposed architecture. Simulation results show that the proposed architecture is highly effective in time-series prediction tasks associated with nonlinear and chaotic dynamic systems with underlying oscillatory modes. This modular architecture with dual upper and lower triangular feedback weight matrices mimics fully recurrent network architectures, while maintaining learning stability with a simplified training process. While training, the block-diagonal weights (hence the eigenvalues) of the dual triangular matrices are constrained to the same values during weight updates aimed at minimizing the possibility of overfitting. The dual triangular architecture also exploits the benefit of parsing the input and selectively applying the parsed inputs to the two subnetworks to facilitate enhanced learning performance.
Simons, Raluca M; Hahn, Austin M; Simons, Jeffrey S; Murase, Hanako
This study examined the relationships between emotion dysregulation, peer drinking norms, drinking motives, and alcohol-related outcomes among 435 college students. We examined the mediating roles of drinking motives when predicting alcohol consumption and related problems from the subscales of the Difficulties in Emotion Regulation Scale (DERS; Gratz and Roemer, 2004) via negative and positive reinforcement models. First, we hypothesized that individuals who lack in emotion regulation strategies or have difficulties in accepting negative emotions are more likely to drink to cope. Additionally, we hypothesized that individuals who act impulsively or become distracted when upset as well as those with higher peer drinking norms are more likely to drink for social and enhancement motives. The results of the path model indicated that limited access to emotion regulation strategies significantly predicted alcohol-related problems via both depression and anxiety coping motives, but did not predict alcohol consumption. Nonacceptance of emotional responses was not significantly associated with coping motives. Impulsivity had a significant direct relationship with alcohol problems. Difficulty in engaging in goal-directed behaviors predicted both enhancement and social motives, but only enhancement motives in turn predicted consumption. Norms indirectly predicted problems via enhancement motives and consumption. The results indicated that using alcohol to reduce negative or to increase positive emotions increases alcohol consumption and alcohol-related problems. Overall, results advance our understanding of the mechanisms of increased alcohol use and problems among college students. Copyright © 2017 Elsevier B.V. All rights reserved.
CSR, Inc., Washington, DC.
This handbook for higher education faculty is designed to inform them of the nature and extent of alcohol and other drug abuse on the nation's campuses and to enlist their involvement in responding to these problems. Based on the premise that each individual can make a difference, the faculty member is encouraged to help shape the campus…
Universal Esperanto Association, Rotterdam (Netherlands).
The Final Act of the Conference on Security and Co-operation in Europe, linguistic problems in the way of cooperation, language differences and the potential for discriminatory practice, and the need for a new linguistic order are discussed. It is suggested that misunderstandings arising from differences of language reduce the ability of the 35…
Smith, Philip G., Ed.
This volume, intended for advanced and specialized teacher education courses, approaches educational problems from one of the standard divisions of general philosophy. Noting that substantive or normative ethics has dominated the literature of values and education, rather than meta-ethnics or analytical ethics, the editor states that his intention…
Koić, Elvira; Filaković, Pavo; Djordjević, Veljko; Nadj, Sanea
Gambling or gaming is a common term for a group of various games, activities and behavior that involve wagering money on an event with an uncertain outcome with the primary intent of winning additional money, i.e., a player risks and hopes to get back what he/she had gambled, or to win more. When the player is unable to resist impulses to gamble, and gambling behavior harmfully affects him or the others, then he/she is suffering from the so called "pathological gambling", which is one of six categories of the "Impulse control disorders" in the International Classification of Diseases. Since, at present, there is no standardized program and approach to the problem of gambling in Croatia, and having in mind the arising accessibility and popularity of the "games of chance", the authors are presenting seven cases of problem and pathological gambling and call for broad public discussion on the problem from medical-psychiatric and forensic-point of view. The first patient was treated on an outpatient basis with cognitive-behavioral and family therapy for problem gambling; for the second patient was treated for impulse control disorders; for the third patient gambling was a symptom of psychotic form of depressive disorder; the fourth had primary diagnosis of personality disorder; and the fifth patient was prosecuted for armed robbery and evaluated by a psychiatric expert. The sixth and the seventh patients were women suffering from primary bipolar affective and major depressive disorder, respectively. The authors conclude that, due to the size of the problem and its consequences, the prevention of pathological gambling is very important. The prevention can be carried out primarily through screening at the school level and primary health care services, whereas secondary screening may be conducted through the system of psychiatric care. It is recommended to invest into research, education of a wider population, and development of preventive programs.
Hengl, Tomislav; Heuvelink, Gerard B. M.; Perčec Tadić, Melita; Pebesma, Edzer J.
A computational framework to generate daily temperature maps using time-series of publicly available MODIS MOD11A2 product Land Surface Temperature (LST) images (1 km resolution; 8-day composites) is illustrated using temperature measurements from the national network of meteorological stations (159) in Croatia. The input data set contains 57,282 ground measurements of daily temperature for the year 2008. Temperature was modeled as a function of latitude, longitude, distance from the sea, elevation, time, insolation, and the MODIS LST images. The original rasters were first converted to principal components to reduce noise and filter missing pixels in the LST images. The residual were next analyzed for spatio-temporal auto-correlation; sum-metric separable variograms were fitted to account for zonal and geometric space-time anisotropy. The final predictions were generated for time-slices of a 3D space-time cube, constructed in the R environment for statistical computing. The results show that the space-time regression model can explain a significant part of the variation in station-data (84%). MODIS LST 8-day (cloud-free) images are unbiased estimator of the daily temperature, but with relatively low precision (±4.1°C); however their added value is that they systematically improve detection of local changes in land surface temperature due to local meteorological conditions and/or active heat sources (urban areas, land cover classes). The results of 10-fold cross-validation show that use of spatio-temporal regression-kriging and incorporation of time-series of remote sensing images leads to significantly more accurate maps of temperature than if plain spatial techniques were used. The average (global) accuracy of mapping temperature was ±2.4°C. The regression-kriging explained 91% of variability in daily temperatures, compared to 44% for ordinary kriging. Further software advancement—interactive space-time variogram exploration and automated retrieval
Purpose: evaluate 1) whether child’s externalizing problems increase or decrease within 12 months period; 2) the change of externalizing problems with respect to child gender and age, and 3) which maternal parenting stress factors and family sociodemographic characteristics can predict the increase and decrease of child’s externalizing problems. Design/methodology/approach: participants were evaluated 2 times (with the interval of 12 months) with the Parenting Stress Index (Abidin, 1990) and ...
Roč. 116, č. 3 (2011), s. 281-304 ISSN 0167-8019 R&D Projects: GA AV ČR IAA100190804 Institutional research plan: CEZ:AV0Z10190503 Keywords : stokes system * Neumann problem * integral equation method Subject RIV: BA - General Mathematics Impact factor: 0.899, year: 2011 http://www.springerlink.com/content/d73174l507577464/
Jelmer P Borst
Full Text Available BACKGROUND: It has been shown that people can only maintain one problem state, or intermediate mental representation, at a time. When more than one problem state is required, for example in multitasking, performance decreases considerably. This effect has been explained in terms of a problem state bottleneck. METHODOLOGY: In the current study we use the complimentary methodologies of computational cognitive modeling and neuroimaging to investigate the neural correlates of this problem state bottleneck. In particular, an existing computational cognitive model was used to generate a priori fMRI predictions for a multitasking experiment in which the problem state bottleneck plays a major role. Hemodynamic responses were predicted for five brain regions, corresponding to five cognitive resources in the model. Most importantly, we predicted the intraparietal sulcus to show a strong effect of the problem state manipulations. CONCLUSIONS: Some of the predictions were confirmed by a subsequent fMRI experiment, while others were not matched by the data. The experiment supported the hypothesis that the problem state bottleneck is a plausible cause of the interference in the experiment and that it could be located in the intraparietal sulcus.
Pino, María J; Castillo, Rosa A; Raya, Antonio; Herruzo, Javier
To identify possible differences in the level of externalizing behavior problems among children with and without hearing impairment and determine whether any relationship exists between this type of problem and parenting practices. The Behavior Assessment System for Children was used to evaluate externalizing variables in a sample of 118 boys and girls divided into two matched groups: 59 with hearing disorders and 59 normal-hearing controls. Significant between-group differences were found in hyperactivity, behavioral problems, and externalizing problems, but not in aggression. Significant differences were also found in various aspects of parenting styles. A model for predicting externalizing behavior problems was constructed, achieving a predicted explained variance of 50%. Significant differences do exist between adaptation levels in children with and without hearing impairment. Parenting style also plays an important role.
María J. Pino
Full Text Available Objective: To identify possible differences in the level of externalizing behavior problems among children with and without hearing impairment and determine whether any relationship exists between this type of problem and parenting practices. Methods: The Behavior Assessment System for Children was used to evaluate externalizing variables in a sample of 118 boys and girls divided into two matched groups: 59 with hearing disorders and 59 normal-hearing controls. Results: Significant between-group differences were found in hyperactivity, behavioral problems, and externalizing problems, but not in aggression. Significant differences were also found in various aspects of parenting styles. A model for predicting externalizing behavior problems was constructed, achieving a predicted explained variance of 50%. Conclusion: Significant differences do exist between adaptation levels in children with and without hearing impairment. Parenting style also plays an important role.
Bai, Sunhye; Lee, Steve S
Given its considerable public health significance, identifying predictors of early expressions of conduct problems is a priority. We examined the predictive validity of daring, a key dimension of temperament, and the Balloon Analog Risk Task (BART), a laboratory-based measure of risk taking behavior, with respect to two-year change in parent, teacher-, and youth self-reported oppositional defiant disorder (ODD), conduct disorder (CD), and antisocial behavior. At baseline, 150 ethnically diverse 6- to 10-year old (M=7.8, SD=1.1; 69.3% male) youth with ( n =82) and without ( n =68) DSM-IV ADHD completed the BART whereas parents rated youth temperament (i.e., daring); parents and teachers also independently rated youth ODD and CD symptoms. Approximately 2 years later, multi-informant ratings of youth ODD, CD, and antisocial behavior were gathered from rating scales and interviews. Whereas risk taking on the BART was unrelated to conduct problems, individual differences in daring prospectively predicted multi-informant rated conduct problems, independent of baseline risk taking, conduct problems, and ADHD diagnostic status. Early differences in the propensity to show positive socio-emotional responses to risky or novel experiences uniquely predicted escalating conduct problems in childhood, even with control of other potent clinical correlates. We consider the role of temperament in the origins and development of significant conduct problems from childhood to adolescence, including possible explanatory mechanisms underlying these predictions.
van den Akker, Alithe L; Deković, Maja; Prinzie, Peter
The present study examined how changes in child Big Five personality characteristics and overreactive parenting during the transition from childhood to adolescence predict adolescent adjustment problems. The sample included 290 children, aged 8-9 years. At three moments, with 2-year intervals, mothers, fathers, and a teacher reported on the child's personality, and mothers and fathers reported on their parenting behavior. At the third measurement moment, mothers, fathers, and children reported on the child's adjustment problems. Rank-order stability of the personality dimensions and overreactive parenting were high. Univariate latent growth models revealed mean-level decreases for extraversion, conscientiousness, and imagination. Mean levels of benevolence, emotional stability, and overreactive parenting were stable. Multivariate latent growth models revealed that decreases in extraversion and emotional stability predicted internalizing problems, whereas decreases in benevolence, conscientiousness, and emotional stability predicted externalizing problems. Increases in overreactive parenting predicted externalizing, but not internalizing problems. The associations were similar for boys and girls. The results indicate that changes in child personality and overreactive parenting during the transition to adolescence are associated with adolescent adjustment problems. Overall, child personality was more important than overreactive parenting, and children were more likely to "act out" than to "withdraw" in reaction to overreactive parenting.
Dash, Y.; Mishra, S. K.; Panigrahi, B. K.
Prediction of northeast/post monsoon rainfall which occur during October, November and December (OND) over Indian peninsula is a challenging task due to the dynamic nature of uncertain chaotic climate. It is imperative to elucidate this issue by examining performance of different machine leaning (ML) approaches. The prime objective of this research is to compare between a) statistical prediction using historical rainfall observations and global atmosphere-ocean predictors like Sea Surface Temperature (SST) and Sea Level Pressure (SLP) and b) empirical prediction based on a time series analysis of past rainfall data without using any other predictors. Initially, ML techniques have been applied on SST and SLP data (1948-2014) obtained from NCEP/NCAR reanalysis monthly mean provided by the NOAA ESRL PSD. Later, this study investigated the applicability of ML methods using OND rainfall time series for 1948-2014 and forecasted up to 2018. The predicted values of aforementioned methods were verified using observed time series data collected from Indian Institute of Tropical Meteorology and the result revealed good performance of ML algorithms with minimal error scores. Thus, it is found that both statistical and empirical methods are useful for long range climatic projections.
Wu Xue-Dong; Liu Wei-Ting; Zhu Zhi-Yu; Wang Yao-Nan
On the assumption that random interruptions in the observation process are modeled by a sequence of independent Bernoulli random variables, we firstly generalize two kinds of nonlinear filtering methods with random interruption failures in the observation based on the extended Kalman filtering (EKF) and the unscented Kalman filtering (UKF), which were shortened as GEKF and GUKF in this paper, respectively. Then the nonlinear filtering model is established by using the radial basis function neural network (RBFNN) prototypes and the network weights as state equation and the output of RBFNN to present the observation equation. Finally, we take the filtering problem under missing observed data as a special case of nonlinear filtering with random intermittent failures by setting each missing data to be zero without needing to pre-estimate the missing data, and use the GEKF-based RBFNN and the GUKF-based RBFNN to predict the ground radioactivity time series with missing data. Experimental results demonstrate that the prediction results of GUKF-based RBFNN accord well with the real ground radioactivity time series while the prediction results of GEKF-based RBFNN are divergent. (geophysics, astronomy, and astrophysics)
Paprocka, I.; Kempa, W. M.; Grabowik, C.; Kalinowski, K.; Krenczyk, D.
In the paper a survey of predictive and reactive scheduling methods is done in order to evaluate how the ability of prediction of reliability characteristics influences over robustness criteria. The most important reliability characteristics are: Mean Time to Failure, Mean Time of Repair. Survey analysis is done for a job shop scheduling problem. The paper answers the question: what method generates robust schedules in the case of a bottleneck failure occurrence before, at the beginning of planned maintenance actions or after planned maintenance actions? Efficiency of predictive schedules is evaluated using criteria: makespan, total tardiness, flow time, idle time. Efficiency of reactive schedules is evaluated using: solution robustness criterion and quality robustness criterion. This paper is the continuation of the research conducted in the paper , where the survey of predictive and reactive scheduling methods is done only for small size scheduling problems.
Calado, Filipa; Alexandre, Joana; Griffiths, Mark D
Background and aims Recent research suggests that youth problem gambling is associated with several factors, but little is known how these factors might influence or interact each other in predicting this behavior. Consequently, this is the first study to examine the mediation effect of coping styles in the relationship between attachment to parental figures and problem gambling. Methods A total of 988 adolescents and emerging adults were recruited to participate. The first set of analyses tested the adequacy of a model comprising biological, cognitive, and family variables in predicting youth problem gambling. The second set of analyses explored the relationship between family and individual variables in problem gambling behavior. Results The results of the first set of analyses demonstrated that the individual factors of gender, cognitive distortions, and coping styles showed a significant predictive effect on youth problematic gambling, and the family factors of attachment and family structure did not reveal a significant influence on this behavior. The results of the second set of analyses demonstrated that the attachment dimension of angry distress exerted a more indirect influence on problematic gambling, through emotion-focused coping style. Discussion This study revealed that some family variables can have a more indirect effect on youth gambling behavior and provided some insights in how some factors interact in predicting problem gambling. Conclusion These findings suggest that youth gambling is a multifaceted phenomenon, and that the indirect effects of family variables are important in estimating the complex social forces that might inﬂuence adolescent decisions to gamble.
Anggraini, Agita Dzulhajh; Fadiawati, Noor; Diawati, Chansyanah
Accuracy educators in selecting and implementing learning models influence students' science process skills. Models of learning that can be applied to improve science process skills and tend constructivist among athers learning model of problem solving. This research was conducted to describe the effectiveness of the learning model of problem solving in improving communication skills and prediction skills. Subjects in this research were students of high school YP Unila Bandar Lampung Even ...
Mead, W.C.; Jones, R.D.; Barnes, C.W.; Lee, L.A.; O'Rourke, M.K.; Lee, Y.C.; Flake, G.W.
We use the Connectionist Normalized Local Spline (CNLS) network to learn the dynamics of the Mackey-Glass time-delay differential equation, for the case τ = 30. We show the optimum network operating mode and determine the accuracy and robustness of predictions. We obtain pedictions of varying accuracy using some 2--120 minutes of execution time on a Sun SPARC-1 workstation. CNLS-net is capable of very good performance in predicting the Mackey-Glass time series. 11 refs., 4 figs
Salvatore, Jessica E; Aliev, Fazil; Edwards, Alexis C; Evans, David M; Macleod, John; Hickman, Matthew; Lewis, Glyn; Kendler, Kenneth S; Loukola, Anu; Korhonen, Tellervo; Latvala, Antti; Rose, Richard J; Kaprio, Jaakko; Dick, Danielle M
Alcohol problems represent a classic example of a complex behavioral outcome that is likely influenced by many genes of small effect. A polygenic approach, which examines aggregate measured genetic effects, can have predictive power in cases where individual genes or genetic variants do not. In the current study, we first tested whether polygenic risk for alcohol problems-derived from genome-wide association estimates of an alcohol problems factor score from the age 18 assessment of the Avon Longitudinal Study of Parents and Children (ALSPAC; n = 4304 individuals of European descent; 57% female)-predicted alcohol problems earlier in development (age 14) in an independent sample (FinnTwin12; n = 1162; 53% female). We then tested whether environmental factors (parental knowledge and peer deviance) moderated polygenic risk to predict alcohol problems in the FinnTwin12 sample. We found evidence for both polygenic association and for additive polygene-environment interaction. Higher polygenic scores predicted a greater number of alcohol problems (range of Pearson partial correlations 0.07-0.08, all p-values ≤ 0.01). Moreover, genetic influences were significantly more pronounced under conditions of low parental knowledge or high peer deviance (unstandardized regression coefficients (b), p-values (p), and percent of variance (R2) accounted for by interaction terms: b = 1.54, p = 0.02, R2 = 0.33%; b = 0.94, p = 0.04, R2 = 0.30%, respectively). Supplementary set-based analyses indicated that the individual top single nucleotide polymorphisms (SNPs) contributing to the polygenic scores were not individually enriched for gene-environment interaction. Although the magnitude of the observed effects are small, this study illustrates the usefulness of polygenic approaches for understanding the pathways by which measured genetic predispositions come together with environmental factors to predict complex behavioral outcomes.
Fujita, N.; Irani, A.A.; Mecham, D.C.; Sawtelle, G.R.; Moore, K.V.
Standard Problem Five was the prediction of test S-02-8 in the Semiscale Mod-1 experimental program. The Semiscale System is an electrically heated experiment designed to produce data on system performance typical of PWR thermal-hydraulic behavior. The RELAP4 program used for these analyses is a digital computer program developed to predict the thermal-hydraulic behavior of experimental systems and water-cooled nuclear reactors subjected to postulated transients. The RELAP4 predictions of Standard Problem 5 were in good overall agreement with the measured hydraulic data. Fortunately, sufficient experience has been gained with the semiscale break configuration and the critical flow models in RELAP4 to accurately predict the break flow and, hence the overall system depressurization. Generally, the hydraulic predictions are quite good in regions where homogeneity existed. Where separation effects occurred, predictions are not as good, and the data oscillations and error bands are larger. A large discrepancy existed among the measured heater rod temperature data as well as between these data and predicted values. Several potential causes for these differences were considered, and several post test analyses were performed in order to evaluate the discrepancies
Hossam A. Gabbar
Full Text Available Premature failure of a gearbox in a wind turbine poses a high risk of increasing the operational and maintenance costs and decreasing the profit margins. Prognostics and health management (PHM techniques are widely used to access the current health condition of the gearbox and project it in future to predict premature failures. This paper proposes such techniques for predicting gearbox health condition index extracted from the vibration signals emanating from the gearbox. The progression of the monitoring index is predicted using two different prediction techniques, adaptive neuro-fuzzy inference system (ANFIS and nonlinear autoregressive model with exogenous inputs (NARX. The proposed prediction techniques are evaluated through sun-spot data-set and applied on vibration based health related monitoring index calculated through psychoacoustic phenomenon. A comparison is given for their prediction accuracy. The results are helpful in understanding the relationship of machine conditions, the corresponding indicating features, the level of damage/degradation, and their progression.
This report is a primer on the analysis of both linear and nonlinear time series with applications in nuclear safeguards and nonproliferation. We analyze eight simulated and two real time series using both linear and nonlinear modeling techniques. The theoretical treatment is brief but references to pertinent theory are provided. Forecasting is our main goal. However, because our most common approach is to fit models to the data, we also emphasize checking model adequacy by analyzing forecast errors for serial correlation or nonconstant variance
Malkin, Z.; Tissen, V. M.
A new method has been developed at the Siberian Research Institute of Metrology (SNIIM) for highly accurate prediction of UT1 and Pole motion (PM). In this study, a detailed comparison was made of real-time UT1 predictions made in 2006-2011 and PMpredictions made in 2009-2011making use of the SNIIM method with simultaneous predictions computed at the International Earth Rotation and Reference Systems Service (IERS), USNO. Obtained results have shown that proposed method provides better accuracy at different prediction lengths.
Wijnia, Lisette; Loyens, Sofie M. M.; Derous, Eva; Koendjie, Nitaasha S.; Schmidt, Henk G.
This study examines whether tutors (N?=?15) in a problem-based learning curriculum were able to predict students' success in their first year and their entire bachelor programme. Tutors were asked to rate each student in their tutorial group in terms of the chance that this student would successfully finish their first year and the entire…
Jaspers, M.; de Winter, A.F.; de Meer, G.; Stewart, R.E.; Verhulst, F.C.; Ormel, J.; Reijneveld, S.A.
Objective To develop and validate a prediction model for psychosocial problems in preadolescence using data on early developmental factors from routine Preventive Child Healthcare (PCH). Study design The data come from the 1692 participants who take part in the TRacking Adolescents' Individual Lives
Jaspers, M.; De Winter, A.F.; de Meer, G.; Stewart, R.E; Verhulst, F.C.; Ormel, J.; Reijneveld, S.A.
Objective To develop and validate a prediction model for psychosocial problems in preadolescence using data on early developmental factors from routine Preventive Child Healthcare (PCH). Study design The data come from the 1692 participants who take part in the TRacking Adolescents' Individual Lives
This study examined mothers' ability to accurately predict their sons' performance on executive functioning tasks in relation to the child's behavior problems. One-hundred thirteen mothers and their 4-7 year old sons participated. From behind a one-way mirror, mothers watched their sons perform tasks assessing inhibition and planning skills.…
Björn, Piia Maria; Aunola, Kaisa; Nurmi, Jari-Erik
This longitudinal study aimed to investigate the extent to which primary school text comprehension predicts mathematical word problem-solving skills in secondary school among Finnish students. The participants were 224 fourth graders (9-10 years old at the baseline). The children's text-reading fluency, text comprehension and basic calculation…
Parkes, Alison; Waylen, Andrea; Sayal, Kapil; Heron, Jon; Henderson, Marion; Wight, Daniel; Macleod, John
Mental health and school adjustment problems are thought to distinguish early sexual behavior from normative timing (16-18 years), but little is known about how early sexual behavior originates from these problems in middle-childhood. Existing studies do not allow for co-occurring problems, differences in onset and persistence, and there is no information on middle-childhood school adjustment in relationship to early sexual activity. This study examined associations between several middle-childhood problems and early sexual behavior, using a subsample (N = 4,739, 53 % female, 98 % white, mean age 15 years 6 months) from a birth cohort study, the Avon Longitudinal Study of Parents and Children. Adolescents provided information at age 15 on early sexual behavior (oral sex and/or intercourse) and sexual risk-taking, and at age 13 on prior risk involvement (sexual behavior, antisocial behavior and substance use). Information on hyperactivity/inattention, conduct problems, depressive symptoms, peer relationship problems, school dislike and school performance was collected in middle-childhood at Time 1 (6-8 years) and Time 2 (10-11 years). In agreement with previous research, conduct problems predicted early sexual behavior, although this was found only for persistent early problems. In addition, Time 2 school dislike predicted early sexual behavior, while peer relationship problems were protective. Persistent early school dislike further characterized higher-risk groups (early sexual behavior preceded by age 13 risk, or accompanied by higher sexual risk-taking). The study establishes middle-childhood school dislike as a novel risk factor for early sexual behavior and higher-risk groups, and the importance of persistent conduct problems. Implications for the identification of children at risk and targeted intervention are discussed, as well as suggestions for further research.
Modeling advances using Rosetta structure prediction to aid in solving difficult molecular-replacement problems are discussed. Recent work has shown the effectiveness of structure-prediction methods in solving difficult molecular-replacement problems. The Rosetta protein structure modeling suite can aid in the solution of difficult molecular-replacement problems using templates from 15 to 25% sequence identity; Rosetta refinement guided by noisy density has consistently led to solved structures where other methods fail. In this paper, an overview of the use of Rosetta for these difficult molecular-replacement problems is provided and new modeling developments that further improve model quality are described. Several variations to the method are introduced that significantly reduce the time needed to generate a model and the sampling required to improve the starting template. The improvements are benchmarked on a set of nine difficult cases and it is shown that this improved method obtains consistently better models in less running time. Finally, strategies for best using Rosetta to solve difficult molecular-replacement problems are presented and future directions for the role of structure-prediction methods in crystallography are discussed
Hengl, T.; Heuvelink, G.B.M.; Percec Tadic, M.; Pebesma, E.J.
A computational framework to generate daily temperature maps using time-series of publicly available MODIS MOD11A2 product Land Surface Temperature (LST) images (1 km resolution; 8-day composites) is illustrated using temperature measurements from the national network of meteorological stations
Jessica E. Salvatore
Full Text Available Alcohol problems represent a classic example of a complex behavioral outcome that is likely influenced by many genes of small effect. A polygenic approach, which examines aggregate measured genetic effects, can have predictive power in cases where individual genes or genetic variants do not. In the current study, we first tested whether polygenic risk for alcohol problems—derived from genome-wide association estimates of an alcohol problems factor score from the age 18 assessment of the Avon Longitudinal Study of Parents and Children (ALSPAC; n = 4304 individuals of European descent; 57% female—predicted alcohol problems earlier in development (age 14 in an independent sample (FinnTwin12; n = 1162; 53% female. We then tested whether environmental factors (parental knowledge and peer deviance moderated polygenic risk to predict alcohol problems in the FinnTwin12 sample. We found evidence for both polygenic association and for additive polygene-environment interaction. Higher polygenic scores predicted a greater number of alcohol problems (range of Pearson partial correlations 0.07–0.08, all p-values ≤ 0.01. Moreover, genetic influences were significantly more pronounced under conditions of low parental knowledge or high peer deviance (unstandardized regression coefficients (b, p-values (p, and percent of variance (R2 accounted for by interaction terms: b = 1.54, p = 0.02, R2 = 0.33%; b = 0.94, p = 0.04, R2 = 0.30%, respectively. Supplementary set-based analyses indicated that the individual top single nucleotide polymorphisms (SNPs contributing to the polygenic scores were not individually enriched for gene-environment interaction. Although the magnitude of the observed effects are small, this study illustrates the usefulness of polygenic approaches for understanding the pathways by which measured genetic predispositions come together with environmental factors to predict complex behavioral outcomes.
Xu, Xijin; Tang, Qian; Xia, Haiyue; Zhang, Yuling; Li, Weiqiu; Huo, Xia
Chaotic time series prediction based on nonlinear systems showed a superior performance in prediction field. We studied prenatal exposure to polychlorinated biphenyls (PCBs) by chaotic time series prediction using the least squares self-exciting threshold autoregressive (SEATR) model in umbilical cord blood in an electronic waste (e-waste) contaminated area. The specific prediction steps basing on the proposal methods for prenatal PCB exposure were put forward, and the proposed scheme’s validity was further verified by numerical simulation experiments. Experiment results show: 1) seven kinds of PCB congeners negatively correlate with five different indices for birth status: newborn weight, height, gestational age, Apgar score and anogenital distance; 2) prenatal PCB exposed group at greater risks compared to the reference group; 3) PCBs increasingly accumulated with time in newborns; and 4) the possibility of newborns suffering from related diseases in the future was greater. The desirable numerical simulation experiments results demonstrated the feasibility of applying mathematical model in the environmental toxicology field.
Sasmita, Yoga; Darmawan, Gumgum
This research aims to evaluate the performance of forecasting by Fourier Series Analysis (FSA) and Singular Spectrum Analysis (SSA) which are more explorative and not requiring parametric assumption. Those methods are applied to predicting the volume of motorcycle sales in Indonesia from January 2005 to December 2016 (monthly). Both models are suitable for seasonal and trend component data. Technically, FSA defines time domain as the result of trend and seasonal component in different frequencies which is difficult to identify in the time domain analysis. With the hidden period is 2,918 ≈ 3 and significant model order is 3, FSA model is used to predict testing data. Meanwhile, SSA has two main processes, decomposition and reconstruction. SSA decomposes the time series data into different components. The reconstruction process starts with grouping the decomposition result based on similarity period of each component in trajectory matrix. With the optimum of window length (L = 53) and grouping effect (r = 4), SSA predicting testing data. Forecasting accuracy evaluation is done based on Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The result shows that in the next 12 month, SSA has MAPE = 13.54 percent, MAE = 61,168.43 and RMSE = 75,244.92 and FSA has MAPE = 28.19 percent, MAE = 119,718.43 and RMSE = 142,511.17. Therefore, to predict volume of motorcycle sales in the next period should use SSA method which has better performance based on its accuracy.
Full Text Available The primary objective of engineering asset management is to optimize assets service delivery potential and to minimize the related risks and costs over their entire life through the development and application of asset health and usage management in which the health and reliability prediction plays an important role. In real-life situations where an engineering asset operates under dynamic operational and environmental conditions, the lifetime of an engineering asset is generally described as monitored nonlinear time-series data and subject to high levels of uncertainty and unpredictability. It has been proved that application of data mining techniques is very useful for extracting relevant features which can be used as parameters for assets diagnosis and prognosis. In this paper, a tutorial on nonlinear time-series data mining in engineering asset health and reliability prediction is given. Besides that an overview on health and reliability prediction techniques for engineering assets is covered, this tutorial will focus on concepts, models, algorithms, and applications of hidden Markov models (HMMs and hidden semi-Markov models (HSMMs in engineering asset health prognosis, which are representatives of recent engineering asset health prediction techniques.
Voyant, Cyril; Motte, Fabrice; Fouilloy, Alexis; Notton, Gilles; Paoli, Christophe; Nivet, Marie-Laure
Integration of unpredictable renewable energy sources into electrical networks intensifies the complexity of the grid management due to their intermittent and unforeseeable nature. Because of the strong increase of solar power generation the prediction of solar yields becomes more and more important. Electrical operators need an estimation of the future production. For nowcasting and short term forecasting, the usual technics based on machine learning need large historical data sets of good quality during the training phase of predictors. However data are not always available and induce an advanced maintenance of meteorological stations, making the method inapplicable for poor instrumented or isolated sites. In this work, we propose intuitive methodologies based on the Kalman filter use (also known as linear quadratic estimation), able to predict a global radiation time series without the need of historical data. The accuracy of these methods is compared to other classical data driven methods, for different horizons of prediction and time steps. The proposed approach shows interesting capabilities allowing to improve quasi-systematically the prediction. For one to 10 h horizons Kalman model performances are competitive in comparison to more sophisticated models such as ANN which require both consistent historical data sets and computational resources. - Highlights: • Solar radiation forecasting with time series formalism. • Trainless approach compared to machine learning methods. • Very simple method dedicated to solar irradiation forecasting with high accuracy.
Mac Iver, Martha Abele; Messel, Matthew
This study of high school outcomes in the Baltimore City Public Schools builds on substantial prior research on the early warning indicators of dropping out. It sought to investigate whether the same variables that predicted a non-graduation outcome in other urban districts--attendance, behavior problems, and course failure--were also significant…
K.O. Dzhaparidze (Kacha)
textabstractIn this paper a convergence class is characterized for special series associated with Gelfond's interpolation problem (a generalization of the Abel-Goncharov problem) when the interpolation nodes are equidistantly distributed within the interval $[0,1]$. As a result, an expansion is
Beutler, D.E.; Halbleib, J.A.; Knott, D.P.
This paper reports pulse-height distributions in two different types of Ge detectors measured for a variety of medium-energy x-ray bremsstrahlung spectra. These measurements have been compared to predictions using the integrated tiger series (ITS) Monte Carlo electron/photon transport code. In general, the authors find excellent agreement between experiments and predictions using no free parameters. These results demonstrate that the ITS codes can predict the combined bremsstrahlung production and energy deposition with good precision (within measurement uncertainties). The one region of disagreement observed occurs for low-energy (<50 keV) photons using low-energy bremsstrahlung spectra. In this case the ITS codes appear to underestimate the produced and/or absorbed radiation by almost an order of magnitude
Hirschman, Isidore Isaac
This text for advanced undergraduate and graduate students presents a rigorous approach that also emphasizes applications. Encompassing more than the usual amount of material on the problems of computation with series, the treatment offers many applications, including those related to the theory of special functions. Numerous problems appear throughout the book.The first chapter introduces the elementary theory of infinite series, followed by a relatively complete exposition of the basic properties of Taylor series and Fourier series. Additional subjects include series of functions and the app
Barnett, Adrian G; Stephen, Dimity; Huang, Cunrui; Wolkewitz, Martin
Time series data are popular in environmental epidemiology as they make use of the natural experiment of how changes in exposure over time might impact on disease. Many published time series papers have used parameter-heavy models that fully explained the second order patterns in disease to give residuals that have no short-term autocorrelation or seasonality. This is often achieved by including predictors of past disease counts (autoregression) or seasonal splines with many degrees of freedom. These approaches give great residuals, but add little to our understanding of cause and effect. We argue that modelling approaches should rely more on good epidemiology and less on statistical tests. This includes thinking about causal pathways, making potential confounders explicit, fitting a limited number of models, and not over-fitting at the cost of under-estimating the true association between exposure and disease. Copyright © 2017 Elsevier Inc. All rights reserved.
Rosbjerg, Dan; Madsen, Henrik; Rasmussen, Peter Funder
As a generalization of the common assumption of exponential distribution of the exceedances in Partial duration series the generalized Pareto distribution has been adopted. Estimators for the parameters are presented using estimation by both method of moments and probability-weighted moments...... distributions (with physically justified upper limit) the correct exceedance distribution should be applied despite a possible acceptance of the exponential assumption by a test of significance....
We present a deep neural network for a model-free prediction of a chaotic dynamical system from noisy observations. The proposed deep learning model aims to predict the conditional probability distribution of a state variable. The Long Short-Term Memory network (LSTM) is employed to model the nonlinear dynamics and a softmax layer is used to approximate a probability distribution. The LSTM model is trained by minimizing a regularized cross-entropy function. The LSTM model is validated against...
Full Text Available Accurate prediction of water level fluctuation is important in lake management due to its significant impacts in various aspects. This study utilizes four model approaches to predict water levels in the Yuan-Yang Lake (YYL in Taiwan: a three-dimensional hydrodynamic model, an artificial neural network (ANN model (back propagation neural network, BPNN, a time series forecasting (autoregressive moving average with exogenous inputs, ARMAX model, and a combined hydrodynamic and ANN model. Particularly, the black-box ANN model and physically based hydrodynamic model are coupled to more accurately predict water level fluctuation. Hourly water level data (a total of 7296 observations was collected for model calibration (training and validation. Three statistical indicators (mean absolute error, root mean square error, and coefficient of correlation were adopted to evaluate model performances. Overall, the results demonstrate that the hydrodynamic model can satisfactorily predict hourly water level changes during the calibration stage but not for the validation stage. The ANN and ARMAX models better predict the water level than the hydrodynamic model does. Meanwhile, the results from an ANN model are superior to those by the ARMAX model in both training and validation phases. The novel proposed concept using a three-dimensional hydrodynamic model in conjunction with an ANN model has clearly shown the improved prediction accuracy for the water level fluctuation.
Marufuzzaman, M; Reaz, M B I; Ali, M A M; Rahman, L F
The goal of smart homes is to create an intelligent environment adapting the inhabitants need and assisting the person who needs special care and safety in their daily life. This can be reached by collecting the ADL (activities of daily living) data and further analysis within existing computing elements. In this research, a very recent algorithm named sequence prediction via enhanced episode discovery (SPEED) is modified and in order to improve accuracy time component is included. The modified SPEED or M-SPEED is a sequence prediction algorithm, which modified the previous SPEED algorithm by using time duration of appliance's ON-OFF states to decide the next state. M-SPEED discovered periodic episodes of inhabitant behavior, trained it with learned episodes, and made decisions based on the obtained knowledge. The results showed that M-SPEED achieves 96.8% prediction accuracy, which is better than other time prediction algorithms like PUBS, ALZ with temporal rules and the previous SPEED. Since human behavior shows natural temporal patterns, duration times can be used to predict future events more accurately. This inhabitant activity prediction system will certainly improve the smart homes by ensuring safety and better care for elderly and handicapped people.
M R Sumathi
Full Text Available According to World Health Organization, 10-20% of children and adolescents all over the world are experiencing mental disorders. Correct diagnosis of mental disorders at an early stage improves the quality of life of children and avoids complicated problems. Various expert systems using artificial intelligence techniques have been developed for diagnosing mental disorders like Schizophrenia, Depression, Dementia, etc. This study focuses on predicting basic mental health problems of children, like Attention problem, Anxiety problem, Developmental delay, Attention Deficit Hyperactivity Disorder (ADHD, Pervasive Developmental Disorder(PDD, etc. using the machine learning techniques, Bayesian Networks and Fuzzy clustering. The focus of the article is on learning the Bayesian network structure using a novel Fuzzy Clustering Based Bayesian network structure learning framework. The performance of the proposed framework was compared with the other existing algorithms and the experimental results have shown that the proposed framework performs better than the earlier algorithms.
Ramli, Nazirah; Mutalib, Siti Musleha Ab; Mohamad, Daud
Fuzzy time series forecasting model has been proposed since 1993 to cater for data in linguistic values. Many improvement and modification have been made to the model such as enhancement on the length of interval and types of fuzzy logical relation. However, most of the improvement models represent the linguistic term in the form of discrete fuzzy sets. In this paper, fuzzy time series model with data in the form of trapezoidal fuzzy numbers and natural partitioning length approach is introduced for predicting the unemployment rate. Two types of fuzzy relations are used in this study which are first order and second order fuzzy relation. This proposed model can produce the forecasted values under different degree of confidence.
Park, Hyangjin; Kim, Suk Sun
The purpose of the study was to develop and test a model for predicting problem gambling in speculative game users based on Blaszczynski and Nower's pathways model of problem and pathological gambling. The participants were 262 speculative game users recruited from seven speculative gambling places located in Seoul, Gangwon, and Gyeonggi, Korea. They completed a structured self-report questionnaire comprising measures of problem gambling, negative emotions, attentional impulsivity, motor impulsivity, non-planning impulsivity, gambler's fallacy, and gambling self-efficacy. Structural Equation Modeling was used to test the hypothesized model and to examine the direct and indirect effects on problem gambling in speculative game users using SPSS 22.0 and AMOS 20.0 programs. The hypothetical research model provided a reasonable fit to the data. Negative emotions, motor impulsivity, gambler's fallacy, and gambling self-efficacy had direct effects on problem gambling in speculative game users, while indirect effects were reported for negative emotions, motor impulsivity, and gambler's fallacy. These predictors explained 75.2% problem gambling in speculative game users. The findings suggest that developing intervention programs to reduce negative emotions, motor impulsivity, and gambler's fallacy, and to increase gambling self-efficacy in speculative game users are needed to prevent their problem gambling. © 2018 Korean Society of Nursing Science.
Sakkal, Leon A; Rajkowski, Kyle Z; Armen, Roger S
Following insights from recent crystal structures of the muscarinic acetylcholine receptor, binding modes of Positive Allosteric Modulators (PAMs) were predicted under the assumption that PAMs should bind to the extracellular surface of the active state. A series of well-characterized PAMs for adenosine (A 1 R, A 2A R, A 3 R) and muscarinic acetylcholine (M 1 R, M 5 R) receptors were modeled using both rigid and flexible receptor CHARMM-based molecular docking. Studies of adenosine receptors investigated the molecular basis of the probe-dependence of PAM activity by modeling in complex with specific agonist radioligands. Consensus binding modes map common pharmacophore features of several chemical series to specific binding interactions. These models provide a rationalization of how PAM binding slows agonist radioligand dissociation kinetics. M 1 R PAMs were predicted to bind in the analogous M 2 R PAM LY2119620 binding site. The M 5 R NAM (ML-375) was predicted to bind in the PAM (ML-380) binding site with a unique induced-fit receptor conformation. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.
Chen, C P; Wan, J Z
A fast learning algorithm is proposed to find an optimal weights of the flat neural networks (especially, the functional-link network). Although the flat networks are used for nonlinear function approximation, they can be formulated as linear systems. Thus, the weights of the networks can be solved easily using a linear least-square method. This formulation makes it easier to update the weights instantly for both a new added pattern and a new added enhancement node. A dynamic stepwise updating algorithm is proposed to update the weights of the system on-the-fly. The model is tested on several time-series data including an infrared laser data set, a chaotic time-series, a monthly flour price data set, and a nonlinear system identification problem. The simulation results are compared to existing models in which more complex architectures and more costly training are needed. The results indicate that the proposed model is very attractive to real-time processes.
Molecular replacement (MR), a method for solving the crystallographic phase problem using phases derived from a model of the target structure, has proven extremely valuable, accounting for the vast majority of structures solved by X-ray crystallography. However, when the resolution of data is low, or the starting model is very dissimilar to the target protein, solving structures via molecular replacement may be very challenging. In recent years, protein structure prediction methodology has emerged as a powerful tool in model building and model refinement for difficult molecular replacement problems. This chapter describes some of the tools available in Rosetta for model building and model refinement specifically geared toward difficult molecular replacement cases.
Full Text Available This paper presents a modified grey model GMC(1,n for use in systems that involve one dependent system behavior and n-1 relative factors. The proposed model was developed from the conventional GMC(1,n model in order to improve its prediction accuracy by modifying the formula for calculating the background value, the system of parameter estimation, and the model prediction equation. The modified GMC(1,n model was verified by two cases: the study of forecasting CO2 emission in Thailand and forecasting electricity consumption in Thailand. The results demonstrated that the modified GMC(1,n model was able to achieve higher fitting and prediction accuracy compared with the conventional GMC(1,n and D-GMC(1,n models.
Full Text Available Intermittent and ephemeral streams represent more than half of the length of the global river network. Dryland freshwater ecosystems are especially vulnerable to changes in human-related water uses as well as shifts in terrestrial climates. Yet, the description and quantification of patterns of flow permanence in these systems is challenging mostly due to difficulties in instrumentation. Here, we took advantage of existing stream temperature datasets in dryland streams in the northwest Great Basin desert, USA, to extract critical information on climate-sensitive patterns of flow permanence. We used a signal detection technique, Hidden Markov Models (HMMs, to extract information from daily time series of stream temperature to diagnose patterns of stream drying. Specifically, we applied HMMs to time series of daily standard deviation (SD of stream temperature (i.e., dry stream channels typically display highly variable daily temperature records compared to wet stream channels between April and August (2015–2016. We used information from paired stream and air temperature data loggers as well as co-located stream temperature data loggers with electrical resistors as confirmatory sources of the timing of stream drying. We expanded our approach to an entire stream network to illustrate the utility of the method to detect patterns of flow permanence over a broader spatial extent. We successfully identified and separated signals characteristic of wet and dry stream conditions and their shifts over time. Most of our study sites within the entire stream network exhibited a single state over the entire season (80%, but a portion of them showed one or more shifts among states (17%. We provide recommendations to use this approach based on a series of simple steps. Our findings illustrate a successful method that can be used to rigorously quantify flow permanence regimes in streams using existing records of stream temperature.
Arismendi, Ivan; Dunham, Jason B.; Heck, Michael; Schultz, Luke; Hockman-Wert, David
Intermittent and ephemeral streams represent more than half of the length of the global river network. Dryland freshwater ecosystems are especially vulnerable to changes in human-related water uses as well as shifts in terrestrial climates. Yet, the description and quantification of patterns of flow permanence in these systems is challenging mostly due to difficulties in instrumentation. Here, we took advantage of existing stream temperature datasets in dryland streams in the northwest Great Basin desert, USA, to extract critical information on climate-sensitive patterns of flow permanence. We used a signal detection technique, Hidden Markov Models (HMMs), to extract information from daily time series of stream temperature to diagnose patterns of stream drying. Specifically, we applied HMMs to time series of daily standard deviation (SD) of stream temperature (i.e., dry stream channels typically display highly variable daily temperature records compared to wet stream channels) between April and August (2015–2016). We used information from paired stream and air temperature data loggers as well as co-located stream temperature data loggers with electrical resistors as confirmatory sources of the timing of stream drying. We expanded our approach to an entire stream network to illustrate the utility of the method to detect patterns of flow permanence over a broader spatial extent. We successfully identified and separated signals characteristic of wet and dry stream conditions and their shifts over time. Most of our study sites within the entire stream network exhibited a single state over the entire season (80%), but a portion of them showed one or more shifts among states (17%). We provide recommendations to use this approach based on a series of simple steps. Our findings illustrate a successful method that can be used to rigorously quantify flow permanence regimes in streams using existing records of stream temperature.
Farmer, William H.; Archfield, Stacey A.; Over, Thomas M.; Hay, Lauren E.; LaFontaine, Jacob H.; Kiang, Julie E.
Effective and responsible management of water resources relies on a thorough understanding of the quantity and quality of available water. Streamgages cannot be installed at every location where streamflow information is needed. As part of its National Water Census, the U.S. Geological Survey is planning to provide streamflow predictions for ungaged locations. In order to predict streamflow at a useful spatial and temporal resolution throughout the Nation, efficient methods need to be selected. This report examines several methods used for streamflow prediction in ungaged basins to determine the best methods for regional and national implementation. A pilot area in the southeastern United States was selected to apply 19 different streamflow prediction methods and evaluate each method by a wide set of performance metrics. Through these comparisons, two methods emerged as the most generally accurate streamflow prediction methods: the nearest-neighbor implementations of nonlinear spatial interpolation using flow duration curves (NN-QPPQ) and standardizing logarithms of streamflow by monthly means and standard deviations (NN-SMS12L). It was nearly impossible to distinguish between these two methods in terms of performance. Furthermore, neither of these methods requires significantly more parameterization in order to be applied: NN-SMS12L requires 24 regional regressions—12 for monthly means and 12 for monthly standard deviations. NN-QPPQ, in the application described in this study, required 27 regressions of particular quantiles along the flow duration curve. Despite this finding, the results suggest that an optimal streamflow prediction method depends on the intended application. Some methods are stronger overall, while some methods may be better at predicting particular statistics. The methods of analysis presented here reflect a possible framework for continued analysis and comprehensive multiple comparisons of methods of prediction in ungaged basins (PUB
García Marín, Andrés; Turégano Fuentes, Fernando; Cuadrado Ayuso, Marta; Andueza Lillo, Juan Antonio; Cano Ballesteros, Juan Carlos; Pérez López, Mercedes
Fournier's gangrene (FG) is the necrotizing fasciitis of the perineum and genital area and presents a high mortality rate. The aim was to assess prognostic factors for mortality, create a new mortality predictive scale and compare it with previously published scales in patients diagnosed with FG in our Emergency Department. Retrospective analysis study between 1998 and 2012. Of the 59 patients, 44 survived (74%) (S) and 15 died (26%) (D). Significant differences were found in peripheral vasculopathy (S 5 [11%]; D 6 [40%]; P=.023), hemoglobin (S 13; D 11; P=.014), hematocrit (S 37; D 31.4; P=.009), white blood cells (S 17,400; D 23,800; P=.023), serum urea (S 58; D 102; PFournier's gangrene severity index score (FGSIS) (S 4; D 7; P=.002) and Uludag Fournier's Gangrene Severity Index (UFGSI) (S 9; D 13; P=.004). Independent predictive factors were peripheral vasculopathy, serum potassium and severe sepsis criteria, and a model was created with an area under the ROC curve of 0.850 (0.760-0.973), higher than FGSIS (0.746 [0.601-0.981]) and UFGSI (0.760 [0.617-0.904]). FG showed a high mortality rate. Independent predictive factors were peripheral vasculopathy, potassium and severe sepsis criteria creating a predictive model that performed better than those previously described. Copyright © 2014 AEC. Publicado por Elsevier España, S.L.U. All rights reserved.
United Nations Educational, Scientific, and Cultural Organization, Santiago (Chile). Regional Office for Education in Latin America and the Caribbean.
This document discusses the teaching of problem solving in environmental education. From an interdisciplinary viewpoint, this study describes some strategies for teaching that can favor the practice of educational activities oriented toward solving the concrete problems of the surrounding environment. The volume is divided into seven chapters. The…
Cyburt, Richard H; Fields, Brian D; Olive, Keith A
The lithium problem arises from the significant discrepancy between the primordial 7 Li abundance as predicted by big bang nucleosynthesis (BBN) theory and the Wilkinson Microwave Anisotropy Probe (WMAP) baryon density, and the pre-Galactic lithium abundance inferred from observations of metal-poor (Population II) stars. This problem has loomed for the past decade, with a persistent discrepancy of a factor of 2–3 in 7 Li/H. Recent developments have sharpened all aspects of the Li problem. Namely: (1) BBN theory predictions have sharpened due to new nuclear data; in particular, the uncertainty on the reaction rate for 3 He(α,γ) 7 Be has reduced to 7.4%, nearly a factor of 2 tighter than previous determinations. (2) The WMAP five-year data set now yields a cosmic baryon density with an uncertainty reduced to 2.7%. (3) Observations of metal-poor stars have tested for systematic effects. With these, we now find that the BBN+WMAP predicts 7 Li/H = (5.24 −0.67 +0.71 ) × 10 −10 . The central value represents an increase by 23%, most of which is due to the upward shift in the 3 He(α,γ) 7 Be rate. More significant is the reduction in the 7 Li/H uncertainty by almost a factor of 2, tracking the reduction in the 3 He(α,γ) 7 Be error bar. These changes exacerbate the Li problem; the discrepancy is now a factor 2.4 or 4.2σ (from globular cluster stars) to 4.3 or 5.3σ (from halo field stars). Possible resolutions to the lithium problem are briefly reviewed, and key experimental and astronomical measurements highlighted
Gupta, R. K.; Bhunia, A. K.; Roy, D.
In this paper, we have considered the problem of constrained redundancy allocation of series system with interval valued reliability of components. For maximizing the overall system reliability under limited resource constraints, the problem is formulated as an unconstrained integer programming problem with interval coefficients by penalty function technique and solved by an advanced GA for integer variables with interval fitness function, tournament selection, uniform crossover, uniform mutation and elitism. As a special case, considering the lower and upper bounds of the interval valued reliabilities of the components to be the same, the corresponding problem has been solved. The model has been illustrated with some numerical examples and the results of the series redundancy allocation problem with fixed value of reliability of the components have been compared with the existing results available in the literature. Finally, sensitivity analyses have been shown graphically to study the stability of our developed GA with respect to the different GA parameters.
Full Text Available Abstract Background With the development of genome-sequencing technologies, protein sequences are readily obtained by translating the measured mRNAs. Therefore predicting protein-protein interactions from the sequences is of great demand. The reason lies in the fact that identifying protein-protein interactions is becoming a bottleneck for eventually understanding the functions of proteins, especially for those organisms barely characterized. Although a few methods have been proposed, the converse problem, if the features used extract sufficient and unbiased information from protein sequences, is almost untouched. Results In this study, we interrogate this problem theoretically by an optimization scheme. Motivated by the theoretical investigation, we find novel encoding methods for both protein sequences and protein pairs. Our new methods exploit sufficiently the information of protein sequences and reduce artificial bias and computational cost. Thus, it significantly outperforms the available methods regarding sensitivity, specificity, precision, and recall with cross-validation evaluation and reaches ~80% and ~90% accuracy in Escherichia coli and Saccharomyces cerevisiae respectively. Our findings here hold important implication for other sequence-based prediction tasks because representation of biological sequence is always the first step in computational biology. Conclusions By considering the converse problem, we propose new representation methods for both protein sequences and protein pairs. The results show that our method significantly improves the accuracy of protein-protein interaction predictions.
Asomaning, S. [Baker Petrolite, Sugar Land, TX (United States)
A series of test methods have been developed to determine the stability of asphaltenes in crude oils. They were developed due to the high cost of remediating asphaltene deposition in offshore operations. This study described the characteristics of the Oliensis Spot Test, two saturates, aromatics, resins and asphaltenes (SARA)-based screens (the Colloidal Instability Index and Asphaltene-Resin ratio), a solvent titration method with near infrared radiation (NIR) solids detection, and live oil depressurization. Each method is used to predict the stability of asphaltenes in crude oils with different API gravity. A complete description of the test methods was provided along with experimental data. The effectiveness of the different tests in predicting the stability of asphaltenes in crude oils was also assessed. Results indicate that the prediction of a crude oil's tendency towards asphaltene precipitation was more accurate with the Colloidal Instability Index and the solvent titration method. Live oil depressurization proved to be very effective in predicting the stability of asphaltenes for light oils, where most stability tests fail. tabs., figs.
Thériault, Marie-Claude G; Bécue, Jean-Cyprien; Lespérance, Paul; Chouinard, Sylvain; Rouleau, Guy A; Richer, Francois
Chronic tic disorders (TD) are associated with a number of psychological problems such as attention-deficit hyperactivity disorder (ADHD), obsessive-compulsive behavior (OCB), oppositional-defiant disorder (ODD) as well as anxious and depressive symptoms. ODD is often considered a risk factor for many psychological symptoms and recent work suggests that different ODD dimensions show independent predictions of later psychological problems. This study examined the longitudinal predictions between ODD dimensions of Irritability and Defiance and the most frequent comorbidities in TD from childhood to early adulthood. From an initial sample of 135, parent reports were obtained on 58 participants with TD using standard clinical questionnaires and semi-structured interviews. Defiance symptoms decreased from baseline to follow-up whereas Irritability symptoms were more stable over time. In multiple regressions, Irritability in childhood predicted anxiety and OCB in early adulthood while Defiance in childhood predicted ADHD and conduct disorder symptoms in early adulthood. No developmental link was found for depressive symptoms. Results indicate that ODD dimensions are developmentally linked to both internalizing and externalizing adult mental health symptoms in TD. Copyright © 2018. Published by Elsevier B.V.
Rosbjerg, Dan; Madsen, Henrik; Rasmussen, Peter Funder
As a generalization of the common assumption of exponential distribution of the exceedances in Partial duration series the generalized Pareto distribution has been adopted. Estimators for the parameters are presented using estimation by both method of moments and probability-weighted moments......-weighted moments. Maintaining the generalized Pareto distribution as the parent exceedance distribution the T-year event is estimated assuming the exceedances to be exponentially distributed. For moderately long-tailed exceedance distributions and small to moderate sample sizes it is found, by comparing mean...... square errors of the T-year event estimators, that the exponential distribution is preferable to the correct generalized Pareto distribution despite the introduced model error and despite a possible rejection of the exponential hypothesis by a test of significance. For moderately short-tailed exceedance...
Full Text Available It is well established that sleep spindles (bursts of oscillatory brain electrical activity are significant indicators of learning, memory and some disease states. Therefore, many attempts have been made to detect these hallmark patterns automatically. In this pilot investigation, we paid special attention to nonlinear chaotic features of EEG signals (in combination with linear features to investigate the detection and prediction of sleep spindles. These nonlinear features included: Higuchi's, Katz's and Sevcik's Fractal Dimensions, as well as the Largest Lyapunov Exponent and Kolmogorov's Entropy. It was shown that the intensity map of various nonlinear features derived from the constructive interference of spindle signals could improve the detection of the sleep spindles. It was also observed that the prediction of sleep spindles could be facilitated by means of the analysis of these maps. Two well-known classifiers, namely the Multi-Layer Perceptron (MLP and the K-Nearest Neighbor (KNN were used to distinguish between spindle and non-spindle patterns. The MLP classifier produced a~high discriminative capacity (accuracy = 94.93%, sensitivity = 94.31% and specificity = 95.28% with significant robustness (accuracy ranging from 91.33% to 94.93%, sensitivity varying from 91.20% to 94.31%, and specificity extending from 89.79% to 95.28% in separating spindles from non-spindles. This classifier also generated the best results in predicting sleep spindles based on chaotic features. In addition, the MLP was used to find out the best time window for predicting the sleep spindles, with the experimental results reaching 97.96% accuracy.
Fereshteh-Saniee, F.; Barati, F.; Badnava, H.; Fallah Nejad, Kh.
Highlights: ► The exponential model can represent flow behaviors of AZ series Mg alloys very well. ► Strain rate sensitivities of AZ series Mg alloys in compression are nearly the same. ► Effect of zinc element on tensile activation energy is higher than on compressive one. ► Activation energies of AZ80 and AZ81 in tension were greater than in compression. ► Tensile and compressive rate sensitivities of AZ80 are not close to each other. -- Abstract: This paper is concerned with flow behaviors of several magnesium alloys, such as AZ31, AZ80 and AZ81, in tension and compression. The experiments were performed at elevated temperatures and for various strain rates. In order to eliminate the effect of inhomogeneous deformation in tensile and compression tests, the Bridgeman’s and numerical correction factors were respectively employed. A two-section exponential mathematical model was also utilized for prediction of flow stresses of different magnesium alloys in tension and compression. Moreover, based on the compressive flow model proposed, the peak stress and the relevant true strain could be estimated. The true stress and strain of the necking point can also be predicted using the corresponding relations. It was found that the flow behaviors estimated by the exponential flow model were encouragingly in very good agreement with experimental findings.
Waller, Rebecca; Dishion, Thomas J.; Shaw, Daniel S.; Gardner, Frances; Wilson, Melvin N.; Hyde, Luke W.
Callous-unemotional (CU) behavior has been linked to behavior problems in children and adolescents. However, few studies have examined whether CU behavior in "early childhood" predicts behavior problems or CU behavior in "late childhood". This study examined whether indicators of CU behavior at ages 2-4 predicted aggression,…
Harlé, Katia M; Stewart, Jennifer L; Zhang, Shunan; Tapert, Susan F; Yu, Angela J; Paulus, Martin P
Bayesian ideal observer models quantify individuals' context- and experience-dependent beliefs and expectations about their environment, which provides a powerful approach (i) to link basic behavioural mechanisms to neural processing; and (ii) to generate clinical predictors for patient populations. Here, we focus on (ii) and determine whether individual differences in the neural representation of the need to stop in an inhibitory task can predict the development of problem use (i.e. abuse or dependence) in individuals experimenting with stimulants. One hundred and fifty-seven non-dependent occasional stimulant users, aged 18-24, completed a stop-signal task while undergoing functional magnetic resonance imaging. These individuals were prospectively followed for 3 years and evaluated for stimulant use and abuse/dependence symptoms. At follow-up, 38 occasional stimulant users met criteria for a stimulant use disorder (problem stimulant users), while 50 had discontinued use (desisted stimulant users). We found that those individuals who showed greater neural responses associated with Bayesian prediction errors, i.e. the difference between actual and expected need to stop on a given trial, in right medial prefrontal cortex/anterior cingulate cortex, caudate, anterior insula, and thalamus were more likely to exhibit problem use 3 years later. Importantly, these computationally based neural predictors outperformed clinical measures and non-model based neural variables in predicting clinical status. In conclusion, young adults who show exaggerated brain processing underlying whether to 'stop' or to 'go' are more likely to develop stimulant abuse. Thus, Bayesian cognitive models provide both a computational explanation and potential predictive biomarkers of belief processing deficits in individuals at risk for stimulant addiction. © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please
Mocellin, Simone; Thompson, John F; Pasquali, Sandro; Montesco, Maria C; Pilati, Pierluigi; Nitti, Donato; Saw, Robyn P; Scolyer, Richard A; Stretch, Jonathan R; Rossi, Carlo R
To improve selection for sentinel node (SN) biopsy (SNB) in patients with cutaneous melanoma using statistical models predicting SN status. About 80% of patients currently undergoing SNB are node negative. In the absence of conclusive evidence of a SNBassociated survival benefit, these patients may be over-treated. Here, we tested the efficiency of 4 different models in predicting SN status. The clinicopathologic data (age, gender, tumor thickness, Clark level, regression, ulceration, histologic subtype, and mitotic index) of 1132 melanoma patients who had undergone SNB at institutions in Italy and Australia were analyzed. Logistic regression, classification tree, random forest, and support vector machine models were fitted to the data. The predictive models were built with the aim of maximizing the negative predictive value (NPV) and reducing the rate of SNB procedures though minimizing the error rate. After cross-validation logistic regression, classification tree, random forest, and support vector machine predictive models obtained clinically relevant NPV (93.6%, 94.0%, 97.1%, and 93.0%, respectively), SNB reduction (27.5%, 29.8%, 18.2%, and 30.1%, respectively), and error rates (1.8%, 1.8%, 0.5%, and 2.1%, respectively). Using commonly available clinicopathologic variables, predictive models can preoperatively identify a proportion of patients ( approximately 25%) who might be spared SNB, with an acceptable (1%-2%) error. If validated in large prospective series, these models might be implemented in the clinical setting for improved patient selection, which ultimately would lead to better quality of life for patients and optimization of resource allocation for the health care system.
Full Text Available Abstract Background Many common inference problems in computational genetics depend on inferring aspects of the evolutionary history of a data set given a set of observed modern sequences. Detailed predictions of the full phylogenies are therefore of value in improving our ability to make further inferences about population history and sources of genetic variation. Making phylogenetic predictions on the scale needed for whole-genome analysis is, however, extremely computationally demanding. Results In order to facilitate phylogeny-based predictions on a genomic scale, we develop a library of maximum parsimony phylogenies within local regions spanning all autosomal human chromosomes based on Haplotype Map variation data. We demonstrate the utility of this library for population genetic inferences by examining a tree statistic we call 'imperfection,' which measures the reuse of variant sites within a phylogeny. This statistic is significantly predictive of recombination rate, shows additional regional and population-specific conservation, and allows us to identify outlier genes likely to have experienced unusual amounts of variation in recent human history. Conclusion Recent theoretical advances in algorithms for phylogenetic tree reconstruction have made it possible to perform large-scale inferences of local maximum parsimony phylogenies from single nucleotide polymorphism (SNP data. As results from the imperfection statistic demonstrate, phylogeny predictions encode substantial information useful for detecting genomic features and population history. This data set should serve as a platform for many kinds of inferences one may wish to make about human population history and genetic variation.
Lu, Xu; Zhao, Tianzhong
Forecasting is the prerequisite for making scientific decisions, it is based on the past information of the research on the phenomenon, and combined with some of the factors affecting this phenomenon, then using scientific methods to forecast the development trend of the future, it is an important way for people to know the world. This is particularly important in the prediction of financial data, because proper financial data forecasts can provide a great deal of help to financial institutions in their strategic implementation, strategic alignment and risk control. However, the current forecasts of financial data generally use the method of forecast of overall data, which lack of consideration of customer behavior and other factors in the financial data forecasting process, and they are important factors influencing the change of financial data. Based on this situation, this paper analyzed the data of Yuebao, and according to the user's attributes and the operating characteristics, this paper classified 567 users of Yuebao, and made further predicted the data of Yuebao for every class of users, the results showed that the forecasting model in this paper can meet the demand of forecasting.
Full Text Available Abstract Background This study examined the use of the Folstein Mini Mental Status Exam (MMSE and the Clock Drawing Test (CDT in predicting retrospective reports of driving problems among the elderly. The utility of existing scoring systems for the CDT was also examined. Methods Archival chart records of 325 patients of a geriatric outpatient clinic were reviewed, of which 162 had CDT results (including original clock drawings. T-test, correlation, and regression procedures were used to analyze the data. Results Both CDT and MMSE scores were significantly worse among non-drivers than individuals who were currently or recently driving. Among current or recent drivers, scores on both instruments correlated significantly with the total number of reported accidents or near misses, although the magnitude of the respective correlations was small. Only MMSE scores, however, significantly predicted whether or not any accidents or near misses were reported at all. Neither MMSE nor CDT scores predicted unique variance in the regressions. Conclusions The overall results suggest that both the MMSE and CDT have limited utility as potential indicators of driving problems in the elderly. The demonstrated predictive power for these instruments appears to be redundant, such that both appear to assess general cognitive function versus more specific abilities. Furthermore, the lack of robust prediction suggests that neither are sufficient to serve as stand-alone instruments on which to solely base decisions of driving capacity. Rather, individuals who evidence impairment should be provided a more thorough and comprehensive assessment than can be obtained through screening tools.
Véronneau, Marie-Hélène; Dishion, Thomas J
The transition into middle school may be a risky period in early adolescence. In particular, friendships, peer status, and parental monitoring during this developmental period can influence the development of problem behavior. This study examined interrelationships among peer and parenting factors that predict changes in problem behavior over the middle school years. A longitudinal sample (580 boys, 698 girls) was assessed in Grades 6 and 8. Peer acceptance, peer rejection, and their interaction predicted increases in problem behavior. Having high-achieving friends predicted less problem behavior. Parental monitoring predicted less problem behavior in general, but also acted as a buffer for students who were most vulnerable to developing problem behavior on the basis of being well liked by some peers, and also disliked by several others. These findings highlight the importance of studying the family-peer mesosystem when considering risk and resilience in early adolescence, and when considering implications for intervention.
Holahan, Charles J; Brennan, Penny L; Schutte, Kathleen K; Holahan, Carole K; Hixon, J Gregory; Moos, Rudolf H
Research on late-middle-aged and older adults has focused primarily on average level of alcohol consumption, overlooking variability in underlying drinking patterns. The purpose of the present study was to examine the independent contributions of an episodic heavy pattern of drinking versus a high average level of drinking as prospective predictors of drinking problems. The sample comprised 1,107 adults ages 55-65 years at baseline. Alcohol consumption was assessed at baseline, and drinking problems were indexed across 20 years. We used prospective negative binomial regression analyses controlling for baseline drinking problems, as well as for demographic and health factors, to predict the number of drinking problems at each of four follow-up waves (1, 4, 10, and 20 years). Across waves where the effects were significant, a high average level of drinking (coefficients of 1.56, 95% CI [1.24, 1.95]; 1.48, 95% CI [1.11, 1.98]; and 1.85, 95% CI [1.23, 2.79] at 1, 10, and 20 years) and an episodic heavy pattern of drinking (coefficients of 1.61, 95% CI [1.30, 1.99]; 1.61, 95% CI [1.28, 2.03]; and 1.43, 95% CI [1.08, 1.90] at 1, 4, and 10 years) each independently increased the number of drinking problems by more than 50%. Information based only on average consumption underestimates the risk of drinking problems among older adults. Both a high average level of drinking and an episodic heavy pattern of drinking pose prospective risks of later drinking problems among older adults.
Major, Clare S; Paul, Jacob M; Reeve, Robert A
Different math indices can be used to assess math potential at school entry. We evaluated whether standardized math achievement (TEMA-2 performance), core number abilities (dot enumeration, symbolic magnitude comparison), non-verbal intelligence (NVIQ) and visuo-spatial working memory (VSWM), in combination or separately, predicted mental addition problem solving speed over time. We assessed 267 children's TEMA-2, magnitude comparison, dot enumeration, and VSWM abilities at school entry (5 years) and NVIQ at 8 years. Mental addition problem solving speed was assessed at 6, 8, and 10 years. Longitudinal path analysis supported a model in which dot enumeration performance ability profiles and previous mental addition speed predicted future mental addition speed on all occasions, supporting a componential account of math ability. Standardized math achievement and NVIQ predicted mental addition speed at specific time points, while VSWM and symbolic magnitude comparison did not contribute unique variance to the model. The implications of using standardized math achievement and dot enumeration ability to index math learning potential at school entry are discussed.
Sah, D.N.; Venkatesh, D.; Ram Adasan, E.
As a part of an IAEA co-ordinated research program D-COM, a code exercise was organised in May 1983, to investigate the predictive capability of fuel performance codes with respect to transient fission gas release. In this exercise the computer code PROFESS was used to calculate the irradiation performance of fuel pins of the D-COM Blind problem circulated to the participants in the exercise. Calculations of fuel centre temperature, fuel-clad gap conductance, fission gas release during steady state and transient, and fuel restructurings for all the fuel pins were made by PROFESS. Comparison of predictions with experimental findings showed good agreement for several performance parameters. The comparison also revealed some areas where improvement was desired in the modelling of fuel behaviour. A recalculation was carried out for all fuel pins after incorporating modifications and adding additional models in the code. This allowed better agreement between the predicted and observed values of fission gas release in the fuel pins. This paper presents the results of blind calculation, recalculation and sensitivity analysis carried out by PROFESS on the D-COM Blind Problem. The paper also gives a brief description of the models of fission gas release and fuel restructuring used in the calculation. (author)
Manninen, Marko; Lindgren, Maija; Huttunen, Matti; Ebeling, Hanna; Moilanen, Irma; Kalska, Hely; Suvisaari, Jaana; Therman, Sebastian
Delinquent adolescents are a known high-risk group for later criminality. Cognitive deficits correlate with adult criminality, and specific cognitive deficits might predict later criminality in the high-risk adolescents. This study aimed to explore the neuropsychological performance and predictors of adult criminal offending in adolescents with severe behavioural problems. Fifty-three adolescents (33 boys and 20 girls), aged 15-18 years, residing in a reform school due to serious conduct problems, were examined for neuropsychological profile and psychiatric symptoms. Results were compared with a same-age general population control sample, and used for predicting criminality 5 years after the baseline testing. The reform school adolescents' neuropsychological performance was weak on many tasks, and especially on the verbal domain. Five years after the baseline testing, half of the reform school adolescents had obtained a criminal record. Males were overrepresented in both any criminality (75% vs. 10%) and in violent crime (50% vs. 5%). When cognitive variables, psychiatric symptoms and background factors were used as predictors for later offending, low verbal intellectual ability turned out to be the most significant predictor of a criminal record and especially a record of violent crime. Neurocognitive deficits, especially in the verbal and attention domains, are common among delinquent adolescents. Among males, verbal deficits are the best predictors for later criminal offending and violence. Assessing verbal abilities among adolescent population with conduct problems might prove useful as a screening method for inclusion in specific therapies for aggression management.
Perotte, Adler; Ranganath, Rajesh; Hirsch, Jamie S; Blei, David; Elhadad, Noémie
As adoption of electronic health records continues to increase, there is an opportunity to incorporate clinical documentation as well as laboratory values and demographics into risk prediction modeling. The authors develop a risk prediction model for chronic kidney disease (CKD) progression from stage III to stage IV that includes longitudinal data and features drawn from clinical documentation. The study cohort consisted of 2908 primary-care clinic patients who had at least three visits prior to January 1, 2013 and developed CKD stage III during their documented history. Development and validation cohorts were randomly selected from this cohort and the study datasets included longitudinal inpatient and outpatient data from these populations. Time series analysis (Kalman filter) and survival analysis (Cox proportional hazards) were combined to produce a range of risk models. These models were evaluated using concordance, a discriminatory statistic. A risk model incorporating longitudinal data on clinical documentation and laboratory test results (concordance 0.849) predicts progression from state III CKD to stage IV CKD more accurately when compared to a similar model without laboratory test results (concordance 0.733, P<.001), a model that only considers the most recent laboratory test results (concordance 0.819, P < .031) and a model based on estimated glomerular filtration rate (concordance 0.779, P < .001). A risk prediction model that takes longitudinal laboratory test results and clinical documentation into consideration can predict CKD progression from stage III to stage IV more accurately than three models that do not take all of these variables into consideration. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.
Clinard, J.A.; Corum, J.M.; Sartory, W.K.
The results of exemplary inelastic analyses are presented for a series of experimental benchmark problems. Consistent analytical procedures and constitutive relations were used in each of the analyses, and published material behavior data were used in all cases. Two finite-element inelastic computer programs were employed. These programs implement the analysis procedures and constitutive equations for Type 304 stainless steel that are currently used in many analyses of elevated-temperature nuclear reactor system components. The analysis procedures and constitutive relations are briefly discussed, and representative analytical results are presented and compared to the test data. The results that are presented demonstrate the feasibility of performing inelastic analyses, and they are indicative of the general level of agreement that the analyst might expect when using conventional inelastic analysis procedures. (U.S.)
Hoffman, F.W.; Hofer, E.
The International Atomic Energy Agency is preparing a Safety Series publication on practical approaches for evaluating the reliability of the predictions made by environmental radiological assessment models. This publication identifies factors that affect the reliability of these predictions and discusses methods for quantifying uncertainty. Emphasis is placed on understanding the quantity of interest specified by the assessment question and distinguishing between stochastic variability and lack of knowledge about either the true value or the true distribution of values for quantity of interest. Among the many approaches discussed, model testing using independent data sets (model validation) is considered the best method for evaluating the accuracy in model predictions. Analytical and numerical methods for propagating the uncertainties in model parameters are presented and the strengths and weaknesses of model intercomparison exercises are also discussed. It is recognized that subjective judgment is employed throughout the entire modelling process, and quantitative reliability statements must be subjectively obtained when models are applied to different situations from those under which they have been tested. (6 refs.)
The paradoxical nature of results of the mean approach in pooling cross-section and time series data has been identified to be caused by the presence in the normal equations of phenomena such as autocovariances, multicollinear covariances, drift covariances and drift multicollinear covariances. This paper considers the problem of autocorrelation and suggests ways of solving it. (author). 4 refs
This teacher's resource book is a guide to three intermediate texts about Eurasia entitled Population Pressure in Indonesia, Problems of Industrialization in Eurasia, and Power Blocs in Eurasia. The texts are part of the series, Man on the Earth, which probes broad-based issues confronting mankind. The resource book distinguishes 18 major concepts…
Azadeh, A.; Maleki Shoja, B.; Ghanei, S.; Sheikhalishahi, M.
This research investigates a redundancy-scheduling optimization problem for a multi-state series parallel system. The system is a flow shop manufacturing system with multi-state machines. Each manufacturing machine may have different performance rates including perfect performance, decreased performance and complete failure. Moreover, warm standby redundancy is considered for the redundancy allocation problem. Three objectives are considered for the problem: (1) minimizing system purchasing cost, (2) minimizing makespan, and (3) maximizing system reliability. Universal generating function is employed to evaluate system performance and overall reliability of the system. Since the problem is in the NP-hard class of combinatorial problems, genetic algorithm (GA) is used to find optimal/near optimal solutions. Different test problems are generated to evaluate the effectiveness and efficiency of proposed approach and compared to simulated annealing optimization method. The results show the proposed approach is capable of finding optimal/near optimal solution within a very reasonable time. - Highlights: • A redundancy-scheduling optimization problem for a multi-state series parallel system. • A flow shop with multi-state machines and warm standby redundancy. • Objectives are to optimize system purchasing cost, makespan and reliability. • Different test problems are generated and evaluated by a unique genetic algorithm. • It locates optimal/near optimal solution within a very reasonable time
Muhtadie, Luma; Zhou, Qing; Eisenberg, Nancy; Wang, Yun
The additive and interactive relations of parenting styles (authoritative and authoritarian parenting) and child temperament (anger/frustration, sadness, and effortful control) to children’s internalizing problems were examined in a 3.8-year longitudinal study of 425 Chinese children (6 – 9 years) from Beijing. At Wave 1, parents self-reported on their parenting styles, and parents and teachers rated child temperament. At Wave 2, parents, teachers, and children rated children’s internalizing problems. Structural equation modeling indicated that the main effect of authoritative parenting, and the interactions of authoritarian parenting × effortful control and authoritative parenting × anger/frustration (parents’ reports only) prospectively and uniquely predicted internalizing problems. The above results did not vary by child sex and remained significant after controlling for co-occurring externalizing problems. These findings suggest that: a) children with low effortful control may be particularly susceptible to the adverse effect of authoritarian parenting, and b) the benefit of authoritative parenting may be especially important for children with high anger/frustration. PMID:23880383
Muhtadie, Luma; Zhou, Qing; Eisenberg, Nancy; Wang, Yun
The additive and interactive relations of parenting styles (authoritative and authoritarian parenting) and child temperament (anger/frustration, sadness, and effortful control) to children's internalizing problems were examined in a 3.8-year longitudinal study of 425 Chinese children (aged 6-9 years) from Beijing. At Wave 1, parents self-reported on their parenting styles, and parents and teachers rated child temperament. At Wave 2, parents, teachers, and children rated children's internalizing problems. Structural equation modeling indicated that the main effect of authoritative parenting and the interactions of Authoritarian Parenting × Effortful Control and Authoritative Parenting × Anger/Frustration (parents' reports only) prospectively and uniquely predicted internalizing problems. The above results did not vary by child sex and remained significant after controlling for co-occurring externalizing problems. These findings suggest that (a) children with low effortful control may be particularly susceptible to the adverse effect of authoritarian parenting and (b) the benefit of authoritative parenting may be especially important for children with high anger/frustration.
Betts, Kim S; Williams, Gail M; Najman, Jakob M; Scott, James; Alati, Rosa
Exposure to stressful life events during pregnancy has been associated with later schizophrenia in offspring. We explore how prenatal stress and neurodevelopmental abnormalities in childhood associate to increase the risk of later psychotic experiences. Participants from the Mater University Study of Pregnancy (MUSP), an Australian based, pre-birth cohort study were examined for lifetime DSM-IV positive psychotic experiences at 21 years by a semi-structured interview (n = 2227). Structural equation modelling suggested psychotic experiences were best represented with a bifactor model including a general psychosis factor and two group factors. We tested for an association between prenatal stressful life events with the psychotic experiences, and examined for potential moderation and mediation by behaviour problems and cognitive ability in childhood. Prenatal stressful life events predicted psychotic experiences indirectly via behaviour problems at child age five years, and this relationship was not confounded by maternal stressful life events at child age five. We found no statistical evidence for an interaction between prenatal stressful life events and behaviour problems or cognitive ability. The measurable effect of prenatal stressful life events on later psychotic experiences in offspring manifested as behaviour problems by age 5. By identifying early abnormal behavioural development as an intermediary, this finding further confirms the role of prenatal stress to later psychotic disorders. Copyright © 2014 Elsevier Ltd. All rights reserved.
Lemmer, Hermanus Hofmeyr
Cricket players' performances can best be judged after a large number of matches had been played. For test or one-day international (ODI) players, career data are normally used to calculate performance measures. These are normally good indicators of future performances, although various factors influence the performance of a player in a specific match. It is often necessary to judge players' performances based on a small number of scores, e.g. to identify the best players after a short series of matches. The challenge then is to use the best available criteria in order to assess performances as accurately and fairly as possible. In the present study the results of the knock-out phase of an International Cricket Council (ICC) World Cup ODI Series are used to predict the names of the best teams by means of a suitably formulated logistic regression model. Despite using very sparse data, the methods used are reasonably successful. It is also shown that if the same technique is applied to career ratings, very good results are obtained.
The occurrence of high concentrations of tropospheric ozone is considered as one of the most important issues of air management programs. The prediction of dangerous ozone levels for the public health and the environment, along with the assessment of air quality control programs aimed at reducing their severity, is of considerable interest to the scientific community and to policy makers. The chemical mechanisms of tropospheric ozone formation are complex, and highly variable meteorological conditions contribute additionally to difficulties in accurate study and prediction of high levels of ozone. Statistical methods offer an effective approach to understand the problem and eventually improve the ability to predict maximum levels of ozone. In this paper an extreme value model is developed to study data sets that consist of periodically collected maxima of tropospheric ozone concentrations and meteorological variables. The methods are applied to daily tropospheric ozone maxima in Guadalajara City, Mexico, for the period January 1997 to December 2006. The model adjusts the daily rate of change in ozone for concurrent impacts of seasonality and present and past meteorological conditions, which include surface temperature, wind speed, wind direction, relative humidity, and ozone. The results indicate that trend, annual effects, and key meteorological variables along with some interactions explain the variation in daily ozone maxima. Prediction performance assessments yield reasonably good results.
Desherevskii, A. V.; Zhuravlev, V. I.; Nikolsky, A. N.; Sidorin, A. Ya.
Technologies for the analysis of time series with gaps are considered. Some algorithms of signal extraction (purification) and evaluation of its characteristics, such as rhythmic components, are discussed for series with gaps. Examples are given for the analysis of data obtained during long-term observations at the Garm geophysical test site and in other regions. The technical solutions used in the WinABD software are considered to most efficiently arrange the operation of relevant algorithms in the presence of observational defects.
Jezior, Kristen L; McKenzie, Meghan E; Lee, Steve S
Although narcissism and callous-unemotional (CU) traits are separable facets of psychopathy, their independent prediction of conduct problems (CP) among young children is not well known. In addition, above-average IQ was central to the original conceptualization of psychopathy, yet IQ is typically inversely associated with youth CP. We examined narcissism and CU traits as independent and prospective predictors of oppositional defiant disorder (ODD), conduct disorder (CD), and youth self-reported antisocial behavior, as well as their moderation by IQ. At baseline, parents and teachers separately rated narcissism and CU traits in 188 6-to-10-year-old children (47.9% non-White; 69.1% male; M = 7.34 years, SD = 1.09) with (n = 99) and without (n = 89) attention-deficit/hyperactivity disorder (ADHD). Approximately 2 years later, parents and teachers separately rated youth ODD and CD symptoms, and youth self-reported antisocial behavior. With control of baseline ADHD and ODD/CD symptoms, narcissism and CU traits independently and positively predicted ODD and CD symptoms at follow-up. IQ did not moderate any CP predictions from baseline narcissism or CU traits. These preliminary findings suggest that individual differences in narcissism and CU traits, even relatively early in development, are uniquely associated with emergent CP. Findings are considered within a developmental framework and the multiple pathways underlying the heterogeneity of CP are discussed.
Diulio, Andrea R; Cero, Ian; Witte, Tracy K; Correia, Christopher J
The present study investigated the role specific types of alcohol-related problems and life satisfaction play in predicting motivation to change alcohol use. Participants were 548 college students mandated to complete a brief intervention following an alcohol-related policy violation. Using hierarchical multiple regression, we tested for the presence of interaction and quadratic effects on baseline data collected prior to the intervention. A significant interaction indicated that the relationship between a respondent's personal consequences and his/her motivation to change differs depending upon the level of concurrent social consequences. Additionally quadratic effects for abuse/dependence symptoms and life satisfaction were found. The quadratic probes suggest that abuse/dependence symptoms and poor life satisfaction are both positively associated with motivation to change for a majority of the sample; however, the nature of these relationships changes for participants with more extreme scores. Results support the utility of using a multidimensional measure of alcohol related problems and assessing non-linear relationships when assessing predictors of motivation to change. The results also suggest that the best strategies for increasing motivation may vary depending on the types of alcohol-related problems and level of life satisfaction the student is experiencing and highlight potential directions for future research. Copyright © 2014. Published by Elsevier Ltd.
Full Text Available Representing the uncertainties with a set of scenarios, the optimization problem resulting from a robust nonlinear model predictive control (NMPC strategy at each sampling instance can be viewed as a large-scale stochastic program. This paper solves these optimization problems using the parallel Schur complement method developed to solve stochastic programs on distributed and shared memory machines. The control strategy is illustrated with a case study of a multidimensional unseeded batch crystallization process. For this application, a robust NMPC based on min–max optimization guarantees satisfaction of all state and input constraints for a set of uncertainty realizations, and also provides better robust performance compared with open-loop optimal control, nominal NMPC, and robust NMPC minimizing the expected performance at each sampling instance. The performance of robust NMPC can be improved by generating optimization scenarios using Bayesian inference. With the efficient parallel solver, the solution time of one optimization problem is reduced from 6.7 min to 0.5 min, allowing for real-time application.
Tan, Yaoyuan V; Elliott, Michael R; Flannagan, Carol A C
As connected autonomous vehicles (CAVs) enter the fleet, there will be a long period when these vehicles will have to interact with human drivers. One of the challenges for CAVs is that human drivers do not communicate their decisions well. Fortunately, the kinematic behavior of a human-driven vehicle may be a good predictor of driver intent within a short time frame. We analyzed the kinematic time series data (e.g., speed) for a set of drivers making left turns at intersections to predict whether the driver would stop before executing the turn. We used principal components analysis (PCA) to generate independent dimensions that explain the variation in vehicle speed before a turn. These dimensions remained relatively consistent throughout the maneuver, allowing us to compute independent scores on these dimensions for different time windows throughout the approach to the intersection. We then linked these PCA scores to whether a driver would stop before executing a left turn using the random intercept Bayesian additive regression trees. Five more road and observable vehicle characteristics were included to enhance prediction. Our model achieved an area under the receiver operating characteristic curve (AUC) of 0.84 at 94m away from the center of an intersection and steadily increased to 0.90 by 46m away from the center of an intersection. Copyright © 2017 Elsevier Ltd. All rights reserved.
Mehrdad Mirsanjari, Mir; Mohammadyari, Fatemeh
Underground water is regarded as considerable water source which is mainly available in arid and semi arid with deficient surface water source. Forecasting of hydrological variables are suitable tools in water resources management. On the other hand, time series concepts is considered efficient means in forecasting process of water management. In this study the data including qualitative parameters (electrical conductivity and sodium adsorption ratio) of 17 underground water wells in Mehran Plain has been used to model the trend of parameters change over time. Using determined model, the qualitative parameters of groundwater is predicted for the next seven years. Data from 2003 to 2016 has been collected and were fitted by AR, MA, ARMA, ARIMA and SARIMA models. Afterward, the best model is determined using information criterion or Akaike (AIC) and correlation coefficient. After modeling parameters, the map of agricultural land use in 2016 and 2023 were generated and the changes between these years were studied. Based on the results, the average of predicted SAR (Sodium Adsorption Rate) in all wells in the year 2023 will increase compared to 2016. EC (Electrical Conductivity) average in the ninth and fifteenth holes and decreases in other wells will be increased. The results indicate that the quality of groundwater for Agriculture Plain Mehran will decline in seven years.
Hummers-Pradier, Eva; Beyer, Martin; Chevallier, Patrick; Eilat-Tsanani, Sophia; Lionis, Christos; Peremans, Lieve; Petek, Davorina; Rurik, Imre; Soler, Jean Karl; Stoffers, Henri Ejh; Topsever, Pinar; Ungan, Mehmet; van Royen, Paul
The 'Research Agenda for General Practice/Family Medicine and Primary Health Care in Europe' summarizes the evidence relating to the core competencies and characteristics of the Wonca Europe definition of GP/FM, and its implications for general practitioners/family doctors, researchers and policy makers. The European Journal of General Practice publishes a series of articles based on this document. The previous articles presented background, objectives, and methodology, as well results on 'primary care management' and 'community orientation' and the person-related core competencies of GP/FM. This article reflects on the general practitioner's 'specific problem solving skills'. These include decision making on diagnosis and therapy of specific diseases, accounting for the properties of primary care, but also research questions related to quality management and resource use, shared decision making, or professional education and development. Clinical research covers most specific diseases, but often lacks pragmatism and primary care relevance. Quality management is a stronghold of GP/FM research. Educational interventions can be effective when well designed for a specific setting and situation. However, their message that 'usual care' by general practitioners is insufficient may be problematic. GP and their patients need more research into diagnostic reasoning with a step-wise approach to increase predictive values in a setting characterized by uncertainty and low prevalence of specific diseases. Pragmatic comparative effectiveness studies of new and established drugs or non-pharmaceutical therapy are needed. Multi-morbidity and complexity should be addressed. Studies on therapy, communication strategies and educational interventions should consider impact on health and sustainability of effects.
Miranian, A; Abdollahzade, M
Local modeling approaches, owing to their ability to model different operating regimes of nonlinear systems and processes by independent local models, seem appealing for modeling, identification, and prediction applications. In this paper, we propose a local neuro-fuzzy (LNF) approach based on the least-squares support vector machines (LSSVMs). The proposed LNF approach employs LSSVMs, which are powerful in modeling and predicting time series, as local models and uses hierarchical binary tree (HBT) learning algorithm for fast and efficient estimation of its parameters. The HBT algorithm heuristically partitions the input space into smaller subdomains by axis-orthogonal splits. In each partitioning, the validity functions automatically form a unity partition and therefore normalization side effects, e.g., reactivation, are prevented. Integration of LSSVMs into the LNF network as local models, along with the HBT learning algorithm, yield a high-performance approach for modeling and prediction of complex nonlinear time series. The proposed approach is applied to modeling and predictions of different nonlinear and chaotic real-world and hand-designed systems and time series. Analysis of the prediction results and comparisons with recent and old studies demonstrate the promising performance of the proposed LNF approach with the HBT learning algorithm for modeling and prediction of nonlinear and chaotic systems and time series.
Lewis, Gary J.; Asbury, Kathryn; Plomin, Robert
Background: Childhood behavior problems predict subsequent educational achievement; however, little research has examined the etiology of these links using a longitudinal twin design. Moreover, it is unknown whether genetic and environmental innovations provide incremental prediction for educational achievement from childhood to adolescence.…
Burger, Huibert; Boks, Marco P.; Hartman, Catharina A.; Aukes, Maartje F.; Verhulst, Frank C.; Ormel, Johan; Reijneveld, Sijmen A.
OBJECTIVE: To construct a risk score for adolescent mental health problems among children, using parental data only and without potentially stigmatizing mental health items. METHODS: We prospectively derived a prediction model for mental health problems at age 16 using data from parent report on
Cacuci, Dan Gabriel
Highlights: • Predictive Modeling of Coupled Multi-Physics Systems (PM_CMPS) methodology is used. • Impact of measurements for reducing predicted uncertainties is highlighted. • Presented thermal-hydraulics benchmark illustrates generally applicable concepts. - Abstract: This work presents the application of the “Predictive Modeling of Coupled Multi-Physics Systems” (PM_CMPS) methodology conceived by Cacuci (2014) to a “test-section benchmark” problem in order to quantify the impact of measurements for reducing the uncertainties in the conceptual design of a proposed experimental facility aimed at investigating the thermal-hydraulics characteristics expected in the conceptual design of the G4M reactor (GEN4ENERGY, 2012). This “test-section benchmark” simulates the conditions experienced by the hottest rod within the conceptual design of the facility's test section, modeling the steady-state conduction in a rod heated internally by a cosinus-like heat source, as typically encountered in nuclear reactors, and cooled by forced convection to a surrounding coolant flowing along the rod. The PM_CMPS methodology constructs a prior distribution using all of the available computational and experimental information, by relying on the maximum entropy principle to maximize the impact of all available information and minimize the impact of ignorance. The PM_CMPS methodology then constructs the posterior distribution using Bayes’ theorem, and subsequently evaluates it via saddle-point methods to obtain explicit formulas for the predicted optimal temperature distributions and predicted optimal values for the thermal-hydraulics model parameters that characterized the test-section benchmark. In addition, the PM_CMPS methodology also yields reduced uncertainties for both the model parameters and responses. As a general rule, it is important to measure a quantity consistently with, and more accurately than, the information extant prior to the measurement. For
Full Text Available -scale structure to guarantee the numerical accuracy of solution. In the present paper the authors propose to use a novel method of solution of the Helmholtz integral equation, which is based on expansion of the integrands in double Fourier series. The main...
Prasetyo, S. Y. J.; Hartomo, K. D.
The Spatial Plan of the Province of Central Java 2009-2029 identifies that most regencies or cities in Central Java Province are very vulnerable to landslide disaster. The data are also supported by other data from Indonesian Disaster Risk Index (In Indonesia called Indeks Risiko Bencana Indonesia) 2013 that suggest that some areas in Central Java Province exhibit a high risk of natural disasters. This research aims to develop an application architecture and analysis methodology in GIS to predict and to map rainfall distribution. We propose our GIS architectural application of “Multiplatform Architectural Spatiotemporal” and data analysis methods of “Triple Exponential Smoothing” and “Spatial Interpolation” as our significant scientific contribution. This research consists of 2 (two) parts, namely attribute data prediction using TES method and spatial data prediction using Inverse Distance Weight (IDW) method. We conduct our research in 19 subdistricts in the Boyolali Regency, Central Java Province, Indonesia. Our main research data is the biweekly rainfall data in 2000-2016 Climatology, Meteorology, and Geophysics Agency (In Indonesia called Badan Meteorologi, Klimatologi, dan Geofisika) of Central Java Province and Laboratory of Plant Disease Observations Region V Surakarta, Central Java. The application architecture and analytical methodology of “Multiplatform Architectural Spatiotemporal” and spatial data analysis methodology of “Triple Exponential Smoothing” and “Spatial Interpolation” can be developed as a GIS application framework of rainfall distribution for various applied fields. The comparison between the TES and IDW methods show that relative to time series prediction, spatial interpolation exhibit values that are approaching actual. Spatial interpolation is closer to actual data because computed values are the rainfall data of the nearest location or the neighbour of sample values. However, the IDW’s main weakness is that some
Wangdi, Kinley; Singhasivanon, Pratap; Silawan, Tassanee; Lawpoolsri, Saranath; White, Nicholas J; Kaewkungwal, Jaranit
Malaria still remains a public health problem in some districts of Bhutan despite marked reduction of cases in last few years. To strengthen the country's prevention and control measures, this study was carried out to develop forecasting and prediction models of malaria incidence in the endemic districts of Bhutan using time series and ARIMAX. This study was carried out retrospectively using the monthly reported malaria cases from the health centres to Vector-borne Disease Control Programme (VDCP) and the meteorological data from Meteorological Unit, Department of Energy, Ministry of Economic Affairs. Time series analysis was performed on monthly malaria cases, from 1994 to 2008, in seven malaria endemic districts. The time series models derived from a multiplicative seasonal autoregressive integrated moving average (ARIMA) was deployed to identify the best model using data from 1994 to 2006. The best-fit model was selected for each individual district and for the overall endemic area was developed and the monthly cases from January to December 2009 and 2010 were forecasted. In developing the prediction model, the monthly reported malaria cases and the meteorological factors from 1996 to 2008 of the seven districts were analysed. The method of ARIMAX modelling was employed to determine predictors of malaria of the subsequent month. It was found that the ARIMA (p, d, q) (P, D, Q)s model (p and P representing the auto regressive and seasonal autoregressive; d and D representing the non-seasonal differences and seasonal differencing; and q and Q the moving average parameters and seasonal moving average parameters, respectively and s representing the length of the seasonal period) for the overall endemic districts was (2,1,1)(0,1,1)12; the modelling data from each district revealed two most common ARIMA models including (2,1,1)(0,1,1)12 and (1,1,1)(0,1,1)12. The forecasted monthly malaria cases from January to December 2009 and 2010 varied from 15 to 82 cases in 2009
Arov, Damir Z
This monograph deals primarily with the prediction of vector valued stochastic processes that are either weakly stationary, or have weakly stationary increments, from finite segments of their past. The main focus is on the analytic counterpart of these problems, which amounts to computing projections onto subspaces of a Hilbert space of p x 1 vector valued functions with an inner product that is defined in terms of the p x p matrix valued spectral density of the process. The strategy is to identify these subspaces as vector valued de Branges spaces and then to express projections in terms of the reproducing kernels of these spaces and/or in terms of a generalized Fourier transform that is obtained from the solution of an associated inverse spectral problem. Subsequently, the projection of the past onto the future and the future onto the past is interpreted in terms of the range of appropriately defined Hankel operators and their adjoints, and, in the last chapter, assorted computations are carried out for rat...
Rhodes, Jessica D; Colder, Craig R; Trucco, Elisa M; Speidel, Carolyn; Hawk, Larry W; Lengua, Liliana J; Das Eiden, Rina; Wieczorek, William
A large literature suggests associations between self-regulation and motivation and adolescent problem behavior; however, this research has mostly pitted these constructs against one another or tested them in isolation. Following recent neural-systems based theories (e.g., Ernst & Fudge, 2009 ), the present study investigated the interactions between self-regulation and approach and avoidance motivation prospectively predicting delinquency and depressive symptoms in early adolescence. The community sample included 387 adolescents aged 11 to 13 years old (55% female; 17% minority). Laboratory tasks were used to assess self-regulation and approach and avoidance motivation, and adolescent self-reports were used to measure depressive symptoms and delinquency. Analyses suggested that low levels of approach motivation were associated with high levels of depressive symptoms, but only at high levels of self-regulation (p = .01). High levels of approach were associated with high levels of rule breaking, but only at low levels of self-regulation (p theories that posit integration of motivational and self-regulatory individual differences via moderational models to understand adolescent problem behavior.
Lewis, Gary J; Asbury, Kathryn; Plomin, Robert
Childhood behavior problems predict subsequent educational achievement; however, little research has examined the etiology of these links using a longitudinal twin design. Moreover, it is unknown whether genetic and environmental innovations provide incremental prediction for educational achievement from childhood to adolescence. We examined genetic and environmental influences on parental ratings of behavior problems across childhood (age 4) and adolescence (ages 12 and 16) as predictors of educational achievement at age 16 using a longitudinal classical twin design. Shared-environmental influences on anxiety, conduct problems, and peer problems at age 4 predicted educational achievement at age 16. Genetic influences on the externalizing behaviors of conduct problems and hyperactivity at age 4 predicted educational achievement at age 16. Moreover, novel genetic and (to a lesser extent) nonshared-environmental influences acting on conduct problems and hyperactivity emerged at ages 12 and 16, adding to the genetic prediction from age 4. These findings demonstrate that genetic and shared-environmental factors underpinning behavior problems in early childhood predict educational achievement in midadolescence. These findings are consistent with the notion that early-childhood behavior problems reflect the initiation of a life-course persistent trajectory with concomitant implications for social attainment. However, we also find evidence that genetic and nonshared-environment innovations acting on behavior problems have implications for subsequent educational achievement, consistent with recent work arguing that adolescence represents a sensitive period for socioaffective development. © 2016 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for Child and Adolescent Mental Health.
van Horik, Jayden O; Madden, Joah R
Rates of innovative foraging behaviours and success on problem-solving tasks are often used to assay differences in cognition, both within and across species. Yet the cognitive features of some problem-solving tasks can be unclear. As such, explanations that attribute cognitive mechanisms to individual variation in problem-solving performance have revealed conflicting results. We investigated individual consistency in problem-solving performances in captive-reared pheasant chicks, Phasianus colchicus , and addressed whether success depends on cognitive processes, such as trial-and-error associative learning, or whether performances may be driven solely via noncognitive motivational mechanisms, revealed through subjects' willingness to approach, engage with and persist in their interactions with an apparatus, or via physiological traits such as body condition. While subjects' participation and success were consistent within the same problems and across similar tasks, their performances were inconsistent across different types of task. Moreover, subjects' latencies to approach each test apparatus and their attempts to access the reward were not repeatable across trials. Successful individuals did not improve their performances with experience, nor were they consistent in their techniques in repeated presentations of a task. However, individuals that were highly motivated to enter the experimental chamber were more likely to participate. Successful individuals were also faster to approach each test apparatus and more persistent in their attempts to solve the tasks than unsuccessful individuals. Our findings therefore suggest that individual differences in problem-solving success can arise from inherent motivational differences alone and hence be achieved without inferring more complex cognitive processes.
Becker, Stephen P; Langberg, Joshua M; Evans, Steven W
Children and adolescents with attention-deficit/hyperactivity disorder (ADHD) experience high rates of sleep problems and are also at increased risk for experiencing comorbid mental health problems. This study provides an initial examination of the 1-year prospective association between sleep problems and comorbid symptoms in youth diagnosed with ADHD. Participants were 81 young adolescents (75 % male) carefully diagnosed with ADHD and their parents. Parents completed measures of their child's sleep problems and ADHD symptoms, oppositional defiant disorder (ODD) symptoms, and general externalizing behavior problems at baseline (M age = 12.2) and externalizing behaviors were assessed again 1 year later. Adolescents completed measures of anxiety and depression at both time-points. Medication use was not associated with sleep problems or comorbid psychopathology symptoms. Regression analyses indicated that, above and beyond demographic characteristics, ADHD symptom severity, and initial levels of comorbidity, sleep problems significantly predicted greater ODD symptoms, general externalizing behavior problems, and depressive symptoms 1 year later. Sleep problems were not concurrently or prospectively associated with anxiety. Although this study precludes making causal inferences, it does nonetheless provide initial evidence of sleep problems predicting later comorbid externalizing behaviors and depression symptoms in youth with ADHD. Additional research is needed with larger samples and multiple time-points to further examine the interrelations of sleep problems and comorbidity.
In a series of critical assembles in SNEAK physics parameters of interest for the prototype fast reactor SNR-300 were investigated and compared to the results of calculations. Since a complete mock-up of the SNR-300 was not possible with the material supply available the measurements were performed in three different assemblies, each being adapted to the investigation of a particular set of problems. Work was concentrated on the following quantities: criticality, breeding ratio, Na-void effect, control rod worths and power distribution. The calculation were performed using the diffusion and transport methods available at KFK and as a data basis the KFKINER cross section set. Detailed descriptions of the assemblies, the majority of the results and extensive discussions of the experimental and calculational methods used can be found in separate KFK reports about each assembly which were already published. This report contains a summary of the results for each quantity investigated including a basic account of the methods used, and an evaluation of the significance of these data for the prediction of parameters of the SNR-300. (orig.) 891 RW [de
McAlpine, Donna D; McCreedy, Ellen; Alang, Sirry
Self-rated health is a valid measure of health that predicts quality of life, morbidity, and mortality. Its predictive value reflects a conceptualization of health that goes beyond a traditional medical model. However, less is known about self-rated mental health (SRMH). Using data from the Medical Expenditure Panel Survey ( N = 2,547), we examine how rating your mental health as good-despite meeting criteria for a mental health problem-predicts outcomes. We found that 62% of people with a mental health problem rated their mental health positively. Persons who rated their mental health as good (compared to poor) had 30% lower odds of having a mental health problem at follow-up. Even without treatment, persons with a mental health problem did better if they perceived their mental health positively. SRMH might comprise information beyond the experience of symptoms. Understanding the unobserved information individuals incorporate into SRMH will help us improve screening and treatment interventions.
Sastre-Garau, X; Coindre, J M; Leroyer, A; Terrier, P; Ollivier, L; Stöckle, E; Bonichon, F; Collin, F; Le Doussal, V; Contesso, G; Vilain, M O; Jacquemier, J; Nguyen, B B
In order to specify the indications for conservative surgery and preoperative therapeutic approaches of soft tissues sarcomas (STS), we looked for the clinico-pathological parameters associated with the failure to obtain a complete removal (CRm) of the tumor. We retrospectively analyzed a series of 592 cases of primary non-metastatic STS. Surgery was performed in 495 cases as a primary treatment and in 88 cases after chemo- or radiotherapy. Nine patients were treated by chemotherapy-radiotherapy. In a univariate analysis, 20 parameters were tested for their association with CRm. A multivariate analysis was then used to define the independent parameters linked to the achievement of a CRm. In the univariate analysis, 15 parameters were found to be linked to the achievement of a CRm. Three of them proved to be independent in the multivariate analysis: T in the TNM classification, tumor location, and tumor necrosis. By the combination of these risk factors, four groups of patients were defined, with respective rates of CRm of 97% (no factor), 95% (one factor), 70% (two factors), and 48% (three factors). The achievement of a CRm after surgery of STS depends not only on the accessibility of the lesion, but also on tumor aggressiveness, a reflection of which is necrosis. The detection of necrosis by imaging procedures may thus help predicting the resectability of tumors and defining the indications for neoadjuvant therapies, likely to broaden the use of conservative surgery.
Zhong Jian; Dong Gang; Sun Yimei; Zhang Zhaoyang; Wu Yuqin
The present work reports the development of nonlinear time series prediction method of genetic algorithm (GA) with singular spectrum analysis (SSA) for forecasting the surface wind of a point station in the South China Sea (SCS) with scatterometer observations. Before the nonlinear technique GA is used for forecasting the time series of surface wind, the SSA is applied to reduce the noise. The surface wind speed and surface wind components from scatterometer observations at three locations in the SCS have been used to develop and test the technique. The predictions have been compared with persistence forecasts in terms of root mean square error. The predicted surface wind with GA and SSA made up to four days (longer for some point station) in advance have been found to be significantly superior to those made by persistence model. This method can serve as a cost-effective alternate prediction technique for forecasting surface wind of a point station in the SCS basin. (paper)
Mikami, Amori Yee; Szwedo, David E; Allen, Joseph P; Evans, Meredyth A; Hare, Amanda L
This study examined online communication on social networking web pages in a longitudinal sample of 92 youths (39 male, 53 female). Participants' social and behavioral adjustment was assessed when they were ages 13-14 years and again at ages 20-22 years. At ages 20-22 years, participants' social networking website use and indicators of friendship quality on their web pages were coded by observers. Results suggested that youths who had been better adjusted at ages 13-14 years were more likely to be using social networking web pages at ages 20-22 years, after statistically controlling for age, gender, ethnicity, and parental income. Overall, youths' patterns of peer relationships, friendship quality, and behavioral adjustment at ages 13-14 years and at ages 20-22 years predicted similar qualities of interaction and problem behavior on their social networking websites at ages 20-22 years. Findings are consistent with developmental theory asserting that youths display cross-situational continuity in their social behaviors and suggest that the conceptualization of continuity may be extended into the online domain. Copyright 2009 APA, all rights reserved.
Vladimir V. Kozoderov
Full Text Available Developing the general statements of the proposed global change theory, outlined in Part 1 of the publication, Kolmogorov's probability space is used to study properties of information measures (unconditional, joint and conditional entropies, information divergence, mutual information, etc.. Sets of elementary events, the specified algebra of their sub-sets and probability measures for the algebra are composite parts of the space. The information measures are analyzed using the mathematical expectance operator and the adequacy between an additive function of sets and their equivalents in the form of the measures. As a result, explanations are given to multispectral satellite imagery visualization procedures using Markov's chains of random variables represented by pixels of the imagery. The proposed formalism of the information measures application enables to describe the natural targets complexity by syntactically governing probabilities. Asserted as that of signal/noise ratios finding for anomalies of natural processes, the predictability problem is solved by analyses of temporal data sets of related measurements for key regions and their background within contextually coherent structures of natural targets and between particular boundaries of the structures.
Nahas, Nabil; Nourelfath, Mustapha; Ait-Kadi, Daoud
The redundancy allocation problem (RAP) is a well known NP-hard problem which involves the selection of elements and redundancy levels to maximize system reliability given various system-level constraints. As telecommunications and internet protocol networks, manufacturing and power systems are becoming more and more complex, while requiring short developments schedules and very high reliability, it is becoming increasingly important to develop efficient solutions to the RAP. This paper presents an efficient algorithm to solve this reliability optimization problem. The idea of a heuristic approach design is inspired from the ant colony meta-heuristic optimization method and the degraded ceiling local search technique. Our hybridization of the ant colony meta-heuristic with the degraded ceiling performs well and is competitive with the best-known heuristics for redundancy allocation. Numerical results for the 33 test problems from previous research are reported and compared. The solutions found by our approach are all better than or are in par with the well-known best solutions
Campbell, Robert E.; And Others
This instructor's handbook is part of a career development unit on coping in the world of work, designed to assist students in developing coping strategies to deal with work-entry and job adjustment problems. (Other components of the unit--student guide, handout/transparency masters, and filmstrip/sound cassette programs--are available…
Carlson, Dennis L.
"The Education of Eros: is the first and only comprehensive history of sexuality education and the "problem" of adolescent sexuality from the mid-20th century to the beginning of the 21st. It explores how professional health educators, policy makers, and social and religious conservatives differed in their approaches, and battled over what gets…
Durham, Robert; And Others
This document presents discussions of four problems that may be found in the workplace. "AIDS in the Workplace: Employee Safety and Rights" (Robert Durham and Burton White) explores issues of employee/employer relationship and the issue of Acquired immune deficiency syndrome (AIDS) in the workplace. It concludes that the management of the AIDS…
Evan Brooks; Valerie Thomas; Wynne Randolph; John Coulston
With the advent of free Landsat data stretching back decades, there has been a surge of interest in utilizing remotely sensed data in multitemporal analysis for estimation of biophysical parameters. Such analysis is confounded by cloud cover and other image-specific problems, which result in missing data at various aperiodic times of the year. While there is a wealth...
Véronneau, Marie-Hélène; Dishion, Thomas J.
The transition into middle school may be a risky period in early adolescence. In particular, friendships, peer status, and parental monitoring during this developmental period can influence the development of problem behavior. This study examined interrelationships among peer and parenting factors that predict changes in problem behavior over the middle school years. A longitudinal sample (580 boys, 698 girls) was assessed in Grades 6 and 8. Peer acceptance, peer rejection, and their interact...
Al-Shahib, A.; Breitling, R.; Gilbert, D.
Abstract: When the standard approach to predict protein function by sequence homology fails, other alternative methods can be used that require only the amino acid sequence for predicting function. One such approach uses machine learning to predict protein function directly from amino acid sequence
Rasmussen, J.; Taylor, J.R.
The basis for plant operator reliability evaluation is described. Principles for plant design, necessary to permit reliability evaluation, are outlined. Five approaches to the plant operator reliability problem are described. Case stories, illustrating operator reliability problems, are given. (author)
The theory of Fourier series deals with periodic functions. By a periodic ..... including Dirichlet, Riemann and Cantor occupied themselves with the problem of ... to converge only on a set which is negligible in a certain sense (Le. of measure ...
Ebaid, Abdelhalim; Wazwaz, Abdul-Majid; Alali, Elham; Masaedeh, Basem S.
Very recently, it was observed that the temperature of nanofluids is finally governed by second-order ordinary differential equations with variable coefficients of exponential orders. Such coefficients were then transformed to polynomials type by using new independent variables. In this paper, a class of second-order ordinary differential equations with variable coefficients of polynomials type has been solved analytically. The analytical solution is expressed in terms of a hypergeometric function with generalized parameters. Moreover, applications of the present results have been applied on some selected nanofluids problems in the literature. The exact solutions in the literature were derived as special cases of our generalized analytical solution.
Martínez-Villarreal, Ashley A; Asz-Sigall, Daniel; Gutiérrez-Mendoza, Daniela; Serena, Thomas E; Lozano-Platonoff, Adriana; Sanchez-Cruz, Lourdes Y; Toussaint-Caire, Sonia; Domínguez-Cherit, Judith; López-García, Lirio A; Cárdenas-Sánchez, Andrea; Contreras-Ruiz, José
Foreign modelling agent reactions (FMAR) are the result of the injection of unapproved high-viscosity fluids with the purpose of cosmetic body modelling. Its consequences lead to ulceration, disfigurement and even death, and it has reached epidemic proportions in several regions of the world. We describe a series of patients treated for FMARs in a specialised wound care centre and a thorough review of the literature. A retrospective chart review was performed from January 1999 to September 2015 of patients who had been injected with non-medical foreign agents and who developed cutaneous ulceration needing treatment at the dermatology wound care centre. This study involved 23 patients whose ages ranged from 22 to 67 years with higher proportion of women and homosexual men. The most commonly injected sites were the buttocks (38·5%), legs (18%), thighs (15·4%) and breasts (11·8%). Mineral oil (39%) and other unknown substances (30·4%) were the most commonly injected. The latency period ranged from 1 week to 17 years. Complications included several skin changes such as sclerosis and ulceration as well as systemic complications. FMAR is a severe syndrome that may lead to deadly complications, and is still very common in Latin America. © 2016 Medicalhelplines.com Inc and John Wiley & Sons Ltd.
Williams, Kate E.; Nicholson, Jan M.; Walker, Sue; Berthelsen, Donna
Background: Children's sleep problems and self-regulation problems have been independently associated with poorer adjustment to school, but there has been limited exploration of longitudinal early childhood profiles that include both indicators. Aims: This study explores the normative developmental pathway for sleep problems and self-regulation…
Full Text Available We examined the relationship between service use and the number of problem areas as reported by parents and teachers on questionnaires among children aged 7–9 years old in the Bergen Child Study, a total population study including more than 9000 children. A problem area was counted as present if the child scored above the 95th percentile on parent and/or teacher questionnaire. A total number of 13 problem areas were included. Odd ratios (ORs for contact with child and adolescent mental health services (CAMH, school psychology services (SPS, health visiting nurse/physician, and school support were calculated with gender as covariate. The number of symptom areas was highly predictive of service use, showing a dose-response relationship for all services. Children scoring on ≥4 problem areas had a more than hundredfold risk of being in contact with CAMH services compared to children without problems. The mean number of problem areas for children in CAMH and SPS was 6.1 and 4.4 respectively, strongly supporting the ESSENCE model predicting multisymptomatology in children in specialized services. Even after controlling for number of problem areas, boys were twice as likely as girls to be in contact with CAMH, replicating previous findings of female gender being a strong barrier to mental health services.
Neece, C; Baker, B
Parents of children with intellectual disabilities (ID) typically report elevated levels of parenting stress, and child behaviour problems are a strong predictor of heightened parenting stress. Interestingly, few studies have examined child characteristics beyond behaviour problems that may also contribute to parenting stress. The present longitudinal study examined the contribution of child social skills to maternal parenting stress across middle childhood, as well as the direction of the relationship between child social skills and parenting stress. Families of children with ID (n = 74) or typical development (TD) (n = 115) participated over a 2-year period. Maternal parenting stress, child behaviour problems and child social skills were assessed at child ages six and eight. Child social skills accounted for unique variance in maternal parenting stress above and beyond child intellectual status and child behaviour problems. As the children matured, there was a significant interaction between child social skills and behaviour problems in predicting parenting stress. With respect to the direction of these effects, a cross-lagged panel analysis indicated that early parenting stress contributed to later social skills difficulties for children, but the path from children's early social skills to later parenting stress was not supported, once child behaviour problems and intellectual status were accounted for. When examining parenting stress, child social skills are an important variable to consider, especially in the context of child behaviour problems. Early parenting stress predicted child social skills difficulties over time, highlighting parenting stress as a key target for intervention.
Lengua, L J; Wolchik, S A; Sandler, I N; West, S G
Investigated the interaction between parenting and temperament in predicting adjustment problems in children of divorce. The study utilized a sample of 231 mothers and children, 9 to 12 years old, who had experienced divorce within the previous 2 years. Both mothers' and children's reports on parenting, temperament, and adjustment variables were obtained and combined to create cross-reporter measures of the variables. Parenting and temperament were directly and independently related to outcomes consistent with an additive model of their effects. Significant interactions indicated that parental rejection was more strongly related to adjustment problems for children low in positive emotionality, and inconsistent discipline was more strongly related to adjustment problems for children high in impulsivity. These findings suggest that children who are high in impulsivity may be at greater risk for developing problems, whereas positive emotionality may operate as a protective factor, decreasing the risk of adjustment problems in response to negative parenting.
Barker, David H; Quittner, Alexandra L; Fink, Nancy E; Eisenberg, Laurie S; Tobey, Emily A; Niparko, John K
The development of language and communication may play an important role in the emergence of behavioral problems in young children, but they are rarely included in predictive models of behavioral development. In this study, cross-sectional relationships between language, attention, and behavior problems were examined using parent report, videotaped observations, and performance measures in a sample of 116 severely and profoundly deaf and 69 normally hearing children ages 1.5 to 5 years. Secondary analyses were performed on data collected as part of the Childhood Development After Cochlear Implantation Study, funded by the National Institutes of Health. Hearing-impaired children showed more language, attention, and behavioral difficulties, and spent less time communicating with their parents than normally hearing children. Structural equation modeling indicated there were significant relationships between language, attention, and child behavior problems. Language was associated with behavior problems both directly and indirectly through effects on attention. Amount of parent-child communication was not related to behavior problems.
Newland, Rebecca P.; Crnic, Keith A.
The current study examined concurrent and longitudinal relations between maternal negative affective behaviour and child negative emotional expression in preschool age children with (n=96) or without (n=126) an early developmental risk, as well as the predictions of later behaviour problems. Maternal negative affective behaviour, child…
Mantymaa, Mirjami; Puura, Kaija; Luoma, Ilona; Latva, Reija; Salmelin, Raili K.; Tamminen, Tuula
This study examined child and parental factors in infancy and toddlerhood predicting subclinical or clinical levels of internalizing and externalizing problems at 5 years of age. Ninety-six children and their families participated. They were assessed when the children were 4-10 weeks old (T1), 2 years (T2) and 5 years old (T3). Child risks…
Schweizer, Fabian; Wustenberg, Sascha; Greiff, Samuel
This study examines the validity of the complex problem solving (CPS) test MicroDYN by investigating a) the relation between its dimensions--rule identification (exploration strategy), rule knowledge (acquired knowledge), rule application (control performance)--and working memory capacity (WMC), and b) whether CPS predicts school grades in…
Accurate estimation of energy expenditure (EE) in children and adolescents is required for a better understanding of physiological, behavioral, and environmental factors affecting energy balance. Cross-sectional time series (CSTS) models, which account for correlation structure of repeated observati...
Full Text Available The paper discusses a nonlinear parabolic equation describing the process of heat conduction for the case of the power dependence of the heat conductivity factor on temperature. Besides heat distribution in space, it describes filtration of a polytropic gas in a porous medium, whereupon, in the English-language literature, this equation is generally referred to as the porous medium equation. A distinctive feature of this equation is the degeneration of its parabolic type when the required function becomes zero, whereupon the equation acquires some properties typical of first-order equations. Particularly, in some cases, it proves possible to substantiate theorems of the existence and uniqueness of heat-wave (filtration-wave type solutions for it. This paper proves a theorem of the existence and uniqueness of the solution to the problem of the motion of a heat wave with a specified front in the instance of two independent variables. At that, since the front has the form of a closed plane curve, a transition t o the polar coordinate system is performed. The solution is constructed in the form of a series, a constructible recurrent procedure for calculating its coefficients being proposed. The series convergence is proved by the majorant method. A boundary-element-based computation algorithm in the form of a computer program has been developed and implemented to solve the problem under study. Test examples are considered, the calculations made by a program designed by the authors being compared with the truncated series. A good agreement of the obtained results has been established.
Manzione, Rodrigo L.; Wendland, Edson; Tanikawa, Diego H.
Stochastic methods based on time-series modeling combined with geostatistics can be useful tools to describe the variability of water-table levels in time and space and to account for uncertainty. Monitoring water-level networks can give information about the dynamic of the aquifer domain in both dimensions. Time-series modeling is an elegant way to treat monitoring data without the complexity of physical mechanistic models. Time-series model predictions can be interpolated spatially, with the spatial differences in water-table dynamics determined by the spatial variation in the system properties and the temporal variation driven by the dynamics of the inputs into the system. An integration of stochastic methods is presented, based on time-series modeling and geostatistics as a framework to predict water levels for decision making in groundwater management and land-use planning. The methodology is applied in a case study in a Guarani Aquifer System (GAS) outcrop area located in the southeastern part of Brazil. Communication of results in a clear and understandable form, via simulated scenarios, is discussed as an alternative, when translating scientific knowledge into applications of stochastic hydrogeology in large aquifers with limited monitoring network coverage like the GAS.
Anderson, Michael L; Chemero, Tony
Clark appears to be moving toward epistemic internalism, which he once rightly rejected. This results from a double over-interpretation of predictive coding's significance. First, Clark argues that predictive coding offers a Grand Unified Theory (GUT) of brain function. Second, he over-reads its epistemic import, perhaps even conflating causal and epistemic mediators. We argue instead for a plurality of neurofunctional principles.
Gerstein, Emily D.; Pedersen y Arbona, Anita; Crnic, Keith A.; Ryu, Ehri; Baker, Bruce L.; Blacher, Jan
Children with early developmental delays are at heightened risk for behavior problems and comorbid psychopathology. This study examined the trajectories of regulatory capabilities and their potentially mediating role in the development of behavior problems for children with and without early developmental delays. A sample of 231 children comprised…
Tenneij, Nienke; Didden, Robert; Koot, Hans M.
Background: Little is known about client characteristics that are related to outcome during inpatient treatment of adults with mild intellectual disability (ID) and severe behavioural problems. Method: We explored variables that were related to a change in behavioural problems in 87 individuals with mild ID during inpatient treatment in facilities…
Brooker, Rebecca J.; Buss, Kristin A.; Lemery-Chalfant, Kathryn; Aksan, Nazan; Davidson, Richard J.; Goldsmith, H. Hill
Using both traditional composites and novel profiles of anger, we examined associations between infant anger and preschool behavior problems in a large, longitudinal data set (N = 966). We also tested the role of life stress as a moderator of the link between early anger and the development of behavior problems. Although traditional measures of…
Borders, Ashley; Earleywine, Mitchell; Huey, Stanley J
Expectancy-value theory emphasizes the importance of outcome expectancies for behavioral decisions, but most tests of the theory focus on a single behavior and a single expectancy. However, the matching law suggests that individuals consider expected outcomes for both the target behavior and alternative behaviors when making decisions. In this study, we expanded expectancy-value theory to evaluate the contributions of two competing expectancies to adolescent behavior problems. One hundred twenty-one high school students completed measures of behavior problems, expectancies for both acting out and academic effort, and perceived academic competence. Students' self-reported behavior problems covaried mostly with perceived competence and academic expectancies and only nominally with problem behavior expectancies. We suggest that behavior problems may result from students perceiving a lack of valued or feasible alternative behaviors, such as studying. We discuss implications for interventions and suggest that future research continue to investigate the contribution of alternative expectancies to behavioral decisions.
Full Text Available A state-of-the-art review of flow observability, estimation, and prediction problems in traffic networks is performed. Since mathematical optimization provides a general framework for all of them, an integrated approach is used to perform the analysis of these problems and consider them as different optimization problems whose data, variables, constraints, and objective functions are the main elements that characterize the problems proposed by different authors. For example, counted, scanned or “a priori” data are the most common data sources; conservation laws, flow nonnegativity, link capacity, flow definition, observation, flow propagation, and specific model requirements form the most common constraints; and least squares, likelihood, possible relative error, mean absolute relative error, and so forth constitute the bases for the objective functions or metrics. The high number of possible combinations of these elements justifies the existence of a wide collection of methods for analyzing static and dynamic situations.
Full Text Available Abstract Background Undetected and untreated developmental problems can have a significant economic and social impact on society. Intervention to ameliorate potential developmental problems requires early identification of children at risk of future learning and behaviour difficulties. The objective of this study was to estimate the prevalence of risk for developmental problems among preschool children born to medically low risk women and identify factors that influence outcomes. Methods Mothers who had participated in a prenatal trial were followed up three years post partum to answer a telephone questionnaire. Questions were related to child health and development, child care, medical care, mother's lifestyle, well-being, and parenting style. The main outcome measure was risk for developmental problems using the Parents' Evaluation of Developmental Status (PEDS. Results Of 791 children, 11% were screened by the PEDS to be at high risk for developmental problems at age three. Of these, 43% had previously been referred for assessment. Children most likely to have been referred were those born preterm. Risk factors for delay included: male gender, history of ear infections, a low income environment, and a mother with poor emotional health and a history of abuse. A child with these risk factors was predicted to have a 53% chance of screening at high risk for developmental problems. This predicted probability was reduced to 19% if the child had a mother with good emotional health and no history of abuse. Conclusion Over 10% of children were identified as high risk for developmental problems by the screening, and more than half of those had not received a specialist referral. Risk factors for problems included prenatal and perinatal maternal and child factors. Assessment of maternal health and effective screening of child development may increase detection of children at high risk who would benefit from early intervention. Trial registration Current
Sweet, D.W.; Haste, T.J.
The REBEKA-6 clad ballooning experiment has been chosen as the basis of a CSNI Open International Standard Problem (ISP14). The test, which was carried out at KfK, Karlsruhe in March 1983, has also been adopted as a Blind German National Problem (DSP7) and this exercise has been extended to include interested organisations outside the FDR. The UKAEA has completed a set of calculations with the intention of contributing to DSP7 but has not formally submitted these because of reservations regarding the problem specification. This memorandum provides a record of the calculations and summarises the difficulties encountered. (author)
Fung, Wenson; Swanson, H Lee
The purpose of this study was to assess whether the differential effects of working memory (WM) components (the central executive, phonological loop, and visual-spatial sketchpad) on math word problem-solving accuracy in children (N = 413, ages 6-10) are completely mediated by reading, calculation, and fluid intelligence. The results indicated that all three WM components predicted word problem solving in the nonmediated model, but only the storage component of WM yielded a significant direct path to word problem-solving accuracy in the fully mediated model. Fluid intelligence was found to moderate the relationship between WM and word problem solving, whereas reading, calculation, and related skills (naming speed, domain-specific knowledge) completely mediated the influence of the executive system on problem-solving accuracy. Our results are consistent with findings suggesting that storage eliminates the predictive contribution of executive WM to various measures Colom, Rebollo, Abad, & Shih (Memory & Cognition, 34: 158-171, 2006). The findings suggest that the storage component of WM, rather than the executive component, has a direct path to higher-order processing in children.
Hart, Trevor A; Noor, Syed W; Adam, Barry D; Vernon, Julia R G; Brennan, David J; Gardner, Sandra; Husbands, Winston; Myers, Ted
Syndemics research shows the additive effect of psychosocial problems on high-risk sexual behavior among gay and bisexual men (GBM). Psychosocial strengths may predict less engagement in high-risk sexual behavior. In a study of 470 ethnically diverse HIV-negative GBM, regression models were computed using number of syndemic psychosocial problems, number of psychosocial strengths, and serodiscordant condomless anal sex (CAS). The number of syndemic psychosocial problems correlated with serodiscordant CAS (RR = 1.51, 95% CI 1.18-1.92; p = 0.001). When adding the number of psychosocial strengths to the model, the effect of syndemic psychosocial problems became non-significant, but the number of strengths-based factors remained significant (RR = 0.67, 95% CI 0.53-0.86; p = 0.002). Psychosocial strengths may operate additively in the same way as syndemic psychosocial problems, but in the opposite direction. Consistent with theories of resilience, psychosocial strengths may be an important set of variables predicting sexual risk behavior that is largely missing from the current HIV behavioral literature.
Full Text Available This research was conducted by prediction of safety stock using Fuzzy Time Series (FTS and technology of Radio Frequency Identification (RFID for stock control at Vendor Managed Inventory (VMI. Well-controlled stock influenced company revenue and minimized cost. It discussed about information system of safety stock prediction developed through programming language of PHP. Input data consisted of demand got from automatic, online and real time acquisition using technology of RFID, then, sent to server and stored at online database. Furthermore, data of acquisition result was predicted by using algorithm of FTS applying universe of discourse defining and fuzzy sets determination. Fuzzy set result was continued to division process of universe of discourse in order to be to final step. Prediction result was displayed at information system dashboard developed. By using 60 data from demand data, prediction score was 450.331 and safety stock was 135.535. Prediction result was done by error deviation validation using Mean Square Percent Error of 15%. It proved that FTS was good enough in predicting demand and safety stock for stock control. For deeper analysis, researchers used data of demand and universe of discourse U varying at FTS to get various result based on test data used.
Mashuri, Chamdan; Suryono; Suseno, Jatmiko Endro
This research was conducted by prediction of safety stock using Fuzzy Time Series (FTS) and technology of Radio Frequency Identification (RFID) for stock control at Vendor Managed Inventory (VMI). Well-controlled stock influenced company revenue and minimized cost. It discussed about information system of safety stock prediction developed through programming language of PHP. Input data consisted of demand got from automatic, online and real time acquisition using technology of RFID, then, sent to server and stored at online database. Furthermore, data of acquisition result was predicted by using algorithm of FTS applying universe of discourse defining and fuzzy sets determination. Fuzzy set result was continued to division process of universe of discourse in order to be to final step. Prediction result was displayed at information system dashboard developed. By using 60 data from demand data, prediction score was 450.331 and safety stock was 135.535. Prediction result was done by error deviation validation using Mean Square Percent Error of 15%. It proved that FTS was good enough in predicting demand and safety stock for stock control. For deeper analysis, researchers used data of demand and universe of discourse U varying at FTS to get various result based on test data used.
Full Text Available The accurate and reliable prediction of groundwater depth is the basis of the sustainable utilization of regional groundwater resources. However, the complexity of the prediction has been ignored in previous studies of regional groundwater depth system analysis and prediction, making it difficult to realize the scientific management of groundwater resources. To address this defect, taking complexity diagnosis as the research foundation, this paper proposes a new coupling forecast strategy for evaluating groundwater depth based on empirical mode decomposition (EMD and a radial basis function neural network (RBFNN. The data used for complexity analysis and modelling are the monthly groundwater depth series monitoring data from 15 long-term monitoring wells from 1997 to 2007, which were collected from the Jiansanjiang Administration of Heilongjiang Agricultural Reclamation in China. The calculation results of the comprehensive complexity index for each groundwater depth series obtained are based on wavelet theory, fractal theory, and the approximate entropy method. The monthly groundwater depth sequence of District 8 of Farm Nongjiang, which has the highest complexity among the five farms in the Jiansanjiang Administration midland, was chosen as the modelling sample series. The groundwater depth series of District 8 of Farm Nongjiang was separated into five intrinsic mode function (IMF sequences and a remainder sequence by applying the EMD method, which revealed that local groundwater depth has a significant one-year periodic character and an increasing trend. The RBFNN was then used to forecast and stack each EMD separation sequence. The results suggest that the future groundwater depth will remain at approximately 10 m if the past pattern of water use continues, exceeding the ideal depth of groundwater. Thus, local departments should take appropriate countermeasures to conserve groundwater resources effectively.
Bibby, Peter A; Ross, Katherine E
Background and aims The aim of this research was to investigate the relationship between alexithymia and loss-chasing behavior in people at risk and not at risk for problem gambling. Methods An opportunity sample of 58 (50 males and 8 females) participants completed the Problem Gambling Severity Index and the Toronto Alexithymia Scale (TAS-20). They then completed the Cambridge Gambling Task from which a measure of loss-chasing behavior was derived. Results Alexithymia and problem gambling risk were significantly positively correlated. Subgroups of non-alexithymic and at or near caseness for alexithymia by low risk and at risk for problem gambling were identified. The results show a clear difference for loss-chasing behavior for the two alexithymia conditions, but there was no evidence that low and at-risk problem gamblers were more likely to loss chase. The emotion-processing components of the TAS-20 were shown to correlate with loss chasing. Discussion and conclusion These findings suggest that loss-chasing behavior may be particularly prevalent in a subgroup of problem gamblers those who are high in alexithymia.
Alikar, Najmeh; Mousavi, Seyed Mohsen; Raja Ghazilla, Raja Ariffin; Tavana, Madjid; Olugu, Ezutah Udoncy
In this paper, we formulate a mixed-integer binary non-linear programming model to study a series-parallel multi-component multi-periodic inventory-redundancy allocation problem (IRAP). This IRAP is a novel redundancy allocation problem (RAP) because components (products) are purchased under an all unit discount (AUD) policy and then installed on a series-parallel system. The total budget available for purchasing the components, the storage space, the vehicle capacities, and the total weight of the system are limited. Moreover, a penalty function is used to penalize infeasible solutions, generated randomly. The overall goal is to find the optimal number of the components purchased for each subsystem so that the total costs including ordering cost, holding costs, and purchasing cost are minimized while the system reliability is maximized, simultaneously. A non-dominated sorting genetic algorithm-II (NSGA-II), a multi-objective particle swarm optimization (MOPSO), and a multi-objective harmony search (MOHS) algorithm are applied to obtain the optimal Pareto solutions. While no benchmark is available in the literature, some numerical examples are generated randomly to evaluate the results of NSGA-II on the proposed IRAP. The results are in favor of NSGA-II. - Highlights: • An inventory control system employing an all-unit discount policy is considered in the proposed model. • The proposed model considers limited total budget, storage space, transportation capacity, and total weight. Moreover, a penalty function is used to penalize infeasible solutions. • The overall goal is to find the optimal number components purchased for each subsystem so that the total costs including ordering cost, holding cost and purchasing cost are minimized and the system reliability are maximized, simultaneously. • A NSGA-II algorithm is derived where a multi-objective particle swarm optimization and a multi-objective harmony search algorithm are used to evaluate the NSGA-II results.
Kuznets, E I; Bobrov, A F; Bekreneva, L N; Mikhailova, L I; Utekhin, B A; Pruzhinina, T I; Iakovleva, E V; Chadov, V I
The problem of evaluating and predicting the thermal status of a cosmonaut in the long-term space mission is a pressing one and remains to be solved. The previous studies indicated that the best plan to be followed is to evaluate the thermal status of a cosmonaut during his egress into outer space with the use of the procedure of parotid thermometry of the mean body temperature.
Full Text Available A new simulation of daily flow for Kaczawa River, south-west Poland for extra long series of generated meteorological data (comparing to previous research and selected climate change scenarios are presented. The Representative Concentration Pathways (RCPs scenarios vs. SRES are introduced for simulations. The flow simulation in the river catchment is made using MIKE SHE hydrological model while the multisite data are generated by spatial weather generator SWGEN. Simulations are done for 2040 and 2060 while the simulations for the year 2000 are used as a background. The large number of new simulated series determined by the lead time, three climate change scenarios (RCP2.6 RCP4.5 and RCP6.0, and number of generated years (1000 for each case is equal to 7000 for a single station. Finally, Pdf function for flow is presented as well probability of exceedance of maximum flow.
Kendler, Kenneth S; Edwards, Alexis; Myers, John; Cho, Seung Bin; Adkins, Amy; Dick, Danielle
A family history (FH) of psychiatric and substance use problems is a potent risk factor for common internalizing and externalizing disorders. In a large web-based assessment of mental health in college students, we developed a brief set of screening questions for a FH of alcohol problems (AP), drug problems (DP) and depression-anxiety in four classes of relatives (father, mother, aunts/uncles/grandparents, and siblings) as reported by the student. Positive reports of a history of AP, DP, and depression-anxiety were substantially correlated within relatives. These FH measures predicted in the student, in an expected pattern, dimensions of personality and impulsivity, alcohol consumption and problems, smoking and nicotine dependence, use of illicit drugs, and symptoms of depression and anxiety. Using the mean score from the four classes of relatives was more predictive than using a familial/sporadic dichotomy. Interactions were seen between the FH of AP, DP, and depression-anxiety and peer deviance in predicting symptoms of alcohol and tobacco dependence. As the students aged, the FH of AP became a stronger predictor of alcohol problems. While we cannot directly assess the validity of these FH reports, the pattern of findings suggest that our brief screening items were able to assess, with some accuracy, the FH of substance misuse and internalizing psychiatric disorders in relatives. If correct, these measures can play an important role in the creation of developmental etiologic models for substance and internalizing psychiatric disorders which constitute one of the central goals of the overall project. © 2015 Wiley Periodicals, Inc.
The IMMEX (Interactive Multi-Media Exercises) Web-based problem set platform enables the online delivery of complex, multimedia simulations, the rapid collection of student performance data, and has already been used in several genetic simulations. The next step is the use of these data to understand and improve student learning in a formative manner. This article describes the development of probabilistic models of undergraduate student problem solving in molecular genetics that detailed the spectrum of strategies students used when problem solving, and how the strategic approaches evolved with experience. The actions of 776 university sophomore biology majors from three molecular biology lecture courses were recorded and analyzed. Each of six simulations were first grouped by artificial neural network clustering to provide individual performance measures, and then sequences of these performances were probabilistically modeled by hidden Markov modeling to provide measures of progress. The models showed that students with different initial problem-solving abilities choose different strategies. Initial and final strategies varied across different sections of the same course and were not strongly correlated with other achievement measures. In contrast to previous studies, we observed no significant gender differences. We suggest that instructor interventions based on early student performances with these simulations may assist students to recognize effective and efficient problem-solving strategies and enhance learning. PMID:15746978
Rudajev, Vladimír; Číž, R.
Roč. 44, č. 3 (2007), s. 457-467 ISSN 1365-1609 R&D Projects: GA ČR GA205/06/0906 Institutional research plan: CEZ:AV0Z30130516; CEZ:AV0Z30460519 Keywords : ultrasonic emission * microfracturing * time series * autocorrelation * fractal dimensions * neural networks Subject RIV: DC - Siesmology, Volcanology, Earth Structure Impact factor: 0.735, year: 2007
Hilary S. Parker
Full Text Available Batch effects are responsible for the failure of promising genomic prognostic signatures, major ambiguities in published genomic results, and retractions of widely-publicized findings. Batch effect corrections have been developed to remove these artifacts, but they are designed to be used in population studies. But genomic technologies are beginning to be used in clinical applications where samples are analyzed one at a time for diagnostic, prognostic, and predictive applications. There are currently no batch correction methods that have been developed specifically for prediction. In this paper, we propose an new method called frozen surrogate variable analysis (fSVA that borrows strength from a training set for individual sample batch correction. We show that fSVA improves prediction accuracy in simulations and in public genomic studies. fSVA is available as part of the sva Bioconductor package.
Daga, Pankaj R; Bolger, Michael B; Haworth, Ian S; Clark, Robert D; Martin, Eric J
When medicinal chemists need to improve bioavailability (%F) within a chemical series during lead optimization, they synthesize new series members with systematically modified properties mainly by following experience and general rules of thumb. More quantitative models that predict %F of proposed compounds from chemical structure alone have proven elusive. Global empirical %F quantitative structure-property (QSPR) models perform poorly, and projects have too little data to train local %F QSPR models. Mechanistic oral absorption and physiologically based pharmacokinetic (PBPK) models simulate the dissolution, absorption, systemic distribution, and clearance of a drug in preclinical species and humans. Attempts to build global PBPK models based purely on calculated inputs have not achieved the optimization. In this work, local GastroPlus PBPK models are instead customized for individual medchem series. The key innovation was building a local QSPR for a numerically fitted effective intrinsic clearance (CL loc ). All inputs are subsequently computed from structure alone, so the models can be applied in advance of synthesis. Training CL loc on the first 15-18 rat %F measurements gave adequate predictions, with clear improvements up to about 30 measurements, and incremental improvements beyond that.
Cappelli, Daniele; Mansour, Nagi N.
Separation can be seen in most aerodynamic flows, but accurate prediction of separated flows is still a challenging problem for computational fluid dynamics (CFD) tools. The behavior of several Reynolds Averaged Navier-Stokes (RANS) models in predicting the separated ow over a wall-mounted hump is studied. The strengths and weaknesses of the most popular RANS models (Spalart-Allmaras, k-epsilon, k-omega, k-omega-SST) are evaluated using the open source software OpenFOAM. The hump ow modeled in this work has been documented in the 2004 CFD Validation Workshop on Synthetic Jets and Turbulent Separation Control. Only the baseline case is treated; the slot flow control cases are not considered in this paper. Particular attention is given to predicting the size of the recirculation bubble, the position of the reattachment point, and the velocity profiles downstream of the hump.
Mushkin, I.; Solomon, S.
We study the inverse contagion problem (ICP). As opposed to the direct contagion problem, in which the network structure is known and the question is when each node will be contaminated, in the inverse problem the links of the network are unknown but a sequence of contagion histories (the times when each node was contaminated) is observed. We consider two versions of the ICP: The strong problem (SICP), which is the reconstruction of the network and has been studied before, and the weak problem (WICP), which requires "only" the prediction (at each time step) of the nodes that will be contaminated at the next time step (this is often the real life situation in which a contagion is observed and predictions are made in real time). Moreover, our focus is on analyzing the increasing accuracy of the solution, as a function of the number of contagion histories already observed. For simplicity, we discuss the simplest (deterministic and synchronous) contagion dynamics and the simplest solution algorithm, which we have applied to different network types. The main result of this paper is that the complex problem of the convergence of the ICP for a network can be reduced to an individual property of pairs of nodes: the "false link difficulty". By definition, given a pair of unlinked nodes i and j, the difficulty of the false link (i,j) is the probability that in a random contagion history, the nodes i and j are not contaminated at the same time step (or at consecutive time steps). In other words, the "false link difficulty" of a non-existing network link is the probability that the observations during a random contagion history would not rule out that link. This probability is relatively straightforward to calculate, and in most instances relies only on the relative positions of the two nodes (i,j) and not on the entire network structure. We have observed the distribution of false link difficulty for various network types, estimated it theoretically and confronted it
Dubois-Comtois, Karine; Moss, Ellen; Cyr, Chantal; Pascuzzo, Katherine
The objective of the study was to examine the longitudinal relation between early school-age measures of maternal psychosocial distress, quality of mother-child interactions, and child attachment behavior, and behavior problem profiles in middle childhood using a multi-informant design. Participants were 243 French-speaking mother-child dyads (122 girls) who were part of an ongoing longitudinal project. Maternal psychosocial distress was assessed when children were between 4 and 6 years of age. Mother-child interactive quality and attachment patterns were observed at age 6 during a laboratory visit. At age 8.5, externalizing and internalizing problems were assessed using mother and child reports. Results show that maternal psychosocial distress predicted later social adaptation reported by the child through the mediation of mother-child interactions. Analyses also revealed that higher maternal psychosocial distress and controlling attachment patterns, either of the punitive or caregiving type, significantly predicted membership in both child internalizing and externalizing clinical problem groups. Lower mother-child interactive quality, male gender, and child ambivalent attachment were also predictors of externalizing clinical problems.
Odgers, Candice L.; Milne, Barry J.; Caspi, Avshalom; Crump, Raewyn; Poulton, Richie; Moffitt, Terrie E.
Objective: Many children with conduct disorder develop life-course persistent antisocial behavior; however, other children exhibit childhood-limited or adolescence-limited conduct disorder symptoms and escape poor adult outcomes. Prospective prediction of long-term prognosis in pediatric and adolescent clinical settings is difficult. Improved…
Kalinkin, B.N.; Gareev, F.A.
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
Prediction models for the airborne and impact sound transmission in buildings have recently been established (EN 12354- 1&2:1999). However, these models do not cover technical installations and machinery as a source of sound in buildings. Yet these can cause unacceptable sound levels and it is
Witvliet, M.J.; Van Gasteren, S.; Van Den Hondel, D.; Hartman, E.E.; Van Heurn, L.W.E.; Van Der Steeg, A.F.W.
AIM. The aim of this study was to examine the prevalence of sexual dysfunction and distress and to assess whether sexual functioning could be predicted by psychosocial factors in childhood and adolescence in patients with an anorectal malformation or Hirschsprung disease. MATERIAL AND METHODS. In
Ammitzbøll, Janni; Thygesen, Lau Caspar; Holstein, Bjørn E
logistic regression analyses adjusted and weighted to adjust for sampling and bias. CIMHS problems of sleep, feeding and eating, emotions, attention, communication, and language were associated with an up to fivefold increased risk of child mental disorders across the diagnostic spectrum of ICD-10...
Jaspers, Merlijne; de Winter, Andrea F.; Huisman, Mark; Verhulst, Frank C.; Ormel, Johan; Stewart, Roy E.; Reijneveld, Sijmen A.
Purpose: To describe trajectories of emotional and behavioral problems in adolescents and to identify early indicators of these trajectories using data from routine well-child assessments at ages 0-4 years. Methods: Data from three assessment waves of adolescents (n = 1,816) of the TRAILS were used
Dube, S.K.; Rao, A.D.; Sinha, P.C.; Murty, T.S.; Bahulayan, N.
to annual economic losses in these countries. Thus, the real time monitoring and warning of storm surge is of great concern for this region. The goal of this paper is to provide an overview of major aspects of the storm surge problem in the Bay of Bengal...
Mikami, Amori Yee; Lorenzi, Jill
Children with attention-deficit/hyperactivity disorder (ADHD) often have poor relationships with peers. However, research on this topic has predominantly focused on boys. This study considered child gender, ADHD status, and dimensionally assessed conduct problems as predictors of peer relationship difficulties. Participants were 125 children (ages…
Reef, Joni; van Meurs, Inge; Verhulst, Frank C.; van der Ende, Jan
Objective: The goal of this study was to determine continuities of a broad range of psychopathology from childhood into middle adulthood in a general population sample across a 24-year follow-up. Method: In 1983, parent ratings of children's problems were collected with the Child Behavior Checklist (CBCL) in a general population sample of 2,076…
Mesman, Judi; Stoel, Reinoud; Bakermans-Kranenburg, Marian J.; van IJzendoorn, Marinus H.; Juffer, Femmie; Koot, Hans M.; Alink, Lenneke R. A.
Using an accelerated longitudinal design, the development of externalizing problems from age 2 to 5 years was investigated in relation to maternal psychopathology, maternal parenting, gender, child temperament, and the presence of siblings. The sample consisted of 150 children selected at age 2-3 years for having high levels of externalizing…
Mason, W. Alex; Kosterman, Rick; Hawkins, J. David; Herrenkohl, Todd I.; Lengua, Liliana J.; McCauley, Elizabeth
Objective: This study examined childhood behavior problems at ages 10 and 11 years as predictors of young adult depression, social phobia, and violence at age 21 years. Method: Data were collected as part of the Seattle Social Development Project, a longitudinal study of 808 elementary school students from high-crime neighborhoods of Seattle.…
Tian, Linlin; Zhu, Wei Jun; Shen, Wen Zhong
The improved analytical wake model named as 2D_k Jensen model (which was proposed to overcome some shortcomes in the classical Jensen wake model) is applied and validated in this work for wind turbine multi-wake predictions. Different from the original Jensen model, this newly developed 2D_k Jensen...... model uses a cosine shape instead of the top-hat shape for the velocity deficit in the wake, and the wake decay rate as a variable that is related to the ambient turbulence as well as the rotor generated turbulence. Coupled with four different multi-wake combination models, the 2D_k Jensen model...... is assessed through (1) simulating two wakes interaction under full wake and partial wake conditions and (2) predicting the power production in the Horns Rev wind farm for different wake sectors around two different wind directions. Through comparisons with field measurements, results from Large Eddy...
Kundrát, Vojtech; Kaspar, Jan; Procházka, Jirí
The standard description of common influence of both the Coulomb and hadronic elastic scattering in the proton - proton elastic collisions at high energies with the help of West and Yennie complete amplitude is shown to be theoretically inconsistent. The approach being based on the eikonal model amplitude removes these troubles. The preference of its applica- tion to the analysis of experimental data and in obtaining the predictions of contemporary models for proton - proton high energy elastic hadronic scattering are discussed.
Mukherjee, Amritendu; Ramachandran, Parthasarathy
Prediction of Ground Water Level (GWL) is extremely important for sustainable use and management of ground water resource. The motivations for this work is to understand the relationship between Gravity Recovery and Climate Experiment (GRACE) derived terrestrial water change (ΔTWS) data and GWL, so that ΔTWS could be used as a proxy measurement for GWL. In our study, we have selected five observation wells from different geographic regions in India. The datasets are unevenly spaced time series data which restricts us from applying standard time series methodologies and therefore in order to model and predict GWL with the help of ΔTWS, we have built Linear Regression Model (LRM), Support Vector Regression (SVR) and Artificial Neural Network (ANN). Comparative performances of LRM, SVR and ANN have been evaluated with the help of correlation coefficient (ρ) and Root Mean Square Error (RMSE) between the actual and fitted (for training dataset) or predicted (for test dataset) values of GWL. It has been observed in our study that ΔTWS is highly significant variable to model GWL and the amount of total variations in GWL that could be explained with the help of ΔTWS varies from 36.48% to 74.28% (0.3648 ⩽R2 ⩽ 0.7428) . We have found that for the model GWL ∼ Δ TWS, for both training and test dataset, performances of SVR and ANN are better than that of LRM in terms of ρ and RMSE. It also has been found in our study that with the inclusion of meteorological variables along with ΔTWS as input parameters to model GWL, the performance of SVR improves and it performs better than ANN. These results imply that for modelling irregular time series GWL data, ΔTWS could be very useful.
Xiang, Zhexin; Soto, Cinque S; Honig, Barry
In this paper, we introduce a method to account for the shape of the potential energy curve in the evaluation of conformational free energies. The method is based on a procedure that generates a set of conformations, each with its own force-field energy, but adds a term to this energy that favors conformations that are close in structure (have a low rmsd) to other conformations. The sum of the force-field energy and rmsd-dependent term is defined here as the "colony energy" of a given conformation, because each conformation that is generated is viewed as representing a colony of points. The use of the colony energy tends to select conformations that are located in broad energy basins. The approach is applied to the ab initio prediction of the conformations of all of the loops in a dataset of 135 nonredundant proteins. By using an rmsd from a native criterion based on the superposition of loop stems, the average rmsd of 5-, 6-, 7-, and 8-residue long loops is 0.85, 0.92, 1.23, and 1.45 A, respectively. For 8-residue loops, 60 of 61 predictions have an rmsd of less than 3.0 A. The use of the colony energy is found to improve significantly the results obtained from the potential function alone. (The loop prediction program, "Loopy," can be downloaded at http://trantor.bioc.columbia.edu.)
Franchi, Loris; Feruglio, Lorenzo; Mozzillo, Raffaele; Corpino, Sabrina
In recent years, thanks to the increase of the know-how on machine-learning techniques and the advance of the computational capabilities of on-board processing, expensive computing algorithms, such as Model Predictive Control, have begun to spread in space applications even on small on-board processor. The paper presents an algorithm for an optimal fault recovery of a 3U CubeSat, developed in MathWorks Matlab & Simulink environment. This algorithm involves optimization techniques aiming at obtaining the optimal recovery solution, and involves a Model Predictive Control approach for the attitude control. The simulated system is a CubeSat in Low Earth Orbit: the attitude control is performed with three magnetic torquers and a single reaction wheel. The simulation neglects the errors in the attitude determination of the satellite, and focuses on the recovery approach and control method. The optimal recovery approach takes advantage of the properties of magnetic actuation, which gives the possibility of the redistribution of the control action when a fault occurs on a single magnetic torquer, even in absence of redundant actuators. In addition, the paper presents the results of the implementation of Model Predictive approach to control the attitude of the satellite.
Clinard, J.A.; Corum, J.M.; Sartory, W.K.
The results of exemplary inelastic analyses for experimental benchmark problems on reactor components are presented. Consistent analytical procedures and constitutive relations were used in each of the analyses, and the material behavior data presented in the Appendix were used in all cases. Two finite-element inelastic computer programs were employed. These programs implement the analysis procedures and constitutive equations for type 304 stainless steel that are currently used in many analyses of elevated-temperature nuclear reactor system components. The analysis procedures and constitutive relations are briefly discussed, and representative analytical results are presented and compared to the test data. The results that are presented demonstrate the feasibility of performing inelastic analyses for the types of problems discussed, and they are indicative of the general level of agreement that the analyst might expect when using conventional inelastic analysis procedures. (U.S.)
Jaspers, Merlijne; de Winter, Andrea F.; Buitelaar, Jan K.; Verhulst, Frank C.; Reijneveld, Sijmen A.; Hartman, Catharina A.
For clinically referred children with Autism Spectrum Disorder (ASD) or Attention Deficit/Hyperactivity Disorder (ADHD) several early indicators have been described. However, knowledge is lacking on early markers of less severe variants of ASD and ADHD from the general population. The aim of the present study is to identify early indicators of high risk groups for ASD and ADHD problems based on routine data from community pediatric services between infancy and age four. Data are from 1,816 pa...
Muhtadie, Luma; Zhou, Qing; Eisenberg, Nancy; Wang, Yun
The additive and interactive relations of parenting styles (authoritative and authoritarian parenting) and child temperament (anger/frustration, sadness, and effortful control) to children’s internalizing problems were examined in a 3.8-year longitudinal study of 425 Chinese children (6 – 9 years) from Beijing. At Wave 1, parents self-reported on their parenting styles, and parents and teachers rated child temperament. At Wave 2, parents, teachers, and children rated children’s internalizing ...
van der Molen, Elsa; Blokland, Arjan A. J.; Hipwell, Alison E.; Vermeiren, Robert R.J.M.; Doreleijers, Theo A.H.; Loeber, Rolf
Background It is widely recognized that early onset of disruptive behavior is linked to a variety of detrimental outcomes in males later in life. In contrast, little is known about the association between girls’ childhood trajectories of disruptive behavior and adjustment problems in early adolescence. Methods The current study used 9 waves of data from the ongoing Pittsburgh Girls Study. A semi-parametric group based model was used to identify trajectories of disruptive behavior in 1,513 girls from age 6 to 12 years. Adjustment problems were characterized by depression, self-harm, PTSD, substance use, interpersonal aggression, sexual behavior, affiliation with delinquent peers, and academic achievement at ages 13 and 14. Results Three trajectories of childhood disruptive behavior were identified: low, medium, and high. Girls in the high group were at increased risk for depression, self-harm, PTSD, illegal substance use, interpersonal aggression, early and risky sexual behavior, and lower academic achievement. The likelihood of multiple adjustment problems increased with trajectories reflecting higher levels of disruptive behavior. Conclusion Girls following the high childhood trajectory of disruptive behavior require early intervention programs to prevent multiple, adverse outcomes in adolescence and further escalation in adulthood. PMID:25302849
Full Text Available We present prediction and variable importance (VIM methods for longitudinal data sets containing continuous and binary exposures subject to missingness. We demonstrate the use of these methods for prognosis of medical outcomes of severe trauma patients, a field in which current medical practice involves rules of thumb and scoring methods that only use a few variables and ignore the dynamic and high-dimensional nature of trauma recovery. Well-principled prediction and VIM methods can provide a tool to make care decisions informed by the high-dimensional patient's physiological and clinical history. Our VIM parameters are analogous to slope coefficients in adjusted regressions, but are not dependent on a specific statistical model, nor require a certain functional form of the prediction regression to be estimated. In addition, they can be causally interpreted under causal and statistical assumptions as the expected outcome under time-specific clinical interventions, related to changes in the mean of the outcome if each individual experiences a specified change in the variable (keeping other variables in the model fixed. Better yet, the targeted MLE used is doubly robust and locally efficient. Because the proposed VIM does not constrain the prediction model fit, we use a very flexible ensemble learner (the SuperLearner, which returns a linear combination of a list of user-given algorithms. Not only is such a prediction algorithm intuitive appealing, it has theoretical justification as being asymptotically equivalent to the oracle selector. The results of the analysis show effects whose size and significance would have been not been found using a parametric approach (such as stepwise regression or LASSO. In addition, the procedure is even more compelling as the predictor on which it is based showed significant improvements in cross-validated fit, for instance area under the curve (AUC for a receiver-operator curve (ROC. Thus, given that 1 our VIM
Full Text Available A potentially powerful predictor for the course of drug (abuse is the approach-bias, that is, the pre-reflective tendency to approach rather than avoid drug-related stimuli. Here we investigated the neural underpinnings of cannabis approach and avoidance tendencies. By elucidating the predictive power of neural approach-bias activations for future cannabis use and problem severity, we aimed at identifying new intervention targets. Using functional Magnetic Resonance Imaging (fMRI, neural approach-bias activations were measured with a Stimulus Response Compatibility task (SRC and compared between 33 heavy cannabis users and 36 matched controls. In addition, associations were examined between approach-bias activations and cannabis use and problem severity at baseline and at six-month follow-up. Approach-bias activations did not differ between heavy cannabis users and controls. However, within the group of heavy cannabis users, a positive relation was observed between total lifetime cannabis use and approach-bias activations in various fronto-limbic areas. Moreover, approach-bias activations in the dorsolateral prefrontal cortex (DLPFC and anterior cingulate cortex (ACC independently predicted cannabis problem severity after six months over and beyond session-induced subjective measures of craving. Higher DLPFC/ACC activity during cannabis approach trials, but lower activity during cannabis avoidance trials were associated with decreases in cannabis problem severity. These findings suggest that cannabis users with deficient control over cannabis action tendencies are more likely to develop cannabis related problems. Moreover, the balance between cannabis approach and avoidance responses in the DLPFC and ACC may help identify individuals at-risk for cannabis use disorders and may be new targets for prevention and treatment.
Cousijn, Janna; Goudriaan, Anna E; Ridderinkhof, K Richard; van den Brink, Wim; Veltman, Dick J; Wiers, Reinout W
A potentially powerful predictor for the course of drug (ab)use is the approach-bias, that is, the pre-reflective tendency to approach rather than avoid drug-related stimuli. Here we investigated the neural underpinnings of cannabis approach and avoidance tendencies. By elucidating the predictive power of neural approach-bias activations for future cannabis use and problem severity, we aimed at identifying new intervention targets. Using functional Magnetic Resonance Imaging (fMRI), neural approach-bias activations were measured with a Stimulus Response Compatibility task (SRC) and compared between 33 heavy cannabis users and 36 matched controls. In addition, associations were examined between approach-bias activations and cannabis use and problem severity at baseline and at six-month follow-up. Approach-bias activations did not differ between heavy cannabis users and controls. However, within the group of heavy cannabis users, a positive relation was observed between total lifetime cannabis use and approach-bias activations in various fronto-limbic areas. Moreover, approach-bias activations in the dorsolateral prefrontal cortex (DLPFC) and anterior cingulate cortex (ACC) independently predicted cannabis problem severity after six months over and beyond session-induced subjective measures of craving. Higher DLPFC/ACC activity during cannabis approach trials, but lower activity during cannabis avoidance trials were associated with decreases in cannabis problem severity. These findings suggest that cannabis users with deficient control over cannabis action tendencies are more likely to develop cannabis related problems. Moreover, the balance between cannabis approach and avoidance responses in the DLPFC and ACC may help identify individuals at-risk for cannabis use disorders and may be new targets for prevention and treatment.
Harty, Seth C; Galanopoulos, Stavroula; Newcorn, Jeffrey H; Halperin, Jeffrey M
To measure the degree to which childhood and adolescent ratings of aggression, attention, and delinquency are related to adolescent substance use outcomes in youth diagnosed with attention-deficit/hyperactivity disorder (ADHD). Childhood externalizing disorders have been shown to predict adolescent maladaptive substance use, but few studies have examined the differential predictive utility of two distinct dimensions of externalizing behavior: aggression and delinquency. Ninety-seven clinically referred children with ADHD initially took part in this research protocol when they were on average 9.05 years of age, and were seen again on average 9.30 years later. Participants' parents were administered the Child Behavior Checklist (CBCL) at baseline and follow-up, and youth completed the Youth Self Report (YSR) in adolescence. At follow-up, substance use severity and diagnosis were assessed using semi-structured psychiatric interviews administered separately to parents and adolescents. Linear and binary logistic regressions were used to determine the association of CBCL- and YSR-rated attention problems, aggression, and delinquency to adolescent substance use. Childhood and adolescent delinquency, but not aggression, as rated by parents and youths, predicted adolescent substance use disorders and substance use severity (all p delinquency and aggression with adolescent substance use, ratings of attention problems in childhood and adolescence were negatively associated with substance use outcome. Children with ADHD who exhibit high rates of delinquency are at risk for later substance use and may require targeted prevention, intervention, and follow-up services. Copyright © American Academy of Addiction Psychiatry.
Søe-Knudsen, Alf; Sorokin, Sergey
This rapid communication is concerned with justification of the 'rule of thumb', which is well known to the community of users of the finite element (FE) method in dynamics, for the accuracy assessment of the wave finite element (WFE) method. An explicit formula linking the size of a window in the dispersion diagram, where the WFE method is trustworthy, with the coarseness of a FE mesh employed is derived. It is obtained by the comparison of the exact Pochhammer-Chree solution for an elastic rod having the circular cross-section with its WFE approximations. It is shown that the WFE power flow predictions are also valid within this window.
The dopamine receptor D4 gene and familial loading interact with perceived parenting in predicting externalizing behavior problems in early adolescence : The TRacking Adolescents' Individual Lives Survey (TRAILS)
Marsman, Rianne; Oldehinkel, Albertine J.; Ormel, Johan; Buitelaar, Jan K.
Although externalizing behavior problems show in general a high stability over time, the course of externalizing behavior problems may vary from individual to individual. Our main goal was to investigate the predictive role of parenting on externalizing behavior problems. In addition, we
Shin-Fu Wu; Chia-Yung Chang; Shie-Jue Lee
Time series prediction has become more popular in various kinds of applications such as weather prediction, control engineering, financial analysis, industrial monitoring, etc. To deal with real-world problems, we are often faced with missing values in the data due to sensor malfunctions or human errors. Traditionally, the missing values are simply omitted or replaced by means of imputation methods. However, omitting those missing values may cause temporal discontinuity. Imputation methods, o...
Parker, Peter A.; Geoffrey, Vining G.; Wilson, Sara R.; Szarka, John L., III; Johnson, Nels G.
The calibration of measurement systems is a fundamental but under-studied problem within industrial statistics. The origins of this problem go back to basic chemical analysis based on NIST standards. In today's world these issues extend to mechanical, electrical, and materials engineering. Often, these new scenarios do not provide "gold standards" such as the standard weights provided by NIST. This paper considers the classic "forward regression followed by inverse regression" approach. In this approach the initial experiment treats the "standards" as the regressor and the observed values as the response to calibrate the instrument. The analyst then must invert the resulting regression model in order to use the instrument to make actual measurements in practice. This paper compares this classical approach to "reverse regression," which treats the standards as the response and the observed measurements as the regressor in the calibration experiment. Such an approach is intuitively appealing because it avoids the need for the inverse regression. However, it also violates some of the basic regression assumptions.
Groen-Blokhuis, Maria M; Middeldorp, Christel M; M van Beijsterveldt, Catharina E; Boomsma, Dorret I
In order to estimate the influence of genetic and environmental factors on 'crying without a cause' and 'being easily upset' in 2-year-old children, a large twin study was carried out. Prospective data were available for ~18,000 2-year-old twin pairs from the Netherlands Twin Register. A bivariate genetic analysis was performed using structural equation modeling in the Mx software package. The influence of maternal personality characteristics and demographic and lifestyle factors was tested to identify specific risk factors that may underlie the shared environment of twins. Furthermore, it was tested whether crying without a cause and being easily upset were predictive of later internalizing, externalizing and attention problems. Crying without a cause yielded a heritability estimate of 60% in boys and girls. For easily upset, the heritability was estimated at 43% in boys and 31% in girls. The variance explained by shared environment varied between 35% and 63%. The correlation between crying without a cause and easily upset (r = .36) was explained both by genetic and shared environmental factors. Birth cohort, gestational age, socioeconomic status, parental age, parental smoking behavior and alcohol use during pregnancy did not explain the shared environmental component. Neuroticism of the mother explained a small proportion of the additive genetic, but not of the shared environmental effects for easily upset. Crying without a cause and being easily upset at age 2 were predictive of internalizing, externalizing and attention problems at age 7, with effect sizes of .28-.42. A large influence of shared environmental factors on crying without a cause and easily upset was detected. Although these effects could be specific to these items, we could not explain them by personality characteristics of the mother or by demographic and lifestyle factors, and we recognize that these effects may reflect other maternal characteristics. A substantial influence of genetic factors
Full Text Available The problem of biased time series mathematical model parameter estimates is well known to be insurmountable. When used to predict future values by extrapolation, even a de minimis bias will eventually grow into a large bias, with misleading results. This paper elucidates how combining antithetic time series' solves this baffling problem of bias in the fitted and forecast values by dynamic bias cancellation. Instead of growing to infinity, the average error can converge to a constant. (original abstract
Dishion, Thomas J; Forgatch, Marion; Van Ryzin, Mark; Winter, Charlotte
In this study we examined the videotaped family interactions of a community sample of adolescents and their parents. Youths were assessed in early to late adolescence on their levels of antisocial behavior. At age 16-17, youths and their parents were videotaped interacting while completing a variety of tasks, including family problem solving. The interactions were coded and compared for three developmental patterns of antisocial behavior: early onset, persistent; adolescence onset; and typically developing. The mean duration of conflict bouts was the only interaction pattern that discriminated the 3 groups. In the prediction of future antisocial behavior, parent and youth reports of transition entropy and conflict resolution interacted to account for antisocial behavior at age 18-19. Families with low entropy and peaceful resolutions predicted low levels of youth antisocial behavior at age 18-19. These findings suggest the need to study both attractors and repellers to understand family dynamics associated with health and social and emotional development.
Eensoo, Diva; Paaver, Marika; Harro, Maarike; Harro, Jaanus
The aim of the study was to characterize the predictive value of socio-economic data, alcohol consumption measures, smoking, platelet monoamine oxidase (MAO) activity, traffic behaviour habits and impulsivity measures for actual drunk driving. Data were collected from 203 male drunk driving offenders and 211 control subjects using self-reported questionnaires, and blood samples were obtained from the two groups. We identified the combination of variables, which predicted correctly, approximately 80% of the subjects' belonging to the drunk driving and control groups. Significant independent discriminators in the final model were, among the health-behaviour measures, alcohol-related problems, frequency of using alcohol, the amount of alcohol consumed and smoking. Predictive traffic behaviour measures were seat belt use and paying for parking. Among the impulsivity measures, dysfunctional impulsivity was the best predictor; platelet MAO activity and age also had an independent predictive value. Our results support the notion that drunk driving is the result of a combination of various behavioural, biological and personality-related risk factors.
Amini, Y; Emdad, H; Farid, M
Piezoelectric energy harvesting (PEH) from ambient energy sources, particularly vibrations, has attracted considerable interest throughout the last decade. Since fluid flow has a high energy density, it is one of the best candidates for PEH. Indeed, a piezoelectric energy harvesting process from the fluid flow takes the form of natural three-way coupling of the turbulent fluid flow, the electromechanical effect of the piezoelectric material and the electrical circuit. There are some experimental and numerical studies about piezoelectric energy harvesting from fluid flow in literatures. Nevertheless, accurate modeling for predicting characteristics of this three-way coupling has not yet been developed. In the present study, accurate modeling for this triple coupling is developed and validated by experimental results. A new code based on this modeling in an openFOAM platform is developed. (paper)
Fox, Mark C; Mitchum, Ainsley L
The trend of rising scores on intelligence tests raises important questions about the comparability of variation within and between time periods. Descriptions of the processes that mediate selection of item responses provide meaningful psychological criteria upon which to base such comparisons. In a recent paper, Fox and Mitchum presented and tested a cognitive theory of rising scores on analogical and inductive reasoning tests that is specific enough to make novel predictions about cohort differences in patterns of item responses for tests such as the Raven's Matrices. In this paper we extend the same proposal in two important ways by (1) testing it against a dataset that enables the effects of cohort to be isolated from those of age, and (2) applying it to two other inductive reasoning tests that exhibit large Flynn effects: Letter Series and Word Series. Following specification and testing of a confirmatory item response model, predicted violations of measurement invariance are observed between two age-matched cohorts that are separated by only 20 years, as members of the later cohort are found to map objects at higher levels of abstraction than members of the earlier cohort who possess the same overall level of ability. Results have implications for the Flynn effect and cognitive aging while underscoring the value of establishing psychological criteria for equating members of distinct groups who achieve the same scores.
Aljoumani, Basem; Kluge, Björn; sanchez, Josep; Wessolek, Gerd
Highways and main roads are potential sources of contamination for the surrounding environment. High traffic rates result in elevated heavy metal concentrations in road runoff, soil and water seepage, which has attracted much attention in the recent past. Prediction of heavy metals transfer near the roadside into deeper soil layers are very important to prevent the groundwater pollution. This study was carried out on data of a number of lysimeters which were installed along the A115 highway (Germany) with a mean daily traffic of 90.000 vehicles per day. Three polyethylene (PE) lysimeters were installed at the A115 highway. They have the following dimensions: length 150 cm, width 100 cm, height 60 cm. The lysimeters were filled with different soil materials, which were recently used for embankment construction in Germany. With the obtained data, we will develop a time series analysis model to predict total and dissolved metal concentration in road runoff and in soil solution of the roadside embankments. The time series consisted of monthly measurements of heavy metals and was transformed to a stationary situation. Subsequently, the transformed data will be used to conduct analyses in the time domain in order to obtain the parameters of a seasonal autoregressive integrated moving average (ARIMA) model. Four phase approaches for identifying and fitting ARIMA models will be used: identification, parameter estimation, diagnostic checking, and forecasting. An automatic selection criterion, such as the Akaike information criterion, will use to enhance this flexible approach to model building
Mark C Fox
Full Text Available The trend of rising scores on intelligence tests raises important questions about the comparability of variation within and between time periods. Descriptions of the processes that mediate selection of item responses provide meaningful psychological criteria upon which to base such comparisons. In a recent paper, Fox and Mitchum presented and tested a cognitive theory of rising scores on analogical and inductive reasoning tests that is specific enough to make novel predictions about cohort differences in patterns of item responses for tests such as the Raven's Matrices. In this paper we extend the same proposal in two important ways by (1 testing it against a dataset that enables the effects of cohort to be isolated from those of age, and (2 applying it to two other inductive reasoning tests that exhibit large Flynn effects: Letter Series and Word Series. Following specification and testing of a confirmatory item response model, predicted violations of measurement invariance are observed between two age-matched cohorts that are separated by only 20 years, as members of the later cohort are found to map objects at higher levels of abstraction than members of the earlier cohort who possess the same overall level of ability. Results have implications for the Flynn effect and cognitive aging while underscoring the value of establishing psychological criteria for equating members of distinct groups who achieve the same scores.
Full Text Available Pine forests (Pinus tabulaeformis have been in danger of defoliation by a caterpillar in the west Liaoning province of China for more than thirty years. This paper aims to assess and predict the degree of damage to pine forests by using remote sensing and ancillary data. Through regression analysis of the pine foliage remaining ratios of field plots with several vegetation indexes of Landsat data, a feasible inversion model was obtained to detect the degree of damage using the Normalized Difference Infrared Index of 5th band (NDII5. After comparing the inversion result of the degree of damage to the pine in 29 years and the historical damage record, quantized results of damage assessment in a long time-series were accurately obtained. Based on the correlation analysis between meteorological variables and the degree of damage from 1984 to 2015, the average degree of damage was predicted in temporal scale. By adding topographic and other variables, a linear prediction model in spatiotemporal scale was constructed. The spatiotemporal model was based on 5015 public pine points for 24 years and reached 0.6169 in the correlation coefficient. This paper provided a feasible and quantitative method in the spatiotemporal prediction of forest pest occurrence by remote sensing.
Khalili-Damghani, Kaveh; Amiri, Maghsoud
In this paper, a procedure based on efficient epsilon-constraint method and data envelopment analysis (DEA) is proposed for solving binary-state multi-objective reliability redundancy allocation series-parallel problem (MORAP). In first module, a set of qualified non-dominated solutions on Pareto front of binary-state MORAP is generated using an efficient epsilon-constraint method. In order to test the quality of generated non-dominated solutions in this module, a multi-start partial bound enumeration algorithm is also proposed for MORAP. The performance of both procedures is compared using different metrics on well-known benchmark instance. The statistical analysis represents that not only the proposed efficient epsilon-constraint method outperform the multi-start partial bound enumeration algorithm but also it improves the founded upper bound of benchmark instance. Then, in second module, a DEA model is supplied to prune the generated non-dominated solutions of efficient epsilon-constraint method. This helps reduction of non-dominated solutions in a systematic manner and eases the decision making process for practical implementations. - Highlights: ► A procedure based on efficient epsilon-constraint method and DEA was proposed for solving MORAP. ► The performance of proposed procedure was compared with a multi-start PBEA. ► Methods were statistically compared using multi-objective metrics.
Full Text Available The main goal of the current study is to take advantage of advanced numerical and intelligent tools to predict the speed of a vehicle using time series. It is clear that the uncertainty caused by temporal behavior of the driver as well as various external disturbances on the road will affect the vehicle speed, and thus, the vehicle power demands. The prediction of upcoming power demands can be employed by the vehicle powertrain control systems to improve significantly the fuel economy and emission performance. Therefore, it is important to systems design engineers and automotive industrialists to develop efficient numerical tools to overcome the risk of unpredictability associated with the vehicle speed profile on roads. In this study, the authors propose an intelligent tool called evolutionary least learning machine (E-LLM to forecast the vehicle speed sequence. To have a practical evaluation regarding the efficacy of E-LLM, the authors use the driving data collected on the San Francisco urban roads by a private Honda Insight vehicle. The concept of sliding window time series (SWTS analysis is used to prepare the database for the speed forecasting process. To evaluate the performance of the proposed technique, a number of well-known approaches, such as auto regressive (AR method, back-propagation neural network (BPNN, evolutionary extreme learning machine (E-ELM, extreme learning machine (ELM, and radial basis function neural network (RBFNN, are considered. The performances of the rival methods are then compared in terms of the mean square error (MSE, root mean square error (RMSE, mean absolute percentage error (MAPE, median absolute percentage error (MDAPE, and absolute fraction of variances (R2 metrics. Through an exhaustive comparative study, the authors observed that E-LLM is a powerful tool for predicting the vehicle speed profiles. The outcomes of the current study can be of use for the engineers of automotive industry who have been
Fiona J. Duff
Full Text Available There is a lack of stability in language difficulties across early childhood: most late talkers (LTs resolve their difficulties by pre-school; and a significant number of children who were not LTs subsequently manifest language difficulties. Greater reliability in predicting individual outcomes is needed, which might be achieved by waiting until later in development when language is more stable. At 18 months, productive vocabulary scores on the Oxford Communicative Developmental Inventory were used to classify children as LTs or average talkers (ATs. Thirty matched-pairs of LTs and ATs were followed up at school-age (average age 7 years, when language and literacy outcomes were assessed. For 18 children, intermediate testing at age 4 had classified them as showing typical development (TD or specific language impairment (SLI. After correcting for multiple comparisons, there were no significant differences between the LTs and ATs on any outcome measure, and the LTs were performing in the average range. However, there were large-sized effects on all outcomes when comparing the TD and SLI groups. LT status on its own is not determinative of language and literacy difficulties. It would therefore not be appropriate to use expressive vocabulary measures alone to screen for language difficulties at 18 months. However, children with language impairment at age 4 are at risk of enduring difficulties.
Zhang, Yingtao; Wang, Tao; Liu, Kangkang; Xia, Yao; Lu, Yi; Jing, Qinlong; Yang, Zhicong; Hu, Wenbiao; Lu, Jiahai
Dengue is a re-emerging infectious disease of humans, rapidly growing from endemic areas to dengue-free regions due to favorable conditions. In recent decades, Guangzhou has again suffered from several big outbreaks of dengue; as have its neighboring cities. This study aims to examine the impact of dengue epidemics in Guangzhou, China, and to develop a predictive model for Zhongshan based on local weather conditions and Guangzhou dengue surveillance information. We obtained weekly dengue case data from 1st January, 2005 to 31st December, 2014 for Guangzhou and Zhongshan city from the Chinese National Disease Surveillance Reporting System. Meteorological data was collected from the Zhongshan Weather Bureau and demographic data was collected from the Zhongshan Statistical Bureau. A negative binomial regression model with a log link function was used to analyze the relationship between weekly dengue cases in Guangzhou and Zhongshan, controlling for meteorological factors. Cross-correlation functions were applied to identify the time lags of the effect of each weather factor on weekly dengue cases. Models were validated using receiver operating characteristic (ROC) curves and k-fold cross-validation. Our results showed that weekly dengue cases in Zhongshan were significantly associated with dengue cases in Guangzhou after the treatment of a 5 weeks prior moving average (Relative Risk (RR) = 2.016, 95% Confidence Interval (CI): 1.845-2.203), controlling for weather factors including minimum temperature, relative humidity, and rainfall. ROC curve analysis indicated our forecasting model performed well at different prediction thresholds, with 0.969 area under the receiver operating characteristic curve (AUC) for a threshold of 3 cases per week, 0.957 AUC for a threshold of 2 cases per week, and 0.938 AUC for a threshold of 1 case per week. Models established during k-fold cross-validation also had considerable AUC (average 0.938-0.967). The sensitivity and specificity
Full Text Available Dengue is a re-emerging infectious disease of humans, rapidly growing from endemic areas to dengue-free regions due to favorable conditions. In recent decades, Guangzhou has again suffered from several big outbreaks of dengue; as have its neighboring cities. This study aims to examine the impact of dengue epidemics in Guangzhou, China, and to develop a predictive model for Zhongshan based on local weather conditions and Guangzhou dengue surveillance information.We obtained weekly dengue case data from 1st January, 2005 to 31st December, 2014 for Guangzhou and Zhongshan city from the Chinese National Disease Surveillance Reporting System. Meteorological data was collected from the Zhongshan Weather Bureau and demographic data was collected from the Zhongshan Statistical Bureau. A negative binomial regression model with a log link function was used to analyze the relationship between weekly dengue cases in Guangzhou and Zhongshan, controlling for meteorological factors. Cross-correlation functions were applied to identify the time lags of the effect of each weather factor on weekly dengue cases. Models were validated using receiver operating characteristic (ROC curves and k-fold cross-validation.Our results showed that weekly dengue cases in Zhongshan were significantly associated with dengue cases in Guangzhou after the treatment of a 5 weeks prior moving average (Relative Risk (RR = 2.016, 95% Confidence Interval (CI: 1.845-2.203, controlling for weather factors including minimum temperature, relative humidity, and rainfall. ROC curve analysis indicated our forecasting model performed well at different prediction thresholds, with 0.969 area under the receiver operating characteristic curve (AUC for a threshold of 3 cases per week, 0.957 AUC for a threshold of 2 cases per week, and 0.938 AUC for a threshold of 1 case per week. Models established during k-fold cross-validation also had considerable AUC (average 0.938-0.967. The sensitivity and
Van Daele, Tom; Van den Bergh, Omer; Van Audenhove, Chantal; Raes, Filip; Hermans, Dirk
Research has shown that overgeneral autobiographical memory (OGM) is a valid predictor for the course of depression. It is not known, however, whether OGM also moderates information uptake and consolidation in a psychoeducation program to prevent stress, anxiety and depression. The present study was designed to investigate whether the Autobiographical Memory Test (AMT; Williams, & Broadbent, 1986) is a valid predictor for the actual unfolding of skills learned through psychoeducation. The questionnaire included primarily the AMT and the Stress Anxiety Depression Means-Ends Problem Solving Questionnaire (SAD-MEPS). It was filled in prior to and after the psychoeducational course by 23 participants. Correlations were calculated for the AMT at baseline and the differences between the pre and post measurements on the SAD-MEPS. Significant correlations were observed between the number of specific responses and the changes in the number of relevant means (r = .49, p < .01). The sample size was rather small, but several checks were able to reduce the chance of spurious findings. These findings may have important implications for the guidance to and the setup of psychoeducational interventions. Suggestions include screening and memory specificity training prior to course commencement. Copyright © 2011 Elsevier Ltd. All rights reserved.
Davis, Alexander L.; Krishnamurti, Tamar
Highlights: • Energy efficiency pilot studies suffer from severe volunteer bias. • We formulate an approach for accommodating volunteer bias. • A short questionnaire and classification trees can control for the bias. - Abstract: This paper discusses volunteer bias in residential energy efficiency studies. We briefly evaluate the bias in existing studies. We then show how volunteer bias can be corrected when not avoidable, using an on-line study of intentions to enroll in an in-home display trial as an example. We found that the best predictor of intentions to enroll was expected benefit from the in-home display. Constraints on participation, such as time in the home and trust in scientists, were also associated with enrollment intentions. Using Breiman’s classification tree algorithm we found that the best model of intentions to enroll contained only five variables: expected enjoyment of the program, presence in the home during morning hours, trust (in friends and in scientists), and perceived ability to handle unexpected problems. These results suggest that a short questionnaire, that takes at most 1 min to complete, would allow better control of volunteer bias than a more extensive questionnaire. This paper should allow researchers who employ field studies involving human behavior to be better equipped to address volunteer bias
Déry, Michèle; Lapalme, Mélanie; Jagiellowicz, Jadzia; Poirier, Martine; Temcheff, Caroline; Toupin, Jean
This study investigated the relationship between the three DSM-5 categories of oppositional defiant disorder (ODD) symptoms (irritable mood, defiant behavior, vindictive behavior) and anxiety/depression in girls and boys with conduct problems (CP) while controlling for comorbid child psychopathology at baseline. Data were drawn from an ongoing longitudinal study of 6- to 9-year-old French-Canadian children (N = 276; 40.8 % girls) receiving special educational services for CP at school and followed for 2 years. Using linear regression analysis, the results showed that irritable mood symptoms predicted a higher level of depression and anxiety in girls and boys 2 years later, whereas the behavioral symptoms of ODD (e.g., defiant, vindictive symptoms) were linked to lower depression scores. The contribution of ODD symptoms to these predictions, while statistically significant, remained modest. The usefulness of ODD irritable symptoms as a marker for identifying girls and boys with CP who are more vulnerable to developing internalizing problems is discussed.
Full Text Available The timing of defibrillation is mostly at arbitrary intervals during cardio-pulmonary resuscitation (CPR, rather than during intervals when the out-of-hospital cardiac arrest (OOH-CA patient is physiologically primed for successful countershock. Interruptions to CPR may negatively impact defibrillation success. Multiple defibrillations can be associated with decreased post-resuscitation myocardial function. We hypothesize that a more complete picture of the cardiovascular system can be gained through non-linear dynamics and integration of multiple physiologic measures from biomedical signals.Retrospective analysis of 153 anonymized OOH-CA patients who received at least one defibrillation for ventricular fibrillation (VF was undertaken. A machine learning model, termed Multiple Domain Integrative (MDI model, was developed to predict defibrillation success. We explore the rationale for non-linear dynamics and statistically validate heuristics involved in feature extraction for model development. Performance of MDI is then compared to the amplitude spectrum area (AMSA technique.358 defibrillations were evaluated (218 unsuccessful and 140 successful. Non-linear properties (Lyapunov exponent > 0 of the ECG signals indicate a chaotic nature and validate the use of novel non-linear dynamic methods for feature extraction. Classification using MDI yielded ROC-AUC of 83.2% and accuracy of 78.8%, for the model built with ECG data only. Utilizing 10-fold cross-validation, at 80% specificity level, MDI (74% sensitivity outperformed AMSA (53.6% sensitivity. At 90% specificity level, MDI had 68.4% sensitivity while AMSA had 43.3% sensitivity. Integrating available end-tidal carbon dioxide features into MDI, for the available 48 defibrillations, boosted ROC-AUC to 93.8% and accuracy to 83.3% at 80% sensitivity.At clinically relevant sensitivity thresholds, the MDI provides improved performance as compared to AMSA, yielding fewer unsuccessful defibrillations
Marseguerra, M.; Minoggio, S.; Rossi, A.; Zio, E.
The correlated noise affecting many industrial plants under stationary or cyclo-stationary conditions - nuclear reactors included -has been successfully modeled by autoregressive moving average (ARMA) due to the versatility of this technique. The relatively recent neural network methods have similar features and much effort is being devoted to exploring their usefulness in forecasting and control. Identifying a signal by means of an ARMA model gives rise to the problem of selecting its correct order. Similar difficulties must be faced when applying neural network methods and, specifically, particular care must be given to the setting up of the appropriate network topology, the data normalization procedure and the learning code. In the present paper the capability of some neural networks of learning ARMA and seasonal ARMA processes is investigated. The results of the tested cases look promising since they indicate that the neural networks learn the underlying process with relative ease so that their forecasting capability may represent a convenient fault diagnosis tool. (Author)
Sklar, Leonard S.; Riebe, Clifford S.; Marshall, Jill A.; Genetti, Jennifer; Leclere, Shirin; Lukens, Claire L.; Merces, Viviane
Sediments link hillslopes to river channels. The size of sediments entering channels is a key control on river morphodynamics across a range of scales, from channel response to human land use to landscape response to changes in tectonic and climatic forcing. However, very little is known about what controls the size distribution of particles eroded from bedrock on hillslopes, and how particle sizes evolve before sediments are delivered to channels. Here we take the first steps toward building a geomorphic transport law to predict the size distribution of particles produced on hillslopes and supplied to channels. We begin by identifying independent variables that can be used to quantify the influence of five key boundary conditions: lithology, climate, life, erosion rate, and topography, which together determine the suite of geomorphic processes that produce and transport sediments on hillslopes. We then consider the physical and chemical mechanisms that determine the initial size distribution of rock fragments supplied to the hillslope weathering system, and the duration and intensity of weathering experienced by particles on their journey from bedrock to the channel. We propose a simple modeling framework with two components. First, the initial rock fragment sizes are set by the distribution of spacing between fractures in unweathered rock, which is influenced by stresses encountered by rock during exhumation and by rock resistance to fracture propagation. That initial size distribution is then transformed by a weathering function that captures the influence of climate and mineralogy on chemical weathering potential, and the influence of erosion rate and soil depth on residence time and the extent of particle size reduction. Model applications illustrate how spatial variation in weathering regime can lead to bimodal size distributions and downstream fining of channel sediment by down-valley fining of hillslope sediment supply, two examples of hillslope control on
Pandey, Devi D.
In Environmental and Human Bio monitoring studies of Hazardous xenobiotics over living system particularly at cell level, it is desirable to have easy and sensitive test system like Cell Viability assay, MNT, Cell Culture photo toxicity Test, PTGT etc. Out of these the PTGT quite better than other because the in vitro culture of pollen grain can provides a sensitive indication of toxicity at cellular level, since germination and growth of pollen tube will inhibited in presence of toxic substance like DDT, Heavy metal, even Radionuclide's. This test system is easy, economical and widely accepted through out world. In PTGT pollen tube never containing Chloroplast or other plastids so pollen tube resembles animals more than a plant organ and is therefore also a suitable as model for Genotoxicity Assessment of compounds harmful to animal and humans. Lack of plastids in PT, PTGT will not identify the toxic effect of compounds that targets Non cyclic and cyclic photoposphorylation of photosynthesis. This test system valid in International Toxicity Testing Protocol. But this method is time consuming and problem in measurement of pollen tube growing in a culture medium became usually bent and make measurement difficult. Other disadvantage of this method is requirement of DMSO to dissolve test substance of low water suitability in culture medium. DMSO shown to have no effect on PTG at Concentration not more than 1% but some extent interfere with results. Values of PTG are quantified in ED50/IC50 that is the concentration of test compounds that reduces pollen tube growth to 50% of control. So PTGT could be very sensitive and easy to assess in common lab in International way. (author)
Full Text Available Early information on treatment response of HCC to local ablative therapy is crucial. Elastography as a non-invasive method has recently been shown to play a potential role in distinguishing between benign and malignant liver lesions. Elastography of hepatocellular carcinoma (HCC in early response to local ablative therapy has not been studied to date.We prospectively included a cohort of 14 patients with diagnosis of HCC who were treated with local ablative therapy (transarterial chemoembolization, TACE and/or radiofrequency ablation, RFA. We used 2D shear-wave elastography (RT 2D-SWE to examine stiffness of HCC lesion before and 3, 30 and 90 days after local ablative therapy. Contrast-enhanced imaging after 90 days was performed to evaluate treatment response. Primary endpoint was stiffness of HCC in response to local ablative therapy. Secondary end point was tumor recurrence.Stiffness of HCC nodules and liver showed no significant difference prior to local ablative therapy. As early as three days after treatment, stiffness of responding HCC was significantly higher compared to non-responding. Higher stiffness before treatment was significantly associated with tumor recurrence.Nodule stiffness in general and RT 2D-SWE in particular could provide a useful tool for early prediction of HCC response to local ablative therapy.
Lang, Peter; Wojcik, Tomasz; Povoden-Karadeniz, Erwin; Falahati, Ahmad; Kozeschnik, Ernst
Highlights: • Comparison of laboratory Al–Zn–Mg alloy to industrial Al 7xxx series. • Heat flow evolution during non-isothermal DSC analysis is calculated. • TEM investigations of laboratory Al–Zn–Mg alloy at three pronounced temperatures. • Simulation and modelling of precipitation sequence. • Calculation and prediction of heat flow curves of Al 7xxx series. - Abstract: The technological properties of heat treatable Al–Zn–Mg alloys originate in the morphology and distribution of metastable particles. Starting from the solution-annealed condition, this paper describes the precipitate evolution during non-isothermal temperature changes, namely continuous heating differential scanning calorimetry (DSC) analysis. The distribution and the morphology of the metastable and stable precipitates and the heat flow accompanying the precipitation process is investigated experimentally and calculated by numerical thermo-kinetic simulations. The computer simulation results of the sizes and distributions are confirmed by transmission electron microscopy (TEM). The theoretical background and the results of the investigations are discussed
Lang, Peter, E-mail: firstname.lastname@example.org [Department of Materials Science and Metallurgy, University of Cambridge, Charles Babbage Road 27, Cambridge CB3 0FS (United Kingdom); Wojcik, Tomasz [Institute of Materials Science and Technology, Vienna University of Technology, Favoritenstraße 9-11, Vienna 1040 (Austria); Povoden-Karadeniz, Erwin [Institute of Materials Science and Technology, Vienna University of Technology, Favoritenstraße 9-11, Vienna 1040 (Austria); Christian Doppler Laboratory “Early Stages of Precipitation”, Institute of Materials Science and Technology, Vienna University of Technology, Favoritenstraße 9-11, Vienna 1040 (Austria); Falahati, Ahmad [Institute of Materials Science and Technology, Vienna University of Technology, Favoritenstraße 9-11, Vienna 1040 (Austria); Kozeschnik, Ernst [Institute of Materials Science and Technology, Vienna University of Technology, Favoritenstraße 9-11, Vienna 1040 (Austria); Christian Doppler Laboratory “Early Stages of Precipitation”, Institute of Materials Science and Technology, Vienna University of Technology, Favoritenstraße 9-11, Vienna 1040 (Austria)
Highlights: • Comparison of laboratory Al–Zn–Mg alloy to industrial Al 7xxx series. • Heat flow evolution during non-isothermal DSC analysis is calculated. • TEM investigations of laboratory Al–Zn–Mg alloy at three pronounced temperatures. • Simulation and modelling of precipitation sequence. • Calculation and prediction of heat flow curves of Al 7xxx series. - Abstract: The technological properties of heat treatable Al–Zn–Mg alloys originate in the morphology and distribution of metastable particles. Starting from the solution-annealed condition, this paper describes the precipitate evolution during non-isothermal temperature changes, namely continuous heating differential scanning calorimetry (DSC) analysis. The distribution and the morphology of the metastable and stable precipitates and the heat flow accompanying the precipitation process is investigated experimentally and calculated by numerical thermo-kinetic simulations. The computer simulation results of the sizes and distributions are confirmed by transmission electron microscopy (TEM). The theoretical background and the results of the investigations are discussed.
Foster, J. R.; D'Amato, A. W.; Itter, M.; Reinikainen, M.; Curzon, M.
The terrestrial carbon cycle is perturbed when disturbances remove leaf biomass from the forest canopy during the growing season. Changes in foliar biomass arise from defoliation caused by insects, disease, drought, frost or human management. As ephemeral disturbances, these often go undetected and their significance to models that predict forest growth from climatic drivers remains unknown. Here, we seek to distinguish the roles of weather vs. canopy disturbance on forest growth by using dense Landsat time-series to quantify departures in mean phenology that in turn predict changes in leaf biomass. We estimated a foliar biomass index (FBMI) from 1984-2016, and predict plot-level wood growth over 28 years on 156 tree-ring monitoring plots in Minnesota, USA. We accessed the entire Landsat archive (sensors 4, 5 & 7) to compute FBMI using Google Earth Engine's cloud computing platform (GEE). GEE allows this pixel-level approach to be applied at any location; a feature we demonstrate with published wood-growth data from flux tower sites. Our Bayesian models predicted biomass changes from tree-ring plots as a function of Landsat FBMI and annual climate data. We expected model parameters to vary by tree functional groups defined by differences in xylem anatomy and leaf longevity, two traits with linkages to phenology, as reported in a recent review. We found that Landsat FBMI was a surprisingly strong predictor of aggregate wood-growth, explaining up to 80% of annual growth variation for some deciduous plots. Growth responses to canopy disturbance varied among tree functional groups, and the importance of some seasonal climate metrics diminished or changed sign when FBMI was included (e.g. fall and spring climatic water deficit), while others remained unchanged (current and lagged summer deficit). Insights emerging from these models can clear up sources of persistent uncertainty and open a new frontier for models of forest productivity.
Singh, A. K.; Toshniwal, D.
The MODIS Joint Atmosphere product, MODATML2 and MYDATML2 L2/3 provided by LAADS DAAC (Level-1 and Atmosphere Archive & Distribution System Distributed Active Archive Center) re-sampled from medium resolution MODIS Terra /Aqua Satellites data at 5km scale, contains Cloud Reflectance, Cloud Top Temperature, Water Vapor, Aerosol Optical Depth/Thickness, Humidity data. These re-sampled data, when used for deriving climatic effects of aerosols (particularly in case of cooling effect) still exposes limitations in presence of uncertainty measures in atmospheric artifacts such as aerosol, cloud, cirrus cloud etc. The effect of uncertainty measures in these artifacts imposes an important challenge for estimation of aerosol effects, adequately affecting precise regional weather modeling and predictions: Forecasting and recommendation applications developed largely depend on these short-term local conditions (e.g. City/Locality based recommendations to citizens/farmers based on local weather models). Our approach inculcates artificial intelligence technique for representing heterogeneous data(satellite data along with air quality data from local weather stations (i.e. in situ data)) to learn, correct and predict aerosol effects in the presence of cloud and other atmospheric artifacts, defusing Spatio-temporal correlations and regressions. The Big Data process pipeline consisting correlation and regression techniques developed on Apache Spark platform can easily scale for large data sets including many tiles (scenes) and over widened time-scale. Keywords: Climatic Effects of Aerosols, Situation-Aware, Big Data, Apache Spark, MODIS Terra /Aqua, Time Series
The dopamine receptor D4 gene and familial loading interact with perceived parenting in predicting externalizing behavior problems in early adolescence: the TRacking Adolescents' Individual Lives Survey (TRAILS).
Marsman, Rianne; Oldehinkel, Albertine J; Ormel, Johan; Buitelaar, Jan K
Although externalizing behavior problems show in general a high stability over time, the course of externalizing behavior problems may vary from individual to individual. Our main goal was to investigate the predictive role of parenting on externalizing behavior problems. In addition, we investigated the potential moderating role of gender and genetic risk (operationalized as familial loading of externalizing behavior problems (FLE), and presence or absence of the dopamine receptor D4 (DRD4) 7-repeat and 4-repeat allele, respectively). Perceived parenting (rejection, emotional warmth, and overprotection) and FLE were assessed in a population-based sample of 1768 10- to 12-year-old adolescents. Externalizing behavior problems were assessed at the same age and 212 years later by parent report (CBCL) and self-report (YSR). DNA was extracted from blood samples. Parental emotional warmth predicted lower, and parental overprotection and rejection predicted higher levels of externalizing behavior problems. Whereas none of the parenting factors interacted with gender and the DRD4 7-repeat allele, we did find interaction effects with FLE and the DRD4 4-repeat allele. That is, the predictive effect of parental rejection was only observed in adolescents from low FLE families and the predictive effect of parental overprotection was stronger in adolescents not carrying the DRD4 4-repeat allele. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.
Tolstov, Georgi P
Richard A. Silverman's series of translations of outstanding Russian textbooks and monographs is well-known to people in the fields of mathematics, physics, and engineering. The present book is another excellent text from this series, a valuable addition to the English-language literature on Fourier series.This edition is organized into nine well-defined chapters: Trigonometric Fourier Series, Orthogonal Systems, Convergence of Trigonometric Fourier Series, Trigonometric Series with Decreasing Coefficients, Operations on Fourier Series, Summation of Trigonometric Fourier Series, Double Fourie
Vetrayan, Jayachandran; Othman, Suhana; Victor Paulraj, Smily Jesu Priya
To assess the effectiveness and feasibility of behavioral sleep intervention for medicated children with ADHD. Six medicated children (five boys, one girl; aged 6-12 years) with ADHD participated in a 4-week sleep intervention program. The main behavioral strategies used were Faded Bedtime With Response Cost (FBRC) and positive reinforcement. Within a case-series design, objective measure (Sleep Disturbance Scale for Children [SDSC]) and subjective measure (sleep diaries) were used to record changes in children's sleep. For all six children, significant decrease was found in the severity of children's sleep problems (based on SDSC data). Bedtime resistance and mean sleep onset latency were reduced following the 4-week intervention program according to sleep diaries data. Gains were generally maintained at the follow-up. Parents perceived the intervention as being helpful. Based on the initial data, this intervention shows promise as an effective and feasible treatment.
Full Text Available Time series prediction has become more popular in various kinds of applications such as weather prediction, control engineering, financial analysis, industrial monitoring, etc. To deal with real-world problems, we are often faced with missing values in the data due to sensor malfunctions or human errors. Traditionally, the missing values are simply omitted or replaced by means of imputation methods. However, omitting those missing values may cause temporal discontinuity. Imputation methods, on the other hand, may alter the original time series. In this study, we propose a novel forecasting method based on least squares support vector machine (LSSVM. We employ the input patterns with the temporal information which is defined as local time index (LTI. Time series data as well as local time indexes are fed to LSSVM for doing forecasting without imputation. We compare the forecasting performance of our method with other imputation methods. Experimental results show that the proposed method is promising and is worth further investigations.
Manshour, Pouya [Physics Department, Persian Gulf University, Bushehr 75169 (Iran, Islamic Republic of)
In order to extract correlation information inherited in stochastic time series, the visibility graph algorithm has been recently proposed, by which a time series can be mapped onto a complex network. We demonstrate that the visibility algorithm is not an appropriate one to study the correlation aspects of a time series. We then employ the horizontal visibility algorithm, as a much simpler one, to map fractional processes onto complex networks. The degree distributions are shown to have parabolic exponential forms with Hurst dependent fitting parameter. Further, we take into account other topological properties such as maximum eigenvalue of the adjacency matrix and the degree assortativity, and show that such topological quantities can also be used to predict the Hurst exponent, with an exception for anti-persistent fractional Gaussian noises. To solve this problem, we take into account the Spearman correlation coefficient between nodes' degrees and their corresponding data values in the original time series.
Emerging research has identified sub-dimensions of oppositional defiant disorder – irritability and defiance -that differentially predict internalizing and externalizing symptoms in preschoolers, children, and adolescents. Using a theoretical approach and confirmatory factor analyses to distinguish between irritability and defiance, we investigate the associations among these dimensions and internalizing (anxiety and depression) and externalizing problems (conduct problems) within and across time in a community-based sample of 662 youth (342 females) spanning ages 12 to 18 years old at baseline. On average, irritability was stable across assessment points and defiance declined. Within time, associations of irritability with internalizing were consistently stronger than associations of irritability with conduct problems. Defiance was similarly associated within time with both internalizing and conduct problems in mid-adolescence, but was more highly related to internalizing than to conduct problems by early adulthood (ages 18 to 25). Over time, increasing irritability was related to changes in both internalizing and conduct problems; whereas increases in defiance predicted increases in conduct problems more strongly than internalizing symptoms. Increases in both internalizing and conduct problems were also associated with subsequent increases in both irritability and defiance. Sex differences in these associations were not significant. PMID:25028284
Kirill A. Chekalov
Full Text Available The essay examines the structure of a 32-volume series of Fantômas novels created by Pierre Souvestre and Marcel Allain; it traces the origins and development of the serial genre and generic novelties related to seriality. The latter include a relative autonomy of each story in each volume and interconnection of the volumes via the figure of the criminal “slipping away” from the hands of justice. The study compares poetological techniques of the fabula development and points out specific features of the Belle époque reality as represented through the introduction of recognizable “cultural signs,” varia- tions of everyday incidents, and newspaper chronicle of criminal events. It also analyzes the image of Fantômas and other recurrent characters of the series (such as Juve, Fan- dor, Hélène, Lady Maud Beltham, etc. The authors examine para-literary features that can be traced in many other different forms such as TV series and graphic novels. They include (1 mythologization of the main character as embodiment of Evil, or a “criminal genius”; (2 confusion of the real and the fictional, verisimilar and extraordinary, horri- ble and comic based on the variations of literary and journalistic clichés; (3 repetition of plot patterns, (4 attempts to guess and to meet reader’s expectations.
Capurso, Gabriele; Marignani, Massimo; Attilia, Fabio; Milione, Massimo; Colarossi, Cristina; Zampaletta, Costantino; Di Giulio, Emilio; Delle Fave, Gianfranco
Microscopic colitis (MC), comprising lymphocytic and collagenous colitis (LC, CC), causes chronic diarrhoea. Lansoprazole can cause MC. Likelihood criteria defining the causative relationship between drugs and MC have not been applied to lansoprazole, nor has lansoprazole-related-MC been characterized. To analyse a series of lansoprazole-related MC cases, and characterize lansoprazole-related CC and LC. Cases were diagnosed over 23 months and causal relationship evaluated by established likelihood criteria. A systematic Medline search was conducted and publications analysed. Eight patients had lansoprazole-related MC. In all cases chronological and causality likelihood scores supported lansoprazole causative role. Discontinuation determined resolution without further treatment. Twenty-five cases of lansoprazole-related MC from 10 publications were grouped with the present series, and differences between CC and LC analysed. CC cases had more macroscopic alterations at colonoscopy (72.2 vs. 6.6%; p=0.0002). Time between lansoprazole start and symptoms onset was longer for CC (median 60 vs. 28 days; p=0.03). Peculiar features of lansoprazole-related CC were described through the analysis of a newly diagnosed lansoprazole-related MC series in which the causative role of lansoprazole was for the first time defined by established likelihood criteria, and by pooled evaluation with other cases retrieved by a systematic literature review. Copyright © 2010 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved.
Bennema, Anne N; Schendelaar, Pamela; Seggers, Jorien; Haadsma, Maaike L; Heineman, Maas Jan; Hadders-Algra, Mijna
General movement (GM) assessment is a well-established tool to predict cerebral palsy in high-risk infants. Little is known on the predictive value of GM assessment in low-risk populations. To assess the predictive value of GM quality in early infancy for the development of the clinically relevant form of minor neurological dysfunction (complex MND) and behavioral problems at preschool age. Prospective cohort study. A total of 216 members of the prospective Groningen Assisted Reproductive Techniques (ART) cohort study were included in this study. ART did not affect neurodevelopmental outcome of these relatively low-risk infants born to subfertile parents. GM quality was determined at 2 weeks and 3 months. At 18 months and 4 years, the Hempel neurological examination was used to assess MND. At 4 years, parents completed the Child Behavior Checklist; this resulted in the total problem score (TPS), internalizing problem score (IPS), and externalizing problem score (EPS). Predictive values of definitely (DA) and mildly (MA) abnormal GMs were calculated. DA GMs at 2 weeks were associated with complex MND at 18 months and atypical TPS and IPS at 4 years (all ppredictive value of DA GMs at 2 weeks were rather low (13%-60%); specificity and negative predictive value were excellent (92%-99%). DA GMs at 3 months occurred too infrequently to calculate prediction. MA GMs were not associated with outcome. GM quality as a single predictor for complex MND and behavioral problems at preschool age has limited clinical value in children at low risk for developmental disorders. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
Sensitivity and specificity of a brief personality screening instrument in predicting future substance use, emotional, and behavioral problems: 18-month predictive validity of the Substance Use Risk Profile Scale.
Castellanos-Ryan, Natalie; O'Leary-Barrett, Maeve; Sully, Laura; Conrod, Patricia
This study assessed the validity, sensitivity, and specificity of the Substance Use Risk Profile Scale (SURPS), a measure of personality risk factors for substance use and other behavioral problems in adolescence. The concurrent and predictive validity of the SURPS was tested in a sample of 1,162 adolescents (mean age: 13.7 years) using linear and logistic regressions, while its sensitivity and specificity were examined using the receiver operating characteristics curve analyses. Concurrent and predictive validity tests showed that all 4 brief scales-hopelessness (H), anxiety sensitivity (AS), impulsivity (IMP), and sensation seeking (SS)-were related, in theoretically expected ways, to measures of substance use and other behavioral and emotional problems. Results also showed that when using the 4 SURPS subscales to identify adolescents "at risk," one can identify a high number of those who developed problems (high sensitivity scores ranging from 72 to 91%). And, as predicted, because each scale is related to specific substance and mental health problems, good specificity was obtained when using the individual personality subscales (e.g., most adolescents identified at high risk by the IMP scale developed conduct or drug use problems within the next 18 months [a high specificity score of 70 to 80%]). The SURPS is a valuable tool for identifying adolescents at high risk for substance misuse and other emotional and behavioral problems. Implications of findings for the use of this measure in future research and prevention interventions are discussed. Copyright © 2012 by the Research Society on Alcoholism.
Borst, J.P.; Taatgen, N.A.; Stocco, A.; Van Rijn, D.H.
Background: It has been shown that people can only maintain one problem state, or intermediate mental representation, at a time. When more than one problem state is required, for example in multitasking, performance decreases considerably. This effect has been explained in terms of a problem state
Fleming, Andrew P; McMahon, Robert J; King, Kevin M
Structured observations of parent-child interactions are commonly used in research and clinical settings, but require additional empirical support. The current study examined the capacity of child-directed play, parent-directed play, and parent-directed chore interaction analogs to uniquely predict the development of conduct problems across a 6-year follow-up period. Parent-child observations were collected from 338 families from high-risk neighborhoods during the summer following the child's first-grade year. Participating children were 49.2 % female, 54.4 % white, and 45.6 % black, and had an average age of 7.52 years at the first assessment. Conduct problems were assessed via parent report and teacher report at five assessment points between first grade and seventh grade. Latent growth curve modeling was used to analyze predictors of conduct problem trajectory across this 6-year follow-up period. When race, sex, socioeconomic status, and maternal depressive symptoms were controlled, parental negative attention during child-directed play predicted higher levels of parent-reported conduct problems concurrently and after a 6-year follow-up period. Parental negative attention during child-directed play also predicted higher teacher-reported conduct problems 6 years later. Findings support the use of child-directed play and parent-directed chore analogs in predicting longitudinal development of conduct problems. The presence of parental negative attention during child-directed play appears to be an especially important predictor of greater conduct problems over time and across multiple domains. Additionally, the potential importance of task-incongruent behavior is proposed for further study.
Teymuri, Ghulam Heidar; Sadeghian, Marzieh; Kangavari, Mehdi; Asghari, Mehdi; Madrese, Elham; Abbasinia, Marzieh; Ahmadnezhad, Iman; Gholizadeh, Yavar
Background: One of the significant dangers that threaten people’s lives is the increased risk of accidents. Annually, more than 1.3 million people die around the world as a result of accidents, and it has been estimated that approximately 300 deaths occur daily due to traffic accidents in the world with more than 50% of that number being people who were not even passengers in the cars. The aim of this study was to examine traffic accidents in Tehran and forecast the number of future accidents using a time-series model. Methods: The study was a cross-sectional study that was conducted in 2011. The sample population was all traffic accidents that caused death and physical injuries in Tehran in 2010 and 2011, as registered in the Tehran Emergency ward. The present study used Minitab 15 software to provide a description of accidents in Tehran for the specified time period as well as those that occurred during April 2012. Results: The results indicated that the average number of daily traffic accidents in Tehran in 2010 was 187 with a standard deviation of 83.6. In 2011, there was an average of 180 daily traffic accidents with a standard deviation of 39.5. One-way analysis of variance indicated that the average number of accidents in the city was different for different months of the year (P accidents occurred in March, July, August, and September. Thus, more accidents occurred in the summer than in the other seasons. The number of accidents was predicted based on an auto-regressive, moving average (ARMA) for April 2012. The number of accidents displayed a seasonal trend. The prediction of the number of accidents in the city during April of 2012 indicated that a total of 4,459 accidents would occur with mean of 149 accidents per day during these three months. Conclusion: The number of accidents in Tehran displayed a seasonal trend, and the number of accidents was different for different seasons of the year. PMID:26120405
Swartz, Johnna R.; Knodt, Annchen R.; Radtke, Spenser R.; Hariri, Ahmad R.
Personality traits such as conscientiousness as self-reported by individuals can help predict a range of outcomes, from job performance to longevity. Asking others to rate the personality of their acquaintances often provides even better predictive power than using self-report. Here, we examine whether peer-reported personality can provide a better link between brain function, namely threat-related amygdala activity, and future health-related behavior, namely problem drinking, than self-reported personality. Using data from a sample of 377 young adult university students who were rated on five personality traits by peers, we find that higher threat-related amygdala activity to fearful facial expressions is associated with higher peer-reported, but not self-reported, conscientiousness. Moreover, higher peer-reported, but not self-reported, conscientiousness predicts lower future problem drinking more than one year later, an effect specific to men. Remarkably, relatively higher amygdala activity has an indirect effect on future drinking behavior in men, linked by peer-reported conscientiousness to lower future problem drinking. Our results provide initial evidence that the perceived conscientiousness of an individual by their peers uniquely reflects variability in a core neural mechanism supporting threat responsiveness. These novel patterns further suggest that incorporating peer-reported measures of personality into individual differences research can reveal novel predictive pathways of risk and protection for problem behaviors. PMID:27717769
S.J. Roza (Sabine); M.B. Hofstra (Marijke); J. van der Ende (Jan); F.C. Verhulst (Frank)
textabstractOBJECTIVE: The goal of this study was to predict the onset of mood and anxiety disorders from parent-reported emotional and behavioral problems in childhood across a 14-year period from childhood into young adulthood. METHOD: In 1983, parent reports of behavioral and
Swartz, Johnna R; Knodt, Annchen R; Radtke, Spenser R; Hariri, Ahmad R
Personality traits such as conscientiousness as self-reported by individuals can help predict a range of outcomes, from job performance to longevity. Asking others to rate the personality of their acquaintances often provides even better predictive power than using self-report. Here, we examine whether peer-reported personality can provide a better link between brain function, namely threat-related amygdala activity, and future health-related behavior, namely problem drinking, than self-reported personality. Using data from a sample of 377 young adult university students who were rated on five personality traits by peers, we find that higher threat-related amygdala activity to fearful facial expressions is associated with higher peer-reported, but not self-reported, conscientiousness. Moreover, higher peer-reported, but not self-reported, conscientiousness predicts lower future problem drinking more than one year later, an effect specific to men. Remarkably, relatively higher amygdala activity has an indirect effect on future drinking behavior in men, linked by peer-reported conscientiousness to lower future problem drinking. Our results provide initial evidence that the perceived conscientiousness of an individual by their peers uniquely reflects variability in a core neural mechanism supporting threat responsiveness. These novel patterns further suggest that incorporating peer-reported measures of personality into individual differences research can reveal novel predictive pathways of risk and protection for problem behaviors. Copyright © 2016 Elsevier Inc. All rights reserved.
Various perturbation series are factorially divergent. The behavior of their high-order terms can be determined by Lipatov's method, which involves the use of instanton configurations of appropriate functional integrals. When the Lipatov asymptotic form is known and several lowest order terms of the perturbation series are found by direct calculation of diagrams, one can gain insight into the behavior of the remaining terms of the series, which can be resummed to solve various strong-coupling problems in a certain approximation. This approach is demonstrated by determining the Gell-Mann-Low functions in φ 4 theory, QED, and QCD with arbitrary coupling constants. An overview of the mathematical theory of divergent series is presented, and interpretation of perturbation series is discussed. Explicit derivations of the Lipatov asymptotic form are presented for some basic problems in theoretical physics. A solution is proposed to the problem of renormalon contributions, which hampered progress in this field in the late 1970s. Practical perturbation-series summation schemes are described both for a coupling constant of order unity and in the strong-coupling limit. An interpretation of the Borel integral is given for 'non-Borel-summable' series. Higher order corrections to the Lipatov asymptotic form are discussed
Of being true, like each man that thinks scientifically that the later atmospheric states are developed of the previous, according to physical laws believes, then it can understand each other that the necessary and enough conditions for a rational solution of the problem of predict in meteorology they are these requirements: it is necessary to know with enough precision the state of the atmosphere in a given instant. It is necessary to know with enough precision the laws that govern the evolution of an atmospheric state toward another. The knowledge of the state of the atmosphere in appropriate instants, fixed by general agreement, is the task of the observational meteorology. For the purposes of a rational forecast of the time, the resolution of this task has not been sufficiently complete. There are two particularly sensitive lagoons. In the first place, the stations that contribute to maintain the service meteorological newspaper are located in earth. In the seas that four recruits of the terrestrial surface cover, and that therefore they exercise a rolling influence, observations are not still made in benefit of the service meteorological newspaper. Also, the observations that are processed for the regular services of meteorology only come from the surface of the soil, and it lacks all fact of the state of the superior layers of air. The technical means that will allow us to fill both lagoons are already in our power. With the help of the radiotelegraphy they will be included, in the environment of the stations, the steam ships with fixed routes, which can send meteorological telegrams daily. And with the big progresses of the aeronautical meteorology in the last years, it will be possible to carry out observations of the superior layers, from stations in surface and, on the sea, from stations steering wheels. It is necessary to wait, because that the time this to arrive in that will be able to prepare, either daily or in certain days, of a complete diagnose of
Chronis, Andrea M.; Lahey, Benjamin B.; Pelham, William E., Jr.; Williams, Stephanie Hall; Baumann, Barbara L.; Kipp, Heidi; Jones, Heather A.; Rathouz, Paul J.
Children with attention-deficit/hyperactivity disorder (ADHD) are at risk for adverse outcomes such as substance abuse and criminality, particularly if they develop conduct problems. Little is known about early predictors of the developmental course of conduct problems among children with ADHD, however. Parental psychopathology and parenting …
Neece, C.; Baker, B.
Background: Parents of children with intellectual disabilities (ID) typically report elevated levels of parenting stress, and child behaviour problems are a strong predictor of heightened parenting stress. Interestingly, few studies have examined child characteristics beyond behaviour problems that may also contribute to parenting stress. The…
Full Text Available Disruptive behavior disorders in children are on the increase. However, there is evidence that the younger a child is at the time of intervention, the more positive the behavioral effects on his/her adjustment at home and at school. Parental education might be an effective way of addressing early problems. The Incredible Years (IY programs were designed to prevent and treat behavior problems when they first appear (in infancy-toddlerhood through middle childhood and to intervene in multiple areas through parent, teacher, and child training. This paper summarizes the literature demonstrating the impact of the IY parent, teacher and child intervention programs, and describes in more detail the work done in Portugal so far to disseminate IY programs with fidelity, with particular emphasis on the IY Basic Preschool Parenting and Teacher Classroom Management programs.
Creswell, Kasey G; Chung, Tammy; Clark, Duncan B; Martin, Christopher S
Adolescent solitary drinking may represent an informative divergence from normative behavior, with important implications for understanding risk for alcohol-use disorders later in life. Within a self-medication framework, we hypothesized that solitary alcohol use would be associated with drinking in response to negative affect and that such a pattern of drinking would predict alcohol problems in young adulthood. We tested these predictions in a longitudinal study in which we examined whether solitary drinking in adolescence (ages 12-18) predicted alcohol-use disorders in young adulthood (age 25) in 466 alcohol-using teens recruited from clinical programs and 243 alcohol-using teens recruited from the community. Findings showed that solitary drinking was associated with drinking in response to negative affect during adolescence and predicted alcohol problems in young adulthood. Results indicate that drinking alone is an important type of alcohol-use behavior that increases risk for the escalation of alcohol use and the development of alcohol problems.
Cherednichenko, V G
The Inverse and Ill-Posed Problems Series is a series of monographs publishing postgraduate level information on inverse and ill-posed problems for an international readership of professional scientists and researchers. The series aims to publish works which involve both theory and applications in, e.g., physics, medicine, geophysics, acoustics, electrodynamics, tomography, and ecology.
This paper discusses Excel applications related to the prediction of drug absorbability from physicochemical constants. PHDISSOC provides a generalized model for pH profiles of electrolytic dissociation, water solubility, and partition coefficient. SKMODEL predicts drug absorbability, based on a log-log plot of water solubility and O/W partitioning; augmented by additional features such as electrolytic dissociation, melting point, and the dose administered. GIABS presents a mechanistic model of g.i. drug absorption. BIODATCO presents a database compiling relevant drug data to be used for quantitative predictions.
Full Text Available To avoid use errors when handling medical equipment, it is important to develop products with a high degree of usability. This can be achieved by performing usability evaluations in the product development process to detect and mitigate potential usability problems. A commonly used method is cognitive walkthrough (CW, but this method shows three weaknesses: poor high-level perspective, insufficient categorisation of detected usability problems, and difficulties in overviewing the analytical results. This paper presents a further development of CW with the aim of overcoming its weaknesses. The new method is called enhanced cognitive walkthrough (ECW. ECW is a proactive analytical method for analysis of potential usability problems. The ECW method has been employed to evaluate user interface designs of medical equipment such as home-care ventilators, infusion pumps, dialysis machines, and insulin pumps. The method has proved capable of identifying several potential use problems in designs.
Full Text Available Time series prediction is an open problem and many researchers are trying to find new predictive methods and improvements for the existing ones. Lately methods based on neural networks are used extensively for time series prediction. Also, support vector machines have solved some of the problems faced by neural networks and they began to be widely used for time series prediction. The main drawback of those two methods is that they are global models and in the case of a chaotic time series it is unlikely to find such model. In this paper it is presented a comparison between three predictive from computational intelligence field one based on neural networks one based on support vector machine and another based on chaos theory. We show that the model based on chaos theory is an alternative to the other two methods.
Comparison of prediction quality of wind speed in hourly wind using different ARMA models; Comparacion de la calidad de las predicciones a corto plazo de velocidades de viento horarias. Realizadas con modelos armas obtenidos con series temporales de diferente longitud
Izco, E.; Prieto, E.; Garcia, A.; Torres, J. L.
In this communication we have used different ARMA (Autoregressive Moving Average Process) models to predict the hourly average wind speed. It has been compared the results in predictions made in hourly average wind speed up to 10 hours in advance, when it is used as basis for establishment of prediction model the data of previous year and the other model is made with an historical series of several years of duration. The study expands to five locations with different topographic characteristics , in mountains surroundings and others in smoother relief area. It has been proven that the RMSE and MBE obtained in the adjustment between the predictions and the future observations with both models are bigger in the model make with data of previous year. (Author)
Polcin, Douglas L; Korcha, Rachael; Bond, Jason; Galloway, Gantt; Nayak, Madhabika
Few studies have examined how changes in psychiatric symptoms over time are associated with changes in drug use and severity of drug problems. No studies have examined these relationships among methamphetamine (MA)-dependent persons receiving motivational interviewing within the context of standard outpatient treatment. Two hundred seventeen individuals with MA dependence were randomly assigned to a standard single session of motivational interviewing (MI) or an intensive 9-session model of MI. Both groups received standard outpatient group treatment. The Addiction Severity Index (ASI) and timeline follow-back (TLFB) for MA use were administered at treatment entry and 2-, 4-, and 6-month follow-ups. Changes in ASI psychiatric severity between baseline and 2 months predicted changes in ASI drug severity during the same time period, but not changes on measures of MA use. Item analysis of the ASI drug scale showed that psychiatric severity predicted how troubled or bothered participants were by their drug us, how important they felt it was for them to get treatment, and the number of days they experienced drug problems. However, it did not predict the number days they used drugs in the past 30 days. These associations did not differ between study conditions, and they persisted when psychiatric severity and outcomes were compared across 4- and 6-month time periods. Results are among the first to track how changes in psychiatric severity over time are associated with changes in MA use and severity of drug problems. Treatment efforts targeting reduction of psychiatric symptoms among MA-dependent persons might be helpful in reducing the level of distress and problems associated with MA use but not how often it is used. There is a need for additional research describing the circumstances under which the experiences and perceptions of drug-related problems diverge from frequency of consumption.
Riper, Heleen; Kramer, Jeannet; Keuken, Max; Smit, Filip; Schippers, Gerard; Cuijpers, Pim
Web-based self-help interventions for problem drinking are coming of age. They have shown promising results in terms of cost-effectiveness, and they offer opportunities to reach out on a broad scale to problem drinkers. The question now is whether certain groups of problem drinkers benefit more from such Web-based interventions than others. We sought to identify baseline, client-related predictors of the effectiveness of Drinking Less, a 24/7, free-access, interactive, Web-based self-help intervention without therapist guidance for problem drinkers who want to reduce their alcohol consumption. The intervention is based on cognitive-behavioral and self-control principles. We conducted secondary analysis of data from a pragmatic randomized trial with follow-up at 6 and 12 months. Participants (N = 261) were adult problem drinkers in the Dutch general population with a weekly alcohol consumption above 210 g of ethanol for men or 140 g for women, or consumption of at least 60 g (men) or 40 g (women) one or more days a week over the past 3 months. Six baseline participant characteristics were designated as putative predictors of treatment response: (1) gender, (2) education, (3) Internet use competence (sociodemographics), (4) mean weekly alcohol consumption, (5) prior professional help for alcohol problems (level of problem drinking), and (6) participants' expectancies of Web-based interventions for problem drinking. Intention-to-treat (ITT) analyses, using last-observation-carried-forward (LOCF) data, and regression imputation (RI) were performed to deal with loss to follow-up. Statistical tests for interaction terms were conducted and linear regression analysis was performed to investigate whether the participants' characteristics as measured at baseline predicted positive treatment responses at 6- and 12-month follow-ups. At 6 months, prior help for alcohol problems predicted a small, marginally significant positive treatment outcome in the RI model only (beta = .18
Namiot, V.A.; Chernavskii, D.S.
It is well known, that in the classical mechanics the dynamic chaos is possible. When it takes place, the exact prediction of events in the future appears impossible. But in the quantum theory the dynamic chaos (connected with perturbations of the initial conditions) formally is absent. Nevertheless, as it is shown in this Letter, in case of the quantum theory there are other reasons related directly to so-called paradoxes of formal logic which do not allow one to predict the future precisely
Fragkaki, Iro; Cima, Maaike; Meesters, Cor
Morality deficits have been linked to callous-unemotional traits and externalizing problems in response to moral dilemmas, but these associations are still obscure in response to antisocial acts in adolescence. Limited evidence on young boys suggested that callous-unemotional traits and externalizing problems were associated with affective but not cognitive morality judgments. The present study investigated these associations in a community sample of 277 adolescents (M age = 15.35, 64 % females). Adolescents with high callous-unemotional traits showed deficits in affective but not cognitive morality, indicating that they can identify the appropriate moral emotions in others, but experience deviant moral emotions when imagining themselves committing antisocial acts. Externalizing problems and male gender were also strongly related to deficits in affective morality, but they had smaller associations with deficits in cognitive morality too. Implications for treatment and the justice system are discussed.
de Vries, Annelou L C; Steensma, Thomas D; Cohen-Kettenis, Peggy T; VanderLaan, Doug P; Zucker, Kenneth J
This study is the third in a series to examine behavioral and emotional problems in children and adolescents with gender dysphoria in a comparative analysis between two clinics in Toronto, Ontario, Canada and Amsterdam, the Netherlands. In the present study, we report Child Behavior Checklist (CBCL) and Youth Self-Report (YSR) data on adolescents assessed in the Toronto clinic (n = 177) and the Amsterdam clinic (n = 139). On the CBCL and the YSR, we found that the percentage of adolescents with clinical range behavioral and emotional problems was higher when compared to the non-referred standardization samples but similar to the referred adolescents. On both the CBCL and the YSR, the Toronto adolescents had a significantly higher Total Problem score than the Amsterdam adolescents. Like our earlier studies of CBCL data of children and Teacher's Report Form data of children and adolescents, a measure of poor peer relations was the strongest predictor of CBCL and YSR behavioral and emotional problems in gender dysphoric adolescents.
U.S. Department of Health & Human Services — The Centers for Medicare and Medicaid Services (CMS) offers several different Chart Series with data on beneficiary health status, spending, operations, and quality...
The Integrated Tiger Series of Coupled Electron/Photon Monte Carlo Transport Codes (ITS 3.0, PC Version) was used at Oak Ridge National Laboratory (ORNL) to compare with and extend the experimental findings of the beta/gamma response of selected health physics instruments. In order to assure that ITS gives correct results, several beta/gamma problems have been tested. ITS was used to simulate these problems numerically, and results for each were compared to the problem's experimental or analytical results. ITS successfully predicted the experimental or analytical results of all tested problems within the statistical uncertainty inherent in the Monte Carlo method
Cousijn, J.; Goudriaan, A.E.; Ridderinkhof, K.R.; van den Brink, W.; Veltman, D.J.; Wiers, R.W.
A potentially powerful predictor for the course of drug (ab)use is the approach-bias, that is, the pre-reflective tendency to approach rather than avoid drug-related stimuli. Here we investigated the neural underpinnings of cannabis approach and avoidance tendencies. By elucidating the predictive
Cousijn, Janna; Goudriaan, Anna E.; Ridderinkhof, K. Richard; van den Brink, Wim; Veltman, Dick J.; Wiers, Reinout W.
A potentially powerful predictor for the course of drug (ab) use is the approach-bias, that is, the pre-reflective tendency to approach rather than avoid drug-related stimuli. Here we investigated the neural underpinnings of cannabis approach and avoidance tendencies. By elucidating the predictive
Winsler, Adam; Kim, Yoon Kyong; Richard, Erin R.
This article analyzes the role that individual differences in children's cognitive, Spanish competence, and socio-emotional and behavioral skills play in predicting the concurrent and longitudinal acquisition of English among a large sample of ethnically diverse, low-income, Hispanic preschool children. Participants assessed at age 4 for language,…
Nilsen, Wendy; Skipstein, Anni; Demerouti, Evangelia
The long-term consequence of experiencing mental health problems may lead to several adverse outcomes. The current study aims to validate previous identified trajectories of mental health problems from 1993 to 2006 in women by examining their implications on subsequent work and family-related outcomes in 2011. Employed women (n = 439) with children were drawn from the Tracking Opportunities and Problems-Study (TOPP), a community-based longitudinal study following Norwegian families across 18 years. Previous identified latent profiles of mental health trajectories (i.e., High; Moderate; Low-rising and Low levels of mental health problems over time) measured at six time points between 1993 and 2006 were examined as predictors of burnout (e.g., exhaustion and disengagement from work) and work-family conflict in 2011 in univariate and multivariate analyses of variance adjusted for potential confounders (age, job demands, and negative emotionality). We found that having consistently High and Moderate symptoms as well as Low-Rising symptoms from 1993 to 2006 predicted higher levels of exhaustion, disengagement from work and work-family conflict in 2011. Findings remained unchanged when adjusting for several potential confounders, but when adjusting for current mental health problems only levels of exhaustion were predicted by the mental health trajectories. The study expands upon previous studies on the field by using a longer time span and by focusing on employed women with children who experience different patterns of mental health trajectories. The long-term effect of these trajectories highlight and validate the importance of early identification and prevention in women experiencing adverse patterns of mental health problems with regards to subsequent work and family-related outcomes.
Mainert, Jakob; Kretzschmar, André; Neubert, Jonas C.; Greiff, Samuel
Transversal skills, such as complex problem solving (CPS) are viewed as central twenty-first-century skills. Recent empirical findings have already supported the importance of CPS for early academic advancement. We wanted to determine whether CPS could also contribute to the understanding of career advancement later in life. Towards this end, we…
Lorber, Michael F.; Slep, Amy M. Smith
In the present investigation we focused on 2 broad sets of questions: Do parental overreactivity, laxness, and corporal punishment show evidence of normative change in early to middle childhood? Are persistently elevated child conduct problems (CPs) associated with deviations from normative changes in, as well as high initial levels of, discipline…
Guo, Ruocheng; Shakarian, Paulo
Information cascades exist in a wide variety of platforms on Internet. A very important real-world problem is to identify which information cascades can go viral. A system addressing this problem can be used in a variety of applications including public health, marketing and counter-terrorism. As a cascade can be considered as compound of the social network and the time series. However, in related literature where methods for solving the cascade prediction problem were proposed, the experimen...
Franklin R. Ward; David V. Sandberg
This publication presents tables on the behavior of fire and the resistance of fuels to control. The information is to be used with the publication, "Photo Series for Quantifying Forest Residues in the Ponderosa Pine Type, Ponderosa Pine and Associated Species Type, Lodgepole Pine Type" (Maxwell, Wayne G.; Ward, Franklin R. 1976. Gen. Tech. Rep. PNW-GTR-052....
New models of radical polymerization with branching and scission predicting molecular weight distribution in tubular and series of continuous stirred tank reactors allowing for multiradicals and gelation
Yaghini, N.; Iedema, P.D.
Modeling of the mol. wt. distribution (MWD) of low-d. Polyethylene (ldPE) has been carried out for a tubular reactor under realistic non-isothermal conditions and for a series of CSTR's. The model allows for the existence of multiradicals and the occurrence of gelation. The deterministic model is
Shuel, Francis; White, Jacquie; Jones, Martin; Gray, Richard
The physical health of people with serious mental illness is a cause of growing concern to clinicians. Life expectancy in this population may be reduced by up to 25 years and patients often live with considerable physical morbidity that can dramatically reduce quality of life and contribute to social exclusion. This study sought to determine whether the serious mental illness health improvement profile [HIP], facilitated by mental health nurses [MHNs], has the clinical potential to identify physical morbidity and inform future evidence-based care. Retrospective documentation audit and qualitative evaluation of patients' and clinicians' views about the use of the HIP in practice. A nurse-led outpatient medication management clinic, for community adult patients with serious mental illness in Scotland. 31 Community patients with serious mental illness seen in the clinic by 2 MHNs trained to use the HIP. All 31 patients, 9 MHNs, 4 consultant psychiatrists and 12 general practitioners [GPs] (primary care physicians) participated in the qualitative evaluation. A retrospective documentation audit of case notes for all patients where the HIP had been implemented. Semi-structured interviews with patients and their secondary care clinicians. Postal survey of GPs. 189 Physical health issues were identified (mean 6.1 per patient). Items most frequently flagged 'red', suggesting that intervention was required, were body mass index [BMI] (n=24), breast self-examination (n=23), waist circumference (n=21), pulse (n=14) and diet (n=13). Some rates of physical health problems observed were broadly similar to those reported in studies of patients receiving antipsychotics in primary care but much lower than those reported in epidemiological studies. Individualised care was planned and delivered with each patient based on the profile. 28 discreet interventions that included providing advice, promoting health behavioural change, performing an electrocardiogram and making a referral to
Simon, Valerie A.; Feiring, Candice
Youth with confirmed histories of sexual abuse (N = 118) were followed longitudinally to examine associations between their initial sexual reactions to abuse and subsequent sexual functioning. Participants were interviewed at abuse discovery (ages 8 through 15) and again 1 and 6 years later. Eroticism and sexual anxiety emerged as distinct indices of abuse-specific sexual reactions and predicted subsequent sexual functioning. Eroticism was associated with indicators of heightened sexuality, i...
Leising, Daniel; Rehbein, Diana; Sporberg, Doreen
The Inventory of Interpersonal Problems (IIP-64; Horowitz, Alden, Wiggins, & Pincus, 2000) is a self-report measure of maladaptive relationship behavior. Ninety-five adult female participants completed the IIP-64 and then interacted with a same-sex confederate in three diagnostic role plays, designed to evoke assertive responses. After each role play, both the participant and the confederate judged how assertive the participant had been, using two subscales from the Interpersonal Adjective Scales (IAS; Wiggins, 1995). The participants' general self-images, assessed with the IIP-64, were quite congruent with how they judged their own assertiveness in the role plays. But when role-play assertiveness was judged by the confederate, the match with the participants' general self-images was considerably lower. Our results indicate that self-reported interpersonal problems do not converge well with external judgments of interpersonal behavior.
Background Research into neural mechanisms of drug abuse risk has focused on the role of dysfunction in neural circuits for reward. In contrast, few studies have examined the role of dysfunction in neural circuits of threat in mediating drug abuse risk. Although typically regarded as a risk factor for mood and anxiety disorders, threat-related amygdala reactivity may serve as a protective factor against substance use disorders, particularly in individuals with exaggerated responsiveness to reward. Findings We used well-established neuroimaging paradigms to probe threat-related amygdala and reward-related ventral striatum reactivity in a sample of 200 young adult students from the ongoing Duke Neurogenetics Study. Recent life stress and problem drinking were assessed using self-report. We found a significant three-way interaction between threat-related amygdala reactivity, reward-related ventral striatum reactivity, and recent stress, wherein individuals with higher reward-related ventral striatum reactivity exhibit higher levels of problem drinking in the context of stress, but only if they also have lower threat-related amygdala reactivity. This three-way interaction predicted both contemporaneous problem drinking and problem drinking reported three-months later in a subset of participants. Conclusions These findings suggest complex interactions between stress and neural responsiveness to both threat and reward mediate problem drinking. Furthermore, they highlight a novel protective role for threat-related amygdala reactivity against drug use in individuals with high neural reactivity to reward. PMID:23151390
Rhodes, C.; Morari, M.
The false-nearest-neighbors (FNN) algorithm was originally developed to determine the embedding dimension for autonomous time series. For noise-free computer-generated time series, the algorithm does a good job in predicting the embedding dimension. However, the problem of predicting the embedding dimension when the time-series data are corrupted by noise was not fully examined in the original studies of the FNN algorithm. Here it is shown that with large data sets, even small amounts of noise can lead to incorrect prediction of the embedding dimension. Surprisingly, as the length of the time series analyzed by FNN grows larger, the cause of incorrect prediction becomes more pronounced. An analysis of the effect of noise on the FNN algorithm and a solution for dealing with the effects of noise are given here. Some results on the theoretically correct choice of the FNN threshold are also presented. copyright 1997 The American Physical Society
Full Text Available The problem of disturbances forecasting in vehicles control systems is considered in the given article. On the basis of nuclear campaign recurrence there have been obtained algorithms of identification and prediction of disturbances time series.
Full Text Available Objetivos. Valorar el efecto predictivo de características claves de pacientes atendidos en salas de emergencia para detectar casos de uso problemático de alcohol. Materiales y Métodos. La muestra de estudio estuvo constituida por 371 personas atendidas en el lapso de siete días completos de enero de 2005 en el servicio de emergencia de un hospital público de Lima, Perú. Se aplicó un cuestionario demográfico, el SIDUC/CICAD para uso reciente de sustancias psicoactivas en salas de emergencias (i.e., uso dentro de las seis horas previas a la atención y el AUDIT para uso problemático de alcohol en el último año. El análisis de regresión logística simple y multivariada permitió valorar el efecto predictor de la edad, sexo, especialidad del servicio de atención, presencia de daño físico y el uso reciente de alcohol para detectar casos problemáticos de su uso. Resultados. El odds de tener uso problemático de alcohol en los varones es 26 veces el odds de tener dicho problema entre las mujeres (pObjectives. To assess the predictive effect of key individual-level characteristics to identify cases of alcohol use problems among patients visiting an emergency room. Materials and methods. The study sample was composed of 371 people attending an emergency room in a public hospital in Lima, Peru, during a period of seven complete days in January, 2005. For data gathering, we used a questionnaire for demographic information, the SIDUC/CICAD for recent use (i.e., in the last 6 hours of psychoactive substances before arriving to the emergency room, and the AUDIT, to identify alcohol use problems in the last year. Univariate and multivariate logistic regression models were used to estimate the predictive effect of age, sex, area of attention in the emergency room, presence of physical injuries and recent use of alcohol. Results. The odds of being a case of alcohol use problem for males is 26 times the odds of having that problem for females (p
Chiapa, Amanda; Smith, Justin D; Kim, Hanjoe; Dishion, Thomas J; Shaw, Daniel S; Wilson, Melvin N
Therapist fidelity to evidence-based family interventions has consistently been linked to child and family outcomes. However, few studies have evaluated the potential ebb and flow of fidelity of therapists over time. We examined therapist drift in fidelity over 4 years in the context of a Family Check-Up prevention services in early childhood (ages 2-5 years). At age 2, families engaging in Women, Infants, and Children Nutritional Supplement Program services were randomized and offered annual Family Check-Ups. Seventy-nine families with a child in the clinical range of problem behaviors at age 2 years were included in this analysis. Latent growth modeling revealed a significant linear decline in fidelity over time (M = -0.35, SD = 0.35) and that steeper declines were related to less improvement in caregiver-reported problem behaviors assessed at ages 7.5/8.5 years (b = -.69, p = .003; β = -.95, 95% CI [-2.11, -0.22]). These findings add to the literature concerning the need to continually monitor therapist fidelity to an evidence-based practice over time to optimize family benefits. Limitations and directions for future research are discussed. (c) 2015 APA, all rights reserved).
Button, E J; Sonuga-Barke, E J; Davies, J; Thompson, M
A number of authors have emphasized the importance of self-esteem in the aetiology of the eating disorders anorexia nervosa and bulimia nervosa. Evidence for such theorizing, however, mainly derives from clinical observations on people being treated for eating disorders. This study is the first prospective study to investigate the role of self-esteem in aetiology prior to the onset of an eating disorder. Self-esteem was measured in 594 schoolgirls aged 11-12 using the Rosenberg Self-Esteem Scale (Rosenberg, 1965). Almost 400 of these girls were successfully followed up at age 15-16 and they completed a questionnaire examining eating and other psychological problems. Results showed that girls with low self-esteem at age 11-12 were at significantly greater risk of developing the more severe signs of eating disorders, as well as other psychological problems, by the age of 15-16. It is argued that more research is needed to replicate and extend these findings. The results also give weight to the case for examining the potential role of self-esteem enhancement in the prevention of eating disorders.
Liu, Sijun; Chen, Jiaping; Wang, Jianming; Wu, Zhuchao; Wu, Weihua; Xu, Zhiwei; Hu, Wenbiao; Xu, Fei; Tong, Shilu; Shen, Hongbing
Hand, foot, and mouth disease (HFMD) is a significant public health issue in China and an accurate prediction of epidemic can improve the effectiveness of HFMD control. This study aims to develop a weather-based forecasting model for HFMD using the information on climatic variables and HFMD surveillance in Nanjing, China. Daily data on HFMD cases and meteorological variables between 2010 and 2015 were acquired from the Nanjing Center for Disease Control and Prevention, and China Meteorological Data Sharing Service System, respectively. A multivariate seasonal autoregressive integrated moving average (SARIMA) model was developed and validated by dividing HFMD infection data into two datasets: the data from 2010 to 2013 were used to construct a model and those from 2014 to 2015 were used to validate it. Moreover, we used weekly prediction for the data between 1 January 2014 and 31 December 2015 and leave-1-week-out prediction was used to validate the performance of model prediction. SARIMA (2,0,0)52 associated with the average temperature at lag of 1 week appeared to be the best model (R 2 = 0.936, BIC = 8.465), which also showed non-significant autocorrelations in the residuals of the model. In the validation of the constructed model, the predicted values matched the observed values reasonably well between 2014 and 2015. There was a high agreement rate between the predicted values and the observed values (sensitivity 80%, specificity 96.63%). This study suggests that the SARIMA model with average temperature could be used as an important tool for early detection and prediction of HFMD outbreaks in Nanjing, China.
calciphylaxis is prevention through rigorous control of phosphate and calcium balance. We here present two ... The authors declared no conflict of interest. Introduction. Calciphylaxis is a rare but serious disorder .... were reported to resolve the calciphylaxis lesions in a chronic renal failure patient . In a series of five.
polynomials are dense in the class of continuous functions! The body of literature dealing with Fourier series has reached epic proportions over the last two centuries. We have only given the readers an outline of the topic in this article. For the full length episode we refer the reader to the monumental treatise of. A Zygmund.
13 oct. 2017 ... This is an Open Access article distributed under the terms of the Creative Commons Attribution ... Bifocal leg fractures pose many challenges for the surgeon due to .... Dans notre serie, le taux d'infection est reste dans un.
Key words: Case report, case series, concept analysis, research design. African Health Sciences 2012; (4): 557 - 562 http://dx.doi.org/10.4314/ahs.v12i4.25. PO Box 17666 .... According to the latest version of the Dictionary of. Epidemiology ...
Full Text Available Penelitian ini bertujuan untuk mengetahui Perbedaan Hasil Belajar Fisika Siswa antara Model Pembelajaran Problem Based Learning (PBL dengan Model Pembelajaran Prediction, Observation, And Explanation (POE di Kelas X SMA Negeri 5 Lubuklinggau Tahun Pelajaran 2015/2016. Jenis penelitian ini adalah penelitian kuantitatif dengan metode penelitian eksperimen semu yang dilaksanakan dengan membandingkan kelompok eksperimen I dan kelompok eksperimen II desain penelitian ini pre-test post-test group design. Populasi penelitian ini adalah seluruh siswa kelas X SMA Negeri 5 Lubuklinggau Tahun Pelajaran 2015/2016, yang terdiri dari 314 siswa dari 9 kelas. Pengambilan sampel dilakukan secara acak (Simple Random Sampling dengan cara pengundian nomor kelas populasi. Pengumpulan data berupa tes, data tes yang sudah dianalisis dengan uji-t, pada taraf a= 0,05, diperoleh thitung > ttabel (2,17 > 2,00. Rata-rata akhir hasil belajar fisika kelas eksperimen I sebesar 73,4 sedangkan pada kelas kelas eksperimen II sebesar 69,14. Sehingga dapat disimpulkan ada Perbedaan Hasil Belajar Fisika Siswa antara Model Pembelajaran Problem Based Learning (PBL Dengan Model Pembelajaran Prediction, Observation, And Explanation (POE Di Kelas X SMA Negeri 5 Lubuklinggau Tahun Pelajaran 2015/2016. The aim of this research was to find out the Comparative Results Between Students Studying Physics Learning Model Problem Based Learning (PBL with Learning Model Prediction, Observation, And Explanation (POE in the Class X SMAN 5 Lubuklinggau 2015/2016 Academic Year . This research was a quantitative research methods of experimental research conducted by comparing the experimental group I and group II experimental research design was a pre-test post-test group design. As the population in this research were all students of class X SMA Negeri 5 Lubuklinggau Academic Year 2015/2016, consisting of 314 students from the ninth grade. Sampling is done randomly (Simple Random Sampling by
Verduzco-Flores, Sergio O; O'Reilly, Randall C
We present a cerebellar architecture with two main characteristics. The first one is that complex spikes respond to increases in sensory errors. The second one is that cerebellar modules associate particular contexts where errors have increased in the past with corrective commands that stop the increase in error. We analyze our architecture formally and computationally for the case of reaching in a 3D environment. In the case of motor control, we show that there are synergies of this architecture with the Equilibrium-Point hypothesis, leading to novel ways to solve the motor error and distal learning problems. In particular, the presence of desired equilibrium lengths for muscles provides a way to know when the error is increasing, and which corrections to apply. In the context of Threshold Control Theory and Perceptual Control Theory we show how to extend our model so it implements anticipative corrections in cascade control systems that span from muscle contractions to cognitive operations.
Sergio Oscar Verduzco-Flores
Full Text Available We present a cerebellar architecture with two main characteristics. The first one is that complex spikes respond to increases in sensory errors. The second one is that cerebellar modules associate particular contexts where errors have increased in the past with corrective commands that stop the increase in error. We analyze our architecture formally and computationally for the case of reaching in a 3D environment. In the case of motor control, we show that there are synergies of this architecture with the Equilibrium-Point hypothesis, leading to novel ways to solve the motor error and distal learning problems. In particular, the presence of desired equilibrium lengths for muscles provides a way to know when the error is increasing, and which corrections to apply. In the context of Threshold Control Theory and Perceptual Control Theory we show how to extend our model so it implements anticipative corrections in cascade control systems that span from muscle contractions to cognitive operations.
Sun, Xiao-Qian; Shen, Hua-Wei; Cheng, Xue-Qi
Stock price prediction is an important and challenging problem for studying financial markets. Existing studies are mainly based on the time series of stock price or the operation performance of listed company. In this paper, we propose to predict stock price based on investors' trading behavior. For each stock, we characterize the daily trading relationship among its investors using a trading network. We then classify the nodes of trading network into three roles according to their connectivity pattern. Strong Granger causality is found between stock price and trading relationship indices, i.e., the fraction of trading relationship among nodes with different roles. We further predict stock price by incorporating these trading relationship indices into a neural network based on time series of stock price. Experimental results on 51 stocks in two Chinese Stock Exchanges demonstrate the accuracy of stock price prediction is significantly improved by the inclusion of trading relationship indices.
Catania, Leopoldo; Grassi, Stefano; Ravazzolo, Francesco
This paper studies the predictability of cryptocurrencies time series. We compare several alternative univariate and multivariate models in point and density forecasting of four of the most capitalized series: Bitcoin, Litecoin, Ripple and Ethereum. We apply a set of crypto–predictors and rely...
Moore, Corey L.; Wang, Ningning; Washington, Janique Tynez
Purpose: This study assessed and demonstrated the efficacy of two select empirical forecast models (i.e., autoregressive integrated moving average [ARIMA] model vs. grey model [GM]) in accurately predicting state vocational rehabilitation agency (SVRA) rehabilitation success rate trends across six different racial and ethnic population cohorts…
This understanding gave birth to the Hierarchical Temporal Memory (HTM) which holds a lot of promises in the area of time-series prediction and anomaly detection problems. This paper demonstrates the behaviour of an HTM model with respect to its learning and prediction of online credit card fraud. The model was ...
Reaktion Books’ Exposures series, edited by Peter Hamilton and Mark Haworth-Booth, is comprised of 13 volumes and counting, each less than 200 pages with 80 high-quality illustrations in color and black and white. Currently available titles include Photography and Australia, Photography and Spirit, Photography and Cinema, Photography and Literature, Photography and Flight, Photography and Egypt, Photography and Science, Photography and Africa, Photography and Italy, Photography and the USA, P...
Jonas, Katherine; Kochanska, Grazyna
Although the association between deficits in effortful control and later externalizing behavior is well established, many researchers (Nigg Journal of Child Psychology and Psychiatry, 47(3-4), 395-422, 2006; Steinberg Developmental Review, 28(1), 78-106, 2008) have hypothesized this association is actually the product of the imbalance of dual systems, or two underlying traits: approach and self-regulation. Very little research, however, has deployed a statistically robust strategy to examine that compelling model; further, no research has done so using behavioral measures, particularly in longitudinal studies. We examined the imbalance of approach and self-regulation (effortful control, EC) as predicting externalizing problems. Latent trait models of approach and EC were derived from behavioral measures collected from 102 children in a community sample at 25, 38, 52, and 67 months (2 to 5 ½ years), and used to predict externalizing behaviors, modeled as a latent trait derived from parent-reported measures at 80, 100, 123, and 147 months (6 ½ to 12 years). The imbalance hypothesis was supported: Children with an imbalance of approach and EC had more externalizing behavior problems in middle childhood and early preadolescence, relative to children with equal levels of the two traits.
Porcerelli, John H; Huth-Bocks, Alissa; Huprich, Steven K; Richardson, Laura
For at-risk (single parent, low income, low support) mothers, healthy adaptation and the ability to manage stress have clear implications for parenting and the social-emotional well-being of their young offspring. The purpose of this longitudinal study was to examine associations between defense mechanisms in pregnant women and their toddlers' attachment security, social-emotional, and behavioral adjustment. Participants were 84 pregnant women during their last trimester of pregnancy, recruited from community agencies primarily serving low-income families. Women were followed prospectively from pregnancy through 2 years after birth and completed several multimethod assessments during that period. Observations of mother-child interactions were also coded after the postnatal visits. Multiple regression analyses revealed that mothers' defense mechanisms were significantly associated with several toddler outcomes. Mature, healthy defenses were significantly associated with greater toddler attachment security and social-emotional competence and fewer behavior problems, and less mature defenses (disavowal in particular) were associated with lower levels of attachment security and social-emotional competence. Associations remained significant, or were only slightly attenuated, after controlling for demographic variables and partner abuse during pregnancy. The study findings suggest that defensive functioning in parents preparing for and parenting toddlers influences the parent-child attachment relationship and social-emotional adjustment in the earliest years of life. Possible mechanisms for these associations may include parental attunement and mentalization, as well as specific caregiving behavior toward the child. Defensive functioning during times of increased stress (such as the prenatal to postnatal period) may be especially important for understanding parental influences on the child.
Treating divergent series properly has been an ongoing issue in mathematics. However, many of the problems in divergent series stem from the fact that divergent series were discovered prior to having a number system which could handle them. The infinities that resulted from divergent series led to contradictions within the real number system, but these contradictions are largely alleviated with the hyperreal number system. Hyperreal numbers provide a framework for dealing with divergent serie...
A method is proposed for the efficient calculation of the Green close-quote s functions and eigenstates for quantum systems of two or more dimensions. For a given Hamiltonian, the best possible separable approximation is obtained from the set of all Hilbert-space operators. It is shown that this determination itself, as well as the solution of the resultant approximation, is a problem of reduced dimensionality. Moreover, the approximate eigenstates constitute the optimal separable basis, in the sense of self-consistent field theory. The full solution is obtained from the approximation via iterative expansion. In the time-independent perturbation expansion for instance, all of the first-order energy corrections are zero. In the Green close-quote s function case, we have a distorted-wave Born series with optimized convergence properties. This series may converge even when the usual Born series diverges. Analytical results are presented for an application of the method to the two-dimensional shifted harmonic-oscillator system, in the course of which the quantum tanh 2 potential problem is solved exactly. The universal presence of bound states in the latter is shown to imply long-lived resonances in the former. In a comparison with other theoretical methods, we find that the reaction path Hamiltonian fails to predict such resonances. copyright 1997 The American Physical Society
Full Text Available This paper aims to accurately characterize the dynamics of the structural indicators of the assets in the credit institutions operating in Romania through an empirical mathematical model of dual function: regulation and control. The model can be used to predict the future evolution of the economic processes involved, or to study how to act upon them (management in case of changes in the environment around them (e.g. the impact of reducing the minimum compulsory reserve requirements on credit etc.
Arterburn, David R
REA's Problem Solvers is a series of useful, practical, and informative study guides. Each title in the series is complete step-by-step solution guide. The Differential Equations Problem Solver enables students to solve difficult problems by showing them step-by-step solutions to Differential Equations problems. The Problem Solvers cover material ranging from the elementary to the advanced and make excellent review books and textbook companions. They're perfect for undergraduate and graduate studies.The Differential Equations Problem Solver is the perfect resource for any class, any exam, and
Frilander, Heikki; Lallukka, Tea; Viikari-Juntura, Eira; Heliövaara, Markku; Solovieva, Svetlana
Disability retirement causes a significant burden on the society and affects the well-being of individuals. Early health problems as determinants of disability retirement have received little attention. The objective was to study, whether interrupting compulsory military service is an early indicator of disability retirement among Finnish men and whether seeking medical advice during military service increases the risk of all-cause disability retirement and disability retirement due to mental disorders and musculoskeletal diseases. We also looked at secular trends in these associations. We examined a nationally representative sample of 2069 men, who had entered military service during 1967-1996. We linked military service health records with cause-specific register data on disability retirement from 1968 to 2008. Secular trends were explored in three service time strata. We used the Cox regression model to estimate proportional hazard ratios and their 95% confidence intervals. During the follow-up time altogether 140 (6.8%) men retired due to disability, mental disorders being the most common cause. The men who interrupted service had a remarkably higher cumulative incidence of disability retirement (18.9%). The associations between seeking medical advice during military service and all-cause disability retirement were similar across the three service time cohorts (overall hazard ratio 1.40 per one standard deviation of the number of visits; 95% confidence interval 1.26-1.56). Visits due to mental problems predicted disability retirement due to mental disorders in the men who served between 1987 and 1996 and a tendency for a similar cause-specific association was seen for musculoskeletal diseases in the men who served in 1967-1976. In conclusion, health problems-in particular mental problems-during late adolescence are strong determinants of disability retirement. Call-up examinations and military service provide access to the entire age cohort of men, where
Amy B. Martin
Full Text Available Disasters serve as shocks and precipitate unanticipated disturbances to the health care system. Public health surveillance is generally focused on monitoring latent health and environmental exposure effects, rather than health system performance in response to these local shocks. The following intervention study sought to determine the long-term effects of the 2005 chlorine spill in Graniteville, South Carolina on primary care access for vulnerable populations. We used an interrupted time-series approach to model monthly visits for Ambulatory Care Sensitive Conditions, an indicator of unmet primary care need, to quantify the impact of the disaster on unmet primary care need in Medicaid beneficiaries. The results showed Medicaid beneficiaries in the directly impacted service area experienced improved access to primary care in the 24 months post-disaster. We provide evidence that a health system serving the medically underserved can prove resilient and display improved adaptive capacity under adverse circumstances (i.e., technological disasters to ensure access to primary care for vulnerable sub-groups. The results suggests a new application for ambulatory care sensitive conditions as a population-based metric to advance anecdotal evidence of secondary surge and evaluate pre- and post-health system surge capacity following a disaster.
The importance of visualisation and multiple representations in mathematics has been stressed, especially in a context of problem solving. Hanna and Sidoli comment that "Diagrams and other visual representations have long been welcomed as heuristic accompaniments to proof, where they not only facilitate the understanding of theorems and their…
Farmer, William H.; Knight, Rodney R.; Eash, David A.; Kasey J. Hutchinson,; Linhart, S. Mike; Christiansen, Daniel E.; Archfield, Stacey A.; Over, Thomas M.; Kiang, Julie E.
Daily records of streamflow are essential to understanding hydrologic systems and managing the interactions between human and natural systems. Many watersheds and locations lack streamgages to provide accurate and reliable records of daily streamflow. In such ungaged watersheds, statistical tools and rainfall-runoff models are used to estimate daily streamflow. Previous work compared 19 different techniques for predicting daily streamflow records in the southeastern United States. Here, five of the better-performing methods are compared in a different hydroclimatic region of the United States, in Iowa. The methods fall into three classes: (1) drainage-area ratio methods, (2) nonlinear spatial interpolations using flow duration curves, and (3) mechanistic rainfall-runoff models. The first two classes are each applied with nearest-neighbor and map-correlated index streamgages. Using a threefold validation and robust rank-based evaluation, the methods are assessed for overall goodness of fit of the hydrograph of daily streamflow, the ability to reproduce a daily, no-fail storage-yield curve, and the ability to reproduce key streamflow statistics. As in the Southeast study, a nonlinear spatial interpolation of daily streamflow using flow duration curves is found to be a method with the best predictive accuracy. Comparisons with previous work in Iowa show that the accuracy of mechanistic models with at-site calibration is substantially degraded in the ungaged framework.
It is known that nonintegrable dynamical systems do not allow precise predictions concerning their behavior for arbitrary long times. The available series solutions are not uniformly convergent according to Poincare's theorem and numerical integrations lose their meaningfulness after the elapse of arbitrary long times. Two approaches are the use of existing global integrals and statistical methods. This paper presents a generalized method along the first approach. As examples long-time predictions in the classical gravitational satellite and planetary problems are treated.
Ramírez-Valiente, Jose A; Koehler, Kari; Cavender-Bares, Jeannine
Climate is a major selective force in nature. Exploring patterns of inter- and intraspecific genetic variation in functional traits may explain how species have evolved and may continue evolving under future climate change. Photoprotective pigments play an important role in short-term responses to climate stress in plants but knowledge of their long-term role in adaptive processes is lacking. In this study, our goal was to determine how photoprotective mechanisms, morphological traits and their plasticity have evolved in live oaks (Quercus series Virentes) in response to different climatic conditions. For this purpose, seedlings originating from 11 populations from four live oak species (Quercus virginiana, Q. geminata, Q. fusiformis and Q. oleoides) were grown under contrasting common environmental conditions of temperature (tropical vs temperate) and water availability (droughted vs well-watered). Xanthophyll cycle pigments, anthocyanin accumulation, chlorophyll fluorescence parameters and leaf anatomical traits were measured. Seedlings originating from more mesic source populations of Q. oleoides and Q. fusiformis increased the xanthophyll de-epoxidation state under water-limiting conditions and showed higher phenotypic plasticity for this trait, suggesting adaptation to local climate. Likewise, seedlings originating from warmer climates had higher anthocyanin concentration in leaves under cold winter conditions but not higher de-epoxidation state. Overall, our findings suggest that (i) climate has been a key factor in shaping species and population differences in stress tolerance for live oaks, (ii) anthocyanins are used under cold stress in species with limited freezing tolerance and (iii) xanthophyll cycle pigments are used when photoprotection under drought conditions is needed. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: email@example.com.
Hunter, Corey W; Yang, Ajax; Davis, Tim
While spinal cord stimulation (SCS) has established itself as an accepted and validated treatment for neuropathic pain, there are a number of conditions where it has experienced less, long-term success: post amputee pain (PAP) being one of them. Dorsal root ganglion (DRG) stimulation has shown great promise, particularly in conditions where traditional SCS has fallen short. One major difference between DRG stimulation and traditional SCS is the ability to provide focal stimulation over targeted areas. While this may be a contributing factor to its superiority, it can also be a limitation insofar stimulating the wrong DRG(s) can lead to failure. This is particularly relevant in conditions like PAP where neuroplastic maladaptation occurs causing the pain to deviate from expected patterns, thus creating uncertainty and variability in predicting targets for stimulation. We propose selective radiofrequency (RF) stimulation of the DRG as a method for preoperatively predicting targets for neuromodulation in patients with PAP. We present four patients with PAP of the lower extremities. RF stimulation was used to selectively stimulate individual DRG's, creating areas of paresthesias to see which most closely correlated/overlapped with the painful area(s). RF stimulation to the DRG's that resulted in the desirable paresthesia coverage in the residual or the missing limb(s) was recorded as "positive." Trial DRG leads were placed based on the positive RF stimulation findings. In each patient, stimulating one or more DRG(s) produced paresthesias patterns that were contradictory to know dermatomal patterns. Upon completion of a one-week trial all four patients reported 60-90% pain relief, with coverage over the painful areas, and opted for permanent implant. Mapping the DRG via RF stimulation appears to provide improved accuracy for determining lead placement in the setting of PAP where pain patterns are known to deviate from conventional dermatomal mapping. © 2017
Romanov, V G
The Inverse and Ill-Posed Problems Series is a series of monographs publishing postgraduate level information on inverse and ill-posed problems for an international readership of professional scientists and researchers. The series aims to publish works which involve both theory and applications in, e.g., physics, medicine, geophysics, acoustics, electrodynamics, tomography, and ecology.
Dena Sadeghi Bahmani
Full Text Available Background: The concept of mental toughness has gained increasing importance among groups other than elite athletes by virtue of its psychological importance and explanatory power for a broad range of health-related behaviors. However, no study has focused so far on the psychological origins of mental toughness. Therefore, the aims of the present study were: to explore, to what extent the psychological profiles of preschoolers aged five were associated with both 1 mental toughness scores and 2 sleep disturbances at age 14, and 3 to explore possible gender differences.Method: Nine years after their first assessment at age five (preschoolers, a total of 77 adolescents (mean age: 14.35 years; SD = 1.22; 42% females took part in this follow-up study. At baseline, both parents and teachers completed the Strengths and Difficulties Questionnaire (SDQ, covering internalizing and externalizing problems, hyperactivity, negative peer relationships, and prosocial behavior. At follow-up, participants completed a booklet of questionnaires covering socio-demographic data, mental toughness, and sleep disturbances.Results: Higher prosocial behavior, lower negative peer relationships, and lower internalizing and externalizing problems at age five, as rated by parents and teachers, were associated with self-reported higher mental toughness and lower sleep disturbances at age 14. At age 14, and relative to males, females had lower MT scores and reported more sleep disturbances.Results: Higher prosocial behavior, lower negative peer relationships, and lower internalizing and externalizing problems at age five, as rated by parents and teachers, predicted self-reported higher mental toughness and lower sleep disturbances at age 14. At age 14, and relative to males, females had lower MT scores and reported more sleep disturbance.Conclusions: The pattern of results suggests that mental toughness traits during adolescence may have their origins in the pre-school years.
Full Text Available Photo essay. A collection of Images produced by intentionally corrupting the circuitry of a Kodak DC280 2 MP digitalcamera. By rewiring the electronics of a digital camera, glitched images are produced in a manner that parallels chemically processing unexposed film or photographic paper to produce photographic images without exposure to light. The DCP Series of Digital Images are direct visualizations of data generated by a digital camera as it takes a picture. Electronic processes associated with the normal operations of the camera, which are usually taken for granted, are revealed through an act of intervention. The camera is turned insideout through complexes of shortcircuits, selected by the artist, transforming the camera from a picture taking device to a data capturing device that renders raw data (electronic signals as images. In essence, these images are snapshots of electronic signals dancing through the camera's circuits, manually rerouted, written directly to the onboard memory device. Rather than seeing images of the world through a lens, we catch a glimpse of what the camera sees when it is forced to peer inside its own mind.
Catania, Leopoldo; Grassi, Stefano; Ravazzolo, Francesco
This paper studies the predictability of cryptocurrencies time series. We compare several alternative univariate and multivariate models in point and density forecasting of four of the most capitalized series: Bitcoin, Litecoin, Ripple and Ethereum. We apply a set of crypto–predictors and rely on Dynamic Model Averaging to combine a large set of univariate Dynamic Linear Models and several multivariate Vector Autoregressive models with different forms of time variation. We find statistical si...
Azevedo, S. M.; Saba, H.; Miranda, J. G. V.; Filho, A. S. Nascimento; Moret, M. A.
Dengue is a complex public health problem that is common in tropical and subtropical regions. This disease has risen substantially in the last three decades, and the physical symptoms depict the self-affine behavior of the occurrences of reported dengue cases in Bahia, Brazil. This study uses detrended fluctuation analysis (DFA) to verify the scale behavior in a time series of dengue cases and to evaluate the long-range correlations that are characterized by the power law α exponent for different cities in Bahia, Brazil. The scaling exponent (α) presents different long-range correlations, i.e. uncorrelated, anti-persistent, persistent and diffusive behaviors. The long-range correlations highlight the complex behavior of the time series of this disease. The findings show that there are two distinct types of scale behavior. In the first behavior, the time series presents a persistent α exponent for a one-month period. For large periods, the time series signal approaches subdiffusive behavior. The hypothesis of the long-range correlations in the time series of the occurrences of reported dengue cases was validated. The observed self-affinity is useful as a forecasting tool for future periods through extrapolation of the α exponent behavior. This complex system has a higher predictability in a relatively short time (approximately one month), and it suggests a new tool in epidemiological control strategies. However, predictions for large periods using DFA are hidden by the subdiffusive behavior.
Schryver, J.C.; Rao, N.
An increasing availability of high-performance computing and data storage media at decreasing cost is making possible the proliferation of large-scale numerical databases and data warehouses. Numeric warehousing enterprises on the order of hundreds of gigabytes to terabytes are a reality in many fields such as finance, retail sales, process systems monitoring, biomedical monitoring, surveillance and transportation. Large-scale databases are becoming more accessible to larger user communities through the internet, web-based applications and database connectivity. Consequently, most researchers now have access to a variety of massive datasets. This trend will probably only continue to grow over the next several years. Unfortunately, the availability of integrated tools to explore, analyze and understand the data warehoused in these archives is lagging far behind the ability to gain access to the same data. In particular, locating and identifying patterns of interest in numerical time series data is an increasingly important problem for which there are few available techniques. Temporal pattern recognition poses many interesting problems in classification, segmentation, prediction, diagnosis and anomaly detection. This research focuses on the problem of classification or characterization of numerical time series data. Highway vehicles and their drivers are examples of complex dynamic systems (CDS) which are being used by transportation agencies for field testing to generate large-scale time series datasets. Tools for effective analysis of numerical time series in databases generated by highway vehicle systems are not yet available, or have not been adapted to the target problem domain. However, analysis tools from similar domains may be adapted to the problem of classification of numerical time series data.
Li, Angel Yee-Lam; Lo, Barbara Chuen-Yee; Cheng, Cecilia
Studies have shown that children frequently experiencing poor parent-child interaction are prone to video gaming-related problems, but it is unclear which specific aspects of such an interaction play a predictive role in the problems. To extend previous research that relies primarily on the self-report method to assess parent-child interaction, we conducted a longitudinal, mixed-methods study. In a laboratory setting, three major aspects of interaction (i.e., affectivity, cohesiveness, and parental behavior) were observed in 241 parent-child dyads (Children: 43 percent female, age range = 8-15, M age = 12.09, SD age = 1.41; Parents: 78 percent female, age range = 27-63, M age = 44.44, SD age = 6.09). In addition, both parent and children participants completed questionnaires that measured children's symptoms of Internet gaming disorder (IGD) and exposure to violent video games at baseline (Time 1) and 12 months later (Time 2). The results revealed that at Time 1, positive affectivity and cohesiveness were inversely associated with child-report symptoms of IGD. Also, Time 1 coerciveness (i.e., control dimension of parental behavior) was positively associated with Time 1 child-report exposure to violent video games and Time 2 child-report symptoms of IGD, respectively. Apart from main effects, the results also showed that Time 1 negative affectivity moderated the protective effects of Time 1 positive affectivity on Time 1 parent-report and Time 2 child-report exposure to violent video games, respectively. Overall, this study identifies various key aspects of parent-child interaction that may serve as concurrent or temporal predictors of video gaming-related issues.
This text for undergraduate and graduate students illustrates the fundamental simplicity of the properties of orthogonal functions and their developments in related series. Starting with a definition and explanation of the elements of Fourier series, the text follows with examinations of Legendre polynomials and Bessel functions. Boundary value problems consider Fourier series in conjunction with Laplace's equation in an infinite strip and in a rectangle, with a vibrating string, in three dimensions, in a sphere, and in other circumstances. An overview of Pearson frequency functions is followe
de Branges, Louis
This text for advanced undergraduate and graduate students introduces Hilbert space and analytic function theory, which is centered around the invariant subspace concept. The book's principal feature is the extensive use of formal power series methods to obtain and sometimes reformulate results of analytic function theory. The presentation is elementary in that it requires little previous knowledge of analysis, but it is designed to lead students to an advanced level of performance. This is achieved chiefly through the use of problems, many of which were proposed by former students. The book's
Twomey, Conal; Prina, A Matthew; Baldwin, David S; Das-Munshi, Jayati; Kingdon, David; Koeser, Leonardo; Prince, Martin J; Stewart, Robert; Tulloch, Alex D; Cieza, Alarcos
Few countries have made much progress in implementing transparent and efficient systems for the allocation of mental health care resources. In England there are ongoing efforts by the National Health Service (NHS) to develop mental health 'payment by results' (PbR). The system depends on the ability of patient 'clusters' derived from the Health of the Nation Outcome Scales (HoNOS) to predict costs. We therefore investigated the associations of individual HoNOS items and the Total HoNOS score at baseline with mental health service costs at one year follow-up. An historical cohort study using secondary care patient records from the UK financial year 2012-2013. Included were 1,343 patients with 'common mental health problems', represented by ICD-10 disorders between F32-48. Costs were based on patient contacts with community-based and hospital-based mental health services. The costs outcome was transformed into 'high costs' vs 'regular costs' in main analyses. After adjustment for covariates, 11 HoNOS items were not associated with costs. The exception was 'self-injury' with an odds ratio of 1.41 (95% CI 1.10-2.99). Population attributable fractions (PAFs) for the contribution of HoNOS items to high costs ranged from 0.6% (physical illness) to 22.4% (self-injury). After adjustment, the Total HoNOS score was not associated with costs (OR 1.03, 95% CI 0.99-1.07). However, the PAF (33.3%) demonstrated that it might account for a modest proportion of the incidence of high costs. Our findings provide limited support for the utility of the self-injury item and Total HoNOS score in predicting costs. However, the absence of associations for the remaining HoNOS items indicates that current PbR clusters have minimal ability to predict costs, so potentially contributing to a misallocation of NHS resources across England. The findings may inform the development of mental health payment systems internationally, especially since the vast majority of countries have not progressed
Full Text Available Few countries have made much progress in implementing transparent and efficient systems for the allocation of mental health care resources. In England there are ongoing efforts by the National Health Service (NHS to develop mental health 'payment by results' (PbR. The system depends on the ability of patient 'clusters' derived from the Health of the Nation Outcome Scales (HoNOS to predict costs. We therefore investigated the associations of individual HoNOS items and the Total HoNOS score at baseline with mental health service costs at one year follow-up.An historical cohort study using secondary care patient records from the UK financial year 2012-2013. Included were 1,343 patients with 'common mental health problems', represented by ICD-10 disorders between F32-48. Costs were based on patient contacts with community-based and hospital-based mental health services. The costs outcome was transformed into 'high costs' vs 'regular costs' in main analyses.After adjustment for covariates, 11 HoNOS items were not associated with costs. The exception was 'self-injury' with an odds ratio of 1.41 (95% CI 1.10-2.99. Population attributable fractions (PAFs for the contribution of HoNOS items to high costs ranged from 0.6% (physical illness to 22.4% (self-injury. After adjustment, the Total HoNOS score was not associated with costs (OR 1.03, 95% CI 0.99-1.07. However, the PAF (33.3% demonstrated that it might account for a modest proportion of the incidence of high costs.Our findings provide limited support for the utility of the self-injury item and Total HoNOS score in predicting costs. However, the absence of associations for the remaining HoNOS items indicates that current PbR clusters have minimal ability to predict costs, so potentially contributing to a misallocation of NHS resources across England. The findings may inform the development of mental health payment systems internationally, especially since the vast majority of countries have not
Tolosana-Delgado, R.; van den Boogaart, K. G.
Common methods for multivariate time series analysis use linear operations, from the definition of a time-lagged covariance/correlation to the prediction of new outcomes. However, when the time series response is a composition (a vector of positive components showing the relative importance of a set of parts in a total, like percentages and proportions), then linear operations are afflicted of several problems. For instance, it has been long recognised that (auto/cross-)correlations between raw percentages are spurious, more dependent on which other components are being considered than on any natural link between the components of interest. Also, a long-term forecast of a composition in models with a linear trend will ultimately predict negative components. In general terms, compositional data should not be treated in a raw scale, but after a log-ratio transformation (Aitchison, 1986: The statistical analysis of compositional data. Chapman and Hill). This is so because the information conveyed by a compositional data is relative, as stated in their definition. The principle of working in coordinates allows to apply any sort of multivariate analysis to a log-ratio transformed composition, as long as this transformation is invertible. This principle is of full application to time series analysis. We will discuss how results (both auto/cross-correlation functions and predictions) can be back-transformed, viewed and interpreted in a meaningful way. One view is to use the exhaustive set of all possible pairwise log-ratios, which allows to express the results into D(D - 1)/2 separate, interpretable sets of one-dimensional models showing the behaviour of each possible pairwise log-ratios. Another view is the interpretation of estimated coefficients or correlations back-transformed in terms of compositions. These two views are compatible and complementary. These issues are illustrated with time series of seasonal precipitation patterns at different rain gauges of the USA
Tubman, Jonathan G.; Des Rosiers, Sabrina E.; Schwartz, Seth J.; O'Hare, Thomas
The present study evaluated the use of the Risky Sex Scale (RSS; O'Hare, 2001) among youth in outpatient treatment for substance use problems. An ethnically diverse sample of 394 adolescents (280 males; Mage = 16.33 years, SDage = 1.15) was recruited from two treatment sites. The study was guided by two aims. First, a confirmatory factor analysis was conducted on RSS item responses. Findings replicated the factor structure identified in previous studies of undergraduate students cited for campus alcohol violations. Second, structural equation modeling (SEM) was used to document associations between RSS subscales and self-reported substance use and sexual risk behaviors. The Risky Sex Expectancies (RSE) subscale was significantly associated with co-occurring alcohol use and sex, alcohol use at last intercourse, and alcohol use during the prior 30 days. The Risky Sex Behaviors (RSB) subscale was significantly associated with cooccurring drug use and sex, condom use at last intercourse and unprotected intercourse during the prior 30 days. The factor structure of the RSS was consistent across age group (12-16 and 16- 18) and across gender, and the links between the RSS subscales and health risk behaviors varied somewhat by gender but not by age group. These findings suggest that the RSS is an appropriate brief screening tool for predicting health risk behaviors among adolescents in substance abuse treatment. PMID:22425202
Mateo, F; Gadea, Rafael; Sovilj, Dusan
In many real world problems, the existence of irrelevant input variables (features) hinders the predictive quality of the models used to estimate the output variables. In particular, time series prediction often involves building large regressors of artificial variables that can contain irrelevant or misleading information. Many techniques have arisen to confront the problem of accurate variable selection, including both local and global search strategies. This paper presents a method based on genetic algorithms that intends to find a global optimum set of input variables that minimize the Delta Test criterion. The execution speed has been enhanced by substituting the exact nearest neighbor computation by its approximate version. The problems of scaling and projection of variables have been addressed. The developed method works in conjunction with MATLAB's Genetic Algorithm and Direct Search Toolbox. The goodness of the proposed methodology has been evaluated on several popular time series examples, and also ...
Bennema, Anne N; Schendelaar, Pamela; Seggers, Jorien; Haadsma, Maaike L; Heineman, Maas Jan; Hadders-Algra, Mijna
Background: General movement (GM) assessment is a well-established tool to predict cerebral palsy in high-risk infants. Little is known on the predictive value of GM assessment in low-risk populations. Aims: To assess the predictive value of GM quality in early infancy for the development of the
van den Akker, R.
This thesis adresses statistical problems in econometrics. The first part contributes statistical methodology for nonnegative integer-valued time series. The second part of this thesis discusses semiparametric estimation in copula models and develops semiparametric lower bounds for a large class of
D'Urso, Pierpaolo; Cappelli, Carmela; Di Lallo, Dario; Massari, Riccardo
This paper addresses the topic of classifying financial time series in a fuzzy framework proposing two fuzzy clustering models both based on GARCH models. In general clustering of financial time series, due to their peculiar features, needs the definition of suitable distance measures. At this aim, the first fuzzy clustering model exploits the autoregressive representation of GARCH models and employs, in the framework of a partitioning around medoids algorithm, the classical autoregressive metric. The second fuzzy clustering model, also based on partitioning around medoids algorithm, uses the Caiado distance, a Mahalanobis-like distance, based on estimated GARCH parameters and covariances that takes into account the information about the volatility structure of time series. In order to illustrate the merits of the proposed fuzzy approaches an application to the problem of classifying 29 time series of Euro exchange rates against international currencies is presented and discussed, also comparing the fuzzy models with their crisp version.
Goodwin, R D; Sourander, A; Duarte, C S; Niemelä, S; Multimäki, P; Nikolakaros, G; Helenius, H; Piha, J; Kumpulainen, K; Moilanen, I; Tamminen, T; Almqvist, F
Previous studies have documented associations between mental and physical health problems in cross-sectional studies, yet little is known about these relationships over time or the specificity of these associations. The aim of the current study was to examine the relationship between mental health problems in childhood at age 8 years and physical disorders in adulthood at ages 18-23 years. Multiple logistic regression analyses were used to examine the relationship between childhood mental health problems, reported by child, parent and teacher, and physical disorders diagnosed by a physician in early adulthood. Significant linkages emerged between childhood mental health problems and obesity, atopic eczema, epilepsy and asthma in early adulthood. Specifically, conduct problems in childhood were associated with a significantly increased likelihood of obesity and atopic eczema; emotional problems were associated with an increased likelihood of epilepsy and asthma; and depression symptoms at age 8 were associated with an increased risk of asthma in early adulthood. Our findings provide the first evidence of an association between mental health problems during childhood and increased risk of specific physical health problems, mainly asthma and obesity, during early adulthood, in a representative sample of males over time. These data suggest that behavioral and emotional problems in childhood may signal vulnerability to chronic physical health problems during early adulthood.
Engl, Heinz W; Lu, James; Müller, Stefan; Flamm, Christoph; Schuster, Peter; Kügler, Philipp
Systems biology is a new discipline built upon the premise that an understanding of how cells and organisms carry out their functions cannot be gained by looking at cellular components in isolation. Instead, consideration of the interplay between the parts of systems is indispensable for analyzing, modeling, and predicting systems' behavior. Studying biological processes under this premise, systems biology combines experimental techniques and computational methods in order to construct predictive models. Both in building and utilizing models of biological systems, inverse problems arise at several occasions, for example, (i) when experimental time series and steady state data are used to construct biochemical reaction networks, (ii) when model parameters are identified that capture underlying mechanisms or (iii) when desired qualitative behavior such as bistability or limit cycle oscillations is engineered by proper choices of parameter combinations. In this paper we review principles of the modeling process in systems biology and illustrate the ill-posedness and regularization of parameter identification problems in that context. Furthermore, we discuss the methodology of qualitative inverse problems and demonstrate how sparsity enforcing regularization allows the determination of key reaction mechanisms underlying the qualitative behavior. (topical review)