Search results for: generalized linear regression

Authors: Wenbo Du, Xiaomei Ma

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With the empowerment of Cognitive Diagnostic Assessment (CDA), various domains of language testing and assessment have been investigated to dig out more diagnostic information. What is noticeable is that most of the extant empirical CDA-based research puts much emphasis on individual-level diagnostic purpose with very few concerned about learners’ group-level performance. Even though the personalized diagnostic feedback is the unique feature that differentiates CDA from other assessment tools, group-level diagnostic information cannot be overlooked in that it might be more practical in classroom setting. Additionally, the group-level diagnostic information obtained via current CDA always results in a “flat pattern”, that is, the mastery/non-mastery of all tested skills accounts for the two highest proportion. In that case, the outcome does not bring too much benefits than the original total score. To address these issues, the present study attempts to apply cluster analysis for group classification and quantile regression analysis to pinpoint learners’ performance at different proficiency levels (beginner, intermediate and advanced) thus to enhance the interpretation of the CDA results extracted from a group of EFL learners’ reading performance on a diagnostic reading test designed by PELDiaG research team from a key university in China. The results show that EM method in cluster analysis yield more appropriate classification results than that of CDA, and quantile regression analysis does picture more insightful characteristics of learners with different reading proficiencies. The findings are helpful and practical for instructors to refine EFL reading curriculum and instructional plan tailored based on the group classification results and quantile regression analysis. Meanwhile, these innovative statistical methods could also make up the deficiencies of CDA and push forward the development of language testing and assessment in the future.

Keywords: cognitive diagnostic assessment, diagnostic feedback, EFL reading, quantile regression

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Authors: Ali Ashraf, Ghazaleh Aghaee, Sedigheh Samimian, Mohaya Farzin

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Objectives: Previous studies have indicated the positive effect of physical activity and sports on different aspects of health, such as muscle endurance and sleep cycle. However, in university students, particularly medical students, who have limited time and a stressful lifestyle, there have been limited studies exploring this matter with proven statistical results. In this regard, this study aims to find out how regular physical activity can influence the academic performance of medical students during their internship period. Methods: This was a descriptive-analytical study. Overall, 160 medical students (including 80 women and 88 men) voluntarily participated in the study. The Baecke Physical Activity Questionnaire was applied to determine the student’s physical activity levels. The student's academic performance was determined based on their total average academic scores. The data were analyzed in SPSS version 16 software using the independent t-test, Pearson correlation, and linear regression. Results: The average age of the students was 26.0±1.5 years. Eighty-eight students (52.4%) were male, and 142 (84.5%) were single. The student's mean total average academic score was 16.2±1.2, and their average physical activity score was 8.3±1.1. The student's average academic score was not associated with their gender (P=0.427), marital status (P=0.645), and age (P=0.320). However, married students had a significantly lower physical activity level compared to single students (P=0.020). The results indicated a significant positive correlation between student's physical activity levels and average academic scores (r=+0.410 and P<0.001). This correlation was independent of the student’s age, gender, and marital status based on the regression analysis. Conclusion: The results of the current study suggested that the physical activity level in medical students was low to moderate in most cases, and there was a significant direct relationship between student’s physical activity level and academic performance, independent of age, gender, and marital status.

Keywords: exercise, education, physical activity, academic performance

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Authors: Marzieh Karimihaghighi, Carlos Castillo

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This work studies how Machine Learning (ML) may be used to increase the effectiveness of a criminal recidivism risk assessment tool, RisCanvi. The two key dimensions of this analysis are predictive accuracy and algorithmic fairness. ML-based prediction models obtained in this study are more accurate at predicting criminal recidivism than the manually-created formula used in RisCanvi, achieving an AUC of 0.76 and 0.73 in predicting violent and general recidivism respectively. However, the improvements are small, and it is noticed that algorithmic discrimination can easily be introduced between groups such as national vs foreigner, or young vs old. It is described how effectiveness and algorithmic fairness objectives can be balanced, applying a method in which a single error disparity in terms of generalized false positive rate is minimized, while calibration is maintained across groups. Obtained results show that this bias mitigation procedure can substantially reduce generalized false positive rate disparities across multiple groups. Based on these results, it is proposed that ML-based criminal recidivism risk prediction should not be introduced without applying algorithmic bias mitigation procedures.

Keywords: algorithmic fairness, criminal risk assessment, equalized odds, recidivism

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Authors: Leonardo Herrera, Shield B. Lin, Stephen J. Montgomery-Smith, Ziraguen O. Williams

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Three control algorithms: Proportional-Integral-Derivative, Linear-Quadratic-Gaussian, and Linear-Quadratic-Gaussian with the shift, were applied to the computer simulation of a one-directional dynamic model of a Stewart Platform. The goal was to compare the dynamic system responses under the three control algorithms and to minimize the impact energy when simulating spacecraft docking operations. Equations were derived for the control algorithms and the input and output of the feedback control system. Using MATLAB, Simulink diagrams were created to represent the three control schemes. A switch selector was used for the convenience of changing among different controllers. The simulation demonstrated the controller using the algorithm of Linear-Quadratic-Gaussian with the shift resulting in the lowest impact energy.

Keywords: controller, Stewart platform, docking operation, spacecraft

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Authors: Sunil Mittal, Sukhpreet Singh, Hardeep Kaur

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The affinity of thiol group with heavy metals is a well-established phenomenon. The present investigation has been focused on electrochemical response of cysteine and thioredoxin against arsenite (As III) on indium tin oxide (ITO) electrodes. It was observed that both the compounds produce distinct response in free and immobilised form at the electrode. The SEM, FTIR, and impedance studies of the modified electrode were conducted for characterization. Various parameters were optimized to achieve As (III) effect on the reduction potential of the compounds. Cyclic voltammetry and linear sweep voltammetry were employed as the analysis techniques. The optimum response was observed at neutral pH in both the cases, at optimum concentration of 2 mM and 4.27 µM for cysteine and thioredoxin respectively. It was observed that presence of As (III) increases the reduction current of both the moieties. The linear range of detection for As (III) with cysteine was from 1 to 10 mg L⁻¹ with detection limit of 0.8 mg L⁻¹. The thioredoxin was found more sensitive to As (III) and displayed a linear range from 0.1 to 1 mg L⁻¹ with detection limit of 10 µg L⁻¹.

Keywords: arsenite, cyclic voltammetry, cysteine, thioredoxin

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Authors: Ghada Soltane

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The objective of this study was to identify factors impacting supply chain collaboration. a quantitative study was carried out on a sample of 84 Tunisian industrial companies. To verify the research hypotheses and test the direct effect of these factors on supply chain collaboration a multiple regression method was used using SPSS 26 software. The results show that there are four factors direct effects that affect supply chain collaboration in a meaningful and positive way, including: trust, engagement, information sharing and information quality

Keywords: supply chain collaboration, factors of collaboration, principal component analysis, multiple regression

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Authors: R. B. Knudsen, O. T. Rasmussen, R. A. Alphinas

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The reason for conducting this research is to develop an algorithm that is capable of classifying news articles from the automobile industry, according to the competitive actions that they entail, with the use of Text Mining (TM) methods. It is needed to test how to properly preprocess the data for this research by preparing pipelines which fits each algorithm the best. The pipelines are tested along with nine different classification algorithms in the realm of regression, support vector machines, and neural networks. Preliminary testing for identifying the optimal pipelines and algorithms resulted in the selection of two algorithms with two different pipelines. The two algorithms are Logistic Regression (LR) and Artificial Neural Network (ANN). These algorithms are optimized further, where several parameters of each algorithm are tested. The best result is achieved with the ANN. The final model yields an accuracy of 0.79, a precision of 0.80, a recall of 0.78, and an F1 score of 0.76. By removing three of the classes that created noise, the final algorithm is capable of reaching an accuracy of 94%.

Keywords: Artificial Neural network, Competitive dynamics, Logistic Regression, Text classification, Text mining

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Authors: Rachida Ouysse

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This paper proposes a constrained principal components (CnPC) estimator for efficient estimation of large-dimensional factor models when errors are cross sectionally correlated and the number of cross-sections (N) may be larger than the number of observations (T). Although principal components (PC) method is consistent for any path of the panel dimensions, it is inefficient as the errors are treated to be homoskedastic and uncorrelated. The new CnPC exploits the assumption of bounded cross-sectional dependence, which defines Chamberlain and Rothschild’s (1983) approximate factor structure, as an explicit constraint and solves a constrained PC problem. The CnPC method is computationally equivalent to the PC method applied to a regularized form of the data covariance matrix. Unlike maximum likelihood type methods, the CnPC method does not require inverting a large covariance matrix and thus is valid for panels with N ≥ T. The paper derives a convergence rate and an asymptotic normality result for the CnPC estimators of the common factors. We provide feasible estimators and show in a simulation study that they are more accurate than the PC estimator, especially for panels with N larger than T, and the generalized PC type estimators, especially for panels with N almost as large as T.

Keywords: high dimensionality, unknown factors, principal components, cross-sectional correlation, shrinkage regression, regularization, pseudo-out-of-sample forecasting

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Authors: N. F. Jamaluddin, N. A. H. Aizam

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This paper explains the educational timetabling problem, a type of scheduling problem that is considered as one of the most challenging problem in optimization and operational research. The university examination timetabling problem (UETP), which involves assigning a set number of exams into a set number of timeslots whilst fulfilling all required conditions, has been widely investigated. The limitation of available timeslots and resources with the increasing number of examinations are the main reasons in the difficulty of solving this problem. Dynamical change in the examination scheduling system adds up the complication particularly in coping up with the demand and new requirements by the communities. Our objective is to investigate these demands and requirements with subjects taken from Universiti Malaysia Terengganu (UMT), through questionnaires. Integer linear programming model which reflects the preferences obtained to produce an effective examination timetabling was formed.

Keywords: demands, educational timetabling, integer linear programming, scheduling, university examination timetabling problem (UETP)

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Authors: Abdel-Razzaq Mugdadi, Ruqayyah Sani

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The Kernel Distribution Function Estimator (KDFE) method is the most popular method for nonparametric estimation of the cumulative distribution function. The kernel and the bandwidth are the most important components of this estimator. In this investigation, we replace the kernel in the KDFE with a linear combination of kernels to obtain a new estimator based on the linear combination of kernels, the mean integrated squared error (MISE), asymptotic mean integrated squared error (AMISE) and the asymptotically optimal bandwidth for the new estimator are derived. We propose a new data-based method to select the bandwidth for the new estimator. The new technique is based on the Plug-in technique in density estimation. We evaluate the new estimator and the new technique using simulations and real-life data.

Keywords: estimation, bandwidth, mean square error, cumulative distribution function

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Authors: Fatma Abidi, Taoufik Harizi, Slah Msahli, Faouzi Sakli

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Nine different Tunisian handmade carpets were used for the investigation. The raw material of the carpet pile yarns was wool. The influence of the different structure parameters (linear density and pile height) on the carpet compression was investigated. Carpets were tested under dynamic loading in order to evaluate and observe the thickness loss and carpet behavior under dynamic loads. To determine the loss of pile height under dynamic loading, the pile height carpets were measured. The test method was treated according to the Tunisian standard NT 12.165 (corresponds to the standard ISO 2094). The pile height measurements are taken and recorded at intervals up to 1000 impacts (measures of this study were made after 50, 100, 200, 500, and 1000 impacts). The loss of pile height is calculated using the variation between the initial height and those measured after the number of reported impacts. The experimental results were statistically evaluated using Design Expert Analysis of Variance (ANOVA) software. As regards the deformation, results showed that both of the structure parameters of the pile yarn and the pile height have an influence. The carpet with the higher pile and the less linear density of pile yarn showed the worst performance. Results of a polynomial regression analysis are highlighted. There is a good correlation between the loss of pile height and the impacts number of dynamic loads. These equations are in good agreement with measured data. Because the prediction is reasonably accurate for all samples, these equations can also be taken into account when calculating the theoretical loss of pile height for the considered carpet samples. Statistical evaluations of the experimen¬tal data showed that the pile material and number of impacts have a significant effect on mean thickness and thickness loss variations.

Keywords: Tunisian handmade carpet, loss of pile height, dynamic loads, performance

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Authors: Shivani Sharma, Prashant Saxena, Inamul Hasan Madar

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Since the time of Darwin, it has been considered that genetic change is the direct indicator of variation in phenotype. But a few studies in system biology in the past years have proposed that epigenetic developmental processes also affect the phenotype thus shifting the focus from a linear genotype-phenotype map to a non-linear G-P map. In this paper, we attempt at explaining the evolution of colour vision in mammals by taking LWS/ Long-wave sensitive gene under consideration.

Keywords: evolution, phenotypes, epigenetics, LWS gene, G-P map

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Authors: M. Tarawneh

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This work is an experimental study on the heat transfer characteristics and pressure drop of different refrigerants during the condensation and evaporation processes in porous media. Four different refrigerants (R134a, R407C, 600a, R290), with different porosities were used to reach a real understanding of the actual heat transfer characteristics and pressure drop when using porous material inside the condenser and evaporator. Steel balls were used as porous media with different porosities (38%, 43%, 48%). The main goal of this project is to enhance the heat transfer coefficient during the condensation and evaporation processes when using different refrigerants and different porosities. Different correlations for the heat transfer coefficient and the pressure drop of the different refrigerants were developed. Also a generalized empirical correlation was developed for the different refrigerants. The experimental and predicted heat transfer coefficients and pressure drops were compared. It was found that, the Absolute standard deviation for the heat transfer coefficient and the pressure drop not exceeded values of 15% and 20%, respectively.

Keywords: condensation, evaporation, porous media, horizontal tubes, heat transfer coefficient, propane, butane

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Authors: Reji Thankachan, Varsha PS

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Both image capturing devices and human visual systems are nonlinear. Hence nonlinear filtering methods outperforms its linear counterpart in many applications. Linear methods are unable to remove impulsive noise in images by preserving its edges and fine details. In addition, linear algorithms are unable to remove signal dependent or multiplicative noise in images. This paper presents an approach to denoise and smoothen the Bipolar impulse noised images and videos using improved Kuwahara filter. It involves a 2 stage algorithm which includes a noise detection followed by filtering. Numerous simulation demonstrate that proposed method outperforms the existing method by eliminating the painting like flattening effect along the local feature direction while preserving edge with improvement in PSNR and MSE.

Keywords: bipolar impulse noise, Kuwahara, PSNR MSE, PDF

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Authors: Christian H. Sanabria-Montaña, Rodrigo Huerta-Quintanilla

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A lattice network is a special type of network in which all nodes have the same number of links, and its boundary conditions are periodic. The most basic lattice network is the ring, a one-dimensional network with periodic border conditions. In contrast, the Cartesian product of d rings forms a d-dimensional lattice network. An analytical expression currently exists for the clustering coefficient in this type of network, but the theoretical value is valid only up to certain connectivity value; in other words, the analytical expression is incomplete. Here we obtain analytically the clustering coefficient expression in d-dimensional lattice networks for any link density. Our analytical results show that the clustering coefficient for a lattice network with density of links that tend to 1, leads to the value of the clustering coefficient of a fully connected network. We developed a model on criminology in which the generalized clustering coefficient expression is applied. The model states that delinquents learn the know-how of crime business by sharing knowledge, directly or indirectly, with their friends of the gang. This generalization shed light on the network properties, which is important to develop new models in different fields where network structure plays an important role in the system dynamic, such as criminology, evolutionary game theory, econophysics, among others.

Keywords: clustering coefficient, criminology, generalized, regular network d-dimensional

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Authors: Protus A. Lumiti, Susan O. Wekesa, Mary Omondi

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Performance is a key pillar to the accomplishment of the goals of all organizations, whether private, public or non- profit. Employees are the intellectual assets of the organization and they are an avenue to the achievement of competitive advantage. An employee welfare service in an organization is vital in fostering employee motivation and improving their productivity. In view of this, the main goal of this research was to determine the relationship between employee welfare practices and the performance of non-governmental organizations in Kenya. The study was guided by four objectives, namely: to establish, determine, evaluate and assess the relationship between employee welfare practices and the performance of non-governmental organizations in Kenya. The study utilized a survey design using both qualitative and quantitative approaches. In this study, a purposive, stratified and simple random sampling technique was used to arrive at a sample of 355 respondents who comprised senior managers, middle level managers and operational employees out of the targeted population of 14,283 employees of non-governmental organizations working in Nairobi County. The primary data collection tools were questionnaires supplemented by an interview schedule, while secondary data was obtained from reviewed journals, published books and articles. Data analysis was done using Statistical Packages for Social Sciences Software version 23. The study utilized multiple linear regression and a structural equation model. The findings of the study were that: employee welfare practices had a positive and significant relationship with the performance of Non-governmental organizations in Kenya. In addition, there was also a linear relationship between the independent variables and the dependent variable and the study concluded that there was a relationship between the predictor variable and the dependent variable of the study. The study recommended that management of No-governmental organization boards in Kenya should come up with a comprehensive policy document on employee welfare practices in order to enhance the performance of non-governmental organizations in Kenya.

Keywords: employee, economic, performance, welfare

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Authors: Surinder Deswal, Mahesh Pal

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The paper investigates the potential of support vector machines based regression approach to model the mass transfer capacity of multiple plunging jets, both vertical (θ = 90°) and inclined (θ = 60°). The data set used in this study consists of four input parameters with a total of eighty eight cases. For testing, tenfold cross validation was used. Correlation coefficient values of 0.971 and 0.981 (root mean square error values of 0.0025 and 0.0020) were achieved by using polynomial and radial basis kernel functions based support vector regression respectively. Results suggest an improved performance by radial basis function in comparison to polynomial kernel based support vector machines. The estimated overall mass transfer coefficient, by both the kernel functions, is in good agreement with actual experimental values (within a scatter of ±15 %); thereby suggesting the utility of support vector machines based regression approach.

Keywords: mass transfer, multiple plunging jets, support vector machines, ecological sciences

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Authors: M. J. Park, S. H. Lee, C. H. Lee, O. M. Kwon

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This paper investigates stability problem for linear systems of neutral type with time-varying delay. By constructing various Lyapunov-Krasovskii functional, and utilizing some mathematical techniques, the sufficient stability conditions for the systems are established in terms of linear matrix inequalities (LMIs), which can be easily solved by various effective optimization algorithms. Finally, some illustrative examples are given to show the effectiveness of the proposed criterion.

Keywords: neutral systems, time-delay, stability, Lyapnov method, LMI

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Authors: Adilah Abdul Ghapor, Yong Zulina Zubairi, A. H. M. R. Imon

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Missing value problem is common in statistics and has been of interest for years. This article considers two modern techniques in handling missing data for linear functional relationship model (LFRM) namely the Expectation-Maximization (EM) algorithm and Expectation-Maximization with Bootstrapping (EMB) algorithm using three performance indicators; namely the mean absolute error (MAE), root mean square error (RMSE) and estimated biased (EB). In this study, we applied the methods of imputing missing values in two types of LFRM namely the full model of LFRM and in LFRM when the slope is estimated using a nonparametric method. Results of the simulation study suggest that EMB algorithm performs much better than EM algorithm in both models. We also illustrate the applicability of the approach in a real data set.

Keywords: expectation-maximization, expectation-maximization with bootstrapping, linear functional relationship model, performance indicators

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Authors: Meng Koon Lee, Mohammad Hosseini Fouladi, Satesh Narayana Namasivayam

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Research is focused on the response of soundboards of Classical guitars at frequencies up to 5 kHz as the soundboard is a major contributor to acoustic radiation at high frequencies when compared to the bridge and sound hole. A thin rectangular plate of variable thickness that is simply-supported on all sides is used as an analytical model of the research. This model is used to study the response of the guitar soundboard as the latter can be considered as a modified form of a rectangular plate. Homotopy Perturbation Method (HPM) is selected as a mathematical method to obtain an analytical solution of the 4th-order parabolic partial differential equation of motion of the rectangular plate of constant thickness viewed as a linear problem. This procedure is generalized to the nonlinear problem of the rectangular plate with variable thickness and an analytical solution can also be obtained. Sound power is used as a parameter to investigate the acoustic radiation of soundboards made from spruce using various bracing patterns. The sound power of soundboards made from Malaysian softwood such as damar minyak, sempilor or podo are investigated to determine the viability of replacing spruce as future materials for soundboards of Classical guitars.

Keywords: rectangular plates, analytical solution, homotopy perturbation, natural frequencies

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Authors: Davood Rajabi, Mojgan Yazdani

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Non-linear Muskingum model is an efficient method for flood routing, however, the efficiency of this method is influenced by three applied parameters. Therefore, efficiency assessment of Imperialist Competition Algorithm (ICA) to evaluate optimum parameters of non-linear Muskingum model was addressed through this study. In addition to ICA, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) were also used aiming at an available criterion to verdict ICA. In this regard, ICA was applied for Wilson flood routing; then, routing of two flood events of DoAab Samsami River was investigated. In case of Wilson flood that the target function was considered as the sum of squared deviation (SSQ) of observed and calculated discharges. Routing two other floods, in addition to SSQ, another target function was also considered as the sum of absolute deviations of observed and calculated discharge. For the first floodwater based on SSQ, GA indicated the best performance, however, ICA was on first place, based on SAD. For the second floodwater, based on both target functions, ICA indicated a better operation. According to the obtained results, it can be said that ICA could be used as an appropriate method to evaluate the parameters of Muskingum non-linear model.

Keywords: Doab Samsami river, genetic algorithm, imperialist competition algorithm, meta-exploratory algorithms, particle swarm optimization, Wilson flood

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Authors: Kristina Colovic, Mari Janikian, Nikolaos Takis, Fotini-Sonia Apergi

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A considerable number of Afghan asylum seekers in Greece are still waiting for answers about their future and status for personal, social and societal advancement. Most have been trapped in a stalemate of continuously postponed or temporarily progressed levels of integration into the EU/Greek process of asylum. Limited quantitative research exists investigating the psychological effects of long-term displacement among Afghans refugees in Greece. The purpose of this study is to investigate factors that are associated with and predict psychological distress symptoms among this population. Data from a sample of native Afghan nationals (N > 70) living in Greece for approximately the last ten years will be collected from May to July 2016. Criteria for participation include the following: being 18 years of age or older, and emigration from Afghanistan to Greece from 2003 onwards (i.e., long-term refugees or part of the 'old system of asylum'). Snowball sampling will be used to recruit participants, as this is considered the most effective option when attempting to study refugee populations. Participants will complete self-report questionnaires, consisting of the Afghan Symptom Checklist (ASCL), a culturally validated measure of psychological distress, the World Health Organization Quality of Life scale (WHOQOL-BREF), an adapted version of the Comprehensive Trauma Inventory-104 (CTI-104), and a modified Psychological Acculturation Scale. All instruments will be translated in Greek, through the use of forward- and back-translations by bilingual speakers of English and Greek, following WHO guidelines. A pilot study with 5 Afghan participants will take place to check for discrepancies in understanding and for further adapting the instruments as needed. Demographic data, including age, gender, year of arrival to Greece and current asylum status will be explored. Three different types of analyses (descriptive statistics, bivariate correlations, and multivariate linear regression) will be used in this study. Descriptive findings for respondent demographics, psychological distress symptoms, traumatic life events and quality of life will be reported. Zero-order correlations will assess the interrelationships among demographic, traumatic life events, psychological distress, and quality of life variables. Lastly, a multivariate linear regression model will be estimated. The findings from the study will contribute to understanding the determinants of acculturation, distress and trauma on daily functioning for Afghans in Greece. The main implications of the current study will be to advocate for capacity building and empower communities through effective program evaluation and design for mental health services for all refugee populations in Greece.

Keywords: Afghan refugees, evaluation, Greece, mental health, quality of life

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Authors: Himansu, Bedanga Talukdar

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The aim of present study is to investigate the prevalence of various health outcomes and quality of life and analyzes the moderating role of social capital on health outcomes (i.e., self-rated good health (SRH), depression, functional health and quality of life) among elderly in India. Using WHO Study on Global AGEing and adults health (SAGE) data, with sample of 6559 elderly between 50 and above (Mage=61.81, SD=9.00) age were selected for analysis. Multivariate analysis accessed the prevalence of SRH, depression, functional limitation and quality of life among older adults. Logistic regression evaluates the effect of social capital along with other co-founders on SRH, depression, and functional limitation, whereas linear regression evaluates the effect of social capital with other co-founders on quality of life (QoL) among elderly. Empirical results reveal that (74%) of respondents were married, (70%) having low social action, (46%) medium sociability, (45%) low trust-solidarity, (58%) high safety, (65%) medium civic engagement and 37% reported medium psychological resources. The multivariate analysis, explains (SRH) is associated with age, female, having education, higher social action great trust, safety and greater psychological resources. Depression among elderly is greatly related to age, sex, education and higher wealth, higher sociability, having psychological resources. QoL is negatively associated with age, sex, being Muslim, whereas positive associated with higher education, currently married, civic engagement, having wealth, social action, trust and solidarity, safeness, and strong psychological resources.

Keywords: depressive symptom, functional limitation, older adults, quality of life, self rated health, social capital

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Authors: Abdulrahman Sumayli, Saad M. AlShahrani

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For the hydrogenation process, knowing the solubility of hydrogen (H2) in hydrocarbons is critical to improve the efficiency of the process. We investigated the H2 solubility computation in four heavy crude oil feedstocks using machine learning techniques. Temperature, pressure, and feedstock type were considered as the inputs to the models, while the hydrogen solubility was the sole response. Specifically, we employed three different models: Support Vector Regression (SVR), Gaussian process regression (GPR), and Bayesian ridge regression (BRR). To achieve the best performance, the hyper-parameters of these models are optimized using the whale optimization algorithm (WOA). We evaluated the models using a dataset of solubility measurements in various feedstocks, and we compared their performance based on several metrics. Our results show that the WOA-SVR model tuned with WOA achieves the best performance overall, with an RMSE of 1.38 × 10− 2 and an R-squared of 0.991. These findings suggest that machine learning techniques can provide accurate predictions of hydrogen solubility in different feedstocks, which could be useful in the development of hydrogen-related technologies. Besides, the solubility of hydrogen in the four heavy oil fractions is estimated in different ranges of temperatures and pressures of 150 ◦C–350 ◦C and 1.2 MPa–10.8 MPa, respectively

Keywords: temperature, pressure variations, machine learning, oil treatment

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Authors: Cherifi Mehdi, Lahdir Mourad, Ameur Soltane

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Image compression is used to reduce the number of bits required to represent an image. The Meteosat Second Generation satellite (MSG) allows the acquisition of 12 image files every 15 minutes. Which results a large databases sizes. The transform selected in the images compression should contribute to reduce the data representing the images. The Radon transform retrieves the Radon points that represent the sum of the pixels in a given angle for each direction. Linear predictive coding (LPC) with filtering provides a good decorrelation of Radon points using a Predictor constitute by the Symmetric Nearest Neighbor filter (SNN) coefficients, which result losses during decompression. Finally, Run Length Coding (RLC) gives us a high and fixed compression ratio regardless of the input image. In this paper, a novel image compression method based on the Radon transform and linear predictive coding (LPC) for MSG images is proposed. MSG image compression based on the Radon transform and the LPC provides a good compromise between compression and quality of reconstruction. A comparison of our method with other whose two based on DCT and one on DWT bi-orthogonal filtering is evaluated to show the power of the Radon transform in its resistibility against the quantization noise and to evaluate the performance of our method. Evaluation criteria like PSNR and the compression ratio allows showing the efficiency of our method of compression.

Keywords: image compression, radon transform, linear predictive coding (LPC), run lengthcoding (RLC), meteosat second generation (MSG)

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Authors: Klary Geisler, Dalit Lev-Arey, Yael Hacohen

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It is a well-known fact that exercise has favorable effects on people's physical health, as well as mental well-being. However, as for as excessive exercise, it may likely elevate negative consequences (e.g., physical injuries, negligence of everyday responsibilities such as work, family life). Lately, there is a growing interest in exercise addiction, sometimes referred to as exercise dependence, which is defined as a craving for physical activity that results in extreme work-out sessions and generates negative physiological and psychological symptoms (e.g., withdrawal symptoms, tolerance, social conflict). Exercise addiction is considered a behavioral addiction, yet it was not included in the latest editions of the diagnostic and statistical manual of mental disorders (DSM-IV), due to lack of significant research. Specifically, there is scarce research on the relationship between exercise addiction and personality dimensions. The purpose of the current research was to examine the relationship between primary exercise addiction symptoms and the big five dimensions, perfectionism (high performance expectations and self-critical performance evaluations) and subjective affect. participants were 152 trainees on a variety of aerobic sports activities (running, cycling, swimming) that were recruited through sports groups and trainers. 88% of participants trained for at least 5 hours per week, 24% of the participants trained above 10 hours per week. To test the predictive ability of the IVs a hierarchical linear regression with forced block entry was performed. It was found that Neuroticism significantly predicted exercise addiction symptoms (20% of the variance, p<0.001), while consciousness was negatively correlated with exercise addiction symptoms (14% of variance p<0.05); both had a unique contribution. Other dimensions of the big five (agreeableness, openness and extraversion) did not have any contribution to the dependent. Moreover, maladaptive perfectionism (self-critical performance evaluations) significantly predicted exercise addiction symptoms as well (10% of the variance P < 0.05). The overall regression model explained 54% of variance.

Keywords: big five, consciousness, excessive exercise, exercise addiction, neuroticism, perfectionism, personality

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Authors: Ridha Fethi Mechlouch, Ahlem Ayadi, Ammar Ben Brahim

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Given the importance of the preservation of polyphenols and carotenoids during thermal processing, we attempted in this study to investigate the variation of these two parameters in faba beans during microwave treatment using different power densities (1; 2; and 3W/g), then to perform a mathematical modeling by using non-linear regression analysis to evaluate the models constants. The variation of the carotenoids and polyphenols ratio of faba beans and the models are tested to validate the experimental results. Exponential models were found to be suitable to describe the variation of caratenoid ratio (R²= 0.945, 0.927 and 0.946) for power densities (1; 2; and 3W/g) respectively, and polyphenol ratio (R²= 0.931, 0.989 and 0.982) for power densities (1; 2; and 3W/g) respectively. The effect of microwave power density Pd(W/g) on the coefficient k of models were also investigated. The coefficient is highly correlated (R² = 1) and can be expressed as a polynomial function.

Keywords: microwave treatment, power density, carotenoid, polyphenol, modeling

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Authors: Ozden Saygili, Eser Cakti

Abstract:

The masonry building stock in Istanbul and in other cities of Turkey are exposed to significant earthquake hazard. Determination of the safety of masonry structures against earthquakes is a complex challenge. This study deals with experimental tests and non-linear dynamic analysis of masonry structures modeled through discrete element method. The 1:10 scale model of Mustafa Paşa Mosque was constructed and the data were obtained from the sensors on it during its testing on the shake table. The results were used in the calibration/validation of the numerical model created on the basis of the 1:10 scale model built for shake table testing. 3D distinct element model was developed that represents the linear and nonlinear behavior of the shake table model as closely as possible during experimental tests. Results of numerical analyses with those from the experimental program were compared and discussed.

Keywords: dynamic analysis, non-linear modeling, shake table tests, masonry

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Authors: Benjamin Bobbia, Matthias Picard

Abstract:

In recent years active learning methodologies based on the representativity of the data seems more promising to limit overfitting. The presented query methodology for regression using the Wasserstein distance measuring the representativity of our labelled dataset compared to the global distribution. In this work a crucial use of GroupSort Neural Networks is made therewith to draw a double advantage. The Wasserstein distance can be exactly expressed in terms of such neural networks. Moreover, one can provide explicit bounds for their size and depth together with rates of convergence. However, heterogeneity of the dataset is also considered by weighting the Wasserstein distance with the error of approximation at the previous step of active learning. Such an approach leads to a reduction of overfitting and high prediction performance after few steps of query. After having detailed the methodology and algorithm, an empirical study is presented in order to investigate the range of our hyperparameters. The performances of this method are compared, in terms of numbers of query needed, with other classical and recent query methods on several UCI datasets.

Keywords: active learning, Lipschitz regularization, neural networks, optimal transport, regression

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Authors: Jie Liu, Jinqu Cheng, Qiyuan Peng, Yong Yin

Abstract:

Urban rail transit (URT) plays a significant role in dealing with traffic congestion and environmental problems in cities. However, equipment failure and obstruction of links often lead to URT links’ capacities loss in daily operation. It affects the reliability and transport service quality of URT network seriously. In order to measure the influence of links’ capacities loss on reliability and transport service quality of URT network, passengers are divided into three categories in case of links’ capacities loss. Passengers in category 1 are less affected by the loss of links’ capacities. Their travel is reliable since their travel quality is not significantly reduced. Passengers in category 2 are affected by the loss of links’ capacities heavily. Their travel is not reliable since their travel quality is reduced seriously. However, passengers in category 2 still can travel on URT. Passengers in category 3 can not travel on URT because their travel paths’ passenger flow exceeds capacities. Their travel is not reliable. Thus, the proportion of passengers in category 1 whose travel is reliable is defined as reliability indicator of URT network. The transport service quality of URT network is related to passengers’ travel time, passengers’ transfer times and whether seats are available to passengers. The generalized travel cost is a comprehensive reflection of travel time, transfer times and travel comfort. Therefore, passengers’ average generalized travel cost is used as transport service quality indicator of URT network. The impact of links’ capacities loss on transport service quality of URT network is measured with passengers’ relative average generalized travel cost with and without links’ capacities loss. The proportion of the passengers affected by links and betweenness of links are used to determine the important links in URT network. The stochastic user equilibrium distribution model based on the improved logit model is used to determine passengers’ categories and calculate passengers’ generalized travel cost in case of links’ capacities loss, which is solved with method of successive weighted averages algorithm. The reliability and transport service quality indicators of URT network are calculated with the solution result. Taking Wuhan Metro as a case, the reliability and transport service quality of Wuhan metro network is measured with indicators and method proposed in this paper. The result shows that using the proportion of the passengers affected by links can identify important links effectively which have great influence on reliability and transport service quality of URT network; The important links are mostly connected to transfer stations and the passenger flow of important links is high; With the increase of number of failure links and the proportion of capacity loss, the reliability of the network keeps decreasing, the proportion of passengers in category 3 keeps increasing and the proportion of passengers in category 2 increases at first and then decreases; When the number of failure links and the proportion of capacity loss increased to a certain level, the decline of transport service quality is weakened.

Keywords: urban rail transit network, reliability, transport service quality, links’ capacities loss, important links

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