Search results for: nonlinear regression

Authors: Luis Fonseca, Filipe Cabral Pinto, Susana Sargento

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The increase of the world population, especially in large urban centers, has resulted in new challenges particularly with the control and optimization of public safety. Thus, in the present work, a solution is proposed for the prediction of criminal occurrences in a city based on historical data of incidents and demographic information. The entire research and implementation will be presented start with the data collection from its original source, the treatment and transformations applied to them, choice and the evaluation and implementation of the Machine Learning model up to the application layer. Classification models will be implemented to predict criminal risk for a given time interval and location. Machine Learning algorithms such as Random Forest, Neural Networks, K-Nearest Neighbors and Logistic Regression will be used to predict occurrences, and their performance will be compared according to the data processing and transformation used. The results show that the use of Machine Learning techniques helps to anticipate criminal occurrences, which contributed to the reinforcement of public security. Finally, the models were implemented on a platform that will provide an API to enable other entities to make requests for predictions in real-time. An application will also be presented where it is possible to show criminal predictions visually.

Keywords: Crime prediction, machine learning, public safety, smart city.

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Authors: Asma Rabaoui, Zied Lachiri, Noureddine Ellouze

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Discrimination between different classes of environmental sounds is the goal of our work. The use of a sound recognition system can offer concrete potentialities for surveillance and security applications. The first paper contribution to this research field is represented by a thorough investigation of the applicability of state-of-the-art audio features in the domain of environmental sound recognition. Additionally, a set of novel features obtained by combining the basic parameters is introduced. The quality of the features investigated is evaluated by a HMM-based classifier to which a great interest was done. In fact, we propose to use a Multi-Style training system based on HMMs: one recognizer is trained on a database including different levels of background noises and is used as a universal recognizer for every environment. In order to enhance the system robustness by reducing the environmental variability, we explore different adaptation algorithms including Maximum Likelihood Linear Regression (MLLR), Maximum A Posteriori (MAP) and the MAP/MLLR algorithm that combines MAP and MLLR. Experimental evaluation shows that a rather good recognition rate can be reached, even under important noise degradation conditions when the system is fed by the convenient set of features.

Keywords: Sounds recognition, HMM classifier, Multi-style training, Environmental Adaptation, Feature combinations.

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Authors: Mohamed Elghorab, Vendra C. Madhav Rao, Jennifer X. Wen

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Turbulence modelling is still evolving, and efforts are on to improve and develop numerical methods to simulate the real turbulence structures by using the empirical and experimental information. The monotonically integrated large eddy simulation (MILES) is an attractive approach for modelling turbulence in high Re flows, which is based on the solving of the unfiltered flow equations with no explicit sub-grid scale (SGS) model. In the current work, this approach has been used, and the action of the SGS model has been included implicitly by intrinsic nonlinear high-frequency filters built into the convection discretization schemes. The MILES solver is developed using the opensource CFD OpenFOAM libraries. The role of flux limiters schemes namely, Gamma, superBee, van-Albada and van-Leer, is studied in predicting turbulent statistical quantities for a fully developed channel flow with a friction Reynolds number, Re_T = 180, and compared the numerical predictions with the well-established Direct Numerical Simulation (DNS) results for studying the wall generated turbulence. It is inferred from the numerical predictions that Gamma, van-Leer and van-Albada limiters produced more diffusion and overpredicted the velocity profiles, while superBee scheme reproduced velocity profiles and turbulence statistical quantities in good agreement with the reference DNS data in the streamwise direction although it deviated slightly in the spanwise and normal to the wall directions. The simulation results are further discussed in terms of the turbulence intensities and Reynolds stresses averaged in time and space to draw conclusion on the flux limiter schemes performance in OpenFOAM context.

Keywords: Flux limiters, MILES, OpenFOAM, turbulence structures, TVD schemes.

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Authors: Isaac O. Asante, Yushi Jiang, Hailin Tao

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Livestreaming marketing, the new electronic commerce element, has become an optional marketing channel following the COVID-19 pandemic, and many sellers are leveraging the features presented by livestreaming to increase sales. This study was conducted to measure real-time observable interactions between consumers and sellers. Based on the affordance theory, this study conceptualized constructs representing the interactive features and examined how they drive consumers’ purchase willingness during livestreaming sessions using 1238 datasets from Amazon Live, following the manual observation of transaction records. Using structural equation modeling, the ordinary least square regression suggests that live viewers, new followers, live chats, and likes positively affect purchase willingness. The Sobel and Monte Carlo tests show that new followers, live chats, and likes significantly mediate the relationship between live viewers and purchase willingness. The study presents a way of measuring interactions in livestreaming commerce and proposes a way to manually gather data on consumer behaviors in livestreaming platforms when the application programming interface (API) of such platforms does not support data mining algorithms.

Keywords: Livestreaming marketing, live chats, live viewers, likes, new followers, purchase willingness.

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Authors: A. P. Singh

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The results reported in this paper are the part of an extensive laboratory investigation undertaken to study the effects of fibre parameters on the permeability and strength characteristics of steel fibre reinforced concrete (SFRC). The effect of varying fibre content and curing age on the water permeability, compressive and split tensile strengths of SFRC was investigated using straight steel fibres having an aspect ratio of 65. Samples containing three different weight fractions of 1.0%, 2.0% and 4.0% were cast and tested for permeability and strength after 7, 14, 28 and 60 days of curing. Plain concrete samples were also cast and tested for reference purposes.

Permeability was observed to decrease significantly with the addition of steel fibres and continued to decrease with increasing fibre content and increasing curing age. An exponential relationship was observed between permeability and compressive and split tensile strengths for SFRC as well as PCC. To evaluate the effect of fibre content on the permeability and strength characteristics, the Analysis of Variance (ANOVA) statistical method was used. An a level (probability of error) of 0.05 was used for ANOVA test. Regression analysis was carried out to develop relationship between permeability, compressive strength and curing age.

Keywords: Permeability, grade of concrete, fibre shape, fibre content, curing age, steady state, Darcy’s law, method of penetration.

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Authors: Lisa J. Piergallini

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This project uses panel regression analyses to investigate the relationships between geography, institutions, and economic development, as guided by the theories of the 18^th century French philosopher Montesquieu. Contemporary scholars of political economy perpetually misinterpret Montesquieu’s theories on climate, and in doing so they miss what could be the key to resolving the geography vs. institutions debate. There is a conspicuous gap in this literature, in that it does not consider whether geography and institutors might have an interactive, dynamic effect on economic development. This project seeks to bridge that gap. Data are used for all available countries over the years 1980-2013. Two interaction terms between geographic and institutional variables are employed within the empirical analyses, and these offer a unique contribution to the ongoing geography vs. institutions debate within the political economy literature. This study finds that there is indeed an interactive effect between geography and institutions, and that this interaction has a statistically significant effect on economic development. Democracy (as measured by Polity score) and rule of law and property rights (as measured by the Fraser index) have positive effects on economic development (as measured by GDP per capita), yet the magnitude of these effects are stronger in contexts where a low percent of the national population lives in the geographical tropics. This has implications for promoting economic development, and it highlights the importance of understanding geographical context.

Keywords: Montesquieu, geography, institutions, economic development, political philosophy, political economy.

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Authors: Ali Ghaffari, Mansour Nikkhah Bahrami, Hesam Parsa

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In this paper a comprehensive model of a fossil fueled power plant (FFPP) is developed in order to evaluate the performance of a newly designed turbine follower controller. Considering the drawbacks of previous works, an overall model is developed to minimize the error between each subsystem model output and the experimental data obtained at the actual power plant. The developed model is organized in two main subsystems namely; Boiler and Turbine. Considering each FFPP subsystem characteristics, different modeling approaches are developed. For economizer, evaporator, superheater and reheater, first order models are determined based on principles of mass and energy conservation. Simulations verify the accuracy of the developed models. Due to the nonlinear characteristics of attemperator, a new model, based on a genetic-fuzzy systems utilizing Pittsburgh approach is developed showing a promising performance vis-à-vis those derived with other methods like ANFIS. The optimization constraints are handled utilizing penalty functions. The effect of increasing the number of rules and membership functions on the performance of the proposed model is also studied and evaluated. The turbine model is developed based on the equation of adiabatic expansion. Parameters of all evaluated models are tuned by means of evolutionary algorithms. Based on the developed model a fuzzy PI controller is developed. It is then successfully implemented in the turbine follower control strategy of the plant. In this control strategy instead of keeping control parameters constant, they are adjusted on-line with regard to the error and the error rate. It is shown that the response of the system improves significantly. It is also shown that fuel consumption decreases considerably.

Keywords: Attemperator, Evolutionary algorithms, Fossil fuelled power plant (FFPP), Fuzzy set theory, Gain scheduling

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Authors: Ke Zhao, Ballarat Colin Richardson, Jerry Courvisanos, John Crawford

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Climate change has profound consequences for the agriculture of south-eastern Australia and its climate-induced water shortage in the Murray-Darling Basin. Post Keynesian Economics (PKE) macro-dynamics, along with Kaleckian investment and growth theory, are used to develop an ecological-economic system dynamics model of this complex nonlinear river basin system. The Murray- Darling Basin Simulation Model (MDB-SM) uses the principles of PKE to incorporate the fundamental uncertainty of economic behaviors of farmers regarding the investments they make and the climate change they face, particularly as regards water ecosystem services. MDB-SM provides a framework for macroeconomic policies, especially for long-term fiscal policy and for policy directed at the sustainability of agricultural water, as measured by socio-economic well-being considerations, which include sustainable consumption and investment in the river basin. The model can also reproduce other ecological and economic aspects and, for certain parameters and initial values, exhibit endogenous business cycles and ecological sustainability with realistic characteristics. Most importantly, MDBSM provides a platform for the analysis of alternative economic policy scenarios. These results reveal the importance of understanding water ecosystem adaptation under climate change by integrating a PKE macroeconomic analytical framework with the system dynamics modelling approach. Once parameterised and supplied with historical initial values, MDB-SM should prove to be a practical tool to provide alternative long-term policy simulations of agricultural water and socio-economic well-being.

Keywords: Agricultural water, Macroeconomic dynamics, Modeling, Investment dynamics, Sustainability, Unemployment, Economics, Keynesian, Kaleckian.

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Authors: Chimaobi Valentine Okolo, Chizoba Obidigbo

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As a basic physiology need, threat to sufficient food production is threat to human survival. Food security has been an issue that has gained global concern. This paper looks at the food security in Nigeria by assessing the availability of food and accessibility of the available food. The paper employed multiple linear regression technique and graphic trends of growth rates of relevant variables to show the situation of food security in Nigeria. Results of the tests revealed that population growth rate was higher than the growth rate of food availability in Nigeria for the earlier period of the study. Commercial bank credit to agricultural sector, foreign exchange utilization for food and the Agricultural Credit Guarantee Scheme Fund (ACGSF) contributed significantly to food availability in Nigeria. Food prices grew at a faster rate than the average income level, making it difficult to access sufficient food. It implies that prior to the year 2012; there was insufficient food to feed the Nigerian populace. However, continued credit to the food and agricultural sector will ensure sustained and sufficient production of food in Nigeria. Microfinance banks should make sufficient credit available to smallholder farmer. Government should further control and subsidize the rising price of food to make it more accessible by the people.

Keywords: Food security, food availability and food accessibility.

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Authors: Yu Shi, Rong Liu, Jingyun Lv

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Compression textiles with ergonomic-fit and controllable pressure performance have demonstrated positive effect on prevention and treatment of chronic venous insufficiency (CVI). Well-designed compression textile products contribute to improving user compliance in their daily application. This study explored the effects of multiple knitting variables (yarn-machinery settings) on the physical-mechanical properties and the produced pressure magnitudes of tubular compression fabrics (TCFs) through experimental testing and multiple regression modeling. The results indicated that fabric physical (stitch densities and circumference) and mechanical (tensile) properties were affected by the linear density of inlay yarns, which, to some extent, influenced pressure magnitudes of the TCFs. Knitting variables (e.g., feeding velocity of inlay yarns and loop size settings) can alter circumferences and tensile properties of tubular fabrics, respectively, and significantly varied pressure values of the TCFs. This study enhanced the understanding of the effects of knitting factors on pressure controlling of TCFs, thus facilitating dimension and pressure design of compression textiles in future development.

Keywords: Laid-in knitted fabric, yarn-machinery settings, pressure magnitudes, quantitative analysis, compression textiles.

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Authors: Gautham Das, Edward Morrone, Erik Treble, Clinton Binder

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The purpose of this study was to evaluate the efficacy of a low-cost filter regarding per- and polyfluoroalkyl substances (PFAS). PFAS is a commonly used man-made chemical that can be found in a variety of household and industrial products with deleterious effects on humans. The filter consists of a combination of low-cost materials which could be locally procured. Water testing results for 4 different PFAS contaminants indicated that for Perfluorooctane sulfonic acid (PFOS), the Agency for Toxic Substances and Disease Registry (ATSDR) regulation is 7 ppt, the initial concentration was 15 ppt, and the final concentration was 3.9 ppt. For Perfluorononanoic acid (PFNA), the ATSDR regulation is 10.5 ppt, the initial concentration was 15 ppt, and the final concentration was 3.9 ppt. For Perfluorooctanoic acid (PFOA), the ATSDR regulation is 11 ppt, the initial concentration was 15 ppt, and the final concentration was 3.9 ppt. For Perfluorohexane sulfonic acid (PFHxS), the ATSDR regulation is 70 ppt, the initial concentration was 15 ppt, and the final concentration was 3.9 ppt. The results indicated a 74% reduction in PFAS concentration in filtered samples. Statistical data through regression analysis showed 0.9 validity of the sample data. Initial tests show the efficiency of the proposed filter described could be far greater if tested at a greater scale. It is highly recommended further testing to be conducted to validate the data for an innovative solution to a ubiquitous problem.

Keywords: PFAS, PFOS, PFOA, PFHxS, low-cost filter.

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Authors: J. Novak, A. Kohoutkova, V. Kristek, J. Vodicka, M. Sramek

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While the use of cast-in-place concrete for an airfield and highway pavement overlay is very common, the application of precast concrete elements is very limited today. The main reasons consist of high production costs and complex structural behavior. Despite that, several precast concrete systems have been developed and tested with the aim to provide a system with rapid construction. The contribution deals with the reinforcement design of a hexagonal element developed for a proposed airfield pavement system. The sub-base course of the system is composed of compacted recycled concrete aggregates and fiber reinforced concrete with recycled aggregates place on top of it. The selected element belongs to a group of precast concrete elements which are being considered for the construction of a surface course. Both high costs of full-scale experiments and the need to investigate various elements force to simulate their behavior in a numerical analysis software by using finite element method instead of performing expensive experiments. The simulation of the selected element was conducted on a nonlinear model in order to obtain such results which could fully compensate results from experiments. The main objective was to design reinforcement of the precast concrete element subject to quasi-static loading from airplanes with respect to geometrical imperfections, manufacturing imperfections, tensile stress in reinforcement, compressive stress in concrete and crack width. The obtained findings demonstrate that the position and the presence of imperfection in a pavement highly affect the stress distribution in the precast concrete element. The precast concrete element should be heavily reinforced to fulfill all the demands. Using under-reinforced concrete elements would lead to the formation of wide cracks and cracks permanently open.

Keywords: Imperfection, numerical simulation, pavement, precast concrete element, reinforcement design, stress analysis.

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Authors: Melodi Mbuyi Mata, Blessing Olamide Taiwo, Afolabi Ayodele David

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The purpose of this research is to create empirical models for assessing the profitability of granite aggregate production in Akure, Ondo state aggregate quarries. In addition, an Artificial Neural Network (ANN) model and multivariate predicting models for granite profitability were developed in the study. A formal survey questionnaire was used to collect data for the study. The data extracted from the case study mine for this study include granite marketing operations, royalty, production costs, and mine production information. The following methods were used to achieve the goal of this study: descriptive statistics, MATLAB 2017, and SPSS16.0 software in analyzing and modeling the data collected from granite traders in the study areas. The ANN and Multi Variant Regression models' prediction accuracy was compared using a coefficient of determination (R2), Root Mean Square Error (RMSE), and mean square error (MSE). Due to the high prediction error, the model evaluation indices revealed that the ANN model was suitable for predicting generated profit in a typical quarry. More quarries in Nigeria's southwest region and other geopolitical zones should be considered to improve ANN prediction accuracy.

Keywords: National development, granite, profitability assessment, ANN models.

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Authors: Manal Sultan Alhussein, Xiang Michelle Liu

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Telehealth is an advanced technology using digital information and telecommunication facilities that provide access to health services from a distance. It slows the transmission factor of COVID-19, especially for elderly patients and patients with chronic diseases during the pandemic. Therefore, understanding patient perspectives on telehealth services and the factors impacting their option of telehealth service will shed light on the measures that healthcare providers can take to improve the quality of telehealth services. This study aimed to evaluate perceptions of telehealth services among different patient groups and explore various aspects of telehealth utilization in the United States during the COVID-19 pandemic. An online survey distributed via social media platforms was used to collect research data. In addition to the descriptive statistics, both correlation and regression analyses were conducted to test research hypotheses. The empirical results highlighted that the factors such as accessibility to telehealth services and the type of specialty clinics that the patients required play important roles in the effectiveness of telehealth services they received. However, the results found that patients’ waiting time to receive telehealth services and their annual income did not significantly influence their desire to select receiving healthcare services via telehealth. The limitations of the study and future research directions are discussed.

Keywords: Telehealth, patient satisfaction, pandemic, healthcare, remote patient monitor.

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Authors: N. Mamode Khan, V. Jowaheer

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In this paper, we apply and compare two generalized estimating equation approaches to the analysis of car breakdowns data in Mauritius. Number of breakdowns experienced by a machinery is a highly under-dispersed count random variable and its value can be attributed to the factors related to the mechanical input and output of that machinery. Analyzing such under-dispersed count observation as a function of the explanatory factors has been a challenging problem. In this paper, we aim at estimating the effects of various factors on the number of breakdowns experienced by a passenger car based on a study performed in Mauritius over a year. We remark that the number of passenger car breakdowns is highly under-dispersed. These data are therefore modelled and analyzed using Com-Poisson regression model. We use the two types of quasi-likelihood estimation approaches to estimate the parameters of the model: marginal and joint generalized quasi-likelihood estimating equation approaches. Under-dispersion parameter is estimated to be around 2.14 justifying the appropriateness of Com-Poisson distribution in modelling underdispersed count responses recorded in this study.

Keywords: Breakdowns, under-dispersion, com-poisson, generalized linear model, marginal quasi-likelihood estimation, joint quasi-likelihood estimation.

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Authors: Jia-Ru Li, Chia-Jung Li, Ching-Wen Lin

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Contrary to negative emotion regulation, coping with positive moods have received less attention in adolescent adjustment. However, some research has found that everyone is different on dealing with their positive emotions, which affects their adaptation and well-being. The purpose of the present study was to investigate the relationship between positive emotions dampening and internalizing behavior problems of adolescent in Taiwan. A survey was conducted and 208 students (12 to14 years old) completed the strengths and difficulties questionnaire (SDQ), the Affect Intensity Measure, and the positive emotions dampening scale. Analysis methods such as descriptive statistics, t-test, Pearson correlations and multiple regression were adapted. The results were as follows: Emotionality and internalizing problem behavior have significant gender differences. Compared to boys, girls have a higher score on negative emotionality and are at a higher risk for internalizing symptoms. However, there are no gender differences on positive emotion dampening. Additionally, in the circumstance that negative emotionality acted as the control variable, positive emotion dampening strategy was (positive) related to internalizing behavior problems. Given the results of this study, it is suggested that coaching deconstructive positive emotion strategies is to assist adolescents with internalizing behavior problems is encouraged.

Keywords: Emotion dampening strategies, internalizing problem behaviors, affect intensity.

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Authors: Hui-Chuan Chu, Min-Ju Liao, Wei-Kai Cheng, William Wei-Jen Tsai, Yuh-Min Chen

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Avoiding learning failures in mathematics e-learning environments caused by emotional problems in students with autism has become an important topic for combining of special education with information and communications technology. This study presents an adaptive emotional adjustment model in mathematics e-learning for students with autism, emphasizing the lack of emotional perception in mathematics e-learning systems. In addition, an emotion classification for students with autism was developed by inducing emotions in mathematical learning environments to record changes in the physiological signals and facial expressions of students. Using these methods, 58 emotional features were obtained. These features were then processed using one-way ANOVA and information gain (IG). After reducing the feature dimension, methods of support vector machines (SVM), k-nearest neighbors (KNN), and classification and regression trees (CART) were used to classify four emotional categories: baseline, happy, angry, and anxious. After testing and comparisons, in a situation without feature selection, the accuracy rate of the SVM classification can reach as high as 79.3-%. After using IG to reduce the feature dimension, with only 28 features remaining, SVM still has a classification accuracy of 78.2-%. The results of this research could enhance the effectiveness of eLearning in special education.

Keywords: Emotion classification, Physiological and facial Expression measures, Students with autism, Mathematics e-learning.

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Authors: Gi-Ryun Park, Gye-Wan Moon, Dong-Hoon Yang

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This study aims to explore the positive effects of self-leadership and innovative behavior that'd been proven in the existing researches proactively and understand the regulation effects of smartphone addiction which has recently become an issue in Korea. This study conducted a convenient sampling of college students attending the four colleges located at Daegu. A total of 210 questionnaires in 5-point Likert scale were distributed to college students. Among which, a total of 200 questionnaires were collected for our final analysis data. Both correlation analysis and regression analysis were carried out to verify those questionnaires through SPSS 20.0. As a result, college students' self-leadership had a significantly positive impact on innovative behavior (B= .210, P= .003). In addition, it is found that the relationship between self-leadership and innovative behavior can be adjusted depending on the degree of smartphone addiction in college students (B= .264, P= .000). This study could first understand the negative effects of smartphone addiction and find that if students' self-leadership is improved in terms of self-management and unnecessary use of smartphone is controlled properly, innovative behavior can be improved. In addition, this study is significant in that it attempts to identify a new impact of smartphone addiction with the recent environmental changes, unlike the existing researches that'd been carried out from the perspective of organizational behavior theory.

Keywords: Innovative Behavior, Revolutionary Behavior, Self-leadership, Smartphone Addiction.

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Authors: Mohammadreza Salek Faramarzi, Touraj Taghikhany

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In this paper, seismic fragility assessment of a recently developed hybrid structural system, known as the strongback system (SBS) is investigated. In this system, to mitigate the occurrence of the soft-story mechanism and improve the distribution of story drifts over the height of the structure, an elastic vertical truss is formed. The strengthened members of the braced span are designed to remain substantially elastic during levels of excitation where soft-story mechanisms are likely to occur and impose a nearly uniform story drift distribution. Due to the distinctive characteristics of near-field ground motions, it seems to be necessary to study the effect of these records on seismic performance of the SBS. To this end, a set of 56 near-field ground motion records suggested by FEMA P695 methodology is used. For fragility assessment, nonlinear dynamic analyses are carried out in OpenSEES based on the recommended procedure in HAZUS technical manual. Four damage states including slight, moderate, extensive, and complete damage (collapse) are considered. To evaluate each damage state, inter-story drift ratio and floor acceleration are implemented as engineering demand parameters. Further, to extend the evaluation of the collapse state of the system, a different collapse criterion suggested in FEMA P695 is applied. It is concluded that SBS can significantly increase the collapse capacity and consequently decrease the collapse risk of the structure during its life time. Comparing the observing mean annual frequency (MAF) of exceedance of each damage state against the allowable values presented in performance-based design methods, it is found that using the elastic vertical truss, improves the structural response effectively.

Keywords: Strongback System, Near-fault, Seismic fragility, Uncertainty, IDA, Probabilistic performance assessment.

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Authors: Alyaa M. Younes, Nermine Harraz, Mohammad H. Elwany

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Lithium ion batteries are currently used for many applications including satellites, electric vehicles and mobile electronics. Their ability to store relatively large amount of energy in a limited space make them most appropriate for critical applications. Evaluation of the life of these batteries and their reliability becomes crucial to the systems they support. Reliability of Li-Ion batteries has been mainly considered based on its lifetime. However, another important factor that can be considered critical in many applications such as in electric vehicles is the cycle duration. The present work presents the results of an experimental investigation on the degradation behavior of a Laptop Li-ion battery (type TKV2V) and the effect of applied load on the battery cycle time. The reliability was evaluated using an accelerated life test. Least squares linear regression with median rank estimation was used to estimate the Weibull distribution parameters needed for the reliability functions estimation. The probability density function, failure rate and reliability function under each of the applied loads were evaluated and compared. An inverse power model is introduced that can predict cycle time at any stress level given.

Keywords: Accelerated life test, inverse power law, lithium ion battery, reliability evaluation, Weibull distribution.

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Authors: Jameson McFarlane, Thorne J. McFarlane, Leon Bernard

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Social network sites (SNS) is an emerging phenomenon that is here to stay. The popularity and the ubiquity of the SNS technology are undeniable. Because most SNS are free and easy to use people from all walks of life and from almost any age are attracted to that technology. College age students are by far the largest segment of the population using SNS. Since most SNS have been adapted for mobile devices, not only do you find students using this technology in their study, while working on labs or on projects, a substantial number of students have been found to use SNS even while listening to lectures. This study found that SNS use has a significant negative impact on the grade point average of college students particularly in the first semester. However, this negative impact is greatly diminished by the end of the third semester partly because the students have adjusted satisfactorily to the challenges of college or because they have learned how to adequately manage their time. It was established that the kinds of activities the students are engaged in during the SNS use are the leading factor affecting academic performance. Of those activities, using SNS during a lecture or while studying is the foremost contributing factor to lower academic performance. This is due to “cognitive” or “information” bottleneck, a condition in which the students find it very difficult to multitask or to switch between resources leading to inefficiency in information retention and thus, educational performance.

Keywords: Social network sites, social network analysis, regression coefficient, psychological engagement.

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Authors: K. S. Sivakumaran, T. Thanga, B. Halabieh

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The end panels of a large rectangular industrial duct, which experience significant internal pressures, also experience considerable transverse shear due to transfer of gravity loads to the supports. The current design practice of such thin plate panels for shear load is based on methods used for the design of plate girder webs. The structural arrangements, the loadings and the resulting behavior associated with the industrial duct end panels are, however, significantly different from those of the web of a plate girder. The large aspect ratio of the end panels gives rise to multiple bands of tension fields, whereas the plate girder web design is based on one tension field. In addition to shear, the industrial end panels are subjected to internal pressure which in turn produces significant membrane action. This paper reports a study which was undertaken to review the current industrial analysis and design methods and to propose a comprehensive method of designing industrial duct end panels for shear resistance. In this investigation, a nonlinear finite element model was developed to simulate the behavior of industrial duct end panel, along with the associated edge stiffeners, subjected to transverse shear and internal pressures. The model considered the geometric imperfections and constitutive relations for steels. Six scale independent dimensionless parameters that govern the behavior of such end panel were identified and were then used in a parametric study. It was concluded that the plate slenderness dominates the shear strength of stockier end panels, and whereas, both the plate slenderness and the aspect ratio influence the shear strength of slender end panels. Based on these studies, this paper proposes design aids for estimating the shear strength of rectangular duct end panels.

Keywords: Thin plate, transverse shear, tension field, finite element analysis, parametric study, design.

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Authors: Masaki Yamaguchi, Daimei Sasayama, Shinsuke Washizuka

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The goal of this study was to examine the possibility of salivary cytokines for the screening of attention deficit hyperactivity disorder (ADHD) in children. We carried out a case-control study, including 19 children with ADHD and 17 healthy children (controls). A multiplex bead array immunoassay was used to conduct a multi-analysis of 27 different salivary cytokines. Six salivary cytokines (interleukin (IL)-1β, IL-8, IL12p70, granulocyte colony-stimulating factor (G-CSF), interferon gamma (IFN-γ), and vascular endothelial growth factor (VEGF)) were significantly associated with the presence of ADHD (p < 0.05). An informative salivary cytokine panel was developed using VEGF by logistic regression analysis (odds ratio: 0.251). Receiver operating characteristic analysis revealed that assessment of a panel using VEGF showed “good” capability for discriminating between ADHD patients and controls (area under the curve: 0.778). ADHD has been hypothesized to be associated with reduced cerebral blood flow in the frontal cortex, due to reduced VEGF levels. Our study highlights the possibility of utilizing differential salivary cytokine levels for point-of-care testing (POCT) of biomarkers in children with ADHD.

Keywords: Cytokine, saliva, attention deficit hyperactivity disorder, child, biomarker.

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Authors: Norazah Mohd Suki, Norbayah Mohd Suki

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This study proposes a conceptual model and empirically tests the relationships between customers and librarians (i.e. tangibles, responsiveness, assurance, reliability and empathy) with a dependent variable (customer satisfaction) regarding library services. The SERVQUAL instrument was administered to 100 respondents which comprises of staff and students at a public higher learning institution in the Federal Territory of Labuan, Malaysia. They were public university library users. Results revealed that all service quality dimensions tested were significant and influenced customer satisfaction of visitors to a public university library. Assurance is the most important factor that influences customer satisfaction with the services rendered by the librarian. It is imperative for the library management to take note that the top five service attributes that gained greatest attention from library visitors- perspective includes employee willingness to help customers, availability of customer representatives online for response to queries, library staff actively and promptly provide services, signs in the building are clear and library staff are friendly and courteous. This study provides valuable results concerning the determinants of the service quality and customer satisfaction of public university library services from the users' perspective.

Keywords: Service Quality, Customer Satisfaction, SERVQUAL Model, Multiple Regression Analysis

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Authors: L. A. Muhammad, M. S. Argungu

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Educational data mining (EDM) has recorded substantial considerations. Techniques of data mining in one way or the other have been proposed to dig out out-of-sight knowledge in educational data. The result of the study got assists academic institutions in further enhancing their process of learning and methods of passing knowledge to students. Consequently, the performance of students boasts and the educational products are by no doubt enhanced. This study adopted a student performance prediction model premised on techniques of data mining with Students' Essential Features (SEF). SEF are linked to the learner's interactivity with the e-learning management system. The performance of the student's predictive model is assessed by a set of classifiers, viz. Bayes Network, Logistic Regression, and Reduce Error Pruning Tree (REP). Consequently, ensemble methods of Bagging, Boosting, and Random Forest (RF) are applied to improve the performance of these single classifiers. The study reveals that the result shows a robust affinity between learners' behaviors and their academic attainment. Result from the study shows that the REP Tree and its ensemble record the highest accuracy of 83.33% using SEF. Hence, in terms of the Receiver Operating Curve (ROC), boosting method of REP Tree records 0.903, which is the best. This result further demonstrates the dependability of the proposed model.

Keywords: Ensemble, bagging, Random Forest, boosting, data mining, classifiers, machine learning.

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Authors: A. M. Pashayev, D. D. Askerov, C. Ardil, R. A. Sadiqov, P. S. Abdullayev

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Researches show that probability-statistical methods application, especially at the early stage of the aviation Gas Turbine Engine (GTE) technical condition diagnosing, when the flight information has property of the fuzzy, limitation and uncertainty is unfounded. Hence the efficiency of application of new technology Soft Computing at these diagnosing stages with the using of the Fuzzy Logic and Neural Networks methods is considered. According to the purpose of this problem training with high accuracy of fuzzy multiple linear and non-linear models (fuzzy regression equations) which received on the statistical fuzzy data basis is made. For GTE technical condition more adequate model making dynamics of skewness and kurtosis coefficients- changes are analysed. Researches of skewness and kurtosis coefficients values- changes show that, distributions of GTE workand output parameters of the multiple linear and non-linear generalised models at presence of noise measured (the new recursive Least Squares Method (LSM)). The developed GTE condition monitoring system provides stage-by-stage estimation of engine technical conditions. As application of the given technique the estimation of the new operating aviation engine technical condition was made.

Keywords: aviation gas turbine engine, technical condition, fuzzy logic, neural networks, fuzzy statistics

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Authors: Yanling Li, Linying Ji, Zita Oravecz, Timothy R. Brick, Michael D. Hunter, Sy-Miin Chow

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Assessing several individuals intensively over time yields intensive longitudinal data (ILD). Even though ILD provide rich information, they also bring other data analytic challenges. One of these is the increased occurrence of missingness with increased study length, possibly under non-ignorable missingness scenarios. Multiple imputation (MI) handles missing data by creating several imputed data sets, and pooling the estimation results across imputed data sets to yield final estimates for inferential purposes. In this article, we introduce dynr.mi(), a function in the R package, Dynamic Modeling in R (dynr). The package dynr provides a suite of fast and accessible functions for estimating and visualizing the results from fitting linear and nonlinear dynamic systems models in discrete as well as continuous time. By integrating the estimation functions in dynr and the MI procedures available from the R package, Multivariate Imputation by Chained Equations (MICE), the dynr.mi() routine is designed to handle possibly non-ignorable missingness in the dependent variables and/or covariates in a user-specified dynamic systems model via MI, with convergence diagnostic check. We utilized dynr.mi() to examine, in the context of a vector autoregressive model, the relationships among individuals’ ambulatory physiological measures, and self-report affect valence and arousal. The results from MI were compared to those from listwise deletion of entries with missingness in the covariates. When we determined the number of iterations based on the convergence diagnostics available from dynr.mi(), differences in the statistical significance of the covariate parameters were observed between the listwise deletion and MI approaches. These results underscore the importance of considering diagnostic information in the implementation of MI procedures.

Keywords: Dynamic modeling, missing data, multiple imputation, physiological measures.

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Authors: Mahtab Davari, Charles Edward Okon, Somayeh Aghanavesi

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Every institute of learning is usually interested in the performance of enrolled students. The level of these performances determines the approach an institute of study may adopt in rendering academic services. The focus of this paper is to evaluate students' academic performance in given courses of study using machine learning methods. This study evaluated various supervised machine learning classification algorithms such as Logistic Regression (LR), Support Vector Machine (SVM), Random Forest, Decision Tree, K-Nearest Neighbors, Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis, using selected features to predict study performance. The accuracy, precision, recall, and F1 score obtained from a 5-Fold Cross-Validation were used to determine the best classification algorithm to predict students’ performances. SVM (using a linear kernel), LDA, and LR were identified as the best-performing machine learning methods. Also, using the LR model, this study identified students' educational habits such as reading and paying attention in class as strong determinants for a student to have an above-average performance. Other important features include the academic history of the student and work. Demographic factors such as age, gender, high school graduation, etc., had no significant effect on a student's performance.

Keywords: Student performance, supervised machine learning, prediction, classification, cross-validation.

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Authors: Tanwarat Kajsongkram, Saowalux Rotamporn, Sirinat Limbunruang, Sirinan Thubthimthed

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High Performance Liquid Chromatography (HPLC) method was developed and validated for simultaneous estimation of 6-Gingerol(6G) and 6-Shogaol(6S) in joint pain relief gel containing ginger extract. The chromatographic separation was achieved by using C18 column, 150 x 4.6mm i.d., 5μ Luna, mobile phase containing acetonitrile and water (gradient elution). The flow rate was 1.0 ml/min and the absorbance was monitored at 282 nm. The proposed method was validated in terms of the analytical parameters such as specificity, accuracy, precision, linearity, range, limit of detection (LOD), limit of quantification (LOQ), and determined based on the International Conference on Harmonization (ICH) guidelines. The linearity ranges of 6G and 6S were obtained over 20- 60 and 6-18 μg/ml respectively. Good linearity was observed over the above-mentioned range with linear regression equation Y= 11016x- 23778 for 6G and Y = 19276x-19604 for 6S (x is concentration of analytes in μg/ml and Y is peak area). The value of correlation coefficient was found to be 0.9994 for both markers. The limit of detection (LOD) and limit of quantification (LOQ) for 6G were 0.8567 and 2.8555 μg/ml and for 6S were 0.3672 and 1.2238 μg/ml respectively. The recovery range for 6G and 6S were found to be 91.57 to 102.36 % and 84.73 to 92.85 % for all three spiked levels. The RSD values from repeated extractions for 6G and 6S were 3.43 and 3.09% respectively. The validation of developed method on precision, accuracy, specificity, linearity, and range were also performed with well-accepted results.

Keywords: Ginger, 6-gingerol, HPLC, 6-shogaol.

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Authors: Amr R. El-Gamal, Ashraf Essa, Ayman Ismail

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The tension leg platform (TLP) is one of the compliant structures which are generally used for deep water oil exploration. With respect to the horizontal degrees of freedom, it behaves like a floating structure moored by vertical tethers which are pretension due to the excess buoyancy of the platform, whereas with respect to the vertical degrees of freedom, it is stiff and resembles a fixed structure and is not allowed to float freely. In the current study, a numerical study for square TLP using modified Morison equation was carried out in the time domain with water particle kinematics using Airy’s linear wave theory to investigate the effect of changing the tether tension force on the stiffness matrix of TLP's, the dynamic behavior of TLP's; and on the fatigue stresses in the cables. The effect was investigated for different parameters of the hydrodynamic forces such as wave periods, and wave heights. The numerical study takes into consideration the effect of coupling between various degrees of freedom. The stiffness of the TLP was derived from a combination of hydrostatic restoring forces and restoring forces due to cables. Nonlinear equation was solved using Newmark’s beta integration method. Only uni-directional waves in the surge direction was considered in the analysis. It was found that for short wave periods (i.e. 10 sec.), the surge response consisted of small amplitude oscillations about a displaced position that is significantly dependent on tether tension force, wave height; whereas for longer wave periods, the surge response showed high amplitude oscillations that is significantly dependent on wave height, and that special attention should be given to tethers fatigue because of their high tensile static and dynamic stress.

Keywords: Tethers tension, tension leg platforms, hydrodynamic wave forces, wave characteristic.

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