Search results for: principal component regression (PCR)

Authors: Luh Eka Suryani, Purhadi

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Poisson regression is a non-linear regression model with response variable in the form of count data that follows Poisson distribution. Modeling for a pair of count data that show high correlation can be analyzed by Poisson Bivariate Regression. Data, the number of infant mortality and maternal mortality, are count data that can be analyzed by Poisson Bivariate Regression. The Poisson regression assumption is an equidispersion where the mean and variance values are equal. However, the actual count data has a variance value which can be greater or less than the mean value (overdispersion and underdispersion). Violations of this assumption can be overcome by applying Generalized Poisson Regression. Characteristics of each regency can affect the number of cases occurred. This issue can be overcome by spatial analysis called geographically weighted regression. This study analyzes the number of infant mortality and maternal mortality based on conditions in East Java in 2016 using Geographically Weighted Bivariate Generalized Poisson Regression (GWBGPR) method. Modeling is done with adaptive bisquare Kernel weighting which produces 3 regency groups based on infant mortality rate and 5 regency groups based on maternal mortality rate. Variables that significantly influence the number of infant and maternal mortality are the percentages of pregnant women visit health workers at least 4 times during pregnancy, pregnant women get Fe3 tablets, obstetric complication handled, clean household and healthy behavior, and married women with the first marriage age under 18 years.

Keywords: adaptive bisquare kernel, GWBGPR, infant mortality, maternal mortality, overdispersion

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Authors: Nargess Fasihmardanloo

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The aim of this study was to comparatively study the motivations of women to physical activity in Iran and selected Arab countries Based on individual, social and Islamic components. The present study was a descriptive comparative study that was performed by field method. The statistical population of the study included female athletes in Iran and Arab countries. A total of 184 people from Iran and 179 people from Arab countries (Iraq, UAE, and Jordan) were selected through available sampling as a research sample. The research tool included a questionnaire. The validity of the questionnaire was confirmed and its reliability in a pilot study was 0.95 through Cronbach's alpha. The questionnaire was translated into Persian in Iran and translated into Arabic for the selected countries and was provided to the participants electronically and through cyberspace. Finally, 363 questionnaires were collected. Manova multivariate analysis of variance using spss22 software was used to analyze the data. Findings showed that between Iranian women athletes and women athletes in selected Arab countries in the components of intrapersonal motivation (p = 0.009 and f = 6.978), interpersonal motivation (p = 0.050 and f = 3.875), There is a significant difference between social motives (p = 0.001 and f = 27.619) and Islamic motives (p = 0.001 and f = 11.339). And this difference is significant at the level of p <0.01 and p <0.05. In other words, in the component of intrapersonal motivations, the average of this component in Iranian female athletes (M = 59.77) was higher than female athletes in selected Arab countries (M = 55.53). In the interpersonal motivations component, the average of this component in Iranian female athletes (M = 26.87) was lower than in female athletes in selected Arab countries (M = 28.62). In the component of social motivations, the average of this component in Iranian female athletes (M = 33.08) was lower than female athletes in selected Arab countries (M = 39.64). In the component of Islamic motives, the average of this component in Iranian female athletes (M = 21.55) was higher than female athletes in selected Arab countries (M = 19.04).

Keywords: athletes, motivation, women, Islamic

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Authors: Elia Favarelli, Enrico Testi, Andrea Giorgetti

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Autonomous structural health monitoring (SHM) of many structures and bridges became a topic of paramount importance for maintenance purposes and safety reasons. This paper proposes a set of machine learning (ML) tools to perform automatic feature selection and detection of anomalies in a bridge from vibrational data and compare different feature extraction schemes to increase the accuracy and reduce the amount of data collected. As a case study, the Z-24 bridge is considered because of the extensive database of accelerometric data in both standard and damaged conditions. The proposed framework starts from the first four fundamental frequencies extracted through operational modal analysis (OMA) and clustering, followed by density-based time-domain filtering (tracking). The fundamental frequencies extracted are then fed to a dimensionality reduction block implemented through two different approaches: feature selection (intelligent multiplexer) that tries to estimate the most reliable frequencies based on the evaluation of some statistical features (i.e., mean value, variance, kurtosis), and feature extraction (auto-associative neural network (ANN)) that combine the fundamental frequencies to extract new damage sensitive features in a low dimensional feature space. Finally, one class classifier (OCC) algorithms perform anomaly detection, trained with standard condition points, and tested with normal and anomaly ones. In particular, a new anomaly detector strategy is proposed, namely one class classifier neural network two (OCCNN2), which exploit the classification capability of standard classifiers in an anomaly detection problem, finding the standard class (the boundary of the features space in normal operating conditions) through a two-step approach: coarse and fine boundary estimation. The coarse estimation uses classics OCC techniques, while the fine estimation is performed through a feedforward neural network (NN) trained that exploits the boundaries estimated in the coarse step. The detection algorithms vare then compared with known methods based on principal component analysis (PCA), kernel principal component analysis (KPCA), and auto-associative neural network (ANN). In many cases, the proposed solution increases the performance with respect to the standard OCC algorithms in terms of F1 score and accuracy. In particular, by evaluating the correct features, the anomaly can be detected with accuracy and an F1 score greater than 96% with the proposed method.

Keywords: anomaly detection, frequencies selection, modal analysis, neural network, sensor network, structural health monitoring, vibration measurement

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Authors: Mansour Al Qubeissi, Sergei S. Sazhin, Cyril Crua, Morgan R. Heikal

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This paper presents the application of the Discrete Component Model for heating and evaporation to multi-component biodiesel fuel droplets in direct injection internal combustion engines. This model takes into account the effects of temperature gradient, recirculation and species diffusion inside droplets. A distinctive feature of the model used in the analysis is that it is based on the analytical solutions to the temperature and species diffusion equations inside the droplets. Nineteen types of biodiesel fuels are considered. It is shown that a simplistic model, based on the approximation of biodiesel fuel by a single component or ignoring the diffusion of components of biodiesel fuel, leads to noticeable errors in predicted droplet evaporation time and time evolution of droplet surface temperature and radius.

Keywords: heat/mass transfer, biodiesel, multi-component fuel, droplet

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Authors: Jiangbo Li, Wenqian Huang

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Early detection of fungal infection in citrus fruit is one of the major problems in the postharvest commercialization process. The automatic and nondestructive detection of infected fruits is still a challenge for the citrus industry. At present, the visual inspection of rotten citrus fruits is commonly performed by workers through the ultraviolet induction fluorescence technology or manual sorting in citrus packinghouses to remove fruit subject with fungal infection. However, the former entails a number of problems because exposing people to this kind of lighting is potentially hazardous to human health, and the latter is very inefficient. Orange is used as a research object. This study would focus on this problem and proposed an effective method based on Vis-NIR hyperspectral imaging in the wavelength range of 400-1000 nm with a spectroscopic resolution of 2.8 nm. In this work, three normalization approaches are applied prior to analysis to reduce the effect of sample curvature on spectral profiles, and it is found that mean normalization was the most effective pretreatment for decreasing spectral variability due to curvature. Then, principal component analysis (PCA) was applied to a dataset composing of average spectra from decayed and normal tissue to reduce the dimensionality of data and observe the ability of Vis-NIR hyper-spectra to discriminate data from two classes. In this case, it was observed that normal and decayed spectra were separable along the resultant first principal component (PC1) axis. Subsequently, five wavelengths (band) centered at 577, 702, 751, 808, and 923 nm were selected as the characteristic wavelengths by analyzing the loadings of PC1. A multispectral combination image was generated based on five selected characteristic wavelength images. Based on the obtained multispectral combination image, the intensity slicing pseudocolor image processing method is used to generate a 2-D visual classification image that would enhance the contrast between normal and decayed tissue. Finally, an image segmentation algorithm for detection of decayed fruit was developed based on the pseudocolor image coupled with a simple thresholding method. For the investigated 238 independent set samples including infected fruits infected by Penicillium digitatum and normal fruits, the total success rate is 100% and 97.5%, respectively, and, the proposed algorithm also used to identify the orange infected by penicillium italicum with a 100% identification accuracy, indicating that the proposed multispectral algorithm here is an effective method and it is potential to be applied in citrus industry.

Keywords: citrus fruit, early rotten, fungal infection, hyperspectral imaging

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Authors: Ekele Alih, Enejo Jalija

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Female Genital Mutilation (FGM) is made up of three types namely: Clitoridectomy, Excision and Infibulation. In this study, we examine the factors responsible for FGM in order to identify the causality variables in a logistic regression approach. From the result of the survey conducted by the Public Health Division, Nigeria Institute of Medical Research, Yaba, Lagos State, the tau statistic, τ was used to screen 9 factors that causes FGM in order to select few of the predictors before multiple regression equation is obtained. The need for this may be that the sample size may not be able to sustain having a regression with all the predictors or to avoid multi-collinearity. A total of 300 respondents, comprising 150 adult males and 150 adult females were selected for the household survey based on the multi-stage sampling procedure. The tau statistic,

Keywords: female genital mutilation, logistic regression, tau statistic, African society

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Authors: Georgios Charizanos, Haydar Demirhan, Duygu Icen

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Two of the most impactful issues in classical logistic regression are class imbalance and complete separation. These can result in model predictions heavily leaning towards the imbalanced class on the binary response variable or over-fitting issues. Fuzzy methodology offers key solutions for handling these problems. However, most studies propose the transformation of the binary responses into a continuous format limited within [0,1]. This is called the possibilistic approach within fuzzy logistic regression. Following this approach is more aligned with straightforward regression since a logit-link function is not utilized, and fuzzy probabilities are not generated. In contrast, we propose a method of fuzzifying binary response variables that allows for the use of the logit-link function; hence, a probabilistic fuzzy logistic regression model with the Monte Carlo method. The fuzzy probabilities are then classified by selecting a fuzzy threshold. Different combinations of fuzzy and crisp input, output, and coefficients are explored, aiming to understand which of these perform better under different conditions of imbalance and separation. We conduct numerical experiments using both synthetic and real datasets to demonstrate the performance of the fuzzy logistic regression framework against seven crisp machine learning methods. The proposed framework shows better performance irrespective of the degree of imbalance and presence of separation in the data, while the considered machine learning methods are significantly impacted.

Keywords: fuzzy logistic regression, fuzzy, logistic, machine learning

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Authors: Ashneel Ajay Singh, Naoki Suzuki, Kazumi Sakuramoto,

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The North Pacific albacore (Thunnus alalunga) is a temperate tuna species distributed in the North Pacific which is of significant economic importance to the Pacific Island Nations and Territories. Despite its importance, the stock dynamics and ecological characteristics of albacore still, have gaps in knowledge. The stock-recruitment relationship of the North Pacific stock of albacore tuna was investigated for different density-dependent effects and a regime shift in the stock characteristics in response to changes in environmental and climatic conditions. Linear regression analysis for recruit per spawning biomass (RPS) and recruitment (R) against the female spawning stock biomass (SSB) were significant for the presence of different density-dependent effects and positive for a regime shift in the stock time series. Application of Deming regression to RPS against SSB with the assumption for the presence of observation and process errors in both the dependent and independent variables confirmed the results of simple regression. However, R against SSB results disagreed given variance level of < 3 and agreed with linear regression results given the assumption of variance ≥ 3. Assuming the presence of different density-dependent effects in the albacore tuna time series, environmental and climatic condition variables were compared with R, RPS, and SSB. The significant relationship of R, RPS and SSB were determined with the sea surface temperature (SST), Pacific Decadal Oscillation (PDO) and multivariate El Niño Southern Oscillation (ENSO) with SST being the principal variable exhibiting significantly similar trend with R and RPS. Recruitment is significantly influenced by the dynamics of the SSB as well as environmental conditions which demonstrates that the stock-recruitment relationship is multidimensional. Further investigation of the North Pacific albacore tuna age-class and structure is necessary for further support the results presented here. It is important for fishery managers and decision makers to be vigilant of regime shifts in environmental conditions relating to albacore tuna as it may possibly cause regime shifts in the albacore R and RPS which should be taken into account to effectively and sustainability formulate harvesting plans and management of the species in the North Pacific oceanic region.

Keywords: Albacore tuna, Thunnus alalunga, recruitment, spawning stock biomass, recruits per spawning biomass, sea surface temperature, pacific decadal oscillation, El Niño southern oscillation, density-dependent effects, regime shift

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Authors: Arezou Rezazadeh, Nasrin Omidvar, Hassan Eini-Zinab, Mahmoud Ghazi-Tabatabaie, Reza Majdzadeh, Saeid Ghavamzadeh, Sakineh Nouri-Saeidlou

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Dietary pattern analysis method can reflect more information about the nutritional etiology of chronic diseases such as obesity. The aim of this study was to determine the relationship between major dietary patterns and anthropometric measures in men and women living in the city of Urmia. In this cross-sectional study, 723 participants (427 women and 296 men), aged 20–64 in Urmia city were selected from all four zones of Urmia city, in the north-west of Iran. Anthropometrics (weight, height, waist and hip circumference) were measured with standard methods. Body Mass Index (BMI) was calculated by dividing weight (in kilograms) by the square of height (in meter). Dietary intake information was collected by a semi-quantitative food frequency questionnaire in the last year. Dietary patterns were determined using principal component analysis. The relationship between dietary patterns and obesity was analyzed by logistic regression. Three major dietary patterns (DPs) were identified that were named ‘Traditional Higher SES (THS)’, ‘Traditional Low SES (TLS)’ and ‘Transitional’. THS DP was positively and Transitional DP was negatively associated with BMI and waist circumference (W.C), however, after adjusting for confounding variables (age, gender, ethnicity, energy intake, physical activity and SES), the associations were not significant. The TLS was not significantly associated with BMI, but after adjusting for confounders, a significant positive association was detected with W.C and Waist to hip ratio (WHR). Findings showed that both traditional patterns were positively and the western type transitional pattern was reversely associated with anthropometric indices. But this relationship was highly affected by demographic, socioeconomic and energy input and output determinants. The results indicate the inevitable effect of environmental factors on the relationship between dietary patterns and anthropometric indices.

Keywords: anthropometric indices, dietary pattern, Iran, North-west

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Authors: S. Benchelha, H. C. Aoudjehane, M. Hakdaoui, R. El Hamdouni, H. Mansouri, T. Benchelha, M. Layelmam, M. Alaoui

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The logistic regression (LR) and multivariate adaptive regression spline (MarSpline) are applied and verified for analysis of landslide susceptibility map in Oudka, Morocco, using geographical information system. From spatial database containing data such as landslide mapping, topography, soil, hydrology and lithology, the eight factors related to landslides such as elevation, slope, aspect, distance to streams, distance to road, distance to faults, lithology map and Normalized Difference Vegetation Index (NDVI) were calculated or extracted. Using these factors, landslide susceptibility indexes were calculated by the two mentioned methods. Before the calculation, this database was divided into two parts, the first for the formation of the model and the second for the validation. The results of the landslide susceptibility analysis were verified using success and prediction rates to evaluate the quality of these probabilistic models. The result of this verification was that the MarSpline model is the best model with a success rate (AUC = 0.963) and a prediction rate (AUC = 0.951) higher than the LR model (success rate AUC = 0.918, rate prediction AUC = 0.901).

Keywords: landslide susceptibility mapping, regression logistic, multivariate adaptive regression spline, Oudka, Taounate

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Authors: Fatemeh Roostaee, Zahra Nikmanesh

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This study was done to investigate the role of religious coping components on occupational stress and the quality of working life of midwives. The method of study was descriptive-correlation. The sample was comprised of all midwives in maternity hospitals in Zahedan during 1393. Participants were selected through applying census method. The instruments of data collection were three questionnaires: the quality of working life, occupational stress, and religious opposition. For statistical analysis, Pearson correlation and step by step regression analysis methods were used. The results showed that there is a significant negative relationship between the component of religious activities (r=-0/454) and occupational stress, and regression analysis was also shown that the variable of religious activities has been explained 45% of occupational stress variable changes. The Pearson correlation test showed that there isn't any significant relationship between religious opposition components and the quality of life. Therefore, it is necessary to present essential trainings on (the field of) strengthening compatibility strategies and religious activities to reduce occupational stress.

Keywords: the quality of working life, occupational stress, religious, midwife

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Authors: S. Kherchaoui, A. Houacine

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This paper presents a facial expression recognition system. It performs identification and classification of the seven basic expressions; happy, surprise, fear, disgust, sadness, anger, and neutral states. It consists of three main parts. The first one is the detection of a face and the corresponding facial features to extract the most expressive portion of the face, followed by a normalization of the region of interest. Then calculus of curvelet coefficients is performed with dimensionality reduction through principal component analysis. The resulting coefficients are combined with two ratios; mouth ratio and face edge ratio to constitute the whole feature vector. The third step is the classification of the emotional state using the SVM method in the feature space.

Keywords: facial expression identification, curvelet coefficient, support vector machine (SVM), recognition system

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Authors: Shokrya Saleh A. Alshqaq, Abdullah Ali H. Ahmadini

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The Schwarz information criterion (SIC) is a popular tool for selecting the best variables in regression datasets. However, SIC is defined using an unbounded estimator, namely, the least-squares (LS), which is highly sensitive to outlying observations, especially bad leverage points. A method for robust variable selection based on SIC for linear regression models is thus needed. This study investigates the robustness properties of SIC by deriving its influence function and proposes a robust SIC based on the MM-estimation scale. The aim of this study is to produce a criterion that can effectively select accurate models in the presence of vertical outliers and high leverage points. The advantages of the proposed robust SIC is demonstrated through a simulation study and an analysis of a real dataset.

Keywords: influence function, robust variable selection, robust regression, Schwarz information criterion

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Authors: Tahmidul Islam

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Propensity Score Matching (PSM) technique has been widely used for estimating causal effect of treatment in observational studies. One major step of implementing PSM is estimating the propensity score (PS). Logistic regression model with additive linear terms of covariates is most used technique in many studies. Logistics regression model is also used with cubic splines for retaining flexibility in the model. However, choosing the functional form of the logistic regression model has been a question since the effectiveness of PSM depends on how accurately the PS been estimated. In many situations, the linearity assumption of linear logistic regression may not hold and non-linear relation between the logit and the covariates may be appropriate. One can estimate PS using machine learning techniques such as random forest, neural network etc for more accuracy in non-linear situation. In this study, an attempt has been made to compare the efficacy of Generalized Additive Model (GAM) in various linear and non-linear settings and compare its performance with usual logistic regression. GAM is a non-parametric technique where functional form of the covariates can be unspecified and a flexible regression model can be fitted. In this study various simple and complex models have been considered for treatment under several situations (small/large sample, low/high number of treatment units) and examined which method leads to more covariate balance in the matched dataset. It is found that logistic regression model is impressively robust against inclusion quadratic and interaction terms and reduces mean difference in treatment and control set equally efficiently as GAM does. GAM provided no significantly better covariate balance than logistic regression in both simple and complex models. The analysis also suggests that larger proportion of controls than treatment units leads to better balance for both of the methods.

Keywords: accuracy, covariate balances, generalized additive model, logistic regression, non-linearity, propensity score matching

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Authors: Raed Fawzi Mohammed Ameen, Haider I. Alyasari, Maryam Altaweel

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Due to rapid urbanization, developing countries faced significant urban challenges that accompanied the population growth such as the inability to provide adequate housing; sustain human and community's health and wellbeing; ensure the safety in urban areas; the prevalence corruption; lack of jobs; and a shortage of investment. The destruction, degradation, and lack of planning are acute in countries such as Iraq that have suffered for more than four decades because of war and international sanctions, resulting in severe damages to the ecology sector, social utilities, housing, infrastructure, as well as the disruption of the economic sector. Many of significant urban development, housing, and regeneration projects are currently underway in different regions in Iraq, labelled as a means to reform the environmental, social, and economic sectors. However, most often with absence of public participation. Hence, there is an urgent need for understanding public perception, especially of urban socio-economic challenges, which represents a crucial concern for many planners, designers, and policy-makers in order to develop effective policies in addition to increasing their participation. The aim of this study is to investigate stakeholder perceptions of the socio-economic challenges of urban development and their priorities in the all Iraqi provinces. A nationwide questionnaire has been conducted (N = 643) across Iraq, using 19- item structured questionnaire where the stakeholder’s perspectives were collected on a 5-point Likert-type scale. The indicators were identified through deep investigation in previous studies. Principal component analysis (PCA) and statistical tests were utilized to the collected responses in order to investigate the linkage between the perceptions of socio- economic challenges and demographic factors. A high value of internal consistency and reliability of the instrument has been achieved (Cronbach’s alpha= 0.867). Five principal components have been identified, namely: economic, cultural aspects, design context, employment, security and housing demands. The item ‘safety of public places' was ranked as the most important, followed by the items 'minimize unplanned housing', and ‘provision of affordable housing’, respectively. Promote high-rise housing from the housing demands group, was ranked the lowest component between all indicators. 'Using sustainable local materials in construction' item had the second lowest mean score. The results also illustrate a link between deficiencies in the social and economic infrastructure because of the destruction and degradation caused by political instability in Iraq in the last few decades.

Keywords: public participation in development, socio-economic challenges, urban development, urban sustainability

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Authors: Frank Kuebler, Rolf Steinhilper

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Artificial neural networks (ANN) as well as Design of Experiments (DOE) based regression analysis (RA) are mainly used for modeling of complex systems. Both methodologies are commonly applied in process and quality control of manufacturing processes. Due to the fact that resource efficiency has become a critical concern for manufacturing companies, these models needs to be extended to predict resource-consumption of manufacturing processes. This paper describes an approach to use neural networks as well as DOE based regression analysis for predicting resource consumption of manufacturing processes and gives a comparison of the achievable results based on an industrial case study of a turning process.

Keywords: artificial neural network, design of experiments, regression analysis, resource efficiency, manufacturing process

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Authors: Entisar A. Elgmati

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Recurrent infant diarrhea is studied using daily data collected in Salvador, Brazil over one year and three months. A logistic regression model is fitted instead of Aalen's additive model using the same covariates that were used in the analysis with the additive model. The model gives reasonably similar results to that using additive regression model. In addition, the problem with the estimated conditional probabilities not being constrained between zero and one in additive model is solved here. Also martingale residuals that have been used to judge the goodness of fit for the additive model are shown to be useful for judging the goodness of fit of the logistic model.

Keywords: additive model, cumulative probabilities, infant diarrhoea, recurrent event

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Authors: Cheng Zeng, George Michailidis, Hitoshi Iyatomi, Leo L. Duan

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The conditional density characterizes the distribution of a response variable y given other predictor x and plays a key role in many statistical tasks, including classification and outlier detection. Although there has been abundant work on the problem of Conditional Density Estimation (CDE) for a low-dimensional response in the presence of a high-dimensional predictor, little work has been done for a high-dimensional response such as images. The promising performance of normalizing flow (NF) neural networks in unconditional density estimation acts as a motivating starting point. In this work, the authors extend NF neural networks when external x is present. Specifically, they use the NF to parameterize a one-to-one transform between a high-dimensional y and a latent z that comprises two components [zₚ, zₙ]. The zₚ component is a low-dimensional subvector obtained from the posterior distribution of an elementary predictive model for x, such as logistic/linear regression. The zₙ component is a high-dimensional independent Gaussian vector, which explains the variations in y not or less related to x. Unlike existing CDE methods, the proposed approach coined Augmented Posterior CDE (AP-CDE) only requires a simple modification of the common normalizing flow framework while significantly improving the interpretation of the latent component since zₚ represents a supervised dimension reduction. In image analytics applications, AP-CDE shows good separation of 𝑥-related variations due to factors such as lighting condition and subject id from the other random variations. Further, the experiments show that an unconditional NF neural network based on an unsupervised model of z, such as a Gaussian mixture, fails to generate interpretable results.

Keywords: conditional density estimation, image generation, normalizing flow, supervised dimension reduction

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Authors: Shan Li, Qiuwen Ma

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Integrated project delivery (IPD) is a project delivery method distinguished by a shared risk/rewards mechanism and multiparty agreement. IPD has drawn increasingly attention from construction industry because of its efficiency of solving adversarial problems and reliability to deliver high-performing buildings. However, some evidence showed that some project participants obtained less profit from IPD projects than the typical projects. They attributed it to the unfair IPD sharing mechanism, which resulted in additional time and cost of negotiation on the sharing fractions among project participants. The study is aimed to investigate the reward distribution by constructing a principal-agent model. Based on cooperative game theory, it is examined how to distribute the shared project rewards between client and non-client parties, and identify the sharing fractions among non-client parties. It is found that at least half of the project savings should be allocated to the non-client parties to motivate them to create more project value. Second, the client should raise his sharing fractions when the integration among project participants is efficient. In addition, the client should allocate higher sharing fractions to the non-client party who is more able. This study can help the IPD project participants make fair and motivated sharing mechanisms.

Keywords: cooperative game theory, IPD, principal agent model, sharing mechanism

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Authors: Fatemeh Valizadeh Gamchi, Edward L. Boone

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This research focuses on Lyme disease, a widespread infectious condition in the United States caused by the bacterium Borrelia burgdorferi sensu stricto. It is critical to identify environmental and economic elements that are contributing to the spread of the disease. This study examined data from Virginia to identify a subset of explanatory variables significant for Lyme disease case numbers. To identify relevant variables and avoid overfitting, linear poisson, and regularization regression methods such as a ridge, lasso, and elastic net penalty were employed. Cross-validation was performed to acquire tuning parameters. The methods proposed can automatically identify relevant disease count covariates. The efficacy of the techniques was assessed using four criteria on three simulated datasets. Finally, using the Virginia Department of Health’s Lyme disease data set, the study successfully identified key factors, and the results were consistent with previous studies.

Keywords: lyme disease, Poisson generalized linear model, ridge regression, lasso regression, elastic net regression

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Authors: Prakash Singh

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For schools to be desirable places in which to work, it is necessary for principals to recognise their teachers’ emotions, and be sensitive to their needs. This necessitates that principals are capable to correctly identify their emotionally intelligent behaviours (EIBs) they need to use in order to be successful leaders. They also need to have knowledge of their emotional intelligence and be able to identify the factors and situations that evoke emotion at an interpersonal level. If a principal is able to do this, then the control and understanding of emotions and behaviours of oneself and others could improve vastly. This study focuses on the interpersonal EIBS of principals affecting the job satisfaction of teachers. The correlation coefficients in this quantitative study strongly indicate that there is a statistical significance between the respondents’ level of job satisfaction, the rating of their principals’ EIBs and how they believe their principals’ EIBs will affect their sense of job satisfaction. It can be concluded from the data obtained in this study that there is a significant correlation between the sense of job satisfaction of teachers and their principals’ interpersonal EIBs. This means that the more satisfied a teacher is at school, the more appropriate and meaningful a principal’s EIBs will be. Conversely, the more dissatisfied a teacher is at school the less appropriate and less meaningful a principal’s interpersonal EIBs will be. This implies that the leaders’ EIBs can be construed as one of the major factors affecting the job satisfaction of employees.

Keywords: emotional intelligence, teachers' emotions, teachers' job satisfaction, principals' emotionally intelligent behaviours

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Authors: Muhammad Bahrul Ilmi

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The purpose of this research is to analyze the influence of Islamic financing and work relation founding simultaneously and partially towards non-performing financing in Islamic banks. This research was regression quantitative field research, and had been done in Muammalat Indonesia Bank and Islamic Danamon Bank in 3 months. The populations of this research were 15 account officers of Muammalat Indonesia Bank and Islamic Danamon Bank in Surakarta, Indonesia. The techniques of collecting data used in this research were documentation, questionnaire, literary study and interview. Regression analysis result shows that Islamic financing and work relation founding simultaneously has positive and significant effect towards non performing financing of two Islamic Banks. It is obtained with probability value 0.003 which is less than 0.05 and F value 9.584. The analysis result of Islamic financing regression towards non performing financing shows the significant effect. It is supported by double linear regression analysis with probability value 0.001 which is less than 0.05. The regression analysis of work relation founding effect towards non-performing financing shows insignificant effect. This is shown in the double linear regression analysis with probability value 0.161 which is bigger than 0.05.

Keywords: Syariah financing, work relation founding, non-performing financing (NPF), Islamic Bank

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Authors: Kali Prasad Sarma, Sanghita Dutta

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Deepor Beel (DB), the lone Ramsar site and an important wetland of the Brahmaputra valley in the state of Assam. The local people from fourteen peripheral villages traditionally utilize the wetland for harvesting vegetables, flowers, aquatic seeds, medicinal plants, fish, molluscs, fodder for domestic cattle etc. Therefore, it is of great importance to understand the concentration and distribution of trace metals in water-sediment system of the beel in order to protect its ecological environment. DB lies between26°05′26′′N to 26°09′26′′N latitudes and 90°36′39′′E to 91°41′25′′E longitudes. Water samples from the surface layer of water up to 40cm deep and sediment samples from the top 5cm layer of surface sediments were collected. The trace metals in waters and sediments were analysed using ICP-OES. The organic Carbon was analysed using the TOC analyser. The different mineral present in the sediments were confirmed by X-ray diffraction method (XRD). SEM images were recorded for the samples using SEM, attached with energy dispersive X-ray unit, with an accelerating voltage of 20 kv. All the statistical analyses were performed using SPSS20.0 for windows. In the present research, distribution, source apportionment, temporal and spatial variability, extent of pollution and the ecological risk of eight toxic trace metals in sediments and water of DB were investigated. The average concentrations of chromium(Cr) (both the seasons), copper(Cu) and lead(Pb) (pre-monsoon) and zinc(Zn) and cadmium(Cd) (post-monsoon) in sediments were higher than the consensus based threshold concentration(TEC). The persistent exposure of toxic trace metals in sediments pose a potential threat, especially to sediment dwelling organisms. The degree of pollution in DB sediments for Pb, Cobalt (Co) Zn, Cd, Cr, Cu and arsenic (As) was assessed using Enrichment Factor (EF), Geo-accumulation index (Igeo) and Pollution Load Index (PLI). The results indicated that contamination of surface sediments in DB is dominated by Pb and Cd and to a lesser extent by Co, Fe, Cu, Cr, As and Zn. A significant positive correlation among the pairs of element Co/Fe, Zn/As in water, and Cr/Zn, Fe/As in sediments indicates similar source of origin of these metals. The effects of interaction among trace metals between water and sediments shows significant variations (F =94.02, P < 0.001), suggesting maximum mobility of trace metals in DB sediments and water. The source apportionment of the heavy metals was carried out using Principal Component Analysis (PCA). SEM-EDS detects the presence of Cd, Cu, Cr, Zn, Pb, As and Fe in the sediment sample. The average concentration of Cd, Zn, Pb and As in the bed sediments of DB are found to be higher than the crustal abundance. The EF values indicate that Cd and Pb are significantly enriched. From source apportionment studies of the eight metals using PCA revealed that Cd was anthropogenic in origin; Pb, As, Cr, and Zn had mixed sources; whereas Co, Cu and Fe were natural in origin.

Keywords: Deepor Beel, enrichment factor, principal component analysis, trace metals

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Authors: Chen Li-Ching

Abstract:

The multinomial logistic regression model is used popularly for inferring the relationship of risk factors and disease with multiple categories. This study based on the discrepancy between the nonparametric maximum likelihood estimator and semiparametric maximum likelihood estimator of the cumulative distribution function to propose a Kolmogorov-Smirnov type test statistic to assess adequacy of the multinomial logistic regression model for case-control data. A bootstrap procedure is presented to calculate the critical value of the proposed test statistic. Empirical type I error rates and powers of the test are performed by simulation studies. Some examples will be illustrated the implementation of the test.

Keywords: case-control studies, goodness-of-fit test, Kolmogorov-Smirnov test, multinomial logistic regression

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Authors: Thuvanan Borvornvitchotikarn, Werasak Kurutach

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Mutual information (MI) is widely used in medical image registration. In the different medical images analysis, it is difficult to choose an optimal bins size number for calculating the probability distributions in MI. As the result, this paper presents a new adaptive bins number selection approach that named a hybrid EMPCA-Scott approach. This work combines an expectation maximization principal component analysis (EMPCA) and the modified Scott’s rule. The proposed approach solves the binning problem from the various intensity values in medical images. Experimental results of this work show the lower registration errors compared to other adaptive binning approaches.

Keywords: mutual information, EMPCA, Scott, probability distributions

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Authors: Yan Wang, Yasushi Asami, Yukio Sadahiro

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In urban area, several landmarks may affect housing price and rents, hedonic analysis should employ distance variables corresponding to each landmarks. Unfortunately, the effects of distances to landmarks on housing prices are generally not consistent with the true price. These distance variables may cause magnitude error in regression, pointing a problem of spatial multicollinearity. In this paper, we provided some approaches for getting the samples with less bias and method on locating the specific sampling area to avoid the multicollinerity problem in two specific landmarks case.

Keywords: landmarks, hedonic regression, distance variables, collinearity, multicollinerity

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Authors: Ren-Jieh Kuo, Chun-Shou Huang

Abstract:

The research of juice flavor forecasting has become more important in China. Due to the fast economic growth in China, many different kinds of juices have been introduced to the market. If a beverage company can understand their customers’ preference well, the juice can be served more attractively. Thus, this study intends to introduce the basic theory and computing process of grapes juice flavor forecasting based on support vector regression (SVR). Applying SVR, BPN and LR to forecast the flavor of grapes juice in real data, the result shows that SVR is more suitable and effective at predicting performance.

Keywords: flavor forecasting, artificial neural networks, Support Vector Regression, China

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Authors: Tai Mei Kin, Omar Abdull Kareem

Abstract:

In light of globalization, educational reform has become a top priority for many countries. However, the task of leading change effectively requires a multidimensional set of competencies. Over the past two decades, technical competencies of principal change leadership have been extensively analysed and discussed. Comparatively, little research has been conducted in Malaysian education context on non-technical competencies or popularly known as emotional intelligence, which is equally crucial for the success of change. This article provides a validation of the Principal Change Leadership Non-Technical Competencies (PCLnTC) Scale, a tool that practitioners can easily use to assess school principals’ level of change leadership non-technical competencies that facilitate change and maximize change effectiveness. The overall coherence of the PCLnTC model was constructed by incorporating three theories: a)the change leadership theory whereby leading change is the fundamental role of a leader; b)competency theory in which leadership can be taught and learned; and c)the concept of emotional intelligence whereby it can be developed, fostered and taught. An exploratory factor analysis (EFA) was used to determine the underlying factor structure of PCLnTC model. Before conducting EFA, five important pilot test approaches were conducted to ensure the validity and reliability of the instrument: a)reviewed by academic colleagues; b)verification and comments from panel; c)evaluation on questionnaire format, syntax, design, and completion time; d)evaluation of item clarity; and e)assessment of internal consistency reliability. A total of 335 teachers from 12 High Performing Secondary School in Malaysia completed the survey. The PCLnTCS with six points Liker-type scale were subjected to Principal Components Analysis. The analysis yielded a three-factor solution namely, a)Interpersonal Sensitivity; b)Flexibility; and c)Motivation, explaining a total 74.326 per cent of the variance. Based on the results, implications for instrument revisions are discussed and specifications for future confirmatory factor analysis are delineated.

Keywords: exploratory factor analysis, principal change leadership non-technical competencies (PCLnTC), interpersonal sensitivity, flexibility, motivation

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Authors: Siti Nurhidayah Naqiah Abdull Rani, Herlina Abdul Rahim

Abstract:

Near infrared (NIR) spectroscopy has always been of great interest in the food and agriculture industries. The development of predictive models has facilitated the estimation process in recent years. In this research, 176 crude palm oil (CPO) samples acquired from Felda Johor Bulker Sdn Bhd were studied. A FOSS NIRSystem was used to tak e absorbance measurements from the sample. The wavelength range for the spectral measurement is taken at 1600nm to 1900nm. Partial Least Square Regression (PLSR) prediction model with 50 optimal number of principal components was implemented to study the relationship between the measured Free Fatty Acid (FFA) values and the measured spectral absorption. PLSR showed predictive ability of FFA values with correlative coefficient (R) of 0.9808 for the training set and 0.9684 for the testing set.

Keywords: palm oil, fatty acid, NIRS, PLSR

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Authors: Santosh B. Phad, Priyanka V. Janbandhu, Dhananjay W. Bansod

Abstract:

Population Ageing is a growing concern for the social scientists. There is a higher level of aged male participation compared to elderly females. Now, the critical question is whether participation in work improves the quality of life among the elderly and the impact of working status on the mental well-being of the elderly. While examining these research questions, the present paper focuses on the workforce participation of the elderly and the reasons behind it, additionally, determines the association between employment status and the mental well-being of the elderly. The present study has a base of two data sources. First one is Census of India data, 2001 and 2011, and another one is – the Study on Global Ageing and Adult Health (SAGE), a survey conducted in 2007. To capture the trend of workforce participation elderly Census data is significant and to obtain other information associated with this issue the SAGE data is studied. The research piece consists of univariate and bivariate analysis along with some statistical methods like principal component analysis (PCA) and regression modeling – to investigate the association between workforce participation of elderly and subjective well-being (SWB). The results show that the percentage of elderly participating in the labor market is gradually reducing, but the share of working elderly has increased within the group of overall workers. i.e., the ratio of aged workers to non-aged workers is rising. The findings from survey data specify that there is a considerable share of the elderly in the labor market; three-fourths of the employed elderly enrolled the workforce unwillingly. They are in need of some earnings mainly to afford the medical expenses on their health or the health of their spouse, also to support their family members who are economically inactive. Apart from need, duration of working is another vital aspect for the elderly, whereas more than 80 percent of the elderly are working for six hours or more, and most of them engaged in self-employment. However, more than one-third of the working elderly falls into a negative cluster of the subjective well-being (SWB) index, and it is consistent with the result of the discriminant analysis. Here, the SWB index calculated from the 12 items and the reliability score of these items is 0.89.

Keywords: ageing, workforce, census of India, SAGE

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