Search results for: multiple regression analysis

Authors: Quangyen Tran, YeZhuang Tian, Chengfeng Li

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Psychological contract violations may lead to damaging an organization through losing its potential employees; it is a very significant concept in understanding the employment relationships. The authors selected contents of psychological contract violation scale based on the nine areas of violation most relevant to managerial samples (High pay, training, job security, career development, pay based on performance, promotion, feedback, expertise and quality of co-workers and support with personal problems), using regression analysis, the degree of psychological contract violations was measured by an adaptation of a multiplicative scale with Cronbach’s alpha as a measure of reliability. Through the regression analysis, psychological contract violations was found have a positive impact on employees’ job satisfaction, the frequency of psychological contract violations was more intense among male employees particularly in terms of training, job security and pay based on performance. Job dissatisfaction will lead to a lowering of employee commitment in the job, enterprises in Hanoi, Vietnam should therefore offer lucrative jobs in terms of salary and other emoluments to their employees.

Keywords: psychological contract, psychological contract violation, job satisfaction, subordinate employees, employers’ obligation

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Authors: F. Massri, M. Shkarneh, B. Almassri

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Water scarcity and increasing water demand especially for residential use are major challenges facing Palestine. The need to accurately forecast water consumption is useful for the planning and management of this natural resource. The main objective of this paper is to (i) study the major factors influencing the water consumption in Palestine, (ii) understand the general pattern of Household water consumption, (iii) assess the possible changes in household water consumption and suggest appropriate remedies and (iv) develop prediction model based on the Artificial Neural Network to the water consumption in Palestinian cities. The paper is organized in four parts. The first part includes literature review of household water consumption studies. The second part concerns data collection methodology, conceptual frame work for the household water consumption surveys, survey descriptions and data processing methods. The third part presents descriptive statistics, multiple regression and analysis of the water consumption in the two Palestinian cities. The final part develops the use of Artificial Neural Network for modeling the water consumption in Palestinian cities.

Keywords: water management, demand forecasting, consumption, ANN, Ramallah

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Authors: Mirjana Dragas, Marli Gonan Bozac, Morena Paulisic

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Corporate social responsibility (CSR) is a balance between strategic and financial goals of companies, as well as social needs. The integration of competitive strategy and CSR in food and beverage industry has allowed companies to find new sources of competitive advantage. The paper discusses the fact that socially responsible companies encourage co-operation with socially responsible suppliers in order to strengthen market competitiveness. In addition to the descriptive interpretation of the results obtained by a questionnaire, factor analysis was used, while principal components analysis was applied as a factor extraction method. The research results based on two multiple regression analyses show that: (1) selecting the CSR supplier explains a statistically significant part of the variance of the results on the scale of financial aspects of competitiveness (as much as 44.7% of the explained variance); and (2) selecting the CSR supplier is a significant predictor of non-financial aspects of competitiveness (explains 43.9% of the variance of the results on the scale of non-financial aspects of competitiveness). A successful competitive strategy must ultimately support the growth strategy. This implies an analytical approach to finding factors that influence competitiveness through socially sustainable solutions and satisfactory top management decisions.

Keywords: competitiveness, corporate social responsibility, food and beverage industry, supply chain decision making

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Authors: Pisit Potjanajaruwit

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The objective of this research was to study the influence of Integrated Marketing Communication on Buying Decision of Consumers in Bangkok. A total of 397 respondents were collected from customers who drive in Bangkok. A questionnaire was utilized as a tool to collect data. Statistics utilized in this research included frequency, percentage, mean, standard deviation, and multiple regression analysis. Data were analyzed by using Statistical Package for the Social Sciences. The findings revealed that the majority of respondents were male with the age between 25-34 years old, hold undergraduate degree, married and stay together. The average income of respondents was between 10,001-20,000 baht. In terms of occupation, the majority worked for private companies. The effect to the Buying Decision of Consumers in Bangkok to including sale promotion with the low interest and discount for an installment, selling by introducing and gave product information through sales persons, public relation by website, direct marketing by annual motor show and advertisement by television media.

Keywords: Bangkok metropolis, ECO car, integrated marketing communication, international standard

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Authors: Pirzada Sami Ullah Sabri

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The present study examines the relationship between work engagement (WE) and employee turnover intentions (TI) in telecommunication sector using human resource development climate (HRDC) as a moderator. Based on 538 employees of telecommunication sector Hierarchal regression analysis is employed to examine the influence of HRDC on the relationship of work engagement and turnover intentions. The result indicates the negative correlation between work engagement and turnover intentions; HRD climate support as a powerful moderator increases the work engagement and lessens the turnover intentions. The study shows the importance of favorable and supportive HRD climate which foster the work engagement of the employees in the organization. By understanding the importance of human resource development climate and work engagement in reducing the turnover intentions can increase the productivity and performance of the organization.

Keywords: turnover intentions, work engagement, human resource development, climate, hierarchal regression analysis, telecommunication sector

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Authors: Zhidong Zhang, Amanda Resendez

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This study is to examine how teachers’ perspective on education technology use in their class influence their students’ achievement. The authors hypothesized that teachers’ perspective can directly or indirectly influence students’ learning, performance, and achievements. In this study, a questionnaire entitled, Teacher’s Perspective on Educational Technology, was delivered to 63 teachers and 1268 students’ mathematics and reading achievement records were collected. The questionnaire consists of four parts: a) demographic variables, b) attitudes on technology integration, c) outside factor affecting technology integration, and d) technology use in the classroom. Kruskal-Wallis and hierarchical regression analysis techniques were used to examine: 1) the relationship between the demographic variables and teachers’ perspectives on educational technology, and 2) how the demographic variables were causally related to students’ mathematics and reading achievements. The study found that teacher demographics were significantly related to the teachers’ perspective on educational technology with p < 0.05 and p < 0.01 separately. These teacher demographical variables included the school district, age, gender, the grade currently teach, teaching experience, and proficiency using new technology. Further, these variables significantly predicted students’ mathematics and reading achievements with p < 0.05 and p < 0.01 separately. The variations of R² are between 0.176 and 0.467. That means 46.7% of the variance of a given analysis can be explained by the model.

Keywords: teacher's perception of technology use, mathematics achievement, reading achievement, Kruskal-Wallis test, hierarchical regression analysis

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Authors: Inten Meutia, Emylia Yuniarti

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The objective of this study is to analyze the effects of interest rate, the rate of return of Islamic banks on the amount of mudarabah deposits in Islamic banks. In analyzing the effect of rate of return in the Islamic banks and interest rate risk in the conventional banks, the 1-month Islamic deposit rate of return and 1 month fixed deposit interest rate of a total Islamic deposit are considered. Using data covering the period from January 2010 to Sepember 2013, the study applies the regression analysis to analyze the effect between variable and independence t-test to analyze the mean difference between rate of return and rate of interest. Regression analysis shows that rate of return have significantly negative influence on mudarabah deposits, while interest rate have negative influence but not significant. The result of independent t test shows that the interest rate is not different from the rate of return in Islamic Bank. It supports the hyphotesis that rate of return in Islamic banking mimic rate of interest in conventional bank. The results of the study have important implications on the risk management practices of the Islamic banks in Indonesia.

Keywords: conventional bank, interest rate, Islamic bank, rate of return

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Authors: Kehinde Peter Oyeduntan, Kayode Oshinubi

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The west African ports have been experiencing large inflow and outflow of containerized cargo in the last decades, and this has created a quest amongst the countries to attain the status of hub port for the sub-region. This study analyzed the relationship between the container throughput and Gross Domestic Products (GDP) of nine west African countries, using Simple Linear Regression (SLR), Polynomial Regression Model (PRM) and Support Vector Machines (SVM) with a time series of 20 years. The results showed that there exists a high correlation between the GDP and container throughput. The model also predicted the container throughput in west Africa for the next 20 years. The findings and recommendations presented in this research will guide policy makers and help improve the management of container ports and terminals in west Africa, thereby boosting the economy.

Keywords: container, ports, terminals, throughput

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Authors: Pisit Potjanajaruwit

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TThe research objective was to study the integrated marketing communication strategy that is affecting the student’s decision making to study at Rajabhat University in Thailand. This research is a quantitative research. The sampling for this study is the first year students of Rajabhat University for 400 sampling. The data collection is made by a questionnaire. The data analysis by the descriptive statistic include frequency, percentage, mean and standardization and influence statistic as the multiple regression. The results show that integrated marketing communication including the advertising, public relation, sale promotion is important and significant with the student’s making decision in terms of brand awareness and brand recognized. The university scholar and word of mouth have an impact on decision-making of the student. The direct marketing such as Facebook also relate to the student decision. In addition, we found that the marketing communication budget, university brand positioning and university mission have the direct effect on the marketing communication.

Keywords: decision making of higher education, integrated marketing communication, rajabhat university, social media

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Authors: Kamyar Kildashti, Rasoul Mirghaderi

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This paper describes the three-dimensional seismic performance assessment of a high-rise steel moment-frame setback tower, designed and detailed per the 2010 ASCE7, under multiple support excitations. The vulnerability analyses are conducted based on nonlinear history analyses under a set of multi-directional strong ground motion records which are scaled to design-based site-specific spectrum in accordance with ASCE41-13. Spatial variation of input motions between far distant supports of each part of the tower is considered by defining time lag. Plastic hinge monotonic and cyclic behavior for prequalified steel connections, panel zones, as well as steel columns is obtained from predefined values presented in TBI Guidelines, PEER/ATC72 and FEMA P440A to include stiffness and strength degradation. Inter-story drift ratios, residual drift ratios, as well as plastic hinge rotation demands under multiple support excitations, are compared to those obtained from uniform support excitations. Performance objectives based on acceptance criteria declared by TBI Guidelines are compared between uniform and multiple support excitations. The results demonstrate that input motion discrepancy results in detrimental effects on the local and global response of the tower.

Keywords: high-rise building, nonlinear time history analysis, multiple support excitation, performance-based design

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Authors: Hossein Alimohammadi, Mohsen Amirmojahedi, Mehrdad Rowhani

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Soil properties are used by geotechnical engineers to evaluate and analyze site conditions for designing purposes. Although basic soil classification tests are easy to perform and provide useful information to determine the properties of soils, it may take time to get the result and add some costs to the projects. Standard Penetration Test (SPT) provides an opportunity to evaluate soil parameters without performing laboratory tests. In addition to its simplicity and cheapness, the results become available immediately. This research provides a guideline on the application of the SPT test method, reliability of adapting the SPT test results in evaluating soil physical and mechanical properties such as Atterberg limits, shear strength, and compressive strength compressibility parameters. A total of 70 boreholes were investigated in this study by taking soil samples between depths of 1.2 to 15.25 meters. The project site was located in Morrow County, Ohio. A regression-based formula was proposed based on Tobit regression with a stepwise variable selection analysis conducted between SPT and other typical soil properties obtained from soil tests. The results of the research illustrated that the shear strength and physical properties of the soil affect the SPT number. The proposed correlation can help engineers to use SPT test results in their design with higher accuracy.

Keywords: standard penetration test, soil properties, soil classification, regression method

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Authors: Kuixi Du, Thomas J. Lipscomb

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The proficiency in Mathematics skills is fundamental to success in the STEM disciplines. In the US, beginning college students who are placed in remedial/developmental Mathematics courses frequently struggle to achieve academic success. Therefore, Mathematics remediation in college has become an important concern, and providing Mathematics remediation is a prevalent way to help the students who may not be fully prepared for college-level courses. Programs vary, however, and the effectiveness of a particular remedial Mathematics program must be empirically demonstrated. The purpose of this study was to apply the sharp regression discontinuity (RD) technique to determine the effectiveness of the Jack Leaps Summer (JLS) Mathematic remediation program in supporting improved Mathematics learning outcomes among newly admitted Mathematics students in the South Dakota State University. The researchers studied the newly admitted Fall 2019 cohort of Mathematics majors (n=423). The results indicated that students whose pretest score was lower than the cut-off point and who were assigned to the JLS program experienced significantly higher scores on the post-test (Math 101 final score). Based on these results, there is evidence that the JLS program is effective in meeting its primary objective.

Keywords: causal inference, mathematisc remedial program evaluation, quasi-experimental research design, regression discontinuity design, cohort studies

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Authors: Nattapong Techarattanased

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This survey research aimed to study the influence of attitude in services, product, and marketing mix affected intention to revisit in boutique hotel of foreign travelers in Bangkok, Thailand. The total 400 sets of closed-ended questionnaires were utilized for conducting data from foreign tourists who come to boutique hotel and can communicate in English. The descriptive statistics and multiple regression analysis were used to analyze data. The research found that tourists’ attitude towards the service of check in and check out process, food and beverage, guest room and other facilities affected in opportunity of revisiting, recommending to others and possibility of revisiting in the future at 0.05 statistically significant levels. Tourists’ attitude towards service and marketing mix in term of people, physical evidence, price, process and channel of distribution could forecast intention to revisit in term of recommending to others and intention to revisit in the future at 0.05 statistically significant levels.

Keywords: boutique hotel, foreign tourists, intention to revisit, service marketing mix

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Authors: Aimen Batool Bint-E-Rashid, Syed Muhammad Dawood Ali Shah, Muhammad Usman Farooq, Mahgul Anwar

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As the world is progressing, the needs and demands of the consumer market are also changing. Nowadays the trends of consumer purchase decisions are dependent upon multiple factors. This study aims to identify the influential impact of country of origin over the perception and devotion towards daily personal care products specifically in reference to the knowledge and awareness regarding that particular brand in Pakistan. To corroborate this study, a 30-item brand origin questionnaire has been used with 300 purchase decision makers belonging to different age groups. To illustrate this study, a model has been developed based on brand origin, brand awareness and brand loyalty. Correlation and regression analysis have been used to find out the results which conclude the findings on the perspective of Pakistan’s consumer market as that brand origin has a direct relationship with brand loyalty provided that the consumer has a positive brand awareness. Support for the fact that brand origin impacts brand loyalty through brand awareness has been presented in this study.

Keywords: brand awareness, brand loyalty, brand origin, personal care products, P&G, Unilever

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Authors: Nattapong Techarattanased

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The purposes of this research are to make comparisons in respect of the behaviors on the use of the services of metered taxi classified by the demographic factor and to study the influence of the recognition on service quality having the effect on usage behaviors of metered taxi services of consumers in Bangkok Metropolitan Areas. The samples used in this research are 400 metered taxi service users in Bangkok Metropolitan Areas and use a questionnaire as the tool for collecting the data. Analysis statistics is mean and multiple regression analysis. Results of the research revealed that the consumers recognize the overall quality of services in each aspect include tangible aspects of the service, responses to customers, assurance on the confidence, understanding and knowing of customers which is rated at the moderate level except the aspect of the assurance on the confidence and trustworthiness which are rated at a high level. For the result of a hypothetical test, it is found that the quality in providing the services on the aspect of the assurance given to the customers has the effect on the usage behaviors of metered taxi services and the aspect of the frequency on the use of the services per month which in this connection. Such variable can forecast at one point nine percent (1.9%). In addition, quality in providing the services and the aspect of the responses to customers have the effect on the behaviors on the use of metered taxi services on the aspect of the expenses on the use of services per month which in this connection, such variable can forecast at two point one percent (2.1%).

Keywords: consumer behavior, metered taxi service, satisfaction, service quality

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Authors: Jihad S. Daba, J. P. Dubois, Yvette Antar

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With the exponential growth of cellular users, a new generation of cellular networks is needed to enhance the required peak data rates. The co-channel interference between neighboring base stations inhibits peak data rate increase. To overcome this interference, multi-cell cooperation known as coordinated multipoint transmission is proposed. Such a solution makes use of multiple-input-multiple-output (MIMO) systems under two different structures: Micro- and macro-diversity. In this paper, we study the capacity and bit error rate in cellular networks using MIMO technology. We analyse both micro- and macro-diversity schemes and develop a hybrid model that switches between macro- and micro-diversity in the case of hard handoff based on a cut-off range of signal-to-noise ratio values. We conclude that our hybrid switched micro-macro MIMO system outperforms classical MIMO systems at the cost of increased hardware and software complexity.

Keywords: cooperative multipoint transmission, ergodic capacity, hard handoff, macro-diversity, micro-diversity, multiple-input-multiple output systems, orthogonal frequency division multiplexing

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Authors: Norbayah Mohd Suki, Norazah Mohd Suki

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This study aims to examine relationships between attitude, self-efficacy, and subjective norm with students’ behavioural intention to use public computing facilities at a library. Data was collected from 200 undergraduate students enrolled at a higher learning institution in the Federal Territory of Labuan, Malaysia via a structured questionnaire comprising closed-ended questions. Data was analyzed using multiple regression analysis. The results show that students’ behavioural intention to use public computing facilities at the library is widely affected by subjective norm factor i.e. influence of the support of family members, friends and neighbours. The findings of this study provide a better understanding of factors likely to influence students’ behavioural intention to use public computing facilities at a library. It also offers valuable insights into factors which university librarians need to focus on to improve students’ behavioural intention to actively use public computing facilities at a library for quality information retrieval. Direction for future research is also presented.

Keywords: attitude, self-efficacy, subjective norm, behavioural intention

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Authors: Shivanand Pawar

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In India, a collective range of measurements had been adopted to control the massive spread of the COVID-19 pandemic, but World Health Organization (2022) revealed 525 930 fatalities and 43,847,065 confirmed cases. There are currently 30,857 cases per million people. Lack of knowledge, attitude and practices are the main causes thought to be increased COVID-19. The present study aims to assess the knowledge, attitude, and practice among Tamil Nadu residents. The participants (N=332) were aged 20 to 50 (mean=42.78, & SD=13.98) and were selected using purposive sampling, and data were collected online using knowledge, attitude and practice scale. Data were analyzed using person correlation and multiple regression analysis. The result found that 31.30% had satisfactory knowledge, 68.70% had non-satisfactory knowledge, followed by 45.20% had a positive attitude, 54.80% had a negative attitude, and 34.30% had a good practice, and 65.70% had poor practice towards COVID-19. Correlation results revealed that age has a negative and significant relationship with Knowledge and Practice towards COVID-19. The current study results contribute to the existing literature on knowledge, attitude and practice of COVID-19 to reduce the COVID-19 cases by managing unhealthy knowledge, attitude and practice to control the massive spread of COVID-19.

Keywords: COVID-19, knowledge, practice, attitude, Fisherman community

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Authors: D. S. Nagesh, G. L. Datta

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In the field of welding, various studies had been made by some of the previous investigators to predict as well as optimize weld bead geometric descriptors. Modeling of weld bead shape is important for predicting the quality of welds. In most of the cases, design of experiments technique to postulate multiple linear regression equations have been used. Nowadays, Genetic Algorithm (GA) an intelligent information treatment system with the characteristics of treating complex relationships as seen in welding processes used as a tool for inverse mapping/optimization of the process is attempted.

Keywords: smaw, genetic algorithm, bead geometry, optimization/inverse mapping

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Authors: D. S. Nagesh, G. L. Datta

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In the field of welding, various studies had been made by some of the previous investigators to predict as well as optimize weld bead geometric descriptors. Modeling of weld bead shape is important for predicting the quality of welds. In most of the cases design of experiments technique to postulate multiple linear regression equations have been used. Nowadays Genetic Algorithm (GA) an intelligent information treatment system with the characteristics of treating complex relationships as seen in welding processes used as a tool for inverse mapping/optimization of the process is attempted.

Keywords: SMAW, genetic algorithm, bead geometry, optimization/inverse mapping

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Authors: Mikiyas Adugna

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Effort estimation is among the activities carried out in software development processes. An accurate model of estimation leads to project success. The method of agile effort estimation is a complex task because of the dynamic nature of software development. Researchers are still conducting studies on agile effort estimation to enhance prediction accuracy. Due to these reasons, we investigated and proposed a model on LASSO and Elastic Net regression to enhance estimation accuracy. The proposed model has major components: preprocessing, train-test split, training with default parameters, and cross-validation. During the preprocessing phase, the entire dataset is normalized. After normalization, a train-test split is performed on the dataset, setting training at 80% and testing set to 20%. We chose two different phases for training the two algorithms (Elastic Net and LASSO) regression following the train-test-split. In the first phase, the two algorithms are trained using their default parameters and evaluated on the testing data. In the second phase, the grid search technique (the grid is used to search for tuning and select optimum parameters) and 5-fold cross-validation to get the final trained model. Finally, the final trained model is evaluated using the testing set. The experimental work is applied to the agile story point dataset of 21 software projects collected from six firms. The results show that both Elastic Net and LASSO regression outperformed the compared ones. Compared to the proposed algorithms, LASSO regression achieved better predictive performance and has acquired PRED (8%) and PRED (25%) results of 100.0, MMRE of 0.0491, MMER of 0.0551, MdMRE of 0.0593, MdMER of 0.063, and MSE of 0.0007. The result implies LASSO regression algorithm trained model is the most acceptable, and higher estimation performance exists in the literature.

Keywords: agile software development, effort estimation, elastic net regression, LASSO

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Authors: Adem Peker, Yüksel Eroğlu, İsmail Ay

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As the information and communication technologies have become embedded in everyday life of adolescents, both their possible benefits and risks to adolescents are being identified. The information and communication technologies provide opportunities for adolescents to connect with peers and to access to information. However, as with other social connections, users of information and communication devices have the potential to meet and interact with in harmful ways. One emerging example of such interaction is cyber bullying. Cyber bullying occurs when someone uses the information and communication technologies to harass or embarrass another person. Cyber bullying can take the form of malicious text messages and e-mails, spreading rumours, and excluding people from online groups. Cyber bullying has been linked to psychological problems for cyber bullies and victims. Therefore, it is important to determine how internet addiction contributes to cyber bullying. Building on this question, this study takes a closer look at the relationship between internet addiction and cyber bullying. For this purpose, in this study, based on descriptive relational model, it was hypothesized that loss of control, excessive desire to stay online, and negativity in social relationships, which are dimensions of internet addiction, would be associated positively with cyber bullying and victimization. Participants were 383 high school students (176 girls and 207 boys; mean age, 15.7 years). Internet addiction was measured by using Internet Addiction Scale. The Cyber Victim and Bullying Scale was utilized to measure cyber bullying and victimization. The scales were administered to the students in groups in the classrooms. In this study, stepwise regression analyses were utilized to examine the relationships between dimensions of internet addiction and cyber bullying and victimization. Before applying stepwise regression analysis, assumptions of regression were verified. According to stepwise regression analysis, cyber bullying was predicted by loss of control (β=.26, p<.001) and negativity in social relationships (β=.13, p<.001). These variables accounted for 9 % of the total variance, with the loss of control explaining the higher percentage (8 %). On the other hand, cyber victimization was predicted by loss of control (β=.19, p<.001) and negativity in social relationships (β=.12, p<.001). These variables altogether accounted for 8 % of the variance in cyber victimization, with the best predictor loss of control (7 % of the total variance). The results of this study demonstrated that, as expected, loss of control and negativity in social relationships predicted cyber bullying and victimization positively. However, excessive desire to stay online did not emerge a significant predictor of both cyberbullying and victimization. Consequently, this study would enhance our understanding of the predictors of cyber bullying and victimization since the results proposed that internet addiction is related with cyber bullying and victimization.

Keywords: cyber bullying, internet addiction, adolescents, regression

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Authors: Yildiz Stella Dak, Jale Tezcan

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Ground motion models that relate a strong motion parameter of interest to a set of predictive seismological variables describing the earthquake source, the propagation path of the seismic wave, and the local site conditions constitute a critical component of seismic hazard analyses. When a sufficient number of strong motion records are available, ground motion relations are developed using statistical analysis of the recorded ground motion data. In regions lacking a sufficient number of recordings, a synthetic database is developed using stochastic, theoretical or hybrid approaches. Regardless of the manner the database was developed, ground motion relations are developed using regression analysis. Development of a ground motion relation is a challenging process which inevitably requires the modeler to make subjective decisions regarding the inclusion criteria of the recordings, the functional form of the model and the set of seismological variables to be included in the model. Because these decisions are critically important to the validity and the applicability of the model, there is a continuous interest on procedures that will facilitate the development of ground motion models. This paper proposes the use of the Least Absolute Shrinkage and Selection Operator (LASSO) in selecting the set predictive seismological variables to be used in developing a ground motion relation. The LASSO can be described as a penalized regression technique with a built-in capability of variable selection. Similar to the ridge regression, the LASSO is based on the idea of shrinking the regression coefficients to reduce the variance of the model. Unlike ridge regression, where the coefficients are shrunk but never set equal to zero, the LASSO sets some of the coefficients exactly to zero, effectively performing variable selection. Given a set of candidate input variables and the output variable of interest, LASSO allows ranking the input variables in terms of their relative importance, thereby facilitating the selection of the set of variables to be included in the model. Because the risk of overfitting increases as the ratio of the number of predictors to the number of recordings increases, selection of a compact set of variables is important in cases where a small number of recordings are available. In addition, identification of a small set of variables can improve the interpretability of the resulting model, especially when there is a large number of candidate predictors. A practical application of the proposed approach is presented, using more than 600 recordings from the National Geospatial-Intelligence Agency (NGA) database, where the effect of a set of seismological predictors on the 5% damped maximum direction spectral acceleration is investigated. The set of candidate predictors considered are Magnitude, Rrup, Vs30. Using LASSO, the relative importance of the candidate predictors has been ranked. Regression models with increasing levels of complexity were constructed using one, two, three, and four best predictors, and the models’ ability to explain the observed variance in the target variable have been compared. The bias-variance trade-off in the context of model selection is discussed.

Keywords: ground motion modeling, least absolute shrinkage and selection operator, penalized regression, variable selection

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Authors: Bitewulign Mekonnen

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Context: This scientific paper focuses on the use of near-infrared (NIR) spectroscopy to determine glucose concentration in aqueous solutions accurately and rapidly. The study compares six different machine learning methods for predicting glucose concentration and also explores the development of a deep learning model for classifying NIR spectra. The objective is to optimize the detection model and improve the accuracy of glucose prediction. This research is important because it provides a comprehensive analysis of various machine-learning techniques for estimating aqueous glucose concentrations. Research Aim: The aim of this study is to compare and evaluate different machine-learning methods for predicting glucose concentration from NIR spectra. Additionally, the study aims to develop and assess a deep-learning model for classifying NIR spectra. Methodology: The research methodology involves the use of machine learning and deep learning techniques. Six machine learning regression models, including support vector machine regression, partial least squares regression, extra tree regression, random forest regression, extreme gradient boosting, and principal component analysis-neural network, are employed to predict glucose concentration. The NIR spectra data is randomly divided into train and test sets, and the process is repeated ten times to increase generalization ability. In addition, a convolutional neural network is developed for classifying NIR spectra. Findings: The study reveals that the SVMR, ETR, and PCA-NN models exhibit excellent performance in predicting glucose concentration, with correlation coefficients (R) > 0.99 and determination coefficients (R²)> 0.985. The deep learning model achieves high macro-averaging scores for precision, recall, and F1-measure. These findings demonstrate the effectiveness of machine learning and deep learning methods in optimizing the detection model and improving glucose prediction accuracy. Theoretical Importance: This research contributes to the field by providing a comprehensive analysis of various machine-learning techniques for estimating glucose concentrations from NIR spectra. It also explores the use of deep learning for the classification of indistinguishable NIR spectra. The findings highlight the potential of machine learning and deep learning in enhancing the prediction accuracy of glucose-relevant features. Data Collection and Analysis Procedures: The NIR spectra and corresponding references for glucose concentration are measured in increments of 20 mg/dl. The data is randomly divided into train and test sets, and the models are evaluated using regression analysis and classification metrics. The performance of each model is assessed based on correlation coefficients, determination coefficients, precision, recall, and F1-measure. Question Addressed: The study addresses the question of whether machine learning and deep learning methods can optimize the detection model and improve the accuracy of glucose prediction from NIR spectra. Conclusion: The research demonstrates that machine learning and deep learning methods can effectively predict glucose concentration from NIR spectra. The SVMR, ETR, and PCA-NN models exhibit superior performance, while the deep learning model achieves high classification scores. These findings suggest that machine learning and deep learning techniques can be used to improve the prediction accuracy of glucose-relevant features. Further research is needed to explore their clinical utility in analyzing complex matrices, such as blood glucose levels.

Keywords: machine learning, signal processing, near-infrared spectroscopy, support vector machine, neural network

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Authors: Hui-Yen Lee, Hsiu-Yun Wei, Guey-Jen Lin, Pi-Yueh Lee Lee

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Background: Unplanned hospital readmissions increase healthcare costs and have been considered a marker of poor healthcare performance. The elderly face a higher risk of unplanned readmission due to elderly-specific characteristics such as deteriorating body functions and the relatively high incidence of complications after treatment of acute diseases. Purpose: The aim of this study was exploring the factors that relate to the unplanned readmission of elderly within 14 days of discharge at our hospital in southern Taiwan. Methods: We retrospectively reviewed the medical records of patients aged ≥65 years who had been re-admitted between January 2018 and December 2018.The Charlson Comorbidity score was calculated using previous used method. Related factors that affected the rate of unplanned readmission within 14 days of discharge were screened and analyzed using the chi-squared test and logistic regression analysis. Results: This study enrolled 829 subjects aged more than 65 years. The numbers of unplanned readmission patients within 14 days were 318 cases, while those did not belong to the unplanned readmission were 511 cases. In 2018, the rate of elderly patients in unplanned 14 days readmissions was 38.4%. The majority patients were females (166 cases, 52.2%), with an average age of 77.6 ± 7.90 years (65-98). The average value of Charlson Comorbidity score was 4.42±2.76. Using logistic regression analysis, we found that the gastric or peptic ulcer (OR=1.917 , P< 0.002), diabetes (OR= 0.722, P< 0.043), hemiplegia (OR= 2.292, P< 0.015), metastatic solid tumor (OR= 2.204, P< 0.025), hypertension (OR= 0.696, P< 0.044), and skin ulcer/cellulitis (OR= 2.747, P< 0.022) have significantly higher risk of 14-day readmissions. Conclusion: The results of the present study may assist the healthcare teams to understand the factors that may affect unplanned readmission in the elderly. We recommend that these teams give efficient approach in their medical practice, provide timely health education for elderly, and integrative healthcare for chronic diseases in order to reduce unplanned readmissions.

Keywords: unplanning readmission, elderly, Charlson comorbidity score, logistic regression analysis

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Authors: Abigail Gragg

Abstract:

The goal of this research study was to adjudicate if implementing Howard Gardner’s multiple levels of intelligence would enhance student achievement levels in an Algebra 1 College Preparatory class. This was conducted within every class by incorporating one level of the eight levels of intelligence into small group work in stations. Every class was conducted utilizing small-group instruction. Achievement levels were measured through various forms of collected data that expressed student understandings in class through formative assessments versus student understandings on summative assessments. The data samples included: assessments (i.e. summative and formative assessments), observable data, video recordings, a daily log book, student surveys, and checklists kept during the observation periods. Formative assessments were analyzed during each class period to measure in-class understanding. Summative assessments were dissected per question per accuracy to review the effects of each intelligence implemented. The data was collated into a coding workbook for further analysis to conclude the resulting themes of the research. These themes include 1) there was no correlation to multiple levels of intelligence enhancing student achievement, 2) bodily-kinesthetic intelligence showed to be the intelligence that had the most improvement on test questions and 3) out of all of the bits of intelligence, interpersonal intelligence enhanced student understanding in class.

Keywords: stations, small group instruction, multiple levels of intelligence, Mathematics, Algebra 1, student achievement, secondary school, instructional Pedagogies

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Authors: Ergham Al Bachir

Abstract:

This study adopts strategic leadership (Upper Echelons) as the core theory and contextual leadership theory as the research lens. This research asks how the external context impacts strategic leadership effectiveness to achieve the outcomes in pluralistic organizations (PO). The study explores how the context influences the selection of CEOs, top management teams (TMT), and their leadership effectiveness. POs are characterized by the multiple objectives of their top management teams, divergent objectives, multiple strategies, and multiple governing authorities. The research question is explored by means of a qualitative multiple-case study focusing on healthcare, real estate, and financial services organizations. The data sources are semi-structured interviews, documents, and direct observations. The data analysis strategy is inductive and deploys thematic analysis and cross-case synthesis. The findings differentiate between national and international CEOs' delegation of authority and relationship with the Board of Directors. The findings identify the elements of the dynamic context that influence TMT and PO outcomes. The emergent hybrid strategic and contextual leadership framework shows how the different contextual factors influence strategic direction, PO context, selection of CEOs and TMT, and the outcomes in four pluralistic organizations. The study offers seven theoretical contributions to Upper Echelons, strategic leadership, and contextual leadership research. (1) The integration of two theories revealed how CEO’s impact on the organization is complementary to the contextual impact. (2) Conducting this study in the Middle East contributes to strategic leadership and contextual leadership research. (3) The demonstration of the significant contextual effects on the selection of CEOs. (4 and 5) Two contributions revealed new links between the context, the Board role, internal versus external CEOs, and national versus international CEOs. (6 and 7) This study offered two definitions: what accounts for CEO leadership effectiveness and organizational outcomes. Two methodological contributions were also identified: (1) Previous strategic leadership and Upper Echelons research are mainly quantitative, while this study adopts qualitative multiple-case research with face-to-face interviews. (2) The extrication of the CEO from the TMT advanced the data analysis in strategic leadership research. Four contributions are offered to practice: (1) The CEO's leadership effectiveness inside and outside the organization. (2) Rapid turnover of predecessor CEOs signifies the need for a strategic and contextual approach to CEOs' succession. (3) TMT composition and education impact on TMT-CEO and TMT-TMT interface. (4) Multilevel strategic contextual leadership development framework.

Keywords: strategic leadership, contextual leadership, upper echelons, pluralistic organizations, cross-cultural leadership

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Authors: Osamu Komori, Shinto Eguchi, Hiroshi Okamura, Momoko Ichinokawa

Abstract:

The long time-series data on population assessments are essential for global ecosystem assessment because the temporal change of biomass in such a database reflects the status of global ecosystem properly. However, the available assessment data usually have limited sample sizes and the ratio of populations with low abundance of biomass (collapsed) to those with high abundance (non-collapsed) is highly imbalanced. To allow for the imbalance and uncertainty involved in the ecological data, we propose a binary regression model with mixed effects for inferring ecosystem status through an asymmetric logistic model. In the estimation equation, we observe that the weights for the non-collapsed populations are relatively reduced, which in turn puts more importance on the small number of observations of collapsed populations. Moreover, we extend the asymmetric logistic regression model using propensity score to allow for the sample biases observed in the labeled and unlabeled datasets. It robustified the estimation procedure and improved the model fitting.

Keywords: double robust estimation, ecological binary data, mixed effect logistic regression model, propensity score

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Authors: Kalpana Poudel-Tandukar

Abstract:

Background: Human Immunodeficiency Virus (HIV) infection has been frequently associated with vitamin D deficiency and depression. Vitamin D deficiency increases the risk of depression in people without HIV. We assessed the cross-sectional and prospective associations between serum concentrations of 25-hydroxyvitamin D (25[OH]D) and depression in a HIV-positive people. Methods: A survey was conducted among 316 HIV-positive people aged 20-60 years residing in Kathmandu, Nepal for a cross-sectional association at baseline, and among 184 participants without depressive symptoms at baseline who responded to both baseline (2010) and follow-up (2011) surveys for prospective association. The competitive protein-binding assay was used to measure 25(OH)D levels and the Beck Depression Inventory-Ia method was used to measure depression, with cut off score 20 or higher. Relationships were assessed using multiple logistic regression analysis with adjustment of potential confounders. Results: The proportion of participants with 25(OH)D level of <20ng/mL, 20-30ng/mL, and >30ng/mL were 83.2%, 15.5%, and 1.3%, respectively. Only four participants with 25(OH)D level of >30ng/mL were excluded in the further analysis. The mean 25(OH)D level in men and women were 15.0ng/mL and 14.4ng/mL, respectively. Twenty six percent of participants (men:23%; women:29%) were depressed. Participants with 25(OH)D level of < 20 ng/mL had a 1.4 fold higher odds of depression in a cross-sectional and 1.3 fold higher odds of depression after 18 months of baseline compared to those with 25(OH)D level of 20-30ng/mL (p=0.40 and p=0.78, respectively). Conclusion: Vitamin D may not have significant impact against depression among HIV-positive people with 25(OH)D level below normal ( > 30ng/mL).

Keywords: depression, HIV, Nepal, vitamin D

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Authors: M. Reza Roshandel, Tannaz Aghaei Badr, Batoul Khoundabi, Sara C. Lewis, Soroush Rais-Bahrami, John Sfakianos, Reza Mehrazin, Ash K. Tewari

Abstract:

Introduction: Prostate biopsy is the most reliable diagnostic method for choosing the appropriate management of prostate cancer. However, discrepancies between Gleason grade groups (GG) of different biopsies remain a significant concern. This study aims to assess the association of the radiological factors with GG discrepancies in patients with index Prostate Imaging Reporting and Data System (PI-RADS) 3 lesions, using radical prostatectomy (RP) specimens as the most accurate and informative pathology. Methods: This single-institutional retrospective study was performed on a total of 2289 consecutive prostate cancer patients with combined targeted and systematic prostate biopsy followed by radical prostatectomy (RP). The database was explored for patients with the index PI-RADS 3 lesions version 2 and 2.1. Cancers with PI-RADS 4 or 5 scoring were excluded from the study. Patient characteristics and radiologic features were analyzed by multivariable logistic regression. Number-density of lesions was defined as the number of lesions per prostatic volume. Results: Of the 151 prostate cancer cases with PI-RADS 3 index lesions, 27% and 17% had upgrades and downgrades at RP, respectively. Analysis of grade changes showed no significant associations between discrepancies and the number or the number density of PI-RADS 3 lesions. Moreover, the study showed no significant association of the GG changes with race, age, location of the lesions, or prostate volume. Conclusions: This study demonstrated that in PI-RADS 3 cancerous nodules, the chance of the pathology changes in the final pathology of RP specimens was low. Furthermore, having multiple PI-RADS 3 nodules did not change the conclusion, as the possibility of grade changes in patients with multiple nodules was similar to those with solitary lesions.

Keywords: prostate, adenocarcinoma, multiparametric MRI, Gleason score, robot-assisted surgery

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