Search results for: regression analysis

Authors: Bader Alkandari, Jamal Y. Madouh, Ahmad M. Alkandari, Anwar A. Alnaqi

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A new formulation of fuzzy linear estimation problem is presented. It is formulated as a linear programming problem. The objective is to minimize the spread of the data points, taking into consideration the type of the membership function of the fuzzy parameters to satisfy the constraints on each measurement point and to insure that the original membership is included in the estimated membership. Different models are developed for a fuzzy triangular membership. The proposed models are applied to different examples from the area of fuzzy linear regression and finally to different examples for estimating the electrical load on a busbar. It had been found that the proposed technique is more suited for electrical load estimation, since the nature of the load is characterized by the uncertainty and vagueness.

Keywords: fuzzy regression, load estimation, fuzzy linear parameters, electrical load estimation

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Authors: Nguyen Ngoc Que Tran

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Purpose: The purpose of this paper is to investigate the relationship between internal market orientation, external market orientation, employee commitment and job satisfaction. Design/methodology/approach: This study collected data through a survey and utilized simple linear regression and multiple regression analysis to determine if there was any support for the research hypotheses as presented in the previous chapter. Findings: Using data from 256 employees of four leading joint stock banks in Vietnam, the empirical results indicates that employee commitment is positively related with external market orientation, job satisfaction is positively related to employee commitment, and employee commitment and job satisfaction are positively related to external market orientation. However, job satisfaction has no significant positive effect on external market orientation. Theoretical contribution: The primary contribution to marketing theory arising from this study is the integration of job satisfaction, employee commitment, and external market orientation in a single research model. Practical implications: The major contribution to practice is an external market oriented bank has to respond rapidly to the future needs and preferences of its customers. This could result in high levels of commitment to the service process and in doing so provide Vietnamese joint-stock commercial banks with a competitive advantage. The finding is important for the banking service sector in general and the Vietnamese banking industry in particular.

Keywords: employee commitment, job satisfaction and external market orientation, vietnam, bank

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Authors: Petr Gurný

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One of the most important tasks in the risk management is the correct determination of probability of default (PD) of particular financial subjects. In this paper a possibility of determination of financial institution’s PD according to the credit-scoring models is discussed. The paper is divided into the two parts. The first part is devoted to the estimation of the three different models (based on the linear discriminant analysis, logit regression and probit regression) from the sample of almost three hundred US commercial banks. Afterwards these models are compared and verified on the control sample with the view to choose the best one. The second part of the paper is aimed at the application of the chosen model on the portfolio of three key Czech banks to estimate their present financial stability. However, it is not less important to be able to estimate the evolution of PD in the future. For this reason, the second task in this paper is to estimate the probability distribution of the future PD for the Czech banks. So, there are sampled randomly the values of particular indicators and estimated the PDs’ distribution, while it’s assumed that the indicators are distributed according to the multidimensional subordinated Lévy model (Variance Gamma model and Normal Inverse Gaussian model, particularly). Although the obtained results show that all banks are relatively healthy, there is still high chance that “a financial crisis” will occur, at least in terms of probability. This is indicated by estimation of the various quantiles in the estimated distributions. Finally, it should be noted that the applicability of the estimated model (with respect to the used data) is limited to the recessionary phase of the financial market.

Keywords: credit-scoring models, multidimensional subordinated Lévy model, probability of default

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Authors: Seyedeh Khadijeh Amirian, Seyed Mohammad Reza Amirian, Narges Hekmati

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The current study aims at examining the relationship between English as a foreign language (EFL) students' self-concept and their willingness to communicate (WTC) in EFL classes. To this effect, two questionnaires, namely 'Willingness to Communicate' (MacIntyre et al., 2001) and 'Self-Concept Scale' (Liu and Wang, 2005), were distributed among 174 (45 males and 129 females) Iranian EFL university students. Correlation and regression analyses were conducted to examine the relationship between the two variables. The results indicated that there was a significantly positive correlation between EFL students' self-concept and their WTC in EFL classes (p < .0.05). Moreover, regression analyses indicated that self-concept has a significantly positive influence on students’ WTC in language classes (B= .302, p < .0.05) and explains .302 percent of the variance in the dependent variable (WTC). The results are discussed with regards to the individual differences in educational contexts, and implications are offered.

Keywords: EFL students, language classes, willingness to communicate, self-concept

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Authors: Asma Alzahrani, Elizabeth Stojanovski

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This study investigated factors that influence mathematics achievement based on a sample of ninth-grade students (N  =  21,444) from the High School Longitudinal Study of 2009 (HSLS09). Key aspects studied included efficacy in mathematics, interest and enjoyment of mathematics, identity with mathematics and future utility beliefs and how these influence mathematics achievement. The predictability of mathematics achievement based on these factors was assessed using correlation coefficients and multiple linear regression. Spearman rank correlations and multiple regression analyses indicated positive and statistically significant relationships between the explanatory variables: mathematics efficacy, identity with mathematics, interest in and future utility beliefs with the response variable, achievement in mathematics.

Keywords: Mathematics achievement, math efficacy, mathematics interest, factors influence

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Authors: Shlomit Hon-Snir, Sharon Teitler-Regev, Anabel Lifszyc Friedlander

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Tourism, one of the world's largest and fastest growing industries, exerts a major economic influence. The number of international tourists is growing every year, and the relative portion of independent (FIT) tourists is growing as well. The characteristics of independent tourists differ from those of tourists who travel in organized trips. The purpose of the research is to identify the factors that affect the individual tourist's expenses in Israel: total expenses, expenses per day, expenses per tourist, expenses per day per tourist, accommodation expenses, dining expenses and transportation expenses. Most of the research analyzed the total expenses using OLS regression. The determinants influencing expenses were divided into four groups: budget constraints, socio-demographic data, psychological characteristics and travel-related characteristics. Since the effect of each variable may change over different levels of total expenses the quantile regression (QR) theory will be applied. The current research will use data collected by the Israeli Ministry of Tourism in 2015 from individual independent tourists at the end of their visit to Israel. Preliminary results show that: At lower levels of expense, only income has a (positive) effect on total expenses, while at higher levels of expense, both income and length of stay have (positive) effects. -The effect of income on total expenses is higher for higher levels of expenses than for lower level of expenses. -The number of sites visited during the trip has a (negative) effect on tourist accommodation expenses only for tourists with a high level of total expenses. Due to the increasing share of independent tourism in Israel and around the world and due to the importance of tourism to Israel, it is very important to understand the factors that influence the expenses and behavior of independent tourists. Understanding the factors that affect independent tourists' expenses in Israel can help Israeli policymakers in their promotional efforts to attract tourism to Israel.

Keywords: independent tourist, quantile regression theory, tourism expenses, tourism

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Authors: Cromwell F. Gopo, Joy L. Picar

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This study aimed to predict the employability of senior high school graduates for S.Y. 2018- 2019 in the Davao del Norte Division through quantitative research design using the descriptive status and predictive approaches among the indicated parameters, namely gender, school type, academics, academic award recipient, skills, values, and strand. The respondents of the study were the 33 secondary schools offering senior high school programs identified through simple random sampling, which resulted in 1,530 cases of graduates’ secondary data, which were analyzed using frequency, percentage, mean, standard deviation, and binary logistic regression. Results showed that the majority of the senior high school graduates who come from large schools were females. Further, less than half of these graduates received any academic award in any semester. In general, the graduates’ performance in academics, skills, and values were proficient. Moreover, less than half of the graduates were not employed. Then, those who were employed were either contractual, casual, or part-time workers dominated by GAS graduates. Further, the predictors of employability were gender and the Information and Communications Technology (ICT) strand, while the remaining variables did not add significantly to the model. The null hypothesis had been rejected as the coefficients of the predictors in the binary logistic regression equation did not take the value of 0. After utilizing the model, it was concluded that Technical-Vocational-Livelihood (TVL) graduates except ICT had greater estimates of employability.

Keywords: employability, senior high school graduates, Davao del Norte, Philippines

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Authors: Yu-Jia Jian, Emily Chia-Yu Su, Hui-Ling Hsu, Jian-Jhih Chen

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Coronary artery is the major supplier of myocardial blood flow. When fat and cholesterol are deposit in the coronary arterial wall, narrowing and stenosis of the artery occurs, which may lead to myocardial ischemia and eventually infarction. According to the World Health Organization (WHO), estimated 740 million people have died of coronary heart disease in 2015. According to Statistics from Ministry of Health and Welfare in Taiwan, heart disease (except for hypertensive diseases) ranked the second among the top 10 causes of death from 2013 to 2016, and it still shows a growing trend. According to American Heart Association (AHA), the risk factors for coronary heart disease including: age (> 65 years), sex (men to women with 2:1 ratio), obesity, diabetes, hypertension, hyperlipidemia, smoking, family history, lack of exercise and more. We have collected a dataset of 421 patients from a hospital located in northern Taiwan who received coronary computed tomography (CT) angiography. There were 300 males (71.26%) and 121 females (28.74%), with age ranging from 24 to 92 years, and a mean age of 56.3 years. Prior to coronary CT angiography, basic data of the patients, including age, gender, obesity index (BMI), diastolic blood pressure, systolic blood pressure, diabetes, hypertension, hyperlipidemia, smoking, family history of coronary heart disease and exercise habits, were collected and used as input variables. The output variable of the prediction module is the degree of coronary artery stenosis. The output variable of the prediction module is the narrow constriction of the coronary artery. In this study, the dataset was randomly divided into 80% as training set and 20% as test set. Four machine learning algorithms, including logistic regression, stepwise regression, neural network and decision tree, were incorporated to generate prediction results. We used area under curve (AUC) / accuracy (Acc.) to compare the four models, the best model is neural network, followed by stepwise logistic regression, decision tree, and logistic regression, with 0.68 / 79 %, 0.68 / 74%, 0.65 / 78%, and 0.65 / 74%, respectively. Sensitivity of neural network was 27.3%, specificity was 90.8%, stepwise Logistic regression sensitivity was 18.2%, specificity was 92.3%, decision tree sensitivity was 13.6%, specificity was 100%, logistic regression sensitivity was 27.3%, specificity 89.2%. From the result of this study, we hope to improve the accuracy by improving the module parameters or other methods in the future and we hope to solve the problem of low sensitivity by adjusting the imbalanced proportion of positive and negative data.

Keywords: decision support, computed tomography, coronary artery, machine learning

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Authors: Abdullah Al. Morshed, Mohammad Nahid Mia

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Introduction: Food security refers to the availability of food and a person’s access to it. It is a complex sustainable development issue, which is closely related to under-nutrition. Food security, in turn, can widely affect the living standard, and is rooted in poverty and leads to poor health, low productivity, low income, food shortage, and hunger. The study's aim was to identify the most vulnerable women who are in insecure positions. Method: 17,842 married women were selected for analysis from the Bangladesh Demographic and Health Survey 2011. Food security defined as dichotomous variables of skipped meals and eaten less food at least once in the last year. The outcome variables were cross-tabulated with women's socio-demographic characteristics and chi2 test was applied to see the significance. Logistic regression models were applied to identify the most vulnerable groups in terms of food security. Result: Only 18.5% of women said that they ever had to skip meals in the last year. 45.7% women from low socioeconomic status had skip meal for at least once whereas only 3.6% were from women with highest socioeconomic status. Women meal skipping was ranged from 1.4% to 34.2% by their educational status. 22% of women were eaten less food during the last year. The rate was higher among the poorest (51.6%), illiterate (39.9%) and household have no electricity connection (38.1) in compared with richest (4.4%), higher educated (2.0%), and household has electricity connection (14.0%). The logistic regression analysis indicated that household socioeconomic status, and women education show strong gradients to skip meals. Poorest have had higher odds (20.9) than richest and illiterate women had 7.7 higher odds than higher educated. In terms of religion, Christianity was 2.3 times more likely to skip their meals than Islam. On the other hand, a similar trend was observed in our other outcome variable eat less food. Conclusion: In this study we able to identify women with lower economics status and women with no education were mostly suffered group from starvation.

Keywords: food security, hunger, under-nutrition, women

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Authors: Helena Baffoe

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The US has developed numerous programs over the past 50 years to enhance the lives of those who suffer from mental health illnesses and substance abuse, as well as the effectiveness of their treatments. Despite these advances over the past 50 years, there hasn't been a corresponding improvement in American public attitudes and beliefs about mental health disorders and substance abuse. Highly publicized acts of violence frequently elicit comments that blame the perpetrator's perceived mental health disorder since such people are thought to be substance abusers. Despite these strong public beliefs and perception about mental disorder and substance abuse, concreate empirical evidence that entail this perception is lacking, and evidence of their effectiveness has not been integrated. A rich data was collected from Substance Abuse and Mental Health Services Administration (SAMHSA) with a hypothesis that people who are diagnosed with a mental health disorder are likely to be diagnosed with substance abuse using logit regression analysis and Instrumental Variable. It was found that depressive, anxiety, and trauma/stressor mental disorders constitute the most common mental disorder in the United States, and the study could not find statistically significant evidence that being diagnosed with these leading mental health disorders in the United States does necessarily imply that such a patient is diagnosed with substances abuse. Thus, the public has a misconception of mental health and substance abuse issues, and social workers' responsibilities are outlined in order to assist ameliorate this attitude and perception.

Keywords: mental health disorder, substance use, empirical evidence, logistic regression

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Authors: Marius Gavrilescu, Sabina-Adriana Floria, Florin Leon, Silvia Curteanu, Costel Anton

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Natural sciences provide a wide range of experimental data whose related problems require study and modeling beyond the capabilities of conventional methodologies. Such problems have solution spaces whose complexity and high dimensionality require correspondingly complex regression methods for proper characterization. In this context, we propose an optimization method which consists in a hybrid dual optimizer setup: a global optimizer based on a modified variant of the popular Imperialist Competitive Algorithm (ICA), and a local optimizer based on a gradient descent approach. The ICA is modified such that intermediate solution populations are more quickly and efficiently pruned of low-fitness individuals by appropriately altering the assimilation, revolution and competition phases, which, combined with an initialization strategy based on low-discrepancy sampling, allows for a more effective exploration of the corresponding solution space. Subsequently, gradient-based optimization is used locally to seek the optimal solution in the neighborhoods of the solutions found through the modified ICA. We use this combined approach to find the optimal configuration and weights of a fully-connected neural network, resulting in regression models used to characterize the process of obtained bricks using silicon-based materials. Installations in the raw ceramics industry, i.e., bricks, are characterized by significant energy consumption and large quantities of emissions. Thus, the purpose of our approach is to determine by simulation the working conditions, including the manufacturing mix recipe with the addition of different materials, to minimize the emissions represented by CO and CH4. Our approach determines regression models which perform significantly better than those found using the traditional ICA for the aforementioned problem, resulting in better convergence and a substantially lower error.

Keywords: optimization, biologically inspired algorithm, regression models, bricks, emissions

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Authors: S. P. Singh, Priya, Komal Sajwan

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With the increasing spread of organic farming in India, costs, returns, efficiency, and social and environmental sustainability of organic vis-a-vis conventional farming systems have become topics of interest among agriculture scientists, economists, and policy analysts. A study on technical efficiency estimation under these farming systems, particularly in the Ganga River Basin, where the promotion of organic farming is incentivized, can help to understand whether the inputs are utilized to their maximum possible level and what measures can be taken to improve the efficiency. This paper, therefore, analyses the technical efficiency of wheat farms operating under organic and conventional farming systems. The study is based on a primary survey of 600 farms (300 organic ad 300 conventional) conducted in 2021 in two districts located in the Middle Ganga River Basin, India. Technical, managerial, and scale efficiencies of individual farms are estimated by applying the data envelopment analysis (DEA) methodology. The per hectare value of wheat production is taken as an output variable, and values of seeds, human labour, machine cost, plant nutrients, farm yard manure (FYM), plant protection, and irrigation charges are considered input variables for estimating the farm-level efficiencies. The post-DEA analysis is conducted using the Tobit regression model to know the efficiency determining factors. The results show that technical efficiency is significantly higher in conventional than organic farming systems due to a higher gap in scale efficiency than managerial efficiency. Further, 9.8% conventional and only 1.0% organic farms are found operating at the most productive scale size (MPSS), and 99% organic and 81% conventional farms at IRS. Organic farms perform well in managerial efficiency, but their technical efficiency is lower than conventional farms, mainly due to their relatively lower scale size. The paper suggests that technical efficiency in organic wheat can be increased by upscaling the farm size by incentivizing group/collective farming in clusters.

Keywords: organic, conventional, technical efficiency, determinants, DEA, Tobit regression

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Authors: M. Qamarul Islam, Ergun Dogan, Mehmet Yazici

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There is a growing body of evidence that non-normal data is more prevalent in nature than the normal one. Examples can be quoted from, but not restricted to, the areas of Economics, Finance and Actuarial Science. The non-normality considered here is expressed in terms of fat-tailedness and asymmetry of the relevant distribution. In this study a skew t distribution that can be used to model a data that exhibit inherent non-normal behavior is considered. This distribution has tails fatter than a normal distribution and it also exhibits skewness. Although maximum likelihood estimates can be obtained by solving iteratively the likelihood equations that are non-linear in form, this can be problematic in terms of convergence and in many other respects as well. Therefore, it is preferred to use the method of modified maximum likelihood in which the likelihood estimates are derived by expressing the intractable non-linear likelihood equations in terms of standardized ordered variates and replacing the intractable terms by their linear approximations obtained from the first two terms of a Taylor series expansion about the quantiles of the distribution. These estimates, called modified maximum likelihood estimates, are obtained in closed form. Hence, they are easy to compute and to manipulate analytically. In fact the modified maximum likelihood estimates are equivalent to maximum likelihood estimates, asymptotically. Even in small samples the modified maximum likelihood estimates are found to be approximately the same as maximum likelihood estimates that are obtained iteratively. It is shown in this study that the modified maximum likelihood estimates are not only unbiased but substantially more efficient than the commonly used moment estimates or the least square estimates that are known to be biased and inefficient in such cases. Furthermore, in conventional regression analysis, it is assumed that the error terms are distributed normally and, hence, the well-known least square method is considered to be a suitable and preferred method for making the relevant statistical inferences. However, a number of empirical researches have shown that non-normal errors are more prevalent. Even transforming and/or filtering techniques may not produce normally distributed residuals. Here, a study is done for multiple linear regression models with random error having non-normal pattern. Through an extensive simulation it is shown that the modified maximum likelihood estimates of regression parameters are plausibly robust to the distributional assumptions and to various data anomalies as compared to the widely used least square estimates. Relevant tests of hypothesis are developed and are explored for desirable properties in terms of their size and power. The tests based upon modified maximum likelihood estimates are found to be substantially more powerful than the tests based upon least square estimates. Several examples are provided from the areas of Economics and Finance where such distributions are interpretable in terms of efficient market hypothesis with respect to asset pricing, portfolio selection, risk measurement and capital allocation, etc.

Keywords: least square estimates, linear regression, maximum likelihood estimates, modified maximum likelihood method, non-normality, robustness

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Authors: Nopadol Burananuth

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This research was aimed to investigate (1) the relationship among local social movements, non-governmental Organization activities and state measures deployment; and (2) the effects of local social movements, non-governmental Organization activities, and state measures deployment on conflict of local people towards the Thai-Malaysian gas pipeline project. These people included 1,000 residents of the four districts in Songkhla province. The methods of data analysis consist of multiple regression analysis. The results of the analysis showed that: (1) local social movements depended on information, and mass communication; deployment of state measures depended on compromise, coordination, and mass communication; and (2) the conflict of local people depended on mobilization, negotiation, and campaigning for participation of people in the project. Thus, it is recommended that to successfully implement any government policy, consideration must be paid to the conflict of local people, mobilization, negotiation, and campaigning for people’s participation in the project.

Keywords: conflict, NGO activities, social movements, state measures

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Authors: Rubab Zafar Kahlon, Ibtisam Butt

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Forest resources are deteriorating and experiencing decline around the globe due to unsustainable use and over exploitation. The present study was an attempt to determine the relationship between human activities, forest resource utilization, extraction methods and practices of forest resource exploitation in the district Jhelum of Pakistan. For this purpose, primary sources of data were used which were collected from 8 villages through structured questionnaire and tabulated in Microsoft Excel 365 and SPSS 22 was used for multiple linear regression analysis. The results revealed that farming, wood cutting, animal husbandry and agro-forestry were the major occupations in the study area. Most commonly used resources included timber 26%, fuelwood 25% and fodder 19%. Methods used for resource extraction included gathering 49%, plucking 34% trapping 11% and cutting 6%. Population growth, increased demand of fuelwood and land conversion were the main reasons behind forest degradation. Results for multiple linear regression revealed that Forest based activities, sources of energy production, methods used for wood harvesting and resource extraction and use of fuelwood for energy production contributed significantly towards extensive forest resource exploitation with p value <0.5 within the study area. The study suggests that effective measures should be taken by forest department to control the unsustainable use of forest resources by stringent management interventions and awareness campaigns in Jhelum district.

Keywords: forest resource, biodiversity, expliotation, human activities

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Authors: Mohammadmehdi Torfehnejada, Serhan Senso

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Aftershock collapse capacity of Special Steel Moment Frames (SSMFs) is evaluated under aftershock earthquakes by considering building heights 8 and 12 stories. The assessment evaluates the residual collapse capacity under aftershock excitation when various levels of damage have been induced by the mainshock. For this purpose, incremental dynamic analysis (IDA) under aftershock follows the mainshock imposing the intended damage level. The study results indicate that aftershock collapse capacity of this structure may decrease remarkably when the structure is subjected to large mainshock damage. The capacity reduction under aftershock is finally related to the mainshock damage level through regression equations.

Keywords: aftershock collapse capacity, special steel moment frames, mainshock-aftershock sequences, incremental dynamic analysis, mainshock damage

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Authors: Silvia Lorenzo, Hongmian Gong

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With the influx of research assessing the economic impact of the global financial crisis of 2007-8, a spatial analysis based on empirical data is needed to better understand the spatial significance of the financial crisis in New York, a key international financial center also considered the origin of the crisis. Using spatial statistics, the existence of financial clusters specializing in credit and securities throughout the New York metropolitan area are identified for 2000 and 2010, the time period before and after the height of the global financial crisis. Geographically Weighted Regressions are then used to examine processes underlying the formation and movement of financial geographies across state, county and ZIP codes of the New York metropolitan area throughout the 2000s with specific attention to tax regimes, employment, household income, technology, and transportation hubs. This analysis provides useful inputs for financial risk management and public policy initiatives aimed at addressing regional economic sustainability across state boundaries, while also developing the groundwork for further research on a spatial analysis of the global financial crisis.

Keywords: financial clusters, New York, global financial crisis, geographically weighted regression

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Authors: Sadixa Sanjel, Ahmed Sadek, Naseef Mansoor, Zelalem Denekew

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Collegiate ice hockey (NCAA) sports analytics is different from the national level hockey (NHL). We apply and compare multiple machine learning models such as Linear Regression, Random Forest, and Neural Networks to predict the win ratio for a team based on their statistics. Data exploration helps determine which statistics are most useful in increasing the win ratio, which would be beneficial to coaches and team managers. We ran experiments to select the best model and chose Random Forest as the best performing. We conclude with how to bridge the gap between the college and national levels of sports analytics and the use of machine learning to enhance team performance despite not having a lot of metrics or budget for automatic tracking.

Keywords: NCAA, NHL, sports analytics, random forest, regression, neural networks, game predictions

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Authors: Uche A. Ibekwe

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Pension funds are pooled assets which are established to provide income for retirees. The funds are usually regulated to check excessive risk taking by fund managers. In Nigeria, the current defined contribution (DC) pension scheme appears to contain some overly stringent restrictions which might be hampering its successful implementation. Notable among these restrictions is the 25 percent maximum limit on investment in ordinary shares of quoted companies. This paper examines the extent to which these restrictions affect pension replacement rates at retirement. The study made use of both simulated and historical asset return distributions using mean-variance, regression analysis and ruin-risk analyses, the study found that the current equity investment restriction policy in Nigeria reduces replacement rates at retirement.

Keywords: equity investment, replacement rates, restrictions, ruin-risk

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Authors: Naushad Mamode Khan

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The Com-Poisson (CMP) model is one of the most popular discrete generalized linear models (GLMS) that handles both equi-, over- and under-dispersed data. In longitudinal context, an integer-valued autoregressive (INAR(1)) process that incorporates covariate specification has been developed to model longitudinal CMP counts. However, the joint likelihood CMP function is difficult to specify and thus restricts the likelihood based estimating methodology. The joint generalized quasilikelihood approach (GQL-I) was instead considered but is rather computationally intensive and may not even estimate the regression effects due to a complex and frequently ill conditioned covariance structure. This paper proposes a new GQL approach for estimating the regression parameters (GQLIII) that are based on a single score vector representation. The performance of GQL-III is compared with GQL-I and separate marginal GQLs (GQL-II) through some simulation experiments and is proved to yield equally efficient estimates as GQL-I and is far more computationally stable.

Keywords: longitudinal, com-Poisson, ill-conditioned, INAR(1), GLMS, GQL

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Authors: Guo Yu, Haoming Ma, Peiru Zhou

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BACKGROUND: In addition to hyperglycemia, hypoglycemia, and glycemic variability, a decrease in the time in the targeted blood glucose range (TIR) may be associated with an increased risk of death for critically ill patients. However, the relationship between the TIR and mortality may be influenced by the presence of diabetes and glycemic variability. METHODS: A total of 998 diabetic and non-diabetic patients with severe diseases in the ICU were selected for this retrospective analysis. The TIR is defined as the percentage of time spent in the target blood glucose range of 3.9–10.0 mmol/L within 24 hours. The relationship between TIR and in-hospital in diabetic and non-diabetic patients was analyzed. The effect of glycemic variability was also analyzed. RESULTS: The binary logistic regression model showed that there was a significant association between the TIR as a continuous variable and the in-hospital death of severely ill non-diabetic patients (OR=0.991, P=0.015). As a classification variable, TIR≥70% was significantly associated with in-hospital death (OR=0.581, P=0.003). Specifically, TIR≥70% was a protective factor for the in-hospital death of severely ill non-diabetic patients. The TIR of severely ill diabetic patients was not significantly associated with in-hospital death; however, glycemic variability was significantly and independently associated with in-hospital death (OR=1.042, P=0.027). Binary logistic regression analysis of comprehensive indices showed that for non-diabetic patients, the C3 index (low TIR & high CV) was a risk factor for increased mortality (OR=1.642, P<0.001). In addition, for diabetic patients, the C3 index was an independent risk factor for death (OR=1.994, P=0.008), and the C4 index (low TIR & low CV) was independently associated with increased survival. CONCLUSIONS: The TIR of non-diabetic patients during ICU hospitalization was associated with in-hospital death even after adjusting for disease severity and glycemic variability. There was no significant association between the TIR and mortality of diabetic patients. However, for both diabetic and non-diabetic critically ill patients, the combined effect of high TIR and low CV was significantly associated with ICU mortality. Diabetic patients seem to have higher blood glucose fluctuations and can tolerate a large TIR range. Both diabetic and non-diabetic critically ill patients should maintain blood glucose levels within the target range to reduce mortality.

Keywords: severe disease, diabetes, blood glucose control, time in targeted blood glucose range, glycemic variability, mortality

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Authors: Louati Salma, Louhichi Awatef, Boujelbene Younes

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Analysis of the trade-off between competition and financial stability has been at the center of academic and policy debate for over two decades and especially since the 2007-2008 global financial crises. We use information on 10 OIC countries from 2005 to 2014 to investigate the influence of bank competition on individual bank stability and risk-taking. Alternatively, we explore whether the quality of prudential regulation may affect the nexus between competition and banking stability/risk-taking. We provide a particular attention to the Islamic banking system which principally involves with the Riba-free instruments as compared to the conventional interest-based system. We first run a dynamic panel regression (GMM), and then we apply a panel vector autoregressive (PVAR) methodology to compare both banking business models.

Keywords: Lerner index, Islamic banks, non-performing loans, prudential regulations, z-score

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Authors: Avinash Thakur, Parmjit S. Panesar, Manohar Singh

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The distribution coefficient, KD for the reactive extraction of lactic acid from aqueous solutions of lactic acid using 10-30% (v/v) tri-n-octyl amine (extractant) dissolved in n-hexane (inert diluent) and 20% (v/v) oleyl alcohol (modifier) was optimized by using response surface methodology (RSM). A three level Box-Behnken design was employed for experimental design, analysis of the results and to depict the combined interactive effect of seven independent variables, viz lactic acid concentration (cl), pH, TOA concentration in organic phase (ψ), treat ratio (φ), temperature (T), agitation speed (ω) and batch agitation time (τ) on distribution coefficient of lactic acid. The regression analysis recommended that the quadratic model is significant (R2 and adjusted R2 are 98.72 % and 98.69 % respectively) for analysis. A numerical optimization had resulted in maximum lactic acid distribution coefficient (KD) of 3.16 at the optimized values for test variables, cl, pH, ψ, φ, T, ω and τ as 0.15 [M], 3.0, 22.75% (v/v), 1.0 (v/v), 26°C, 145 rpm and 23 min respectively. A good agreement between the predicted and experimentally obtained values for distribution coefficient using the optimized conditions was exhibited.

Keywords: Distribution coefficient, tri-n-octylamine, lactic acid, response surface methodology

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Authors: Babatunde Akeem Lawal

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In any organization, the primary aim is to maximize profit, but the major challenges facing them is the increase in cost of operation because of this there is increase in cost of production that could lead to inevitable cost control and cost reduction scheme which make it difficult for most organizations to operate at the cost efficient frontier. The study aims to critically examine and evaluate the application of cost control and cost reduction in organization performance and also to review budget as an effective tool of cost control and cost reduction. A descriptive survey research was adopted. A total number of 40 respondent retrieved were used for the study. The analysis of data collected was undertaken by applying appropriate statistical tools. Regression analysis was used to test the hypothesis with the use of SPSS. Based on the findings; it was evident that cost control has a positive impact on organizational performance and also the style of management has a positive impact on organizational performance.

Keywords: organization, cost reduction, cost control, performance, budget, profit

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Authors: Seyed Sadegh Naseralavi, Najmeh Bemani

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In this paper, different concrete codes including America, New Zealand, Mexico, Italy, India, Canada, Hong Kong, Euro Code and Britain are compared with the Iranian concrete design code. First, by using Adaptive Neuro Fuzzy Inference System (ANFIS), the codes having the most correlation with the Iranian ninth issue of the national regulation are determined. Consequently, two anticipated methods are used for comparing the codes: Artificial Neural Network (ANN) and Multi-variable regression. The results show that ANN performs better. Predicting is done by using only tensile steel ratio and with ignoring the compression steel ratio.

Keywords: adaptive neuro fuzzy inference system, anticipate method, artificial neural network, concrete design code, multi-variable regression

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Authors: Neha Ahirwar

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In the contemporary digital era, the rise of credit card fraud poses a significant threat to both financial institutions and consumers. As fraudulent activities become more sophisticated, there is an escalating demand for robust and effective fraud detection mechanisms. Advanced machine learning algorithms have become crucial tools in addressing this challenge. This paper conducts a thorough examination of the design and evaluation of a credit card fraud detection system, utilizing four prominent machine learning algorithms: random forest, logistic regression, decision tree, and XGBoost. The surge in digital transactions has opened avenues for fraudsters to exploit vulnerabilities within payment systems. Consequently, there is an urgent need for proactive and adaptable fraud detection systems. This study addresses this imperative by exploring the efficacy of machine learning algorithms in identifying fraudulent credit card transactions. The selection of random forest, logistic regression, decision tree, and XGBoost for scrutiny in this study is based on their documented effectiveness in diverse domains, particularly in credit card fraud detection. These algorithms are renowned for their capability to model intricate patterns and provide accurate predictions. Each algorithm is implemented and evaluated for its performance in a controlled environment, utilizing a diverse dataset comprising both genuine and fraudulent credit card transactions.

Keywords: efficient credit card fraud detection, random forest, logistic regression, XGBoost, decision tree

Procedia PDF Downloads 35

Authors: Burcu Kaya, Jan-Martin Kaiser, Karl-Friedrich Becker, Tanja Braun, Klaus-Dieter Lang

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Quality of the molded packages is strongly influenced by the process parameters of the transfer molding. To achieve a better package quality and a stable transfer molding process, it is necessary to understand the influence of the process parameters on the package quality. This work aims to comprehend the relationship between the process parameters, and to identify the optimum process parameters for the transfer molding process in order to achieve less voids and wire sweep. To achieve this, a DoE is executed for process optimization and a regression analysis is carried out. A systematic approach is represented to generate models which enable an estimation of the number of voids and wire sweep. Validation experiments are conducted to verify the model and the results are presented.

Keywords: dielectric analysis, electronic packages, epoxy molding compounds, transfer molding process

Procedia PDF Downloads 366

Authors: Hirbaye Mokona, Abebaw Gebeyehu, Aemro Zerihun

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Introduction: Attention deficit hyperactivity disorder is serious public health problem affecting millions of children throughout the world. Method: A cross-sectional study conducted from May to June 2015 among children age 6 to 17 years living in rural area of Girja district. Multi-stage cluster sampling technique was used to select 1302 study participants. Disruptive Behavior Disorder rating scale was used to collect the data. Data were coded, entered and cleaned by Epi-Data version 3.1 and analyzed by SPSS version 20. Logistic regression analysis was used and Variables that have P-values less than 0.05 on multivariable logistic regression was considered as statistically significant. Results: Prevalence of Attention deficit hyperactivity disorder (ADHD) among children age 6 to 17 years was 7.3%. Being male [AOR=1.81, 95%CI: (1.13, 2.91)]; living with single parent [AOR=5.0, 95%CI: (2.35, 10.65)]; child birth order/rank [AOR=2.35, 95%CI: (1.30, 4.25)]; low family socio-economic status [AOR= 2.43, 95%CI: (1.29, 4.59)]; maternal alcohol/khat use during pregnancy [AOR=3.14, 95%CI: (1.37, 7.37)] and complication at delivery [AOR=3.56, 95%CI: (1.19, 10.64)] were more likely to develop Attention deficit hyperactivity disorder. Conclusion: In this study, the prevalence of Attention deficit hyperactivity disorder was similar with worldwide prevalence. Prevention and early management of its modifiable risk factors should be carryout alongside increasing community awareness.

Keywords: attention deficit hyperactivity disorder, ADHD, associated factors, children, prevalence

Procedia PDF Downloads 160

Authors: Benchamat Laksaniyanon, Padcharee Phasuk, Rungtawan Boonphanakan

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With globalization, organizations had to adjust to an unstable environment in order to survive in a competitive arena. Typically within the field of management, different types of organizations include mechanistic, bureaucratic and organic ones. In fact, bureaucratic and mechanistic organizations have some characteristics in common. Bureaucracy is one type of Thailand organization which adapted from mechanistic concept to develop an organization that is suitable for the characteristic and culture of Thailand. The objective of this study is to compare the adjustment strategies of both organizations in order to find key performance indicators (KPI) suitable for improving organization in Thailand. The methodology employed is binary logistic regression. The results of this study will be valuable for developing future management strategies for both bureaucratic and mechanistic organizations.

Keywords: mechanistic, bureaucratic and organic organization, binary logistic regression, key performance indicators (KPI)

Procedia PDF Downloads 335

Authors: Jelena S. Kiurski, Vesna S. Kecić, Snežana M. Aksentijević

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The identification and evaluation of organic and inorganic pollutants were performed in a flexographic facility in Novi Sad, Serbia. Air samples were collected and analyzed in situ, during 4-hours working time at five sampling points by the mobile gas chromatograph and ozonometer at the printing of collagen casing. Experimental results showed that the concentrations of isopropyl alcohol, acetone, total volatile organic compounds and ozone varied during the sampling times. The highest average concentrations of 94.80 ppm and 102.57 ppm were achieved at 200 minutes from starting the production for isopropyl alcohol and total volatile organic compounds, respectively. The mutual dependences between target hazardous and microclimate parameters were confirmed using a multiple linear regression model with software package STATISTICA 10. Obtained multiple coefficients of determination in the case of ozone and acetone (0.507 and 0.589) with microclimate parameters indicated a moderate correlation between the observed variables. However, a strong positive correlation was obtained for isopropyl alcohol and total volatile organic compounds (0.760 and 0.852) with microclimate parameters. Higher values of parameter F than F_critical for all examined dependences indicated the existence of statistically significant difference between the concentration levels of target pollutants and microclimates parameters. Given that, the microclimate parameters significantly affect the emission of investigated gases and the application of eco-friendly materials in production process present a necessity.

Keywords: flexographic printing, indoor air, multiple regression analysis, pollution emission

Procedia PDF Downloads 177