Search results for: logistic regression factor analysis

Authors: James Purnama, Riri Fitri Sari

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We investigate the machine learning algorithm selection problem in the term of a depth image based eye gaze estimation, with respect to its essential difficulty in reducing the number of required training samples and duration time of training. Statistics based prediction accuracy are increasingly used to assess and evaluate prediction or estimation in gaze estimation. This article evaluates Root Mean Squared Error (RMSE) and R-Squared statistical analysis to assess machine learning methods on depth camera data for gaze estimation. There are 4 machines learning methods have been evaluated: Random Forest Regression, Regression Tree, Support Vector Machine (SVM), and Linear Regression. The experiment results show that the Random Forest Regression has the lowest RMSE and the highest R-Squared, which means that it is the best among other methods.

Keywords: gaze estimation, gaze tracking, eye tracking, kinect, regression model, orange python

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Authors: Alanoud Moraya Aldalan, Abdulaziz Almaleh

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A crucial component of maintaining a customer-oriented business as in the telecom industry is understanding the reasons and factors that lead to customer churn. Competition between telecom companies has greatly increased in recent years. It has become more important to understand customers’ needs in this strong market of telecom industries, especially for those who are looking to turn over their service providers. So, predictive churn is now a mandatory requirement for retaining those customers. Machine learning can be utilized to accomplish this. Churn Prediction has become a very important topic in terms of machine learning classification in the telecommunications industry. Understanding the factors of customer churn and how they behave is very important to building an effective churn prediction model. This paper aims to predict churn and identify factors of customers’ churn based on their past service usage history. Aiming at this objective, the study makes use of feature selection, normalization, and feature engineering. Then, this study compared the performance of four different machine learning algorithms on the Orange dataset: Logistic Regression, Random Forest, Decision Tree, and Gradient Boosting. Evaluation of the performance was conducted by using the F1 score and ROC-AUC. Comparing the results of this study with existing models has proven to produce better results. The results showed the Gradients Boosting with feature selection technique outperformed in this study by achieving a 99% F1-score and 99% AUC, and all other experiments achieved good results as well.

Keywords: machine learning, gradient boosting, logistic regression, churn, random forest, decision tree, ROC, AUC, F1-score

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Authors: Muhamad Hafiz Ismail, Ahmad H., Nelfianty M. R.

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The longitudinal study is conducted to identify predictive factors of soccer talent among male players aged 14 to 17 years. Convenience sampling involving elite respondents (n=20) and sub-elite respondents (n=20) male soccer players. Descriptive statistics were reported as frequencies and percentages. The inferential statistical analysis is used to report the status of reliability, independent samples t-test, paired samples t-test, and multiple regression analysis. Generally, there are differences in mean of height, muscular strength, muscular endurance, cardiovascular endurance, task orientation, cognitive anxiety, self-confidence, juggling skills, short pass skills, long pass skills, dribbling skills, and shooting skills for 20 elite players and sub-elite players. Accordingly, there was a significant difference between pre and post-test for thirteen variables of height, weight, fat percentage, muscle strength, muscle endurance, cardiovascular endurance, flexibility, BMI, task orientation, juggling skills, short pass skills, a long pass skills, and dribbling skills. Based on the first predictive factors (physical), second predictive factors (fitness), third predictive factors (psychological), and fourth predictive factors (skills in playing football) pledged to the soccer talent; four multiple regression models were produced. The first predictive factor (physical) contributed 53.5 percent, supported by height and percentage of fat in soccer talents. The second predictive factor (fitness) contributed 63.2 percent and the third predictive factors (psychology) contributed 66.4 percent of soccer talent. The fourth predictive factors (skills) contributed 59.0 percent of soccer talent. The four multiple regression models could be used as a guide for talent scouting for soccer players of the future.

Keywords: soccer talent identification, fitness and physical test, soccer skills test, psychological test

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Authors: Hongmeng Su, Luyu Zhao, Yanyan Qian, Hong Fan

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Aim: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors that endanger human health seriously. RNA methylation, especially N6-methyladenosine (m⁶A) and 5-methylcytosine (m⁵C), a crucial epigenetic transcriptional regulatory mechanism, plays an important role in tumorigenesis, progression and prognosis. This research aims to systematically evaluate the prognostic value of m⁶A and m⁵C modulators in HCC patients. Methods: Twenty-four modulators of m⁶A and m⁵C were candidates to analyze their expression level and their contribution to predict the prognosis of HCC. Consensus clustering analysis was applied to classify HCC patients. Cox and LASSO regression were used to construct the risk model. According to the risk score, HCC patients were divided into high-risk and low/medium-risk groups. The clinical pathology factors of HCC patients were analyzed by univariate and multivariate Cox regression analysis. Results: The HCC patients were classified into 2 clusters with significant differences in overall survival and clinical characteristics. Nine-gene risk model was constructed including METTL3, VIRMA, YTHDF1, YTHDF2, NOP2, NSUN4, NSUN5, DNMT3A and ALYREF. It was indicated that the risk score could serve as an independent prognostic factor for patients with HCC. Conclusion: This study constructed a Nine-gene risk model by modulators of m⁶A and m⁵C and investigated its effect on the clinical prognosis of HCC. This model may provide important consideration for the therapeutic strategy and prognosis evaluation analysis of patients with HCC.

Keywords: hepatocellular carcinoma, m⁶A, m⁵C, prognosis, RNA methylation

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Authors: Hussain A. Alkharusi, Said S. Aldhafri, Hilal Z. Alnabhani, Muna Alkalbani

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Analysing classroom assessments is one of the responsibilities of the teacher. It aims improving teacher’s instruction and assessment as well as student learning. The present study investigated factors that might explain variation in teachers’ practices regarding analysis of classroom assessments. The factors considered in the investigation included gender, in-service assessment training, teaching load, teaching experience, knowledge in assessment, attitude towards quantitative aspects of assessment, and self-perceived competence in analysing assessments. Participants were 246 in-service teachers in Oman. Results of a stepwise multiple linear regression analysis revealed that self-perceived competence was the only significant factor explaining the variance in teachers’ analysis of assessments. Implications for research and practice are discussed.

Keywords: analysis of assessment, classroom assessment, in-service teachers, self-competence

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Authors: Nora Gavira Duron, Cinthya Moreda Gonzalez-Ortega, Reyna Susana Garcia Ruiz

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This paper objective is to analyze the mathematics performance in the Learning Evaluation National Plan (PLANEA for its Spanish initials: Plan Nacional para la Evaluación de los Aprendizajes), applied to Mexican students who are enrolled in the last elementary-school year over the 2017-2018 academic year. Such test was conducted nationwide in 3,573 schools, using a sample of 108,083 students, whose average in mathematics, on a scale of 0 to 100, was 45.6 points. 75% of the sample analyzed did not reach the sufficiency level (60 points). It should be noted that only 2% got a 90 or higher score result. The performance is analyzed while considering whether there are differences in gender, marginalization level, public or private school enrollment, parents’ academic background, and living-with-parents situation. Likewise, this variable impact (among other variables) on school performance by gender is evaluated, considering multivariate logistic (Logit) regression analysis. The results show there are no significant differences in mathematics performance regarding gender in elementary school; nevertheless, the impact exerted by mothers who studied at least high school is of great relevance for students, particularly for girls. Other determining variables are students’ resilience, their parents’ economic status, and the fact they attend private schools, strengthened by the mother's education.

Keywords: multivariate regression analysis, academic performance, learning evaluation, mathematics result per gender

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Authors: Kiurski S. Jelena, Aksentijević M. Snežana, Oros B. Ivana, Kecić S. Vesna, Djogo Z. Maja

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The paper investigates the effect of light intensity on the formation of two ketones, acetone and methyl ethyl ketone, in working premises of five pad printing departments in Novi Sad, Serbia. Multiple linear regression analysis examined the form of interdependency concentrations of methyl ethyl ketone, acetone and light intensity in five printing presses at seven sampling points, using Statistica software package version 10th. The results show an average stacking variation investigated variable and can be presented by the general regression model: y = b0 + b1xi1 + b2xi2.

Keywords: acetone, methyl ethyl ketone, multiple linear regression analysis, pad printing

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Authors: Sadia Arshad, Yifa Tang, Dumitru Baleanu

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A fractional order mathematical model is proposed that incorporate CD8+ cells, natural killer cells, cytokines and tumor cells. The tumor cells growth in the absence of an immune response is modeled by logistic law as it was the simplest form for which predictions also agreed with the experimental data. Natural Killer Cells are our first line of defense. NK cells directly kill tumor cells through several mechanisms, including the release of cytoplasmic granules containing perforin and granzyme, expression of tumor necrosis factor (TNF) family members. The effect of the NK cells on the tumor cell population is expressed with the product term. Rational form is used to describe interaction between CD8+ cells and tumor cells. A number of cytokines are produced by NKs, including tumor necrosis factor TNF, IFN, and interleukin (IL-10). Source term for cytokines is modeled by Michaelis-Menten form to indicate the saturated effects of the immune response. Stability of the equilibrium points is discussed for biologically significant values of bifurcation parameters. We studied the treatment of fractional order system by investigating analytical conditions of tumor eradication. Numerical simulations are presented to illustrate the analytical results.

Keywords: cancer model, fractional calculus, numerical simulations, stability analysis

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Authors: Ryan Dale B. Elnar

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This study developed a multidimensional need assessment scale for college students called The University of Mindanao Needs Assessment Scale (UMNAS). Although there are context-specific instruments measuring the needs of clinical and non-clinical samples, literature reveals no standardized scales to measure the needs of the college students thus a four-phase item development process was initiated to support its content validity. Comprising seven broad facets namely spiritual-moral, intrapersonal, socio-personal, psycho-emotional, cognitive, physical and sexual, a pyramid model of college needs was deconstructed through FGD sample to support the literature review. Using various construct validity procedures, the model was further tested using a total of 881 Filipino college samples. The result of the study revealed evidences of the reliability and validity of the UMNAS. The reliability indices range from .929-.933. Exploratory and confirmatory factor analyses revealed a one-factor-six-dimensional instrument to measure the needs of the college students. Using multivariate regression analysis, year level and course are found predictors of students’ needs. Content analysis attested the usefulness of the instrument to diagnose students’ personal and academic issues and concerns in conjunction with other measures. The norming process includes 1728 students from the different colleges of the University of Mindanao. Further validation is recommended to establish a national norm for the instrument.

Keywords: needs assessment scale, validity, factor analysis, college students

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Authors: Avan Al-Saffar, Eun-Jin Kim

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Both the logistic growth model and the gompertz growth model are used to describe growth processes. Both models driven by perturbations in different cases are investigated using information theory as a useful measure of sustainability and the variability. Specifically, we study the effect of different oscillatory modulations in the system's parameters on the evolution of the system and Probability Density Function (PDF). We show the maintenance of the initial conditions for a long time. We offer Fisher information analysis in positive and/or negative feedback and explain its implications for the sustainability of population dynamics. We also display a finite amplitude solution due to the purely fluctuating growth rate whereas the periodic fluctuations in negative feedback can lead to break down the system's self-regulation with an exponentially growing solution. In the cases tested, the gompertz and logistic systems show similar behaviour in terms of information and sustainability although they develop differently in time.

Keywords: dynamical systems, fisher information, probability density function (pdf), sustainability

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Authors: Suvimon Wajeetongratana, Prateep Wajeetongratana

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The purpose of this research will study the organizational behavior including self-efficacy, hope, optimism, and resiliency that affect to the work motivation in the Dusit workplace and the sample consisted of the production workers in a private company in Dusit area for four hundred workers with approximately 10,000 employees and in this study will provide the multiple regression analysis was used to analyze the questionnaire survey data. The results of the analysis indicate the latent core confidence factor derived from the four components of self-efficacy, hope, optimism, and resiliency provided a significant positive impact on performance. The impact of the integrated latent core confidence factor was, in fact, more effective than derived from any one individual component, as well as any core trait-like self-evaluations such as self-esteem, general efficacy, internal locus of control, and emotional stability.

Keywords: firm performance effectiveness, organizational behavior, work motivation, Dusit workplace

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Authors: Ferry Kurniawan

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In this paper, we nowcast quarterly output growth in Indonesia by exploiting higher frequency data (monthly indicators) using a mixed-frequency factor model and exploiting both quarterly and monthly data. Nowcasting quarterly GDP in Indonesia is particularly relevant for the central bank of Indonesia which set the policy rate in the monthly Board of Governors Meeting; whereby one of the important step is the assessment of the current state of the economy. Thus, having an accurate and up-to-date quarterly GDP nowcast every time new monthly information becomes available would clearly be of interest for central bank of Indonesia, for example, as the initial assessment of the current state of the economy -including nowcast- will be used as input for longer term forecast. We consider a small scale mixed-frequency factor model to produce nowcasts. In particular, we specify variables as year-on-year growth rates thus the relation between quarterly and monthly data is expressed in year-on-year growth rates. To assess the performance of the model, we compare the nowcasts with two other approaches: autoregressive model –which is often difficult when forecasting output growth- and Mixed Data Sampling (MIDAS) regression. In particular, both mixed frequency factor model and MIDAS nowcasts are produced by exploiting the same set of monthly indicators. Hence, we compare the nowcasts performance of the two approaches directly. To preview the results, we find that by exploiting monthly indicators using mixed-frequency factor model and MIDAS regression we improve the nowcast accuracy over a benchmark simple autoregressive model that uses only quarterly frequency data. However, it is not clear whether the MIDAS or mixed-frequency factor model is better. Neither set of nowcasts encompasses the other; suggesting that both nowcasts are valuable in nowcasting GDP but neither is sufficient. By combining the two individual nowcasts, we find that the nowcast combination not only increases the accuracy - relative to individual nowcasts- but also lowers the risk of the worst performance of the individual nowcasts.

Keywords: nowcasting, mixed-frequency data, factor model, nowcasts combination

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Authors: Muna Al-Kalbani, Thuwayba Al Barwani, Otherine Neisler, Hussain Alkharusi, David Clayton, Humaira Al-Sulaimani, Mohammad Khan, Hamad Al-Yahmadi

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This study describes the psychometric properties and factor structure of the University Readiness Survey (URS). Survey data were collected from sample of 2652 students from Sultan Qaboos University. Exploratory factor analysis identified ten significant factors underlining the structure. The results of Confirmatory factor analysis showed a good fit to the data where the indices for the revised model were χ2(df = 1669) = 6093.4; CFI = 0.900; GFI =0.926; PCLOSE = 1.00 and RMSAE = 0.030 where each of these indices were above threshold. The overall value of Cronbach’s alpha was 0.899 indicating that the instrument score was reliable. Results imply that the URS is a valid measure describing the college readiness pattern among Sultan Qaboos University students and the Arabic version could be used by university counselors to identify students’ readiness factors. Nevertheless, further validation of the of the USR is recommended.

Keywords: college readiness, confirmatory factor analysis, reliability, validity

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Authors: Rezvan Hosseinian

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Aim: Systemic sclerosis (SSc) is a chronic connective tissue disease with the clinical hallmark of skin thickening and tethering. The correlation of musculoskeletal features with other parameters should be considered in SSc patients. Methods: We reviewed the records of all patients who had more than one visit and standard anteroposterior radiography of hand. We used univariate analysis, and factors with p<0.05 were included in logistic regression to find out dependent factors. Results: Overall, 180 SSc patients were enrolled in our study, 161 (89.4%) of whom were women. The median age (IQR) was 47.0 years (16), and 52% had a diffuse subtype of the disease. In multivariate analysis, tendon friction rubs (TFRs) were associated with the presence of calcinosis, muscle tenderness, and flexion contracture (FC) on physical examination (p<0.05). Arthritis showed no differences in the two subtypes of the disease (p=0.98), and in multivariate analysis, there were no correlations between radiographic arthritis and serological and clinical features. The radiographic results indicated that disease duration correlated with joint erosion, acro-osteolysis, resorption of the distal ulna, calcinosis and radiologic FC (p< 0.05). Acro-osteolysis was more frequent in the dcSSc subtype, TFRs, and anti-TOPO I antibody. Radiologic FC showed an association with skin score, calcinosis and haematocrit <30% (p<0.05). Joint flexion on radiography was associated with disease duration, modified Rodnan skin score, calcinosis, and low hematocrit (P<0.01). Conclusion: Disease duration was a main dependent factor for developing joint erosion, acro-osteolysis, bone resorption, calcinosis, and flexion contracture on hand radiography. Acro-osteolysis presented in the severe form of the disease. Acro-osteolysis was the only dependent variable associated with bone demineralization.

Keywords: disease subsets, hand radiography, joint erosion, sclerosis

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Authors: Nasrin Azarbani

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Aim: Systemic sclerosis (SSc) is a chronic connective tissue disease with the clinical hallmark of skin thickening and tethering. Correlation of musculoskeletal features with other parameters should be considered in SSc patients. Methods: We reviewed the records of all patients who had more than one visit and standard anteroposterior radiography of hand. We used univariate analysis, and factors with p<0.05 were included in logistic regression to find out dependent factors. Results: Overall, 180 SSc patients were enrolled in our study, 161 (89.4%) of whom were women. Median age (IQR) was 47.0 years (16), and 52% had diffuse subtype of the disease. In multivariate analysis, tendon friction rubs (TFRs) was associated with the presence of calcinosis, muscle tenderness, and flexion contracture (FC) on physical examination (p<0.05). Arthritis showed no differences in the two subtypes of the disease (p=0.98), and in multivariate analysis, there were no correlations between radiographic arthritis and serological and clinical features. The radiographic results indicated that disease duration correlated with joint erosion, acro-osteolysis, resorption of distal ulna, calcinosis and radiologic FC (p< 0.05). Acro-osteolysis was more frequent in the dcSSc subtype, TFRs, and anti-TOPO I antibody. Radiologic FC showed an association with skin score, calcinosis and haematocrit <30% (p<0.05). Joint flexion on radiography was associated with disease duration, modified Rodnan skin score, calcinosis, and low haematocrit (P<0.01). Conclusion: Disease duration was a main dependent factor for developing joint erosion, acro-osteolysis, bone resorption, calcinosis, and flexion contracture on hand radiography. Acro-osteolysis presented in the severe form of the disease. Acro-osteolysis was the only dependent variable associated with bone demineralization.

Keywords: sclerosis, disease subsets, joint erosion, musculoskeletal

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Authors: Lars Laußat, Manfred Helmus, Kamil Szczesny, Markus König

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As one result of the project “Reactive Construction Project Scheduling using Real Time Construction Logistic Data and Simulation”, a procedure for using data about uncertain resource availability assumptions in reactive scheduling processes has been developed. Prediction data about resource availability is generated in a formalized way using real-time monitoring data e.g. from auto-ID systems on the construction site and in the supply chains. The paper focuses on the formalization of the procedure for monitoring construction logistic processes, for the detection of disturbance and for generating of new and uncertain scheduling assumptions for the reactive resource constrained simulation procedure that is and will be further described in other papers.

Keywords: auto-ID, construction logistic, fuzzy, monitoring, RFID, scheduling

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Authors: Ebenezer Kwesi Armah-Ansah, Kenneth Fosu Oteng, Esther Selasi Avinu, Eugene Budu, Edward Kwabena Ameyaw

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Background: Maternal mortality, particularly in Benin, is a major public health concern in Sub-Saharan Africa. To provide a positive pregnancy experience and reduce maternal morbidities, all pregnant women must get appropriate and timely prenatal support. However, many pregnant women in developing countries, including Benin, begin antenatal care late. There is a paucity of empirical literature on the prevalence and predictors of early antenatal care visits in Benin. As a result, the purpose of this study is to investigate the prevalence and predictors of early antenatal care visits among women of productive age in Benin. Methods: This is a secondary analysis of the 2017/2018 Benin Demographic and Health Survey (BDHS) data. The study involved 6,919 eligible women. Data analysis was conducted using Stata version 14.2 for Mac OS. We adopted a multilevel logistic regression to examine the predictors of early ANC visits in Benin. The results were presented as odds ratios (ORs) associated with 95% confidence intervals (CIs) and p-value <0.05 to determine the significant associations. Results: The prevalence of early ANC visits among pregnant women in Benin was 57.03% [95% CI: 55.41-58.64]. In the final multilevel logistic regression, early ANC visit was higher among women aged 30-34 [aOR=1.60, 95% CI=1.17-2.18] compared to those aged 15-19, women with primary education [aOR=1.22, 95% CI=1.06-142] compared to the non-educated women, women who were covered by health insurance [aOR=3.03, 95% CI=1.35-6.76], women without a big problem in getting the money needed for treatment [aOR=1.31, 95% CI=1.16-1.49], distance to the health facility, not a big problem [aOR=1.23, 95% CI=1.08-1.41], and women whose partners had secondary/higher education [aOR=1.35, 95% CI=1.15-1.57] compared with those who were not covered by health insurance, had big problem in getting money needed for treatment, distance to health facility is a big problem and whose partners had no education respectively. However, women who had four or more births [aOR=0.60, 95% CI=0.48-0.74] and those in Atacora Region [aOR=0.50, 95% CI=0.37-0.68] had lower odds of early ANC visit. Conclusion: This study revealed a relatively high prevalence of early ANC visits among women of reproductive age in Benin. Women's age, educational status of women and their partners, parity, health insurance coverage, distance to health facilities, and region were all associated with early ANC visits among women of reproductive in Benin. These factors ought to be taken into account when developing ANC policies and strategies in order to boost early ANC visits among women in Benin. This will significantly reduce maternal and newborn mortality and help achieve the World Health Organization’s recommendation that all pregnant women should initiate early ANC visits within the first three months of pregnancy.

Keywords: antenatal care, Benin, maternal health, pregnancy, DHS, public health

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Authors: L. S. Chathurika

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Early prediction of student performance is an important factor to be gained academic excellence. Whatever the study stream in secondary education, students lay the foundation for higher studies during the first year of their degree or diploma program in Sri Lanka. The information technology (IT) field has certain improvements in the education domain by selecting specialization areas to show the talents and skills of students. These specializations can be software engineering, network administration, database administration, multimedia design, etc. After completing the first-year, students attempt to select the best path by considering numerous factors. The purpose of this experiment is to predict the best study path using machine learning algorithms. Five classification algorithms: decision tree, support vector machine, artificial neural network, Naïve Bayes, and logistic regression are selected and tested. The support vector machine obtained the highest accuracy, 82.4%. Then affecting features are recognized to select the best study path.

Keywords: algorithm, classification, evaluation, features, testing, training

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Authors: Abdullah Pandang, Mantasiah Rivai, Nur Fadhilah Umar, Azam Arifyadi

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This study aims to measure the validity of the politeness behaviour scale and obtain a model that fits the scale. The researcher developed the Politeness Behavior on Communicating (PBC) scale. The research method uses descriptive quantitative by developing the PBC scale. The population in this study were students in three provinces, namely South Sulawesi, West Sulawesi, and Central Sulawesi, recorded in the 2022/2023 academic year. The sampling technique used stratified random sampling by determining the number of samples using the Slovin formula. The sample of this research is 1200 students. This research instrument uses the PBC scale, which consists of 5 (five) indicators: self-regulation of compensation behaviour, self-efficacy of compensation behaviour, fulfilment of social expectations, positive feedback, and no strings attached. The PBC scale consists of 34 statement items. The data analysis technique is divided into two types: the validity test on the correlated item values and the item reliability test referring to Cronbach's and McDonald's alpha standards using the JASP application. Furthermore, the data were analyzed using confirmatory factor analysis (CFA) and exploratory factor analysis (EFA). The results showed that the adaptation of the Politeness Behavior on Communicating (PBC) scale was on the Fit Index with a chi-square value (711,800/375), RMSEA (0.53), GFI (0.990), CFI (0.987), GFI (0.985).

Keywords: polite behavior in communicating, positive communication, exploration factor analysis, confirmatory factor analysis

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Authors: Watcharapong Yaowarat, Thanee Kaewthummanukul, Waruntorn Jongrungrotsakul

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Noise-induced hearing loss, the most significant occupational and safety problem among the working population, can be effectively prevented through hearing protection devices (HPDs) use. This study aimed to examine whether the following factors, perceived benefits, perceived barriers, perceived self-efficacy, and interpersonal and situational influences about using hearing protection could predict HPD use among 132 qualified workers in production lines at Kaolin Mineral Dressing factories, Uttaradit and Lampang provinces. Data collection was undertaken from August to September 2020 according to the interview form developed by Yaruang et al. (2010), which was assured by a panel of experts and its reliability value was at an acceptable level. Data analysis was performed using logistic regression analysis. The results revealed that only the situational factor of using hearing protection could predict HPD use, which accounted for 21.80 percent of the total variance for HPD use. It was also found that the study sample who had a score for the situational factors on using hearing protection greater than or equal to the median was 4.16 times more likely to use HPDs than those who had lower median scores. (OR = 4.16, p < .05). The results, thus, indicate that organization policies addressing worker health along with enhancing a supportive environment for HPD use, in particular, the provision of various HPDs, are of great importance. Therefore, occupational health nurses and related health teams should enhance workers’ use of HPDs effectively through knowledge dissemination by adopting strategies appropriate to the workplace context leading to an achievement of worker health policy focusing on work safety.

Keywords: predicting factors, hearing protection device, factors predicting hearing protection device use, kaolin mineral dressing factories

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Authors: Kisan Sarda, Bhavika Shingote

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This paper aims to evaluate regression modelling for prediction of Energy storage of solar photovoltaic (PV) system using Semi parametric regression techniques because there are some parameters which are known while there are some unknown parameters like humidity, dust etc. Here irradiation of solar energy is different for different places on the basis of Latitudes, so by finding out areas which give more storage we can implement PV systems at those places and our need of energy will be fulfilled. This regression modelling is done for daily, monthly and seasonal prediction of solar energy storage. In this, we have used R modules for designing the algorithm. This algorithm will give the best comparative results than other regression models for the solar PV cell energy storage.

Keywords: semi parametric regression, photovoltaic (PV) system, regression modelling, irradiation

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Authors: Sheena K. J., Jyoti Badge, Sayed Mohammed Zeeshan

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A Comparative Study of Exponential and Logistic Population Growth Models in India India is the second most populous city in the world, just behind China, and is going to be in the first place by next year. The Indian population has remarkably at higher rate than the other countries from the past 20 years. There were many scientists and demographers who has formulated various models of population growth in order to study and predict the future population. Some of the models are Fibonacci population growth model, Exponential growth model, Logistic growth model, Lotka-Volterra model, etc. These models have been effective in the past to an extent in predicting the population. However, it is essential to have a detailed comparative study between the population models to come out with a more accurate one. Having said that, this research study helps to analyze and compare the two population models under consideration - exponential and logistic growth models, thereby identifying the most effective one. Using the census data of 2011, the approximate population for 2016 to 2031 are calculated for 20 Indian states using both the models, compared and recorded the data with the actual population. On comparing the results of both models, it is found that logistic population model is more accurate than the exponential model, and using this model, we can predict the future population in a more effective way. This will give an insight to the researchers about the effective models of population and how effective these population models are in predicting the future population.

Keywords: population growth, population models, exponential model, logistic model, fibonacci model, lotka-volterra model, future population prediction, demographers

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Authors: Linnan Zhang

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Linguists, as well as psychologists, have shown great interests in implicit causality in reference processing. However, most frequently-used approaches to this issue are psychological experiments (such as eye tracking or self-paced reading, etc.). This research is a corpus-based one and is assisted with statistical tool – software R. The main focus of the present study is about the competition between verb-based implicit causality and theme structure’s influence on anaphora bias in Mandarin Chinese sentences. In Accessibility Theory, it is believed that salience, which is also known as accessibility, and relevance are two important factors in reference processing. Theme structure, which is a special syntactic structure in Chinese, determines the salience of an antecedent on the syntactic level while verb-based implicit causality is a key factor to the relevance between antecedent and anaphora. Therefore, it is a study about anaphora, combining psychology with linguistics. With analysis of the sentences from corpus as well as the statistical analysis of Multinomial Logistic Regression, major findings of the present study are as follows: 1. When the sentence is stated in a ‘cause-effect’ structure, the theme structure will always be the antecedent no matter forward biased verbs or backward biased verbs co-occur; in non-theme structure, the anaphora bias will tend to be the opposite of the verb bias; 2. When the sentence is stated in a ‘effect-cause’ structure, theme structure will not always be the antecedent and the influence of verb-based implicit causality will outweigh that of theme structure; moreover, the anaphora bias will be the same with the bias of verbs. All the results indicate that implicit causality functions conditionally and the noun in theme structure will not be the high-salience antecedent under any circumstances.

Keywords: accessibility theory, anaphora, theme strcture, verb-based implicit causality

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Authors: Harinder Singh

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The paper sheds light on the perceptions of the uninsured workers employed in the urban informal sector of India, towards the health insurance. In addition to this, it also explores the association of the identified perceptions with household decisions to enroll for health insurance schemes in India. Firstly the data taken from the primary survey of the uninsured workers employed in the urban informal sector was analyzed using exploratory factor analysis to evaluate the perceptions. Thereafter, logistic regression was employed to determine the association of the identified perceptions regarding the enrollment. Our study identifies twelve perceptions related to the health insurance enrollment of the uninsured workers employed in the urban informal sector of India. The study demonstrates that perceptions have the strongest association with the voluntary enrollment. These specifically relate to the lack of awareness about the need to buy health insurance; comprehensive coverage; income constraint; future contingencies and social obligations; lack of information; availability of subsidized government health care; linkage with government hospitals and preference for government schemes. Conclusions: Along with the food security, health security has become a crying need of the workers employed in the informal sector and the time has come to scale up the health insurance schemes for them in the country. Policy makers or marketers of health insurance policies should recognize the household perceptions as a potential barrier and try to develop a health insurance package as per the actual needs of the informal sector (low income) in India.

Keywords: association, enrollment, health insurance, informal sector, perceptions, uninsured

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Authors: Luh Eka Suryani, Purhadi

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

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

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Authors: Yakubu Suleiman, S. A. Musa

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Nigeria is the largest cowpea producer in the world, accounting for about 45%, followed by Brazil with about 17%. Cowpea is grown in Kano, Bauchi, Katsina, Borno in the north, Oyo in the west, and to the lesser extent in Enugu in the east. This study was conducted to determine the input–output relationship of Cowpea production in Kano, Katsina, and Jigawa states of Nigeria. The data were collected with the aid of 1000 structured questionnaires that were randomly distributed to Cowpea farmers in the three states mentioned above of the study area. The data collected were analyzed using regression analysis (Cobb–Douglass production function model). The result of the regression analysis revealed the coefficient of multiple determinations, R2, to be 72.5% and the F ration to be 106.20 and was found to be significant (P < 0.01). The regression coefficient of constant is 0.5382 and is significant (P < 0.01). The regression coefficient with respect to labor and seeds were 0.65554 and 0.4336, respectively, and they are highly significant (P < 0.01). The regression coefficient with respect to fertilizer is 0.26341 which is significant (P < 0.05). This implies that a unit increase of any one of the variable inputs used while holding all other variables inputs constants, will significantly increase the total Cowpea output by their corresponding coefficient. This indicated that farmers in the study area are operating in stage II of the production function. The result revealed that Cowpea farmer in Kano, Jigawa and Katsina States realized a profit of N15,997, N34,016 and N19,788 per hectare respectively. It is hereby recommended that more attention should be given to Cowpea production by government and research institutions.

Keywords: coefficient, constant, inputs, regression

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Authors: Fang Yao, Minglei Chen

Abstract:

City shrinkage is one of the thorny problems that many European cities have to face with nowadays. It is mainly expressed as the decrease of population in these cities. Eastern Germany is one of the pioneers of European shrinking cities with long shrinking history. Selecting one representative shrinking city Halle(Saale) in eastern Germany as research objective, collecting and investigating nearly 20 years (1993-2010) municipal data after the reunification of Germany. These data based on five dimensions, which are demographic, economic, social, spatial and environmental and total 16 eligible variables. Using Factor Analysis to dealing with these variables in order to assess the most important factors affecting shrinking Halle. The Factor Analysis shows that there are three main factors determine the shrinkage of Halle, namely demographical and economical factor, social stability factor, and city vitality factor. Three factors acts at different period of Halle’s shrinkage: from 1993 to 1997 the demographical and economical factor played an important role; from 1997 to 2004 the social stability is significant to city shrinkage; since 2005 city vitality factors determines the shrinkage of Halle. In recent years, the shrinkage in Halle mitigates that shows the sign of growing population. Thus the city Halle should focus on attaching more importance on the city vitality factor to prevent the city from shrinkage. Meanwhile, the city should possess a positive perspective that to shift the growth-oriented development to tap the potential of shrinking cities. This method is expected to apply to further research and other shrinking cities.

Keywords: demography, factor analysis, Halle, shrinking cities

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Authors: Raya Al Khayari, Rasha Al Jassim, Muna Al Balushi, Fatma Al Moqbali, Said El Hajjar

Abstract:

This study utilizes linear regression analysis to investigate the correlation between user account passwords and the probability of civil ID exposure, offering statistical insights into civil ID security. The study employs multiple linear regression (MLR) analysis to further investigate the elements that influence consumers’ views of civil ID security. This aims to increase awareness and improve preventive measures. The results obtained from the MLR analysis provide a thorough comprehension and can guide specific educational and awareness campaigns aimed at promoting improved security procedures. In summary, the study’s results offer significant insights for improving existing security measures and developing more efficient tactics to reduce risks related to civil ID security in Oman. By identifying key factors that impact consumers’ perceptions, organizations can tailor their strategies to address vulnerabilities effectively. Additionally, the findings can inform policymakers on potential regulatory changes to enhance civil ID security in the country.

Keywords: civil-id disclosure, awareness, linear regression, multiple regression

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Authors: Anthony J. Kondracki, Bonzo I. Reddick, Jennifer L. Barkin

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We sought to examine the association of maternal Chlamydia trachomatis (CT), Neisseria gonorrhoeae (NG), and treponema pallidum (TP) (syphilis) infections with preterm birth (PTB) (<37 weeks gestation), low birth weight (LBW) (<2500 grams) and prenatal care (PNC) attendance. This cross-sectional study was based on data drawn from the 2020 United States National Center for Health Statistics (NCHS) Natality File. We estimated the prevalence of all births, early/late PTBs, moderately/very LBW, and the distribution of sexually transmitted infections (STIs) according to maternal characteristics in the sample. In multivariable logistic regression models, we examined adjusted odds ratios (aORs) and their corresponding 95% confidence intervals (CIs) of PTB and LBW subcategories in the association with maternal/infant characteristics, PNC status, and maternal CT, NG, and TP infections. In separate logistic regression models, we assessed the risk of these newborn outcomes stratified by PNC status. Adjustments were made for race/ethnicity, age, education, marital status, health insurance, liveborn parity, previous preterm birth, gestational hypertension, gestational diabetes, PNC status, smoking, and infant sex. Additionally, in a sensitivity analysis, we assessed the association with early, full, and late term births and the potential impact of unmeasured confounding using the E-value. CT (1.8%) was most prevalent STI in pregnancy, followed by NG (0.3%), and TP (0.1%). Non-Hispanic Black women, 20-24 years old, with a high school education, and on Medicaid had the highest rate of STIs. Around 96.6% of women reported receiving PNC and about 60.0% initiated PNC early in pregnancy. PTB and LBW were strongly associated with NG infection (12.2% and 12.1%, respectively) and late initiation/no PNC (8.5% and 7.6%, respectively), and ≤10 prenatal visits received (13.1% and 10.3%, respectively). The odds of PTB and LBW were 2.5- to 3-foldhigher for each STI among women who received ≤10 prenatal visits than >10 visits. Adequate prenatal care utilization and timely screening and treatment of maternal STIs can substantially reduce the burden of adverse newborn outcomes.

Keywords: low birthweight, prenatal care, preterm birth, sexually transmitted infections

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Authors: Hochan Seok, Woosik Jang, Seung-Heon Han

Abstract:

The stagnant global construction market has intensified competition since 2008 among firms that aim to win overseas contracts. Against this backdrop, subcontractor selection is identified as one of the most critical success factors in overseas construction project. However, it is difficult to select qualified subcontractors due to the lack of evaluation standards and reliability. This study aims to identify the problems associated with existing subcontractor evaluations using a correlations analysis and a multiple regression analysis with pre-qualification and performance evaluation of 121 firms in six countries.

Keywords: subcontractor evaluation system, pre-qualification, performance evaluation, correlation analysis, multiple regression analysis

Procedia PDF Downloads 344