Search results for: regression analysis (RA)

Authors: Abdul Rauf, Xiaoxing Liu, Rizwan Qaisar Danish, Waqas Amin

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The core intention of this study is to analyze the linkage of training, job attitudes and motivation through a mediation model in the banking sector of Pakistan. Moreover, this study is executed to answer a range of queries regarding the consideration of employees about training, job satisfaction, motivation and organizational commitment. Hence, the association of training with job satisfaction, job satisfaction with motivation, organizational commitment with job satisfaction, organization commitment as independently with motivation and training directly related to motivation is determined in this course of study. A questionnaire crafted for comprehending the purpose of this study by including four variables such as training, job satisfaction, motivation and organizational commitment which have to measure. A sample of 450 employees from seventeen private (17) banks and two (2) public banks was taken on the basis of convenience sampling from Pakistan. However, 357 questionnaires, completely filled were received back. AMOS used for assessing the conformity factor analysis (CFA) model and statistical techniques practiced to scan the collected data (i.e.) descriptive statistics, regression analysis and correlation analysis. The empirical findings revealed that training and organizational commitment has a significant and positive impact directly on job satisfaction and motivation as well as through the mediator (job satisfaction) also the impact sensing in the same way on the motivation of employees in the financial Banks of Pakistan. In this research study, the banking sector is under discussion, so the findings could not generalize on other sectors such as manufacturing, textiles, telecom, and medicine, etc. The low sample size is also the limitation of this study. On the foundation of these results the management fascinates to make the revised strategies regarding training program for the employees as it enhances their motivation level, and job satisfaction on a regular basis.

Keywords: job satisfaction, motivation, organizational commitment, Pakistan, training

Procedia PDF Downloads 257

Authors: M. Illias Kanchan

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The death of a woman during pregnancy and childbirth is not only a health issue, but also a matter of social injustice. This study makes an attempt to explore the association between maternal death and associated factors in West Bengal using the approaches of facility-based and community-based maternal death review. Bivariate and binary logistic regression analysis have been performed to understand the causes and circumstances of maternal deaths in West Bengal. Delay in seeking care was the major contributor in maternal deaths, near about one-third women died due to this factor. The most common cause of maternal death is found to be hypertensive disorders of pregnancy or eclampsia. We believe that these deaths can be averted by reducing hypertensive disorders of pregnancy or eclampsia.

Keywords: maternal death, facility-based, community-based, review, west Bengal, eclampsia

Procedia PDF Downloads 436

Authors: Punam Kumari Mandal, Namita Yangden, Bhumika Rai, Achala Niraula, Sabitra Subedi

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introduction: Family planning not only improves women’s health but also promotes gender equality, better child health, and improved education outcomes, including poverty reduction. The objective of this study is to assess the utilization of family planning methods and associated factors in Sunsari, Nepal. methodology: A cross-sectional analytical study was conducted among women of the reproductive age group (15-49 years) in Sunsari in 2020. Nonprobability purposive sampling was used to collect information from 212 respondents through face-to-face interviews using a Semi-structured interview schedule from ward no 1 of Barju rural municipality. Data processing was done by using SPSS “statistics for windows, version 17.0(SPSS Inc., Chicago, III.USA”). Descriptive analysis and inferential analysis (binary logistic regression) were used to find the association of the utilization of family planning methods with selected demographic variables. All the variables with P-value <0.1 in bivariate analysis were included in multivariate analysis. A P-value of <0.05 was considered to indicate statistical significance at a level of significance of 5%. results: This study showed that the mean age and standard deviation of the respondents were 26±7.03, and 91.5 % of respondent’s age at marriage was less than 20 years. Likewise, 67.5% of respondents use any methods of family planning, and 55.2% of respondents use family planning services from the government health facility. Furthermore, education (AOR 1.579, CI 1.013-2.462)., husband’s occupation (AOR 1.095, CI 0.744-1.610)., type of family (AOR 2.741, CI 1.210-6.210)., and no of living son (AOR 0.259 CI 0.077-0.872)are the factors associated with the utilization of family planning methods. conclusion: This study concludes that two-thirds of reproductive-age women utilize family planning methods. Furthermore, education, the husband’s occupation, the type of family, and no of living sons are the factors associated with the utilization of family planning methods. This reflects that awareness through mass media, including behavioral communication, is needed to increase the utilization of family planning methods.

Keywords: family planning methods, utilization. factors, women, community

Procedia PDF Downloads 143

Authors: Seun Mayowa Sunday

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Loan prediction is one of the most significant and recognised fields of research in the banking, insurance, and the financial security industries. Some prediction systems on the market include the construction of static software. However, due to the fact that static software only operates with strictly regulated rules, they cannot aid customers beyond these limitations. Application of many machine learning (ML) techniques are required for loan prediction. Four separate machine learning models, random forest (RF), decision tree (DT), k-nearest neighbour (KNN), and logistic regression, are used to create the loan prediction model. Using the anaconda navigator and the required machine learning (ML) libraries, models are created and evaluated using the appropriate measuring metrics. From the finding, the random forest performs with the highest accuracy of 80.17% which was later implemented into the Django framework. For real-time testing, the web application is deployed on the Alibabacloud which is among the top 4 biggest cloud computing provider. Hence, to the best of our knowledge, this research will serve as the first academic paper which combines the model development and the Django framework, with the deployment into the Alibaba cloud computing application.

Keywords: k-nearest neighbor, random forest, logistic regression, decision tree, django, cloud computing, alibaba cloud

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Authors: Soha A. Bahanshal, Vaibhav Verdhan, Bayong Kim

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Graduation rates at six-year colleges are becoming a more essential indicator for incoming fresh students and for university rankings. Predicting student graduation is extremely beneficial to schools and has a huge potential for targeted intervention. It is important for educational institutions since it enables the development of strategic plans that will assist or improve students' performance in achieving their degrees on time (GOT). A first step and a helping hand in extracting useful information from these data and gaining insights into the prediction of students' progress and performance is offered by machine learning techniques. Data analysis and visualization techniques are applied to understand and interpret the data. The data used for the analysis contains students who have graduated in 6 years in the academic year 2017-2018 for science majors. This analysis can be used to predict the graduation of students in the next academic year. Different Predictive modelings such as logistic regression, decision trees, support vector machines, Random Forest, Naïve Bayes, and KNeighborsClassifier are applied to predict whether a student will graduate. These classifiers were evaluated with k folds of 5. The performance of these classifiers was compared based on accuracy measurement. The results indicated that Ensemble Classifier achieves better accuracy, about 91.12%. This GOT prediction model would hopefully be useful to university administration and academics in developing measures for assisting and boosting students' academic performance and ensuring they graduate on time.

Keywords: prediction, decision trees, machine learning, support vector machine, ensemble model, student graduation, GOT graduate on time

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Authors: Jawad Taheri

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This paper investigates the impact of foreign aid-funded development projects on corruption perception in Afghanistan, utilizing instrumental variable (IV) approaches in regression analysis setting. Seven sub-categories of development projects are examined in order to have a glance over comparative usefulness of each project. The study incorporates natural disaster indicators as exogenous variables to mitigate endogeneity concerns and control variables. The findings reveal significant associations between certain types of projects and corruption perception. Road and bridge construction, irrigation, and agriculture projects are found to decrease corruption perception, fostering optimism and trust within affected communities. Mosque construction and healthcare initiatives aligned with religious beliefs also contribute to reduced corruption perception. However, drinking water projects are associated with increased corruption perception, highlighting potential challenges in their implementation. The study emphasizes the importance of well-implemented projects, cultural contexts, and effective governance in mitigating corruption and fostering trust within communities. These findings contribute to a refined understanding of the relationship between development projects and corruption perception in Afghanistan.

Keywords: foreign aid, development projects, corruption perception, Afghanistan, instrumental variable analysis, survey of Afghan people (SAP), natural disasters, exogenous variation

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Authors: Lindiwe Ngcobo

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The aim of the study is to describe and understand the benefits and costs of having biogas digesters at both household and society level. On a household level, the purpose is to understand how rural households benefit from the biogas digesters, for example, by converting animal and human waste through biogas digesters, and at what costs the benefits are realized. At a societal level, the purpose is to understand the costs and benefits of biogas digesters relative to the situation of rural communities who do not have flush toilets and have no appropriate waste disposal services while they incur electricity costs. Multiple regression analysis was used to determine the effect of biogas digesters on electricity availability and waste management. The results showed that beneficiaries spent less on electricity using household waste, and also waste disposal costs were eliminated from household expenses. A move to biogas energy production can be beneficial to rural households. It is economically and environmentally friendly. Small-scale farmers need to be introduced to agricultural innovations that can assist them in producing nutritious crops at a low cost. This can be a good opportunity to start an agribusiness that focuses on organic crops. Extensions and training institutions have to play a part in supporting households to develop entrepreneurial skills. Cost-benefit analysis showed that the benefits of biogas exceed the costs of the biogas projects. This implies that this technology should be promoted in rural households. Government financial incentives must be put in place to motivate a generation of organic Agri-prenuers.

Keywords: Agri-prenuers, biogas digester, biogas energy, disposal costs

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Authors: Prateek Singh, Dilshad Ahmad

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Growing demand for high-quality steel products has inspired researchers to investigate the unit operations involved in the manufacturing of these products (slabs, rods, sheets, etc.). One such operation is tundish operation, in which a vessel (tundish) acts as a buffer of molten steel for the solidification operation in mold. It is observed that tundish also plays a crucial role in the quality and cleanliness of the steel produced, besides merely acting as a reservoir for the mold. It facilitates removal of dissolved oxygen (inclusions) from the molten steel thus improving its cleanliness. Inclusion removal can be enhanced by increasing the residence time of molten steel in the tundish by incorporation of flow modifiers like dams, weirs, turbo-pad, etc. These flow modifiers also help in reducing the dead or short circuit zones within the tundish which is significant for maintaining thermal and chemical homogeneity of molten steel. Thus, it becomes important to analyze the flow of molten steel in the tundish for different configuration of flow modifiers. In the present work, effect of varying positions and heights/depths of dam and weir on the dead volume in tundish is studied. Steady state thermal and flow profiles of molten steel within the tundish are obtained using OpenFOAM. Subsequently, Residence Time Distribution analysis is performed to obtain the percentage of dead volume in the tundish. Design of Experiment method is then used to configure different tundish geometries for varying positions and heights/depths of dam and weir, and dead volume for each tundish design is obtained. A second-degree polynomial with two-term interactions of independent variables to predict the dead volume in the tundish with positions and heights/depths of dam and weir as variables are computed using Multiple Linear Regression model. This polynomial is then used in an optimization framework to obtain the optimal tundish geometry for minimizing dead volume using Sequential Quadratic Programming optimization.

Keywords: design of experiments, multiple linear regression, OpenFOAM, residence time distribution, sequential quadratic programming optimization, steel, tundish

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Authors: F. Pagliara, F. Mauriello

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The objective of this paper is to investigate the relationship between the increase of accessibility brought by high speed rail (HSR) systems and the tourism market in Italy. The impacts of HSR projects on tourism can be quantified in different ways. In this manuscript, an empirical analysis has been carried out with the aid of a dataset containing information both on tourism and transport for 99 Italian provinces during the 2006-2016 period. Panel data regression models have been considered, since they allow modelling a wide variety of correlation patterns. Results show that HSR has an impact on the choice of a given destination for Italian tourists while the presence of a second level hub mainly affects foreign tourists. Attraction variables are also significant for both categories and the variables concerning security, such as number of crimes registered in a given destination, have a negative impact on the choice of a destination.

Keywords: tourists, overnights, high speed rail, attractions, security

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Authors: Vladimir Korovkin, Albert Park, Evgeny Kaganer

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The paper studies the second-level digital divide (the one defined by the way how digital technology is used in everyday life) between regions of the Russian Federation. The paper offers a systemic review of literature on the measurement of the digital divide; based upon this it suggests a composite Digital Life Index, that captures the complex multi-dimensional character of the phenomenon. The model of the index studies separately the digital supply and demand across seven independent dimensions providing for 14 subindices. The Index is based on Internet-borne data, a distinction from traditional research approaches that rely on official statistics or surveys. Regression analysis is used to determine the relative importance of factors like income, human capital, and policy in determining the digital divide. The result of the analysis suggests that the digital divide is driven more by the differences in demand (defined by consumer competencies) than in supply; the role of income is insignificant, and the quality of human capital is the key determinant of the divide. The paper advances the existing methodological literature on the issue and can also inform practical decision-making regarding the strategies of national and regional digital development.

Keywords: digital transformation, second-level digital divide, composite index, digital policy, regional development, Russia

Procedia PDF Downloads 192

Authors: Hassam Ahmed, Yuanzhi Liu, Yassine Selami, Wei Tao, Hui Zhao

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The purpose of this research is to develop a real time axial force measurement system for a smart bearing through the use of strain-gauges, whereby the data acquisition is performed by an Arduino microcontroller due to its easy manipulation and low-cost. The measured signal is acquired and then discretized using a Wheatstone Bridge and an Analog-Digital Converter (ADC) respectively. For bearing monitoring, a real time monitoring system based on Internet of things (IoT) and Bluetooth were developed. Experimental tests were performed on a bearing within a force range up to 600 kN. The experimental results show that there is a proportional linear relationship between the applied force and the output voltage, and the error R squared is within 0.9878 based on the regression analysis.

Keywords: bearing, force measurement, IoT, strain gauge

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Authors: Anucha Taiwong, Nirobol Kanogsunthornrat

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This predictive research aimed to investigate the symptom clusters that influence the quality of life among patients with chronic kidney disease, as indicated in the Theory of Unpleasant Symptoms. The purposive sample consisted of 150 patients with stage 3-4 chronic kidney disease who received care at an outpatient chronic kidney disease clinic of a tertiary hospital in Roi-Et province. Data were collected from January to March 2016 by using a patient general information form, unpleasant symptom form, and quality of life (SF-36) and were analyzed by using descriptive statistics, factor analysis, and multiple regression analysis. Findings revealed six core symptom clusters including symptom cluster of the mental and emotional conditions, peripheral nerves abnormality, fatigue, gastro-intestinal tract, pain and, waste congestion. Significant predictors for quality of life were the two symptom clusters of pain (Beta = -.220; p < .05) and the mental and emotional conditions (Beta=-.204; p<.05) which had predictive value of 19.10% (R2=.191, p<.05). This study indicated that the symptom cluster of pain and the mental and emotional conditions would worsen the patients’ quality of life. Nurses should be attentive in managing the two symptom clusters to facilitate the quality of life among patients with chronic kidney disease.

Keywords: chronic kidney disease, symptom clusters, predictors of quality of life, pre-dialysis

Procedia PDF Downloads 323

Authors: Fatemeh Torfi

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Estimation of stochastic demand in physical distribution in general and efficient transport routs management in particular is emerging as a crucial factor in urban planning domain. It is particularly important in some municipalities such as Tehran where a sound demand management calls for a realistic analysis of the routing system. The methodology involved critically investigating a fuzzy least-squares linear regression approach (FLLRs) to estimate the stochastic demands in the vehicle routing problem (VRP) bearing in mind the customer's preferences order. A FLLR method is proposed in solving the VRP with stochastic demands. Approximate-distance fuzzy least-squares (ADFL) estimator ADFL estimator is applied to original data taken from a case study. The SSR values of the ADFL estimator and real demand are obtained and then compared to SSR values of the nominal demand and real demand. Empirical results showed that the proposed methods can be viable in solving problems under circumstances of having vague and imprecise performance ratings. The results further proved that application of the ADFL was realistic and efficient estimator to face the stochastic demand challenges in vehicle routing system management and solve relevant problems.

Keywords: fuzzy least-squares, stochastic, location, routing problems

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Authors: Almontas Vilutis, Vytenis Jankauskas

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The paper presents the results of the investigation of the dry sliding friction of wood-plastic composites (WPCs) against WC-Co cemented carbide. The dependence of the dynamic coefficient of friction on the main influencing factors (vertical load, temperature, and sliding distance) was investigated by evaluating their mutual interaction. Multiple regression analysis showed a high polynomial dependence (adjusted R2 > 0.98). The resistance of the composite to thermo-mechanical effects determines how temperature and force factors affect the magnitude of the coefficient of friction. WPC-B composite has the lowest friction and highest resistance compared to WPC-A, while composite and cemented carbide materials wear the least. Energy dispersive spectroscopy (EDS), based on elemental composition, provided important insights into the friction process.

Keywords: friction, composite, carbide, factors

Procedia PDF Downloads 89

Authors: Tsung Chin Wu, Yu Chen Tsai, Rhay Hung Weng, Weir Sen Lin

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In recent years, nursing staff’s lower job satisfaction has led to higher turnover rates, and high turnover rates not only cause medical institution costs to increase but also the quality of medical care to decrease. From the perspective of internal marketing, institution staffs are internal customers, and institutions should focus and meet the needs of staff, so that staff will strive to meet the needs of external customers and provide them with the required care. However, few previous studies have investigated the impact of internal staff satisfaction on external customers. Therefore, this study aimed to conduct job satisfaction surveys on internal staff to investigate the relationship between job satisfaction and quality of medical care through statistical analysis of the study results. The related study results may serve as a reference for healthcare managers. This study was conducted using a questionnaire and the subjects were nursing staff from four hospitals. A total of 600 questionnaires were distributed and 577 valid questionnaires were returned with a response rate of 96.1%. After collecting the data, the reliability and validity of the study variables were confirmed by confirmatory factor analysis. The impact of internal marketing and job satisfaction on turnover intention of nursing staff was analyzed using descriptive analysis, one-way ANOVA, Pearson correlation analysis and multiple regression analysis. The study results showed that there was a significant difference between nursing staff’s job title and ‘professional participation’ and ‘shifts’. There was a significant difference between salary and ‘shifts’ and ‘turnover intention’, as well as between marriage and ‘remuneration’ and ‘turnover intention’. A significant difference was found between professional advancement and ‘professional growth’ and ‘type of leave’, as well as between division of service and ‘shifts’ and ‘turnover intention’. Pearson correlation analysis revealed a significant negative correlation between turnover intention and ‘internal marketing’, ‘interaction’, ‘professional participation’, ‘grasp of environment’, ‘remuneration’ and ‘shifts’, meaning that the higher the satisfaction, the lower the turnover intention. It is recommended that hospitals establish a comprehensive internal marketing mechanism to enhance staff satisfaction and in turn, reduce intention to resign, and the key to increasing job satisfaction is by establishing effective methods of internal communication.

Keywords: internal marketing, job satisfaction, turnover intention, nursing staff

Procedia PDF Downloads 193

Authors: Hassan Barau Singhry

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Purpose: Leadership or the absence of it is an important behaviour affecting employees’ job satisfaction. Although, there are many models of leadership, one that stands out in a period of change is the transformational behaviour. The aim of this study is to investigate the role of an organizational justice on the relationship between transformational leadership and employee job satisfaction. The study is based on the assumption that change begins with leaders and leaders should be fair and just. Methodology: A cross-sectional survey through structured questionnaire was employed to collect the data of this study. The population is selected the three tiers of government such as the local, state, and federal governments in Nigeria. The sampling method used in this research is stratified random sampling. 418 middle managers of public organizations respondents to the questionnaire. Multiple regression aided by structural equation modeling was employed to test 4 hypothesized relationships. Finding: The regression results support for the mediating role of organizational justice such as distributive, procedural, interpersonal and informational justice in the link between transformational leadership and job satisfaction. Originality/value: This study adds to the literature of human resource management by empirically validating and integrating transformational leadership behaviour with the four dimensions of organizational justice theory. The study is expected to be beneficial to the top and middle-level administrators as well as theory building and testing.

Keywords: distributive justice, job satisfaction, organizational justice, procedural justice, transformational leadership

Procedia PDF Downloads 180

Authors: Vera Karla S. Caingles, Glen A. Lorenzo

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Soil, as the main material of landslides, plays a vital role in landslide assessment. An efficient and accurate method of doing an assessment is significantly important to prevent damage of properties and loss of lives. The study has two phases: to establish an empirical correlation of the residual soil thickness with the slope angle and to investigate the geotechnical characteristics of residual soil. Digital Elevation Model (DEM) in Geographic Information System (GIS) was used to establish the slope map and to program sampling points for field investigation. Physical and index property tests were undertaken on the 20 soil samples obtained from the area with Pliocene-Pleistocene geology and different slope angle in Kibawe, Bukidnon. The regression analysis result shows that the best fitting model that can describe the soil thickness-slope angle relationship is an exponential function. The physical property results revealed that soils contain a high percentage of clay and silts ranges from 41% - 99.52%. Based on the index properties test results, the soil exhibits a high degree of plasticity and expansion but not collapsible. It is deemed that this compendium will serve as primary data for slope stability analysis and deterministic landslide assessment.

Keywords: collapsibility, correlation, expansiveness, landslide, plasticity

Procedia PDF Downloads 163

Authors: Yijun Shao, Yan Cheng, Rashmee U. Shah, Charlene R. Weir, Bruce E. Bray, Qing Zeng-Treitler

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Deep neural network (DNN) models are being explored in the clinical domain, following the recent success in other domains such as image recognition. For clinical adoption, outcome prediction models require explanation, but due to the multiple non-linear inner transformations, DNN models are viewed by many as a black box. In this study, we developed a deep neural network model for predicting 1-year mortality of patients who underwent major cardio vascular procedures (MCVPs), using temporal image representation of past medical history as input. The dataset was obtained from the electronic medical data warehouse administered by Veteran Affairs Information and Computing Infrastructure (VINCI). We identified 21,355 veterans who had their first MCVP in 2014. Features for prediction included demographics, diagnoses, procedures, medication orders, hospitalizations, and frailty measures extracted from clinical notes. Temporal variables were created based on the patient history data in the 2-year window prior to the index MCVP. A temporal image was created based on these variables for each individual patient. To generate the explanation for the DNN model, we defined a new concept called impact score, based on the presence/value of clinical conditions’ impact on the predicted outcome. Like (log) odds ratio reported by the logistic regression (LR) model, impact scores are continuous variables intended to shed light on the black box model. For comparison, a logistic regression model was fitted on the same dataset. In our cohort, about 6.8% of patients died within one year. The prediction of the DNN model achieved an area under the curve (AUC) of 78.5% while the LR model achieved an AUC of 74.6%. A strong but not perfect correlation was found between the aggregated impact scores and the log odds ratios (Spearman’s rho = 0.74), which helped validate our explanation.

Keywords: deep neural network, temporal data, prediction, frailty, logistic regression model

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Authors: Seongryong Kang, Woonjin Kim, Sungjoo Lee

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Under the environment of fierce competition and globalized economy, international technology collaboration has gained increasing attention as a way to improve innovation efficiency. While international technology transfer helps a firm to acquire necessary technology in a short period of time, it also has a risk; embedding external technology from overseas partners may cause a transaction cost due to the regional, cultural and language barriers, which tend to offset the benefits of such transfer. Though a number of previous studies have focused on the effects of technology in-transfer on firm performance, few have conducted in the context of international technology transfer. To fill this gap, this study aims to investigate the impact of international technology in-transfer on firm performance – both innovation and financial performance, with a particular emphasis on the diversity and intensity of such transfer. To do this, we adopted technology balance payment (TBP) data of Korean firms from 2010 to 2011, where an intermediate regression analysis was used to identify the intermediate effects of absorptive capacity. The analysis results indicate that i) the diversity and intensity of international technology transfer influence innovation performance by improving R&D capability positively; and ii) the diversity has a positive impact but the intensity has a negative impact on financial performance through the intermediation of R&D intensity. The research findings are expected to provide meaningful implications for establishing global technology strategy and developing policy programs to facilitate technology transfer.

Keywords: diversity, intensity, international technology acquisition, performance, technology transfer

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Authors: Kanchan Negi

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Background: Modern and urban occupational culture have driven demands for people to work long hours and weekends and take work to home at times. Research on the health effects of these exhaustive temporal work patterns is scant or contradictory. This study examines the relationship between work patterns and wellbeing in a sample of women living in the metropolitan hub of Delhi. Method: This study is based on the data collected from 360 currently married women between age 29 and 49 years, working in the urban capital hub of India, i.e., Delhi. The women interviewed were professionals from the education, health, banking and information and technology (IT) sector. Bivariate analysis was done to study the characteristics of the sample. Logistic regression analysis was used to estimate the physical and psychological wellbeing across occupational characteristics. Results: Most of the working women were below age 35 years; around 30% of women worked in the education sector, 23% in health, 21% in banking and 26% in the IT sector. Over 55% of women were employed in the private sector and only 36% were permanent employees. Nearly 30% of women worked for more than the standard 8 hours a day. The findings from logistic regression showed that compared to women working in the education sector, those who worked in the banking and IT sector more likely to have physical and psychological health issues (OR 2.07-4.37, CI 1.17-4.37); women who bear dual burden of responsibilities had higher odds of physical and psychological health issues than women who did not (OR 1.19-1.85 CI 0.96-2.92). Women who worked for more than 8 hours a day (OR 1.15, CI 1.01-1.30) and those who worked for more than five days a week (OR 1.25, CI 1.05-1.35) were more likely to have physical health issues than women who worked for 6-8 hours a day and five days e week, respectively. Also, not having flexible work timings and compensatory holidays increased the odds of having physical and psychological health issues among working women (OR 1.17-1.29, CI 1.01-1.47). Women who worked in the private sector, those employed temporarily and who worked in the non-conducive environments were more likely to have psychological health issues as compared to women in the public sector, permanent employees and those who worked in a conducive environment, respectively (OR 1.33-1.67, CI 1.09-2.91). Women who did not have poor work-life balance had reduced the odds of psychological health issues than women with poor work-life balance (OR 0.46, CI 0.25-0.84). Conclusion: Poor wellbeing significantly linked to strenuous and rigid work patterns, suggesting that modern and urban work culture may contribute to the poor wellbeing of working women. Noticing the recent decline in female workforce participation in Delhi, schemes like Flexi-timings, compensatory holidays, work-from-home and daycare facilities for young ones must be welcomed; these policies already exist in some private sector firms, and the public sectors companies should also adopt such changes to ease the dual burden as homemaker and career maker. This could encourage women in the urban areas to readily take up the jobs with less juggle to manage home and work.

Keywords: occupational characteristics, urban India, well-being, working women

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Authors: Maria Reichenber Arcilla

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Objective: To determine the risk factors for maternal and neonatal complications associated with operative vaginal deliveries. Methods: A retrospective chart review of 435 patients who underwent operative vaginal deliveries was done. Patient profiles – age, parity, AOG, duration of labor – and outcomes – birthweight, maternal and neonatal complications - were tabulated and multivariable analysis and logistic regression were performed using SPSS® Statistics Base. Results and Conclusion: There was no significant difference in the incidence of maternal and neonatal complications between those that underwent vacuum and forceps extraction. Among the variables analysed, parity and duration of labor reached statistical significance. The odds of maternal complications were 3 times higher among nulliparous patients. Neonatal complications were seen in those whose labor lasted more than 9 hours.

Keywords: operative vaginal deliveries, maternal, neonatal, morbidity

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Authors: Sukanya Sripho

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The purpose of this research was to study the role of local administration management to promote culture based on philosophy of sufficiency economy to many communities in Thailand. The philosophy was given to the Thai people by their King and become one of the important policies from the Thai government. A total of 375 local people in main district, Amnadcharoen province were selected by random sampling. A questionnaire was used as the tool for collecting data. Descriptive statistics in this research included percentage, mean, and multiple regression analysis. The findings revealed that the role of facilitator was utilized the most from the management in order to promote culture based on philosophy of sufficiency economy to many communities in Thailand.

Keywords: administration, management, philosophy of sufficiency economy, facilitator

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Authors: Muller Salah Zaky Toudry

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The complexity arises in defining the development great due to its notion, based on inherent market situations and their requirements, the diverse stakeholders itself and their desired output. An quantitative survey based totally approach was adopted in this optimistic examine. A questionnaire-primarily based survey was performed for the assessment of production fine belief and expectations within the context of excellent development technique. The survey feedback of experts of the leading creation corporations/companies of Pakistan production industry have been analyzed. The monetary ability, organizational shape, and production revel in of the construction companies shaped basis for their selection. The great belief become located to be venture-scope-orientated and taken into consideration as an extra cost for a production assignment. Any excellent improvement technique changed into expected to maximize the profit for the employer, via enhancing the productiveness in a creation project. The look at is beneficial for the construction specialists to evaluate the prevailing creation great perception and the expectations from implementation of any pleasant improvement approach in production projects.

Keywords: correlation analysis, lean construction tools, lean construction, logistic regression analysis, risk management, safety construction quality, expectation, improvement, perception client loyalty, NPS, pre-construction, schedule reduction

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Authors: Sayeeda Laeque Bangi

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Objectives: Treating white spot lesions (WSL) to create a sound and esthetically pleasing enamel surface is a question yet to be fully answered. The objective of this randomized controlled trial was to measure and compare the degree of regression of WSL during orthodontic treatment achieved by using three commercially available materials. Methods: A single-blinded randomized prospective clinical trial, comprising 80 patients categorized into four groups (one control group and three experimental groups, with 20 subjects per group) using block randomization, was conducted. Group A (control group): Colgate strong toothpaste; and experiments groups were Group B: GC tooth mousse, Group C: Phos-Flur mouthwash and Group D: SHY-NM. Subjects were instructed to use the designated dentifrice/mouthwash and photographs were taken at baseline, third and sixth months, and white spot lesions were reassessed in the maxillomandibular anterior teeth. Results: All the three groups had shown an improvement in WSL. But Group B has shown the greatest difference in mean values of decalcification index (DI) scores. Conclusion: All three commercially available products showed a regression of WSL over a 6-month duration. GC tooth mousse proved to be the most effective means of treating WSL over other regimens.

Keywords: white spot lesions, dentifrices, orthodontic therapy, remineralization

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Authors: Wiji Wijaya

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The aim of this research is to examine the effect of internal control weaknesses and audit opinion on the state’s loss findings of audit compliance to the regulation in public sector. The samples of this research consisted of 175 local government financial statements in the area of Central Java Province at 2009 until 2013. Area sampling design was used to select the financial statements. This study using quantitative descriptive statistical analysis and regression was run for data analysis and hypothesis examination. Result of this study indicated that internal control weaknesses and audit opinion contributes a positive influence which is significant to the state’s loss findings of audit compliance to the regulation. The internal control weaknesses that affect the state's loss finding are weakness control system of accounting and reporting with the value of the critical ratio 0.010 p 2.613 ; weakness budget execution control system with critical ratio value of 3.421 p 0.001 and weaknesses internal control structure with critical ratio value of 2.246 p 0.026 . While the audit opinion with a critical ratio value of 4.401 p 0.000. The implications of this research so that policy makers at the local government should give more attention to the implementation and improvement of internal control system.

Keywords: audit compliance findings, state’s loss, audit opinion, internal control, local government

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Authors: Mahtab Davari, Charles Edward Okon, Somayeh Aghanavesi

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

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

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Authors: Wondmnew Derebe Yohannis

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Farming irrigation plays a crucial role in rural development strategies, impacting both annual household income and livelihood. This research aims to evaluate the factors influencing irrigation participation and assess the impact of small-scale irrigation on rural households' annual income. The study collected data from 287 farmers in the Dahana district of northern Ethiopia. The research investigates the driving forces behind farmers' decisions to adopt small-scale irrigation and its effect on annual income gain. The findings reveal that several factors positively influence the probability of adoption, including access to credit, cultivated land size, livestock holding, extension contact, and the education level of the household head. Conversely, the distance to local markets and water schemes negatively affects the likelihood of adoption. To understand the differences in annual income between farm households that adopted irrigation and those that did not, a simultaneous equations model with endogenous switching regression is estimated. This accounts for the heterogeneity in the adoption decision and unobservable characteristics of farmers and their farms. The analysis compares the expected income gain under actual and counterfactual scenarios, considering whether the farm household adopted irrigation or not. The study reveals that the group of farm households that adopted irrigation has distinct characteristics compared to those that did not adopt it. Furthermore, the research demonstrates that the adoption of irrigation practices leads to an increase in annual income. Interestingly, the impact of small-scale irrigation on annual income is greater for the farm households that actually adopted irrigation compared to those in the counterfactual scenario where they did not adopt. Based on the findings, the researcher concludes that small-scale irrigation is a practical solution for meeting household financial needs in the study area. It is recommended that investments in small-scale irrigation continue to further improve the livelihoods of rural farming communities by enhancing annual income gains.

Keywords: small-scale irrigation, income, rural farm households, endogenous switching regression, user, non-user

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Authors: Swathi Ganesan, Nalinda Somasiri, Rebecca Jeyavadhanam, Gayathri Karthick

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The COVID-19 crisis presents a substantial and critical hazard to worldwide health. Since the occurrence of the disease in late January 2020 in the UK, the number of infected people confirmed to acquire the illness has increased tremendously across the country, and the number of individuals affected is undoubtedly considerably high. The purpose of this research is to figure out a predictive machine learning archetypal that could forecast COVID-19 cases within the UK. This study concentrates on the statistical data collected from 31st January 2020 to 31st March 2021 in the United Kingdom. Information on total COVID cases registered, new cases encountered on a daily basis, total death registered, and patients’ death per day due to Coronavirus is collected from World Health Organisation (WHO). Data preprocessing is carried out to identify any missing values, outliers, or anomalies in the dataset. The data is split into 8:2 ratio for training and testing purposes to forecast future new COVID cases. Support Vector Machines (SVM), Random Forests, and linear regression algorithms are chosen to study the model performance in the prediction of new COVID-19 cases. From the evaluation metrics such as r-squared value and mean squared error, the statistical performance of the model in predicting the new COVID cases is evaluated. Random Forest outperformed the other two Machine Learning algorithms with a training accuracy of 99.47% and testing accuracy of 98.26% when n=30. The mean square error obtained for Random Forest is 4.05e11, which is lesser compared to the other predictive models used for this study. From the experimental analysis Random Forest algorithm can perform more effectively and efficiently in predicting the new COVID cases, which could help the health sector to take relevant control measures for the spread of the virus.

Keywords: COVID-19, machine learning, supervised learning, unsupervised learning, linear regression, support vector machine, random forest

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Authors: Ethelbert C. Njoku

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The primary determination of any individual is the achievement of wholesome health. This is applicable to the government too. This desire becomes a reality with the efforts of medical practitioners who work day and night to ensure that the health of people is not compromised in any form. To achieve this laudable goal, the psychological wellbeing of the practitioners must be unparalleled. They must be psychologically fit in order to deliver as expected. More so, the organization must be able to provide the basic ingredients of trust in the daily management of the organization. Significantly, proper Interpersonal Communication Competence remains a necessity in the overall realization of this goal. 200 participants took part in the study, and they were selected through convenient sampling method from hospitals in Imo State. The current study adopted cross sectional survey design in trying to find out if Interpersonal Communication Competence and Organizational Trust can predict Psychological Wellbeing of medical practitioners in Imo State. Standard Multiple Regression Analysis was used for data analysis. Interestingly, the results indicate that interpersonal communication competence and organizational trust predicted psychological wellbeing among medical practitioners. The implication of this study hinges on the fact that since Interpersonal Communication Competence and Organizational Trust are important for psychological wellbeing of medical practitioners, the government and managers should try to provide opportunities that enhance these variables in the organization for the psychological wellbeing of medical practitioners.

Keywords: interpersonal communication competence, medical practitioners, organizational trust, psychological wellbeing

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Authors: Morteza Raei Dehaghi, Seyed Mohammad Mirhashemi

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The current study explored the effect of economic development, financial development and institutional quality on environmental destruction in upper-middle income countries during the time period of 1999-2011. The dependent variable is logarithm of carbon dioxide emissions that can be considered as an index for destruction or quality of the environment given to its effects on the environment. Financial development and institutional development variables as well as some control variables were considered. In order to study cross-sectional correlation among the countries under study, Pesaran and Friz test was used. Since the results of both tests show cross-sectional correlation in the countries under study, seemingly unrelated regression method was utilized for model estimation. The results disclosed that Kuznets’ environmental curve hypothesis is confirmed in upper-middle income countries and also, financial development and institutional quality have a significant effect on environmental quality. The results of this study can be considered by policy makers in countries with different income groups to have access to a growth accompanied by improved environmental quality.

Keywords: economic development, environmental destruction, financial development, institutional development, seemingly unrelated regression

Procedia PDF Downloads 351