Search results for: panel data regression

Authors: Ali Hesari, Arezoo Esmaeeli

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Regarding to dramatic changes of fertility and higher order births during recent decades in Iran, access to knowledge about affecting factors on different birth orders has crucial importance. In this study, According to hierarchical structure of many of social sciences data and the effect of variables of different levels of social phenomena that determine different birth orders in 365 days ending to 1390 census have been explored by multilevel approach. In this paper, 2% individual row data for 1390 census is analyzed by HLM software. Three different hierarchical linear regression models are estimated for data analysis of the first and second, third, fourth and more birth order. Research results displays different outcomes for three models. Individual level variables entered in equation are; region of residence (rural/urban), age, educational level and labor participation status and province level variable is GDP per capita. Results show that individual level variables have different effects in these three models and in second level we have different random and fixed effects in these models.

Keywords: fertility, birth order, hierarchical approach, fixe effects, random effects

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Authors: Frank Adusah-Poku, John Bosco Dramani, Prince Boakye Frimpong

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Climate change is considered a principal threat to human existence and livelihood. The persistence and intensity of droughts and floods in recent years have adversely affected food production systems and value chains, making it impossible to end global hunger by 2030. Thus, this study aims to examine the effect of climate change on food consumption for both farm and non-farm households in Ghana. An important focus of the analysis is to investigate how climate change affects alternative dimensions of food security, examine the extent to which these effects vary across heterogeneous groups, and explore the channels through which climate change affects food consumption. Finally, we conducted a pilot study to understand the significance of farm and non-farm diversification measures in reducing the harmful impact of climate change on farm households. The approach of this article is to use two secondary and one primary datasets. The first secondary dataset is the Ghana Socioeconomic Panel Survey (GSPS). The GSPS is a household panel dataset collected during the period 2009 to 2019. The second dataset is monthly district rainfall and temperature gridded data from the Ghana Meteorological Agency. This data was matched to the GSPS dataset at the district level. Finally, the primary data was obtained from a survey of farm and non-farm adaptation practices used by farmers in three regions in Northern Ghana. The study employed the household fixed effects model to estimate the effect of climate change (measured by temperature and rainfall) on food consumption in Ghana. Again, it used the spatial and temporal variation in temperature and rainfall across the districts in Ghana to estimate the household-level model. Evidence of potential mechanisms through which climate change affects food consumption was explored using two steps. First, the potential mechanism variables were regressed on temperature, rainfall, and the control variables. In the second and final step, the potential mechanism variables were included as extra covariates in the first model. The results revealed that extreme average temperature and drought had caused a decrease in food consumption as well as reduced the intake of important food nutrients such as carbohydrates, protein and vitamins. The results further indicated that low rainfall increased food insecurity among households with no education compared with those with primary and secondary education. Again, non-farm activity and silos have been revealed as the transmission pathways through which the effect of climate change on farm households can be moderated. Finally, the results indicated over 90% of the small-holder farmers interviewed had no farm diversification adaptation strategies for climate change, and a little over 50% of the farmers owned unskilled or manual non-farm economic ventures. This makes it very difficult for the majority of the farmers to withstand climate-related shocks. These findings suggest that achieving the Sustainable Development Goal of Zero Hunger by 2030 needs an integrated approach, such as reducing the over-reliance on rainfed agriculture, educating farmers, and implementing non-farm interventions to improve food consumption in Ghana.

Keywords: climate change, food consumption, Ghana, non-farm activity

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Authors: Enno R. Farahzehan, Wustari L. Mangundjaya

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Organizational change is necessary to develop innovation and to compete with other competitors. Organizational changes were also made to defend the existence of the organization itself. Success in implementing organizational change consists of a variety of factors, one of which is individual (employee) who run changes. The employee must have the willingness and ability in carrying out the changes. Besides, employees must also have a commitment to change for creation of the successful organizational change. This study aims to execute the effect of change communication towards commitment to change through the role of organizational trust. The respondents of this study were employees who work in organizations, which have been or are currently running organizational changes. The data were collected using Change Communication, Commitment to Change, and Organizational Trust Inventory. The data were analyzed using regression. The result showed that there is an effect among change communication towards commitment to change which is higher when mediated by organizational trust. This paper will contribute to the knowledge and implications of organizational change, that shows change communication can affect commitment to change among employee if there is trust in the organization.

Keywords: change communication, commitment to change, organizational trust, organizational change

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Authors: B. Gepts, E. Meex, E. Nuyts, E. Knaepen, G. Verbeeck

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The importance of resource efficiency and environmental impact assessment has raised the interest in knowing the amount of materials used in buildings. If no BIM model or energy performance certificate is available, material quantities can be obtained through an estimation or time-consuming calculation. For the interior wall area, no validated estimation method exists. However, in the case of environmental impact assessment or evaluating the existing building stock as future material banks, knowledge of the material quantities used in interior walls is indispensable. This paper presents a validated method for the estimation of the interior wall area for dwellings based on easy-to-collect building characteristics. A database of 4963 residential buildings spread all over Belgium is used. The data are collected through onsite measurements of the buildings during the construction phase (between mid-2010 and mid-2017). The interior wall area refers to the area of all interior walls in the building, including the inner leaf of exterior (party) walls, minus the area of windows and doors, unless mentioned otherwise. The two predictive modelling techniques used are 1) a (stepwise) linear regression and 2) a decision tree. The best estimation method is selected based on the best R² k-fold (5) fit. The research shows that the building volume is by far the most important variable to estimate the interior wall area. A stepwise regression based on building volume per building, building typology, and type of house provides the best fit, with R² k-fold (5) = 0.88. Although the best R² k-fold value is obtained when the other parameters ‘building typology’ and ‘type of house’ are included, the contribution of these variables can be seen as statistically significant but practically irrelevant. Thus, if these parameters are not available, a simplified estimation method based on only the volume of the building can also be applied (R² k-fold = 0.87). The robustness and precision of the method (output) are validated three times. Firstly, the prediction of the interior wall area is checked by means of alternative calculations of the building volume and of the interior wall area; thus, other definitions are applied to the same data. Secondly, the output is tested on an extension of the database, so it has the same definitions but on other data. Thirdly, the output is checked on an unrelated database with other definitions and other data. The validation of the estimation methods demonstrates that the methods remain accurate when underlying data are changed. The method can support environmental as well as economic dimensions of impact assessment, as it can be used in early design. As it allows the prediction of the amount of interior wall materials to be produced in the future or that might become available after demolition, the presented estimation method can be part of material flow analyses on input and on output.

Keywords: buildings as material banks, building stock, estimation method, interior wall area

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Authors: H. Mahdavinezhad, M. Labbaf, H. R. Tavakoli

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In recent decades, the study and control of the destructive effects of explosive vibration in construction projects has received more attention, and several experimental equations in the field of vibration prediction as well as allowable vibration limit for various structures are presented. Researchers have developed a number of experimental equations to estimate the peak particle velocity (PPV), in which the experimental constants must be obtained at the site of the explosion by fitting the data from experimental explosions. In this study, the most important of these equations was evaluated for strong massive conglomerates around Dez Dam by collecting data on explosions, including 30 particle velocities, 27 displacements, 27 vibration frequencies and 27 acceleration of earth vibration at different distances; they were recorded in the form of two types of detonation systems, NUNEL and electric. Analysis showed that the data from the explosion had the best correlation with the cube root of the explosive, R2=0.8636, but overall the correlation coefficients are not much different. To estimate the vibration in this project, data regression was performed in the other formats, which resulted in the presentation of new equation with R2=0.904 correlation coefficient. Finally according to the importance of the studied structures in order to ensure maximum non damage to adjacent structures for each diagram, a range of application was defined so that for distances 0 to 70 meters from blast site, exponent n=0.33 and for distances more than 70 m, n =0.66 was suggested.

Keywords: blasting, blast-induced vibration, empirical equations, PPV, tunnel

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Authors: Petr Gurný

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

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

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Authors: Liaqat Ali, Khalid Farooq, Shafieullah Khan, Nasir Orakzai, Qudratullah

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Objectives: To determine the risk factors for high resistance of ciprofloxacin in complicated urinary tract infections. Materials and Methods: It is an analytical study that was conducted in the department of Urology (Team ‘C’) at Institute of Kidney Diseases Hayatabad Peshawar from 1st June 2012 till 31st December 2012. Total numbers of 100 patients with complicated UTI was selected in the study. Multivariate analysis and linear regression were performed for the detection of risk factors. All the data was recorded on structured Proforma and was analyzed on SPSS version 17. Results: The mean age of the patient was 55.6 years (Range 3-82 years). 62 patients were male while 38 patients were female. 66 isolates of E-Coli were found sensitive to ciprofloxacin while 34 isolates were found Resistant for ciprofloxacin. Using multivariate analysis and linear regression, an increasing age above 50 (p=0.002) History of urinary catheterization especially for bladder outflow obstruction (p=0.001) and previous multiple use of ciprofloxacin (p=0.001) and poor brand of ciprofloxacin were found to be independent risk factors for high resistance of ciprofloxacin. Conclusion: UTI is common illness across the globe with increasing trend of antimicrobial resistance for ciprofloxacin against E Coli in complicated UTI. The risk factors for emerging resistance are increasing age, urinary catheterization and multiple use and poor brand of ciprofloxacin.

Keywords: urinary tract infection, ciprofloxacin, urethral catheterization, antimicrobial resistance

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Authors: Samuel Nii Tackie, Oyebade K. Oyedotun, Ebenezer O. Olaniyi, Adnan Khashman

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Artificial neural networks have been used in different fields of artificial intelligence, and more specifically in machine learning. Although, other machine learning options are feasible in most situations, but the ease with which neural networks lend themselves to different problems which include pattern recognition, image compression, classification, computer vision, regression etc. has earned it a remarkable place in the machine learning field. This research exploits neural networks as a data mining tool in predicting the number of times a student repeats a course, considering some attributes relating to the course itself, the teacher, and the particular student. Neural networks were used in this work to map the relationship between some attributes related to students’ course assessment and the number of times a student will possibly repeat a course before he passes. It is the hope that the possibility to predict students’ performance from such complex relationships can help facilitate the fine-tuning of academic systems and policies implemented in learning environments. To validate the power of neural networks in data mining, Turkish students’ performance database has been used; feedforward and radial basis function networks were trained for this task; and the performances obtained from these networks evaluated in consideration of achieved recognition rates and training time.

Keywords: artificial neural network, data mining, classification, students’ evaluation

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Authors: Faiz N. K. Yusufi, Aquil Ahmed, Jamal Ahmad

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Diabetes in India is growing at an alarming rate and the complications caused by it need to be controlled. Coronary heart disease (CHD) is one of the complications that will be discussed for prediction in this study. India has the second most number of diabetes patients in the world. To the best of our knowledge, there is no CHD risk score for Indian type 2 diabetes patients. Any form of CHD has been taken as the event of interest. A sample of 750 was determined and randomly collected from the Rajiv Gandhi Centre for Diabetes and Endocrinology, J.N.M.C., A.M.U., Aligarh, India. Collected variables include patients data such as sex, age, height, weight, body mass index (BMI), blood sugar fasting (BSF), post prandial sugar (PP), glycosylated haemoglobin (HbA1c), diastolic blood pressure (DBP), systolic blood pressure (SBP), smoking, alcohol habits, total cholesterol (TC), triglycerides (TG), high density lipoprotein (HDL), low density lipoprotein (LDL), very low density lipoprotein (VLDL), physical activity, duration of diabetes, diet control, history of antihypertensive drug treatment, family history of diabetes, waist circumference, hip circumference, medications, central obesity and history of CHD. Predictive risk scores of CHD events are designed by cox proportional hazard regression. Model calibration and discrimination is assessed from Hosmer Lemeshow and area under receiver operating characteristic (ROC) curve. Overfitting and underfitting of the model is checked by applying regularization techniques and best method is selected between ridge, lasso and elastic net regression. Youden’s index is used to choose the optimal cut off point from the scores. Five year probability of CHD is predicted by both survival function and Markov chain two state model and the better technique is concluded. The risk scores for CHD developed can be calculated by doctors and patients for self-control of diabetes. Furthermore, the five-year probabilities can be implemented as well to forecast and maintain the condition of patients.

Keywords: coronary heart disease, cox proportional hazard regression, ROC curve, type 2 diabetes Mellitus

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Authors: Emmanuel Osaze Oshoiribhor, Adetokunbo MacGregor John-Otumu

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In recent years, there has been a desire to forecast student academic achievement prior to graduation. This is to help them improve their grades, particularly for individuals with poor performance. The goal of this study is to employ supervised learning techniques to construct a predictive model for student academic achievement. Many academics have already constructed models that predict student academic achievement based on factors such as smoking, demography, culture, social media, parent educational background, parent finances, and family background, to name a few. This feature and the model employed may not have correctly classified the students in terms of their academic performance. This model is built using a logistic regression classifier with basic features such as the previous semester's course score, attendance to class, class participation, and the total number of course materials or resources the student is able to cover per semester as a prerequisite to predict if the student will perform well in future on related courses. The model outperformed other classifiers such as Naive bayes, Support vector machine (SVM), Decision Tree, Random forest, and Adaboost, returning a 96.7% accuracy. This model is available as a desktop application, allowing both instructors and students to benefit from user-friendly interfaces for predicting student academic achievement. As a result, it is recommended that both students and professors use this tool to better forecast outcomes.

Keywords: artificial intelligence, ML, logistic regression, performance, prediction

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Authors: Helena Baffoe

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

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

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Authors: Tareq Nikzad

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Since the first bank was established in 1933, Afghanistan's banking sector has seen a number of variations but hasn't been able to grow to its full potential because of the civil war. The implementation of dual banks in Afghanistan is investigated in this study in relation to the effects of inflation and interest rates. This research took data from World Bank Data (WBD) over a period of nineteen years. For the banking sector, inflation, which is the general rise in prices of goods and services over time, presents considerable difficulties. The objectives of this research are to analyze the effect of inflation and interest rates on conventional and Islamic banks in Afghanistan, identify potential differences between these two banking models, and provide insights for policymakers and practitioners. A mixed-methods approach is used in the research to analyze quantitative data and qualitatively examine the unique difficulties that banks in Afghanistan's economic atmosphere encounter. The findings contribute to the understanding of the relationship between interest rate, inflation rate, and the performance of both banking systems in Afghanistan. The paper concludes with recommendations for policymakers and banking institutions to enhance the stability and growth of the banking sector in Afghanistan. Interest is described as "a prefixed rate for use or borrowing of money" from an Islamic perspective. This "prefixed rate," known in Islamic economics as "riba," has been described as "something undesirable." Furthermore, by using the time series regression data technique on the annual data from 2003 to 2021, this research examines the effect of CPI inflation rate and interest rate of Banking in Afghanistan.

Keywords: inflation, Islamic banking, conventional banking, interest, Afghanistan, impact

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Authors: Shinoy Vengaramkode Bhaskaran, Kaushik Sathupadi, Sandesh Achar

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Human locomotion recognition is important in a variety of sectors, such as robotics, security, healthcare, fitness tracking and cloud computing. With the increasing pervasiveness of peripheral devices, particularly Inertial Measurement Units (IMUs) sensors, researchers have attempted to exploit these advancements in order to precisely and efficiently identify and categorize human activities. This research paper introduces a state-of-the-art methodology for the recognition of human locomotion patterns in a cloud environment. The methodology is based on a publicly available benchmark dataset. The investigation implements a denoising and windowing strategy to deal with the unprocessed data. Next, feature extraction is adopted to abstract the main cues from the data. The SelectKBest strategy is used to abstract optimal features from the data. Furthermore, state-of-the-art ML classifiers are used to evaluate the performance of the system, including logistic regression, random forest, gradient boosting and SVM have been investigated to accomplish precise locomotion classification. Finally, a detailed comparative analysis of results is presented to reveal the performance of recognition models.

Keywords: artificial intelligence, cloud computing, IoT, human locomotion, gradient boosting, random forest, neural networks, body-worn sensors

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Authors: Iacopo Odoardi, Emanuele Felice, Dario D'Ingiullo

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We investigate the role of human capital in the Chinese inland-coastal inequality and how the consequences of the 2007-2008 crisis may induce China to refocus its development path on human capital. We compare panel data analyses for two periods for the richer/coastal and the relatively poor/inland provinces. Considering the rapid evolution of the Chinese economy and the changes forced by the international crisis, we wonder if these events can lead to rethinking local development paths, fostering greater attention on the diffusion of higher education. We expect that the consequences on human capital may, in turn, have consequences on the inland/coastal dualism. The focus on human capital is due to the fact that the growing differences between inland and coastal areas can be explained by the different local endowments. In this respect, human capital may play a major role and should be thoroughly investigated. To assess the extent to which human capital has an effect on economic growth, we consider a fixed-effects model where differences among the provinces are considered parametric shifts in the regression equation. Data refer to the 31 Chinese provinces for the periods 1998-2008 and 2009-2017. Our dependent variable is the annual variation of the provincial gross domestic product (GDP) at the prices of the previous year. Among our regressors, we include two proxies of advanced human capital and other known factors affecting economic development. We are aware of the problem of conceptual endogeneity of variables related to human capital with respect to GDP; we adopt an instrumental variable approach (two-stage least squares) to avoid inconsistent estimates. Our results suggest that the economic strengths that influenced the Chinese take-off and the dualism are confirmed in the first period. These results gain relevance in comparison with the second period. An evolution in local economic endowments is taking place: first, although human capital can have a positive effect on all provinces after the crisis, not all types of advanced education have a direct economic effect; second, the development path of the inland area is changing, with an evolution towards more productive sectors which can favor higher returns to human capital. New strengths (e.g., advanced education, transport infrastructures) could be useful to foster development paths of inland-coastal desirable convergence, especially by favoring the poorer provinces. Our findings suggest that in all provinces, human capital can be useful to promote convergence in growth paths, even if investments in tertiary education seem to have a negative role, most likely due to the inability to exploit the skills of highly educated workers. Furthermore, we observe important changes in the economic characteristics of the less developed internal provinces. These findings suggest an evolution towards more productive economic sectors, a greater ability to exploit both investments in fixed capital and the available infrastructures. All these aspects, if connected with the improvement in the returns to human capital (at least at the secondary level), lead us to assume a better reaction (i.e., resilience) of the less developed provinces to the crisis effects.

Keywords: human capital, inland-coastal inequality, Great Recession, China

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

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

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

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Authors: Asma Alzahrani, Elizabeth Stojanovski

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

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

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Authors: Marianne Dæhlen

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Periods of apprenticeships are characteristic of many vocational educational training (VET) systems. In many countries, becoming a skilled worker implies that the journey starts with an application for apprenticeships at a company or another relevant training establishment. In Norway, where this study is conducted, VET students start their journey with two years of school-based training before applying for two years of apprenticeship. Previous research has shown that access to apprenticeships differs by family background (socio-economic, immigrant, etc.), gender, school grades, and region. The question we raise in this study is whether the status, reputation, or position of the vocational school contributes to VET students’ access to apprenticeships. Data and methods: Register data containing information about schools’ and VET students’ characteristics will be analyzed in multilevel regression analyses. At the school level, the data will contain information on school size, shares of immigrants and/or share of male/female students, and grade requirements for admission. At the VET-student level, the register contains information on e.g., gender, school grades, educational program/trade, obtaining apprenticeship or not. The data set comprises about 3,000 students. Results: The register data is expected to be received in November 2024 and consequently, any results are not present at the point of this call. The planned article is part of a larger research project granted from the Norwegian Research Council and will, accordingly to the plan, start up in December 2024.

Keywords: apprenticeships, VET-students’ characteristics, vocational schools, quantitative methods

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Authors: Ming-Chan Chung, Wen-Ming Huang, Yi-Chu Liu, Li-Hui Yang, Ming-Jyh Chen

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introduction: Hospital buildings require considerable energy, including air conditioning, lighting, elevators, heating, and medical equipment. Energy consumption in hospitals is expected to increase significantly due to innovative equipment and continuous development plans. Consequently, the environment and climate will be adversely affected. Hospitals should therefore consider transforming from their traditional role of saving lives to being at the forefront of global efforts to reduce carbon dioxide emissions. As healthcare providers, it is our responsibility to provide a high-quality environment while using as little energy as possible. Purpose / Methods: Compare the energy-saving benefits of solar photovoltaic systems and solar hot water systems. The proportion of electricity consumption effectively reduced after the installation of solar photovoltaic systems. To comprehensively assess the potential benefits of utilizing solar energy for both photovoltaic (PV) and solar thermal applications in hospitals, a solar PV system was installed covering a total area of 28.95 square meters in 2021. Approval was obtained from the Taiwan Power Company to integrate the system into the hospital's electrical infrastructure for self-use. To measure the performance of the system, a dedicated meter was installed to track monthly power generation, which was then converted into area output using an electric energy conversion factor. This research aims to compare the energy efficiency of solar PV systems and solar thermal systems. Results: Using the conversion formula between electrical and thermal energy, we can compare the energy output of solar heating systems and solar photovoltaic systems. The comparative study draws upon data from February 2021 to February 2023, wherein the solar heating system generated an average of 2.54 kWh of energy per panel per day, while the solar photovoltaic system produced 1.17 kWh of energy per panel per day, resulting in a difference of approximately 2.17 times between the two systems. Conclusions: After conducting statistical analysis and comparisons, it was found that solar thermal heating systems offer higher energy and greater benefits than solar photovoltaic systems. Furthermore, an examination of literature data and simulations of the energy and economic benefits of solar thermal water systems and solar-assisted heat pump systems revealed that solar thermal water systems have higher energy density values, shorter recovery periods, and lower power consumption than solar-assisted heat pump systems. Through monitoring and empirical research in this study, it has been concluded that a heat pump-assisted solar thermal water system represents a relatively superior energy-saving and carbon-reducing solution for medical institutions. Not only can this system help reduce overall electricity consumption and the use of fossil fuels, but it can also provide more effective heating solutions.

Keywords: sustainable development, energy conservation, carbon reduction, renewable energy, heat pump system

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Authors: Daniela Evtimovska, Ivana Srbinovska, Padraig O’Rourke

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Kostal Macedonia has the challenge to mill a rigid-flex printed circuit board (PCB). The PCB elaborated in this paper is made of FR4 material covered with polyimide through the whole surface on the one side, including the tabs where PCBs need to be separated. After milling only 1.44 meters, the updraft routing tool isn’t effective and causes polyimide debris on all PCB cuts if it continues to mill with the same tool. Updraft routing tool is used for all another product in Kostal Macedonia, and it is changing after milling 60 meters. Changing the tool adds 80 seconds to the cycle time. One solution is using a laser-cut machine. Buying a laser-cut machine for cutting only one product doesn’t make financial sense. The focus is given to find an internal solution among the options under review to solve the issue with polyimide debris. In the paper, the design of the rigid-flex panel is described deeply. It is evaluated downdraft routing tool as a possible solution which could be used for the flex rigid panel as a specific product. It is done a comparison between updraft and down draft routing tools from a technical and financial aspect of view, taking into consideration the customer requirements for the rigid-flex PCB. The results show that using the downdraft routing tool is the best solution in this case. This tool is more expensive for 0.62 euros per piece than updraft. The downdraft routing tool needs to be changed after milling 43.44 meters in comparison with the updraft tool, which needs to be changed after milling only 1.44 meters. It is done analysis which actions should be taken in order further improvements and the possibility of maximum serving of downdraft routing tool.

Keywords: Kostal Macedonia, rigid flex PCB, polyimide, debris, milling process, up/down draft routing tool

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Authors: L. Lamola, E. Manolas, A. Krause

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Background: The incidence of childhood cancer incidence is increasing gradually in low-middle income countries, such as South Africa. Globally, there is an extensive range of familial- and hereditary-cancer syndromes, where underlying germline variants increase the likelihood of developing cancer in childhood. Next-Generation Sequencing (NGS) technologies have been key in determining the occurrence and genetic contribution of germline variants to paediatric cancer development. We aimed to design and evaluate a candidate gene panel specific to inherited cancer-predisposing genes to provide a comprehensive insight into the contribution of germline variants to childhood cancer. Methods: 32 paediatric patients (aged 0-18 years) diagnosed with a malignant tumour were recruited, and biological samples were obtained. After quality control, DNA was sequenced using an ion Ampliseq 50 candidate gene panel design and Ion Torrent S5 technologies. Sequencing variants were called using Ion Torrent Suite software and were subsequently annotated using Ion Reporter and Ensembl's VEP. High priority variants were manually analysed using tools such as MutationTaster, SIFT-INDEL and VarSome. Putative identified candidates were validated via Sanger Sequencing. Results: The patients studied had a variety of cancers, the most common being nephroblastoma (13), followed by osteosarcoma (4) and astrocytoma (3). We identified 10 pathogenic / likely pathogenic variants in 10 patients, most of which were novel. Conclusions: According to the literature, we expected ~10% of our patient population to harbour pathogenic or likely pathogenic germline variants, however, we reported about 3 times (~30%) more than we expected. Majority of the identified variants are novel; this may be because this is the first study of its kind in an understudied South African population.

Keywords: Africa, genetics, germline-variants, paediatric-cancer

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Authors: Dieudonne Uwizeye

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Introduction: Diarrhoea is one of the major causes of morbidity and mortality among communities living in urban informal settlements of developing countries. It is assumed that mountainous environment introduces variations of the burden among residents of the same settlements. Design and Objective: A cross-sectional study was done in Kigali to explore the effect of mountainous informal settlements on diarrhoea risk variations. Data were collected among 1,152 households through household survey and transect walk to observe the status of sanitation. The outcome variable was the incidence of diarrhoea among household members of any age. The study used the most knowledgeable person in the household as the main respondent. Mostly this was the woman of the house as she was more likely to know the health status of every household member as she plays various roles: mother, wife, and head of the household among others. The analysis used cross tabulation and logistic regression analysis. Results: Results suggest that risks for diarrhoea vary depending on home location in the settlements. Diarrhoea risk increased as the distance from the road increased. The results of the logistic regression analysis indicate the adjusted odds ratio of 2.97 with 95% confidence interval being 1.35-6.55 and 3.50 adjusted odds ratio with 95% confidence interval being 1.61-7.60 in level two and three respectively compared with level one. The status of sanitation within and around homes was also significantly associated with the increase of diarrhoea. Equally, it is indicated that stable households were less likely to have diarrhoea. The logistic regression analysis indicated the adjusted odds ratio of 0.45 with 95% confidence interval being 0.25-0.81. However, the study did not find evidence for a significant association between diarrhoea risks and household socioeconomic status in the multivariable model. It is assumed that environmental factors in mountainous settings prevailed. Households using the available public water sources were more likely to have diarrhoea in their households. Recommendation: The study recommends the provision and extension of infrastructure for improved water, drainage, sanitation and wastes management facilities. Equally, studies should be done to identify the level of contamination and potential origin of contaminants for water sources in the valleys to adequately control the risks for diarrhoea in mountainous urban settings.

Keywords: urbanisation, diarrhoea risk, mountainous environment, urban informal settlements in Rwanda

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Authors: Rubab Zafar Kahlon, Ibtisam Butt

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

Keywords: forest resource, biodiversity, expliotation, human activities

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Authors: Sakul Jariyachansit

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The purposes of this research were to investigate and to compare the factors affecting the decision for Tourism in Thailand by ASEAN Tourists and among ASEAN community tourists. Samples in this research were 400 ASEAN Community Tourists who travel in Thailand at Suvarnabhumi Airport during November 2016 - February 2016. The researchers determined the sample size by using the formula Taro Yamane at 95% confidence level tolerances 0.05. The English questionnaire, research instrument, was distributed by convenience sampling, for gathering data. Descriptive statistics was applied to analyze percentages, mean and standard deviation and used for hypothesis testing. The statistical analysis by multiple regression analysis (Multiple Regression) was employed to prove the relationship hypotheses at the significant level of 0.01. The results showed that majority of the respondents indicated the factors affecting the decision for Tourism in Thailand by ASEAN Tourists, in general there were a moderate effects and the mean of each side is moderate. Transportation was the most influential factor for tourism in Thailand. Therefore, the mode of transport, information, infrastructure and personnel are very important to factor affecting decision making for tourism in Thailand by ASEAN tourists. From the hypothesis testing, it can be predicted that the decision for choosing Tourism in Thailand is at R2 = 0.449. The predictive equation is decision for choosing Tourism in Thailand = 1.195 (constant value) + 0.425 (tourist attraction) +0.217 (information received) and transportation factors, tourist attraction, information, human resource and infrastructure at the significant level of 0.01.

Keywords: factor, decision making, ASEAN tourists, tourism in Thailand

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Authors: Muhammad Ajmair

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This empirical study followed structural vector auto regression (SVAR) approach proposed by the so-called AB-model of Amisano and Giannini (1997) to check the impact of relevant macroeconomic determinants on economic growth in Pakistan. Before that auto regressive distributive lag (ARDL) bound testing technique and time varying parametric approach along with general to specific approach was employed to find out relevant significant determinants of economic growth. To our best knowledge, no author made such a study that employed auto regressive distributive lag (ARDL) bound testing and time varying parametric approach with general to specific approach in empirical literature, but current study will bridge this gap. Annual data was taken from World Development Indicators (2014) during period 1976-2014. The widely-used Schwarz information criterion and Akaike information criterion were considered for the lag length in each estimated equation. Main findings of the study are that remittances received, gross national expenditures and inflation are found to be the best relevant positive and significant determinants of economic growth. Based on these empirical findings, we conclude that government should focus on overall economic growth augmenting factors while formulating any policy relevant to the concerned sector.

Keywords: economic growth, gross national expenditures, inflation, remittances

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Authors: Acumsiri Tantikarnpanit

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This paper aims to examine the relationship between urbanization and income inequality in Thailand during the period 2002–2020. Using a panel of data for 76 provinces collected from Thailand’s National Statistical Office (Labor Force Survey: LFS), as well as geospatial data from the U.S. Air Force Defense Meteorological Satellite Program (DMSP) and the Visible Infrared Imaging Radiometer Suite Day/Night band (VIIRS-DNB) satellite for nineteen selected years. This paper employs two different definitions to identify urban areas: 1) Urban areas defined by Thailand's National Statistical Office (Labor Force Survey: LFS), and 2) Urban areas estimated using nighttime light data from the DMSP and VIIRS-DNB satellite. The second method includes two sub-categories: 2.1) Determining urban areas by calculating nighttime light density with a population density of 300 people per square kilometer, and 2.2) Calculating urban areas based on nighttime light density corresponding to a population density of 1,500 people per square kilometer. The empirical analysis based on Ordinary Least Squares (OLS), fixed effects, and random effects models reveals a consistent U-shaped relationship between income inequality and urbanization. The findings from the econometric analysis demonstrate that urbanization or population density has a significant and negative impact on income inequality. Moreover, the square of urbanization shows a statistically significant positive impact on income inequality. Additionally, there is a negative association between logarithmically transformed income and income inequality. This paper also proposes the inclusion of satellite imagery, geospatial data, and spatial econometric techniques in future studies to conduct quantitative analysis of spatial relationships.

Keywords: income inequality, nighttime light, population density, Thailand, urbanization

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

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Samples of tap and wastewater were collected in three offset printing facilities in Novi Sad, Serbia. Ten physicochemical parameters were analyzed within all collected samples: pH, conductivity, m - alkalinity, p - alkalinity, acidity, carbonate concentration, hydrogen carbonate concentration, active oxygen content, chloride concentration and total alkali content. All measurements were conducted using the standard analytical and instrumental methods. Comparing the obtained results for tap water and wastewater, a clear quality difference was noticeable, since all physicochemical parameters were significantly higher within wastewater samples. The study also involves the application of simple linear regression analysis on the obtained dataset. By using software package ORIGIN 5 the pH value was mutually correlated with other physicochemical parameters. Based on the obtained values of Pearson coefficient of determination a strong positive correlation between chloride concentration and pH (r = -0.943), as well as between acidity and pH (r = -0.855) was determined. In addition, statistically significant difference was obtained only between acidity and chloride concentration with pH values, since the values of parameter F (247.634 and 182.536) were higher than F_critical (5.59). In this way, results of statistical analysis highlighted the most influential parameter of water contamination in offset printing, in the form of acidity and chloride concentration. The results showed that variable dependence could be represented by the general regression model: y = a₀ + a₁x+ k, which further resulted with matching graphic regressions.

Keywords: pollution, printing industry, simple linear regression analysis, wastewater

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Authors: Kehinde Peter Oyeduntan, Kayode Oshinubi

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

Keywords: container, ports, terminals, throughput

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Authors: Sophia Liang Zhou

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For investors, businesses, consumers, and governments, an accurate assessment of future housing prices is crucial to critical decisions in resource allocation, policy formation, and investment strategies. Previous studies are contradictory about macroeconomic determinants of housing price and largely focused on one or two areas using point prediction. This study aims to develop data-driven models to accurately predict future housing market trends in different markets. This work studied five different metropolitan areas representing different market trends and compared three-time lagging situations: no lag, 6-month lag, and 12-month lag. Linear regression (LR), random forest (RF), and artificial neural network (ANN) were employed to model the real estate price using datasets with S&P/Case-Shiller home price index and 12 demographic and macroeconomic features, such as gross domestic product (GDP), resident population, personal income, etc. in five metropolitan areas: Boston, Dallas, New York, Chicago, and San Francisco. The data from March 2005 to December 2018 were collected from the Federal Reserve Bank, FBI, and Freddie Mac. In the original data, some factors are monthly, some quarterly, and some yearly. Thus, two methods to compensate missing values, backfill or interpolation, were compared. The models were evaluated by accuracy, mean absolute error, and root mean square error. The LR and ANN models outperformed the RF model due to RF’s inherent limitations. Both ANN and LR methods generated predictive models with high accuracy ( > 95%). It was found that personal income, GDP, population, and measures of debt consistently appeared as the most important factors. It also showed that technique to compensate missing values in the dataset and implementation of time lag can have a significant influence on the model performance and require further investigation. The best performing models varied for each area, but the backfilled 12-month lag LR models and the interpolated no lag ANN models showed the best stable performance overall, with accuracies > 95% for each city. This study reveals the influence of input variables in different markets. It also provides evidence to support future studies to identify the optimal time lag and data imputing methods for establishing accurate predictive models.

Keywords: linear regression, random forest, artificial neural network, real estate price prediction

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Authors: Sandeep Sharma, Sarabjit Singh

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As per the user demand and growth trends of large free data the storage solutions are now becoming more challenge-able to protect, store and to retrieve data. The days are not so far when the storage companies and organizations are start saying 'no' to store our valuable data or they will start charging a huge amount for its storage and protection. On the other hand as per the environmental conditions it becomes challenge-able to maintain and establish new data warehouses and data centers to protect global warming threats. A challenge of small data is over now, the challenges are big that how to manage the exponential growth of data. In this paper we have analyzed the growth trend of big data and its future implications. We have also focused on the impact of the unstructured data on various concerns and we have also suggested some possible remedies to streamline big data.

Keywords: big data, unstructured data, volume, variety, velocity

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Authors: Chakraborty Sudipta, A. R. Kambekar, Sarma Arnab

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Sea-level rise being one of the most important impacts of anthropogenic induced climate change resulting from global warming and melting of icebergs at Arctic and Antarctic, the investigations done by various researchers both on Indian Coast and elsewhere during the last decade has been reviewed in this paper. The paper aims to ascertain the propensity of consistency of different suggested methods to predict the near-accurate future sea level rise along the coast of Mumbai. Case studies at East Coast, Southern Tip and West and South West coast of India have been reviewed. Coastal Vulnerability Index of several important international places has been compared, which matched with Intergovernmental Panel on Climate Change forecasts. The application of Geographic Information System mapping, use of remote sensing technology, both Multi Spectral Scanner and Thematic Mapping data from Landsat classified through Iterative Self-Organizing Data Analysis Technique for arriving at high, moderate and low Coastal Vulnerability Index at various important coastal cities have been observed. Instead of data driven, hindcast based forecast for Significant Wave Height, additional impact of sea level rise has been suggested. Efficacy and limitations of numerical methods vis-à-vis Artificial Neural Network has been assessed, importance of Root Mean Square error on numerical results is mentioned. Comparing between various computerized methods on forecast results obtained from MIKE 21 has been opined to be more reliable than Delft 3D model.

Keywords: climate change, Coastal Vulnerability Index, global warming, sea level rise

Procedia PDF Downloads 132