Search results for: panel data regression analysis

Authors: Amir Ikram, Qin Su, Muhammad Fiaz, Muhammad Waqas Shabbir

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Women entrepreneurship witnessed a healthy rise in the last decade or so, and the scenario in Pakistan is not different. However female leaders are facing various, cultural, career oriented, and professional challenges. The study investigates the impact of social and industry-specific challenges on female entrepreneurship; social challenges was evaluated in terms of culture, and industry-specific challenges was measured in terms of team management and career growth. Purposive sampling was employed to collect data from 75 multicultural organizations operating in the culturally diverse and historic city of Lahore, Pakistan. Cronbach’s alpha was conducted to endorse the reliability of survey questionnaire, while correlation and regression analysis were used to test hypotheses. Industry-specific challenges were found to be more significant as compared to cultural factors. The paper also highlights the importance of female entrepreneurship for emerging economies, and suggests that bringing women to mainstream professions can lead to economic success.

Keywords: cultural challenges, emerging economy, female entrepreneurship, leadership

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Authors: Minto Rattan, Tania Bose, Neeraj Chamoli

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The present paper investigates the effect of linear thermal gradient on the steady-state creep behavior of rotating isotropic disc using threshold stress based Sherby’s creep law. The composite discs made of aluminum matrix reinforced with silicon carbide particulate has been taken for analysis. The stress and strain rate distributions have been calculated for discs rotating at linear thermal gradation using von Mises’ yield criterion. The material parameters have been estimated by regression fit of the available experimental data. The results are displayed and compared graphically in designer friendly format for the above said temperature profile with the disc operating under uniform temperature profile. It is observed that radial and tangential stresses show minor variation and the strain rates vary significantly in the presence of thermal gradation as compared to disc having uniform temperature.

Keywords: creep, isotropic, steady-state, thermal gradient

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Authors: Nordila Ahmad, Thamer Mohammad, Bruce W. Melville, Zuliziana Suif

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Current methods for predicting local scour at wide bridge piers, were developed on the basis of laboratory studies and very limited scour prediction were tested with field data. Laboratory wide pier scour equation from previous findings with field data were presented. A wide range of field data were used and it consists of both live-bed and clear-water scour. A method for assessing the quality of the data was developed and applied to the data set. Three other wide pier-scour equations from the literature were used to compare the performance of each predictive method. The best-performing scour equation were analyzed using statistical analysis. Comparisons of computed and observed scour depths indicate that the equation from the previous publication produced the smallest discrepancy ratio and RMSE value when compared with the large amount of laboratory and field data.

Keywords: field data, local scour, scour equation, wide piers

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Authors: M. Sakthivel, A. Devaki, D. Balasubramanyam, P. Kumarasamy, A. Raja, R. Anilkumar, H. Gopi

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The influence of inbreeding on growth traits in the New Zealand White rabbits maintained at Sheep Breeding and Research Station, Sandynallah, The Nilgiris, India was studied in a closed herd. Data were collected over a period of 15 years (1998 to 2012). The traits studied were body weights at weaning (W42), post-weaning (W70) and marketing (W135) age and growth efficiency traits viz., average daily gain (ADG), relative growth rate (RGR) and Kleiber ratio (KR) estimated on a daily basis at different age intervals (1=42 to 70 days; 2=70 to 135 days and 3=42 to 135 days) from weaning to marketing. The effects of inbreeding along with other non-genetic factors (sex of the kit, season and period of birth of the kit) were analyzed using least-squares method. The inbreeding (F) and equivalent inbreeding (EF) coefficients were taken as fixed classes as well as covariates in separate analyses. When taken as covariate, the effect was analyzed as partial regression of respective growth trait on individual inbreeding coefficient (F or EF). The mean body weights at weaning, post-weaning and marketing were 0.715, 1.276 and 2.187 kg, respectively. The maximum growth efficiency was noticed between weaning and post-weaning. Season and period had highly significant influence on all the growth parameters studied and sex of the kit had significant influence on certain growth efficiency traits only. The average coefficients of inbreeding and equivalent inbreeding in the population were 13.233 and 17.585 percent, respectively. About 11.17 percent of total matings were highly inbred in which full-sib, half-sib and parent-offspring matings were 1.20, 6.30 and 3.67 percent, respectively. The regression of body weight traits on F and EF showed negative effect whereas most of the growth efficiency traits showed positive effects. Significant inbreeding depression was observed in W42 and W70. The depression in W42 was 0.214 kg and 0.139 kg and in W70 was 0.269 kg and 0.172 kg for every one unit increase in F and EF, respectively. Though the trait W135 showed positive value and ADG1 showed depression, the effects of inbreeding and equivalent inbreeding were non-significant in these traits. Higher values of inbreeding depression could be due to more variance of F or EF in the population. The analysis of the effect of level of inbreeding on growth traits revealed that the inbreeding class was significant on W70, ADG2, RGR2 and KR2 while EF classes had significant influence only on ADG2, RGR2 and KR2. Obviously, inbreeding does not have a positive effect, therefore, these results suggest that inbreeding had no effect on these traits.

Keywords: growth parameters, equivalent inbreeding, inbreeding effects, rabbit genetics

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Authors: Sidharth Talan, Rajlakshmi G. Majumdar

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Problem-solving skill is one the few skills which is constantly endeavored to improve upon by the professionals and academicians around the world in order to sustain themselves in the ever-growing competitive environment. The given paper focuses on evaluating a hypothesized relationship between the problems solving efficiency of an individual with spaced repetitions, conducted with a time interval of one day over a period of two weeks. The paper has utilized uni-variate regression analysis technique to assess the best fit curve that can explain the significant relationship between the given two variables. The paper has incorporated Anagrams solving as the appropriate testing process for the analysis. Since Anagrams solving involves rearranging a jumbled word to form a correct word, it projects to be an efficient process to observe the attention span, visual- motor coordination and the verbal ability of an individual. Based on the analysis for a sample population of 30, it was observed that problem-solving efficiency of an individual, measured in terms of the score in each test was found to be significantly correlated with time period measured in days.

Keywords: Anagrams, histogram plot, moving average curve, spacing effect

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Authors: B. Madhevan, R. Sakkaravarthi, R. Diya

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The number and variety of mobile robot applications are increasing day by day, both in an industry and in our daily lives. First developed as a tool, nowadays mobile robots can be integrated as an entity in Internet-accessible resources. The present work is organized around four potential resources such as cloud computing, Internet of things, Big data analysis and Co-simulation. Further, the focus relies on integrating, analyzing and discussing the need for integrating Internet-accessible resources and the challenges deriving from such integration, and how these issues have been tackled. Hence, the research work investigates the concepts of the Internet-accessible resources from the aspect of the autonomous mobile robots with an overview of the performances of the currently available database systems. IaR is a world-wide network of interconnected objects, can be considered an evolutionary process in mobile robots. IaR constitutes an integral part of future Internet with data analysis, consisting of both physical and virtual things.

Keywords: internet-accessible resources, cloud computing, big data analysis, internet of things, mobile robot

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Authors: Oualid Walid Ben Ali

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Big Data has become one of the buzzwords today. The recent explosion of digital data has led the organization, either private or public, to a new era towards a more efficient decision making. At some point, business decided to use that concept in order to learn what make their clients tick with phrases like ‘sales funnel’ analysis, ‘actionable insights’, and ‘positive business impact’. So, it stands to reason that Big Data was viewed through green (read: money) colored lenses. Somewhere along the line, however someone realized that collecting and processing data doesn’t have to be for business purpose only, but also could be used for other purposes to assist law enforcement or to improve policing or in road safety. This paper presents briefly, how Big Data have been used in the fields of policing order to improve the decision making process in the daily operation of the police. As example, we present a big-data driven system which is sued to accurately dispatch the patrol cars in a geographic environment. The system is also used to allocate, in real-time, the nearest patrol car to the location of an incident. This system has been implemented and applied in the Emirate of Abu Dhabi in the UAE.

Keywords: big data, big data analytics, patrol car allocation, dispatching, GIS, intelligent, Abu Dhabi, police, UAE

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Authors: Deng Tingting

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The coordinated and integrated development of regions is an inevitable requirement for China to move towards high-quality, sustainable development. As one of the regions with the best economic foundation and the strongest economic strength in western China, it is a typical area with national importance and strong network connection characteristics in terms of the comprehensive effect of linking the inland hinterland and connecting the western and national urban networks. The integrated development of the Chengdu-Chongqing economic circle is of great strategic significance for the rapid and high-quality development of the western region. In the context of new urbanization, this paper takes 16 urban units within the economic circle as the research object, based on the 5-year panel data of population, regional economy, and spatial construction and development from 2016 to 2020, using the entropy method and Theil index to analyze the three target layers, and cause analysis. The research shows that there are temporal and spatial differences in the Chengdu-Chongqing economic circle, and there are significant differences between the core city and the surrounding cities. Therefore, by reforming and innovating the regional coordinated development mechanism, breaking administrative barriers, and strengthening the "polar nucleus" radiation function to release the driving force for economic development, especially in the gully areas of economic development belts, not only promote the coordinated development of internal regions but also promote the coordinated and sustainable development of the western region and take a high-quality development path.

Keywords: Chengdu-Chongqing economic circle, new urbanization, coordinated regional development, Theil Index

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Authors: Saqib Amina, Waqas Mehmoodb, Iftikhar Yasinc

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This study analyzes the robust association between ethnic violence and mental health from a global perspective (201-countries across the world) by using the panel data for the period 1970 to 2020. Mental health has been determined through various dimensions such as mental disorders, anxiety disorder, depressive disorder, schizophrenia, and bipolar disorder. The empirical findings show that ethnic violence has a significant positive association with all types of mental health indicators, including mental disorders, anxiety disorder, depressive disorder, schizophrenia, and bipolar disorder. The study also reveals that institutional quality may reduce the effect of mental health disorders in a better way than promoting democracy. This study suggests the non-stigmatizing social form of community-based support services instead of a stigmatizing the concept of mental illnesses that promotes hospitalization and drug treatment.

Keywords: ethnic violence, unemployment, mental health, violence

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Authors: Adji Achmad Rinaldo Fernandes

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Path analysis is an analytical technique to study the causal relationship between independent and dependent variables. In this study, the integration of Clusters in the Ward Linkage method was used in a variety of clusters with path analysis. The variables used are character (x₁), capacity (x₂), capital (x₃), collateral (x₄), and condition of economy (x₄) to on time pay (y₂) through the variable willingness to pay (y₁). The purpose of this study was to compare the Ward Linkage method cluster integration in various clusters with path analysis to classify willingness to pay (y₁). The data used are primary data from questionnaires filled out by customers of Bank X, using purposive sampling. The measurement method used is the average score method. The results showed that the Ward linkage method cluster integration with path analysis on 2 clusters is the best method, by comparing the coefficient of determination. Variable character (x₁), capacity (x₂), capital (x₃), collateral (x₄), and condition of economy (x₅) to on time pay (y₂) through willingness to pay (y₁) can be explained by 58.3%, while the remaining 41.7% is explained by variables outside the model.

Keywords: cluster integration, linkage, path analysis, compliant paying behavior

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Authors: Mayank Raj, V. G. Narendra

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Ever since the DARPA challenge on autonomous vehicles in 2005, there has been a lot of buzz about ‘Autonomous Vehicles’ amongst the major tech giants such as Google, Uber, and Tesla. Numerous approaches have been adopted to solve this problem, which can have a long-lasting impact on mankind. In this paper, we have used Deep Learning techniques and TensorFlow framework with the goal of building a neural network model to predict (speed, acceleration, steering angle, and brake) features needed for navigation of autonomous vehicles. The Deep Neural Network has been trained on images and sensor data obtained from the comma.ai dataset. A heatmap was used to check for correlation among the features, and finally, four important features were selected. This was a multivariate regression problem. The final model had five convolutional layers, followed by five dense layers. Finally, the calculated values were tested against the labeled data, where the mean squared error was used as a performance metric.

Keywords: autonomous vehicles, deep learning, computer vision, artificial intelligence

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Authors: Zahid Ullah, Atlas Khan

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The rapid growth of data in various domains has created a pressing need for effective methods to transform this data into meaningful knowledge. In this era of big data, mathematical and statistical innovations play a crucial role in unlocking insights and facilitating informed decision-making in data analytics. This abstract aims to explore the transformative potential of these innovations and their impact on converting raw data into actionable knowledge. Drawing upon a comprehensive review of existing literature, this research investigates the cutting-edge mathematical and statistical techniques that enable the conversion of data into knowledge. By evaluating their underlying principles, strengths, and limitations, we aim to identify the most promising innovations in data analytics. To demonstrate the practical applications of these innovations, real-world datasets will be utilized through case studies or simulations. This empirical approach will showcase how mathematical and statistical innovations can extract patterns, trends, and insights from complex data, enabling evidence-based decision-making across diverse domains. Furthermore, a comparative analysis will be conducted to assess the performance, scalability, interpretability, and adaptability of different innovations. By benchmarking against established techniques, we aim to validate the effectiveness and superiority of the proposed mathematical and statistical innovations in data analytics. Ethical considerations surrounding data analytics, such as privacy, security, bias, and fairness, will be addressed throughout the research. Guidelines and best practices will be developed to ensure the responsible and ethical use of mathematical and statistical innovations in data analytics. The expected contributions of this research include advancements in mathematical and statistical sciences, improved data analysis techniques, enhanced decision-making processes, and practical implications for industries and policymakers. The outcomes will guide the adoption and implementation of mathematical and statistical innovations, empowering stakeholders to transform data into actionable knowledge and drive meaningful outcomes.

Keywords: data analytics, mathematical innovations, knowledge extraction, decision-making

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Authors: Eui Jun Jeong, Hye Rim Lee, Ji Hye Yoo

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Recent studies have verified the significant relationship of user personality with Internet use. However, in game studies, little research has emphasized on the effects of personality traits on game addiction. This study examined whether big five personality traits affect game addiction with control of psychological, social, and demographic factors. Specifically, using data from a survey of 789 game users in Korea, we conducted a regression analysis to see the associations of psychological (loneliness/depression), social (activities with family/friends), self-efficacy (game/general), gaming (daily gaming time/perception), demographic (age/gender), and personality traits (extraversion, neuroticism conscientiousness, agreeableness, & openness) with the degree of game addiction. Results showed that neuroticism increase game addiction with no effect of extraversion on the addiction. General self-efficacy negatively affected game addiction, whereas game self-efficacy increased the degree of game addiction. Loneliness enhanced game addiction while depression showed a negative effect on the addiction. Results and implications are discussed.

Keywords: game addiction, big five personality, social activities, self-efficacy, loneliness, depression

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Authors: Syed Toqueer Akhter, Muhammad Awais

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Pakistan is a country which is gifted by naturally abundant resources these resources are a pioneer towards a prospect and developed country. Pakistan is the fourth largest exporter of the textile in the world and with the passage of time the competitiveness of these exports is subject to a decline. With a lot of International players in the textile world like China, Bangladesh, India, and Sri Lanka, Pakistan needs to put up a lot of effort to compete with these countries. This research paper would determine the impact of Foreign Direct Investment upon technological diffusion and that how significantly it may be affecting on export performance of the country. It would also demonstrate that with the increase in Foreign Direct Investment, technological diffusion, strong property rights, and using different policy tools, export competitiveness of the country could be improved. The research has been carried out using time series data from 1995 to 2013 and the results have been estimated by using competing Econometrics modes such as Robust regression and Generalized least squares so that to consolidate the impact of the Foreign Investments and Technological diffusion upon export competitiveness comprehensively. Distributed Lag model has also been used to encompass the lagged effect of policy tools variables used by the government. Model estimates entail that 'FDI' and 'Technological Diffusion' do have a significant impact on the competitiveness of the exports of Pakistan. It may also be inferred that competitiveness of Textile Sector requires integrated policy framework, primarily including the reduction in interest rates, providing subsides, and manufacturing of value added products.

Keywords: high technology export, robust regression, patents, technological diffusion, export competitiveness

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Authors: Daphine Namara, Jeremy I. Schwartz, Andrew K. Tusubira, Willi McFarland, Caroline Birungi, Fred C. Semitala, Martin Muddu

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Emerging evidence suggests a possible association between hyperglycemia and dolutegravir (DTG), a preferred first-line antiretroviral agent in sub-Saharan Africa (SSA). There is a need for rigorous studies to validate this association in the face of increasing DTG use and the burden of non-communicable diseases among people living with HIV (PLHIV). We conducted a case-control study to assess the risk of hyperglycemia associated with the use of DTG among PLHIV attending Mulago ISS Clinic in Kampala. Cases had hyperglycemia, while controls had no hyperglycemia, as confirmed by fasting plasma glucose and oral glucose tolerance tests. Demographic, laboratory, and clinical data were collected using interviewer-administered questionnaires and medical record abstraction. The analysis compared cases and controls on DTG use prior to diagnosis of hyperglycemia while controlling for potential confounders using multivariable logistic regression. We included 204 cases and 231 controls. In multivariable analysis, patients with prior DTG use had seven times greater odds of subsequent diagnosis of hyperglycemia compared to those who had non-DTG-based regimens (adjusted odds ratio [aOR] 7.01, 95% CI 1.96-25.09). The odds of hyperglycemia also increased with age (56 years and above vs. 18-35, aOR 12.38, 95% CI 3.79-40.50) and hypertension (aOR 5.78, 95% CI 2.53-13.21). Our study demonstrates a strong association between prior DTG exposure and subsequent diagnosis of hyperglycemia. Given the benefits of DTG, wide-scale use, and the growing burden of diabetes mellitus (DM) in SSA, there is a need for systematic screening for hyperglycemia and consideration of alternate regimens for those at risk for DM.

Keywords: HIV, hyperglycemia, doluteravir, diabetes

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Authors: S. H. Arnaud Kanga, O. Hili, S. Dabo-Niang

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Spatial prediction is an issue appealing and attracting several fields such as agriculture, environmental sciences, ecology, econometrics, and many others. Although multiple non-parametric prediction methods exist for spatial data, those are based on the conditional expectation. This paper took a different approach by examining a non-parametric spatial predictor of the conditional quantile. The study especially observes the stationary multidimensional spatial process over a rectangular domain. Indeed, the proposed quantile is obtained by inverting the conditional distribution function. Furthermore, the proposed estimator of the conditional distribution function depends on three kernels, where one of them controls the distance between spatial locations, while the other two control the distance between observations. In addition, the almost complete convergence and the convergence in mean order q of the kernel predictor are obtained when the sample considered is alpha-mixing. Such approach of the prediction method gives the advantage of accuracy as it overcomes sensitivity to extreme and outliers values.

Keywords: conditional quantile, kernel, nonparametric, stationary

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Authors: ِAbdallah A. A. Shaheen

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Gaza Strip faces economic and political issues such as conflict, siege and urbanization; all these have led to an increase in the air pollution over Gaza Strip. In this study, Particulate matter 10 (PM10) concentration over Gaza Strip has been estimated by Landsat Thematic Mapper (TM) and Landsat Enhanced Thematic Mapper Plus (ETM+) data, based on a multispectral algorithm. Simultaneously, in-situ measurements for the corresponding particulate are acquired for selected time period. Landsat and ground data for eleven years are used to develop the algorithm while four years data (2002, 2006, 2010 and 2014) have been used to validate the results of algorithm. The developed algorithm gives highest regression, R coefficient value i.e. 0.86; RMSE value as 9.71 µg/m³; P values as 0. Average validation of algorithm show that calculated PM10 strongly correlates with measured PM10, indicating high efficiency of algorithm for the mapping of PM10 concentration during the years 2000 to 2014. Overall results show increase in minimum, maximum and average yearly PM10 concentrations, also presents similar trend over urban area. The rate of urbanization has been evaluated by supervised classification of the Landsat image. Urban sprawl from year 2000 to 2014 results in a high concentration of PM10 in the study area.

Keywords: PM10, landsat, atmospheric reflectance, Gaza strip, urbanization

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Authors: Smah Almotiri

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One of the most common fields of natural language processing (NLP) is sentimental analysis. The inferred feeling in the text can be successfully mined for various events using sentiment analysis. Twitter is viewed as a reliable data point for sentimental analytics studies since people are using social media to receive and exchange different types of data on a broad scale during the COVID-19 epidemic. The processing of such data may aid in making critical decisions on how to keep the situation under control. The aim of this research is to look at how sentimental states differed in a single geographic region during the lockdown at two different times.1162 tweets were analyzed related to the COVID-19 pandemic lockdown using keywords hashtags (lockdown, COVID-19) for the first sample tweets were from March 23, 2020, until April 23, 2020, and the second sample for the following year was from March 1, 2020, until April 4, 2020. Natural language processing (NLP), which is a form of Artificial intelligence, was used for this research to calculate the sentiment value of all of the tweets by using AFINN Lexicon sentiment analysis method. The findings revealed that the sentimental condition in both different times during the region's lockdown was positive in the samples of this study, which are unique to the specific geographical area of New Zealand. This research suggests applying machine learning sentimental methods such as Crystal Feel and extending the size of the sample tweet by using multiple tweets over a longer period of time.

Keywords: sentiment analysis, Twitter analysis, lockdown, Covid-19, AFINN, NodeJS

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Authors: Proud Arunrangsiwed, Sarinya Kongtieng

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Teachers and university lecturers face an unsolved problem, which is students’ multitasking behaviors during class time, such as texting or playing a game. It is important to examine the most powerful predictor that can result in students’ educational performances. Meta-analysis was used to analyze the research articles, which were published with the keywords, multitasking, class performance, and texting. We selected 14 research articles published during 2008-2013 from online databases, and four articles met the predetermined inclusion criteria. Effect size of each pair of variables was used as the dependent variable. The findings revealed that the students’ expectancy and value on SNSs usages is the best significant predictor of their educational performances, followed by their motivation and ability in using SNSs, prior educational performances, usage behaviors of SNSs in class, and their personal characteristics, respectively. Future study should conduct a longitudinal design to better understand the effect of multitasking in the classroom.

Keywords: meta-regression analysis, social networking sites, academic Performances, multitasking, motivation

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Authors: Mostafa Kazemi, Zahra N. Farkhani

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This paper proposes an integrated Data Envelopment Analysis (DEA) and Variance Inflation Factor (VIF) model for measuring the technical efficiency of decision making units. The model is validated using a set of 69% sales representatives’ dairy products. The analysis is done in two stages, in the first stage, VIF technique is used to distinguish independent effective factors of resellers, and in the second stage we used DEA for measuring efficiency for both constant and variable return to scales status. Further DEA is used to examine the utilization of environmental factors on efficiency. Results of this paper indicated an average managerial efficiency of 83% in the whole sales representatives’ dairy products. In addition, technical and scale efficiency were counted 96% and 80% respectively. 38% of sales representative have the technical efficiency of 100% and 72% of the sales representative in terms of managerial efficiency are quite efficient.High levels of relative efficiency indicate a good condition for sales representative efficiency.

Keywords: data envelopment analysis (DEA), relative efficiency, sales representatives’ dairy products, variance inflation factor (VIF)

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Authors: Gizem Akcan, Erdinc Ozturk

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The purpose of the study was to determine some psychosocial variables that predict dissociation in university students. These psychosocial variables were perceived childhood trauma, alexithymia, and gender. 150 (75 males, 75 females) university students (bachelor, master and postgraduate) were enrolled in this study. They were chosen from universities in Istanbul at the education year of 2016-2017. Dissociative Experiences Scale (DES), Childhood Trauma Questionnaire (CTQ) and Toronto Alexithymia Scale were used to assess related variables. Demographic Information Form was given to students in order to have their demographic information. Frequency Distribution, Linear Regression Analysis, and t-test analysis were used for statistical analysis. Childhood trauma and alexithymia were found to have predictive value on dissociation among university students. However, physical abuse, physical neglect and emotional neglect sub dimensions of childhood trauma and externally-oriented thinking sub dimension of alexithymia did not have predictive value on dissociation. Moreover, there was no significant difference between males and females in terms of dissociation scores of participants.

Keywords: childhood trauma, dissociation, alexithymia, gender

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Authors: Rock-Hyun Choi, Won-Seok Kang, Chang-Sik Son

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Physical activity is important for healthy living. Recently wearable devices which motivate physical activity are quickly developing, and become cheaper and more comfortable. In particular, fitness trackers provide a variety of information and need to provide well-analyzed, and user-friendly results. In this study, frequency analysis was performed to classify various data sets of Fitbit into simple activity status. The data from Fitbit cloud server consists of 263 subjects who were healthy factory and office workers in Korea from March 7th to April 30th, 2016. In the results, we found assumptions of activity state classification seem to be sufficient and reasonable.

Keywords: activity status, fitness tracker, heart rate, steps

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Authors: Claudineia Brazil, Elison Eduardo Jardim Bierhals, Luciane Teresa Salvi, Rafael Haag

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Renewable energy sources are dependent on climatic variability, so for adequate energy planning, observations of the meteorological variables are required, preferably representing long-period series. Despite the scientific and technological advances that meteorological measurement systems have undergone in the last decades, there is still a considerable lack of meteorological observations that form series of long periods. The reanalysis is a system of assimilation of data prepared using general atmospheric circulation models, based on the combination of data collected at surface stations, ocean buoys, satellites and radiosondes, allowing the production of long period data, for a wide gamma. The third generation of reanalysis data emerged in 2010, among them is the Climate Forecast System Reanalysis (CFSR) developed by the National Centers for Environmental Prediction (NCEP), these data have a spatial resolution of 0.50 x 0.50. In order to overcome these difficulties, it aims to evaluate the performance of solar radiation estimation through alternative data bases, such as data from Reanalysis and from meteorological satellites that satisfactorily meet the absence of observations of solar radiation at global and/or regional level. The results of the analysis of the solar radiation data indicated that the reanalysis data of the CFSR model presented a good performance in relation to the observed data, with determination coefficient around 0.90. Therefore, it is concluded that these data have the potential to be used as an alternative source in locations with no seasons or long series of solar radiation, important for the evaluation of solar energy potential.

Keywords: climate, reanalysis, renewable energy, solar radiation

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Authors: Dial Tassadit Rania, Figueiredo De Oliveira Gabriel

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In this paper, we investigate the relationship between ports and their hinterland and foreland environments and the competitive relationship between the ports themselves. These two environments are changing, evolving and introducing new challenges for commercial and economic development at the regional, national and international levels. Because of the rise of the containerization phenomenon, shipping costs and port handling costs have considerably decreased due to economies of scale. The volume of maritime trade has increased substantially and the markets served by the ports have expanded. On these bases, overlapping hinterlands can give rise to the phenomenon of competition between ports. Our main contribution comparing to the existing literature on this issue, is to build a set of hinterland, foreland and competition indicators. Using these indicators? we investigate the effect of hinterland accessibility, foreland connectivity and inter-ports competition on containerized traffic of Europeans ports. For this, we have a 10-year panel database from 2004 to 2014. Our hinterland indicators are given by two indicators of accessibility; they describe the market potential of a port and are calculated using information on population and wealth (GDP). We then calculate population and wealth for different neighborhoods within a distance from a port ranging from 100 to 1000km. For the foreland, we produce two indicators: port connectivity and number of partners for each port. Finally, we compute the two indicators of inter-port competition and a market concentration indicator (Hirshmann-Herfindhal) for different neighborhood-distances around the port. We then apply a fixed-effect model to test the relationship above. Again, with a fixed effects model, we do a sensitivity analysis for each of these indicators to support the results obtained. The econometric results of the general model given by the regression of the accessibility indicators, the LSCI for port i, and the inter-port competition indicator on the containerized traffic of European ports show a positive and significant effect for accessibility to wealth and not to the population. The results are positive and significant for the two indicators of connectivity and competition as well. One of the main results of this research is that the port development given here by the increase of its containerized traffic is strongly related to the development of its hinterland and foreland environment. In addition, it is the market potential, given by the wealth of the hinterland that has an impact on the containerized traffic of a port. However, accessibility to a large population pool is not important for understanding the dynamics of containerized port traffic. Furthermore, in order to continue to develop, a port must penetrate its hinterland at a deep level exceeding 100 km around the port and seek markets beyond this perimeter. The port authorities could focus their marketing efforts on the immediate hinterland, which can, as the results shows, not be captive and thus engage new approaches of port governance to make it more attractive.

Keywords: accessibility, connectivity, European containerization, European hinterland and foreland, inter-port competition

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Authors: Chernet Mulugeta, Girma Seyoum, Yeshineh Demrew, Kehabtimer Shiferaw

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Background: Obstetric fistula (OF) is a serious medical condition that includes an abnormal opening between the vagina and bladder (vesico-vaginal fistula) or the vagina and rectum (recto-vaginal fistula). It is usually caused by prolonged obstructed labour. Despite its serious health and psychosocial consequences, there is a paucity of evidence regarding the time it takes to heal from OF. Objective: The aim of this study was to assess the time to cure from obstetric fistula and its predictors among women admitted to Addis Ababa Hamlin Fistula Hospital, Addis Ababa, Ethiopia. Methodology: An institution-based retrospective cohort study was conducted from January 2015 to December 2020 among a randomly selected 434 women with OF in Addis Ababa Hamlin Fistula Hospital. Data was collected using a structured checklist adapted from a similar study. The open data kit (ODK) collected data was exported and analyzed by using STATA (14.2). Kaplan Meir was used to compare the recovery time from OF. To identify the predictors of OF, a Cox regression model was fitted, and an adjusted hazard ratio with a 95% confidence interval was used to estimate the strength of the associations. Results: The average time to recover from obstetric fistula was 3.95 (95% CI: 3.0-4.6) weeks. About ¾ of the women [72.8% (95% CI - 0.65-1.2)] were physically cured of obstetric fistula. Having secondary education and above [AHR=3.52; 95% CI (1.98, 6.25)] compared to no formal education, having a live birth [AHR=1.64; 95% CI (1.22, 2.21)], having an intact bladder [AHR=2.47; 95% CI (1.1, 5.54)] compared to totally destructed, and having a grade 1 fistula [AHR=1.98; 95% CI (1.19, 3.31)] compared to grade 3 were the significant predictors of shorter time to cure from an obstetric fistula. Conclusion and recommendation: Overall, the proportion of women with OF who were not being cured was unacceptably high. The time it takes for them to recover from the fistula was also extended. It connotes us to work on the identified predictors to improve the time to recovery from OF.

Keywords: time to recovery, obstetric fistula, predictors, Ethiopia

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Authors: Yassir Mahmood, Nadia Ehsan

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In current research stream, empirical work regarding knowledge and motivation management along their dimensions is sparse. This study partially filled this void by investigating the influence of knowledge management (tacit and explicit) and motivation management (intrinsic and extrinsic) on firm performance with the mediating effects of innovative performance. Based on the quantitative research method, data were collected through questionnaire from 284 employees working in 18 different firms across the citrus industry located in Sargodha region (Pakistan). The proposed relationships were tested through regression analysis while mediation relations were analyzed through Barron and Kenny (1986) technique. The results suggested that knowledge management (KM) and motivation management (MM) have significant positive impacts on innovative performance (IP). In addition, the role of IP as full mediator between KM and firm performance (FP) is confirmed. Also, IP proved to be a partial mediator between MM and FP. From the managerial perspective, the findings of the study are vital as some of the important constituents of FP have been highlighted. The study produced important underpinnings for managers. In last, implications for policymakers along with future research directions are discussed.

Keywords: innovative performance, firm performance, knowledge management, motivation management, Sargodha

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Authors: Tchapo Tchaga Sophia, Cai Chun

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This study measures the efficiency level of corporate governance to improve the quality level of value relevance in the resolution of market value efficiency increase issues, transparency problems, risk frauds, agency problems, investors' confidence, and decision-making issues using IFRS and Corporate Governance Acts (CGA). The final sample of this study contains 3660 firms from ten countries' stock markets from 2010 to 2020. Based on the efficiency market theory and the positive accounting theory, this paper uses multiple econometrical methods (DID method, multivariate and univariate regression methods) and models (Ohlson model and compliance index model) regression to see the incidence results of corporate governance mechanisms on the value relevance level under the influence of IFRS and corporate governance regulations act framework in Africa's stock exchanges for non-financial firms. The results on value relevance show that the corporate governance system, strengthened by the adoption of IFRS and enforcement of new corporate governance regulations, produces better financial statement information when its compliance level is high. And that is both value-relevant and comparable to results in more developed markets. Similar positive and significant results were obtained when predicting future book value per share and earnings per share through the determination of stock price and stock return. The findings of this study have important implications for regulators, academics, investors, and other users regarding the effects of IFRS and the Corporate Governance Act (CGA) on the relationship between corporate governance and accounting information relevance in the African stock market. The contributions of this paper are also based on the uniqueness of the data used in this study. The unique data is from Africa, and not all existing findings provide evidence for Africa and of the DID method used to examine the relationship between corporate governance and value relevance on African stock exchanges.

Keywords: corporate governance value, market efficiency value, value relevance, African stock market, stock return-stock price

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Authors: Lily Ingsrisawang, Tasanee Nacharoen

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Introduction: The problems of unbalanced data sets generally appear in real world applications. Due to unequal class distribution, many research papers found that the performance of existing classifier tends to be biased towards the majority class. The k -nearest neighbors’ nonparametric discriminant analysis is one method that was proposed for classifying unbalanced classes with good performance. Hence, the methods of discriminant analysis are of interest to us in investigating misclassification error rates for class-imbalanced data of three diabetes risk groups. Objective: The purpose of this study was to compare the classification performance between parametric discriminant analysis and nonparametric discriminant analysis in a three-class classification application of class-imbalanced data of diabetes risk groups. Methods: Data from a healthy project for 599 staffs in a government hospital in Bangkok were obtained for the classification problem. The staffs were diagnosed into one of three diabetes risk groups: non-risk (90%), risk (5%), and diabetic (5%). The original data along with the variables; diabetes risk group, age, gender, cholesterol, and BMI was analyzed and bootstrapped up to 50 and 100 samples, 599 observations per sample, for additional estimation of misclassification error rate. Each data set was explored for the departure of multivariate normality and the equality of covariance matrices of the three risk groups. Both the original data and the bootstrap samples show non-normality and unequal covariance matrices. The parametric linear discriminant function, quadratic discriminant function, and the nonparametric k-nearest neighbors’ discriminant function were performed over 50 and 100 bootstrap samples and applied to the original data. In finding the optimal classification rule, the choices of prior probabilities were set up for both equal proportions (0.33: 0.33: 0.33) and unequal proportions with three choices of (0.90:0.05:0.05), (0.80: 0.10: 0.10) or (0.70, 0.15, 0.15). Results: The results from 50 and 100 bootstrap samples indicated that the k-nearest neighbors approach when k = 3 or k = 4 and the prior probabilities of {non-risk:risk:diabetic} as {0.90:0.05:0.05} or {0.80:0.10:0.10} gave the smallest error rate of misclassification. Conclusion: The k-nearest neighbors approach would be suggested for classifying a three-class-imbalanced data of diabetes risk groups.

Keywords: error rate, bootstrap, diabetes risk groups, k-nearest neighbors

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Authors: Laraib Niaz

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This paper analyzes the ways in which education and economic deprivation are linked to terrorism in Pakistan using data for terrorist incidents from the Global Terrorism Database (GTD). It employs the technique of negative binomial regression for the years between 1990 and 2013, presenting evidence for a positive association between education and terrorism. Conversely, a negative correlation with economic deprivation is signified in the results. The study highlights the element of radicalization as witnessed in the curriculum and textbooks of public schools as a possible reason for extremism, which in turn may lead to terrorism.

Keywords: education, Pakistan, terrorism, poverty

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Authors: W. Schellong

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Industry accounts for one-third of global final energy demand. Crossover technologies (e.g. motors, pumps, process heat, and air conditioning) play an important role in improving energy efficiency. These technologies are used in many applications independent of the production branch. Especially electrical power is used by drives, pumps, compressors, and lightning. The paper demonstrates the algorithm of the energy analysis by some selected case studies for typical industrial processes. The energy analysis represents an essential part of energy management systems (EMS). Generally, process control system (PCS) can support EMS. They provide information about the production process, and they organize the maintenance actions. Combining these tools into an integrated process allows the development of an energy critical equipment strategy. Thus, asset and energy management can use the same common data to improve the energy efficiency.

Keywords: crossover technologies, data management, energy analysis, energy efficiency, process control

Procedia PDF Downloads 209