Search results for: robust penalized regression

Authors: Muhammad Afzal Malik

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Traditional Learning & Distance based learning are the two systems prevailing in Pakistan. These systems affect the level of education standard. The purpose of this study was to compare the traditional classroom learning and distance learning in Pakistan: (a) To explore the effectiveness of the traditional to Distance learning in Pakistan; (b) To identify the factors that affect traditional and distance learning. This review found that, on average, students in traditional classroom conditions performed better than those receiving education in and distance learning. The difference between student outcomes for traditional Classroom and distance learning classes —measured as the difference between treatment and control means, divided by the pooled standard deviation— was larger in those studies contrasting conditions that blended elements of online and face-to-face instruction with conditions taught entirely face-to-face. This research was conducted to highlight the impact of distance learning education system on education standard. The education standards were institutional support, course development, learning process, student support, faculty support, evaluation and assessment. A well developed questionnaire was administered and distributed among 26 faculty members of GCET, H-9 and Virtual University of Pakistan from each. Data was analyzed through correlation and regression analysis. Results confirmed that there is a significant relationship and impact of DLE system on education standards. This will also provide baseline for future research. It will add value to the existing body of knowledge.

Keywords: distance learning education, higher education, education standards, student performance

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Authors: Innocent Uzougbo Onwuegbuzie

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Healthcare delivery in low-income markets is fraught with numerous challenges, including limited access to essential medical resources, inadequate healthcare infrastructure, and a significant shortage of trained healthcare professionals. These constraints lead to suboptimal health outcomes and a higher incidence of preventable diseases. This paper explores the application of Wireless Sensor Networks (WSNs) as a transformative solution to enhance healthcare delivery in these underserved regions. WSNs, comprising spatially distributed sensor nodes that collect and transmit health-related data, present opportunities to address critical healthcare needs. Leveraging WSN technology facilitates real-time health monitoring and remote diagnostics, enabling continuous patient observation and early detection of medical issues, especially in areas with limited healthcare facilities and professionals. The implementation of WSNs can enhance the overall efficiency of healthcare systems by enabling timely interventions, reducing the strain on healthcare facilities, and optimizing resource allocation. This paper highlights the potential benefits of WSNs in low-income markets, such as cost-effectiveness, increased accessibility, and data-driven decision-making. However, deploying WSNs involves significant challenges, including technical barriers like limited internet connectivity and power supply, alongside concerns about data privacy and security. Moreover, robust infrastructure and adequate training for local healthcare providers are essential for successful implementation. It further examines future directions for WSNs, emphasizing innovation, scalable solutions, and public-private partnerships. By addressing these challenges and harnessing the potential of WSNs, it is possible to revolutionize healthcare delivery and improve health outcomes in low-income markets.

Keywords: wireless sensor networks (WSNs), healthcare delivery, low-Income markets, remote patient monitoring, health data security

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Authors: Mehdi Modabberifar, Milad Roodi, Ehsan Souri

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In recent years, honeycomb fiber reinforced plastic (FRP) sandwich panels have been increasingly used in various industries. Low weight, low price, and high mechanical strength are the benefits of these structures. However, their mechanical properties and behavior have not been fully explored. The objective of this study is to conduct a combined numerical-statistical investigation of honeycomb FRP sandwich beams subject to torsion load. In this paper, the effect of geometric parameters of the sandwich panel on the maximum shear strain in both face and core and angle of torsion in a honeycomb FRP sandwich structures in torsion is investigated. The effect of Parameters including core thickness, face skin thickness, cell shape, cell size, and cell thickness on mechanical behavior of the structure were numerically investigated. Main effects of factors were considered in this paper and regression equations were derived. Taguchi method was employed as experimental design and an optimum parameter combination for the maximum structure stiffness has been obtained. The results showed that cell size and face skin thickness have the most significant impacts on torsion angle, maximum shear strain in face and core.

Keywords: finite element, honeycomb FRP sandwich panel, torsion, civil engineering

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Authors: Seongcheol Kwon, Jeongmin Kim, Kwang Ryel Ryu

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We assume an IoT-based smart-home environment where the on-off status of each of the electrical appliances including the room lights can be recognized in a real time by monitoring and analyzing the smart meter data. At any moment in such an environment, we can recognize what the household or the user is doing by referring to the status data of the appliances. In this paper, we focus on a smart-home service that is to activate a robot vacuum cleaner at right time by recognizing the user situation, which requires a situation-aware model that can distinguish the situations that allow vacuum cleaning (Yes) from those that do not (No). We learn as our candidate models a few classifiers such as naïve Bayes, decision tree, and logistic regression that can map the appliance-status data into Yes and No situations. Our training and test data are obtained from simulations of user behaviors, in which a sequence of user situations such as cooking, eating, dish washing, and so on is generated with the status of the relevant appliances changed in accordance with the situation changes. During the simulation, both the situation transition and the resulting appliance status are determined stochastically. To compare the performances of the aforementioned classifiers we obtain their learning curves for different types of users through simulations. The result of our empirical study reveals that naïve Bayes achieves a slightly better classification accuracy than the other compared classifiers.

Keywords: situation-awareness, smart home, IoT, machine learning, classifier

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Authors: Yosra Makni Fourati, Sana Masmoudi Mardassi

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This paper investigates the effect of Book-tax conformity on audit quality regarding the proxies of audit fees, auditors’ industry specialization and audit report lag. Using a sample of Canadian firms listed on the Toronto Stock Exchange spanning the years 2006- 2016, we applied an Ordinary Least Squares (OLS) regression to test hypotheses of this research. The authors find that higher Book-tax conformity leads to lower audit fees. They also provide evidence that there is a negative association between Book-tax conformity and auditors’ industry specialization, whereas there is a positive association between Book-tax conformity and audit report lag. Overall, the findings are prominent to better understanding the effect of Book-tax conformity on audit quality and are relevant for academic researchers, practitioners, and regulators. As the paper investigates the relationship of Book-tax conformity and audit quality using a sample of Canadian firms, it brings original insights regarding the importance of audit fees and Book-tax conformity. In addition, it considers the role of auditor’s industry specialization in the relation between audit quality and Book-tax conformity by considering a sample listed on the Toronto Stock Exchange. This paper contributes to the existing literature by highlighting the Canadian setting, to our best knowledge. In addition, our results are prominent to the auditing literature as they introduce a different determinant of auditors’ industry specialization and audit report lag.

Keywords: audit fees, auditors' industry specialization, audit report lag, book-tax conformity

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Authors: Peter Fornaro, Lothar Schmitt

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The Digital Humanities Lab and the Institute of Art History at the University of Basel are collaborating in the SNSF research project ‘Digital Materiality’. Its goal is to develop and enhance existing methods for the digital reproduction of cultural heritage objects in order to support art historical research. One part of the project focuses on the visualization of a small eye-catching group of early prints that are noteworthy for their subtle reliefs and glossy surfaces. Additionally, this group of objects – known as ‘paste prints’ – is characterized by its fragile state of preservation. Because of the brittle substances that were used for their production, most paste prints are heavily damaged and thus very hard to examine. These specific material properties make a photographic reproduction extremely difficult. To obtain better results we are working with Reflectance Transformation Imaging (RTI), a computational photographic method that is already used in archaeological and cultural heritage research. This technique allows documenting how three-dimensional surfaces respond to changing lighting situations. Our first results show that RTI can capture the material properties of paste prints and their current state of preservation more accurately than conventional photographs, although there are limitations with glossy surfaces because the mathematical models that are included in RTI are kept simple in order to keep the software robust and easy to use. To improve the method, we are currently developing tools for a more detailed analysis and simulation of the reflectance behavior. An enhanced analytical model for the representation and visualization of gloss will increase the significance of digital representations of cultural heritage objects. For collaborative efforts, we are working on a web-based viewer application for RTI images based on WebGL in order to make acquired data accessible to a broader international research community. At the ICDH Conference, we would like to present unpublished results of our work and discuss the implications of our concept for art history, computational photography and heritage science.

Keywords: art history, computational photography, paste prints, reflectance transformation imaging

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Authors: Shabana Mohammad, Muhammad Irfan

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The aim of this study was to bring out the facts regarding the effects of gender discrimination in fulfilling the basic needs of females at home. The purpose of the study was to observe whether gender discrimination affects the fulfillment of their basic needs in comparison to male siblings. Balochistan is the largest province of Pakistan geographically and has a tribal system. Due to having tribal systems, the women are not treated equally as men at home because males are considered the strength and privilege of tribes; males are more valuable because they support their families economically as well, and females are not allowed to work outside the home. That’s why females are deprived of their basic needs of life. The females (women) are neglected to have better nourishment, health facilities, easy access to get an education, safe house, and self-confidence. The type of research is quantitative, and data was collected from all government girls’ degree colleges of Quetta city (the capital of Balochistan province) under the age of 18. Two hundred (200) Students of all FA arts faculty (first year) were selected through simple random sampling (fishbowl draw). Data were analyzed by SPSS, and a coefficient test was applied to test the hypothesis. The regression of coefficient has a significant association between gender discrimination and basic needs (P-value =.000). The results showed that women are underprivileged from all basic needs (fundamental rights) of life, which are entitled to everyone by their birth because of male preference that creates gender disparity between men and women.

Keywords: basic needs, discrimination, value of women at home, hurdles of women

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Authors: Sohaib Raza, Parvez Mantry

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Frailty is a multi-system impairment associated with stressors such as age, disease, and invasive surgical procedures. This multi-system impairment can lead to increased post-transplant mortality and functional decline. Additionally, the prevalence and/or severity of frailty increases when patient pre-habilitation is unsatisfactory or lacking. We conducted a retrospective study to examine whether the COVID-19 Pandemic, and subsequent lack of patient access to pre-habilitation and physical therapy resources, led to an increase in the prevalence and severity of frailty, sarcopenia, and other comorbidities including diabetes, hypertension, and COPD. Secondarily, we examined the correlation between patient survival rate and liver frailty index as well as muscle wasting/sarcopenia. Data were analyzed in order to correlate variables associated with these parameters. Three hundred sixty-nine liver transplant candidates at Methodist Dallas Medical Center were administered pre-transplant frailty assessments, which consisted of chair stands, grip strength, and position balance time. A frailty score less than 3.2 indicated a robust condition, a score from 3.3 to 4.4 indicated a pre-frail condition, and a score greater than 4.5 indicated a frail condition. Greater than 50 percent of patients were found to have muscle wasting in the COVID-19 period (March 13, 2020 to February 28, 2022), an increase of 16.5 percent from the pre-COVID period (April 1st, 2018 to March 12, 2020). Additionally, sarcopenia was associated with a two-fold increase in patient mortality rate. Furthermore, high liver frailty index scores were associated with increased patient mortality. However, there was no significant difference in liver frailty index or number of comorbidities between patients in the two cohorts. Conclusion: The COVID-19 Pandemic exacerbated sarcopenia-related muscle wasting in liver transplant candidates, and patient survival rate was directly correlated with liver frailty index score and the presence of sarcopenia.

Keywords: frailty, sarcopenia, covid-19, patient mortality, pre-habilitation, liver transplant candidates

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Authors: Hakeem Ade Omokayode Idowu

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The extent of the role played by human capabilities developments as well as knowledge management on employees’ performance in the nuclear energy sector of Nigeria remains unclear. This is in view of the important role which human resource capabilities could play in the desire to generate energy using nuclear resources. This study appraised the extent of human resource capabilities available in the nuclear energy sector of Nigeria. It further examined the relationship between knowledge management and employees’ performance in the nuclear energy sector. The study adopted a descriptive research design with a population that comprised all the 1736 members of staff of the selected centres, institutes, and the headquarters of the Nigeria Atomic Energy Commission (NAEC), Nigerian Nuclear Regulatory Authority (NNRA), and Energy Commission of Nigeria (ECN) and a sample size of 332 employees was selected using purposive and convenience sampling techniques. Data collected were subjected to analysis using frequency counts and simple regression. The results showed that majority of the employees perceived that they have to a high extent of availability of knowledge (118, 35.5%), credibility (134, 40.4%), alignment (130, 39.2%), performance (126, 38%) and innovation (138, 41.6%) The result of the hypothesis tested indicated that knowledge management has a positive and significant effect on employees’ performance (Beta weight = 0.336, R2 =0.113, F-value = 41.959, p-value = 0.000< 0.05). The study concluded that human resource capabilities and knowledge management could enhance employee performance within the nuclear energy sector of Nigeria.

Keywords: human resource capabilities, knowledge management, employees productivity, national development

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Authors: Ranjitkumar Patil

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Age estimation has its importance in forensic dentistry. Dental age estimation has emerged as an alternative to skeletal age determination. The methods based on stages of tooth formation, as appreciated on radiographs, seems to be more appropriate in the assessment of age than those based on skeletal development. The study was done to evaluate dental age in north Indian population using Cameriere’s method. Aims/Objectives: The study was conducted to assess the dental age of North Indian children using Cameriere’smethodand to compare the chronological age and dental age for validation of the Cameriere’smethod in the north Indian population. A comparative study of 02 year duration on the OPG (using PLANMECA Promax 3D) data of 497 individuals with age ranging from 5 to 15 years was done based on simple random technique ethical approval obtained from the institutional ethical committee. The data was obtained based on inclusion and exclusion criteria was analyzed by a software for dental age estimation. Statistical analysis: Student’s t test was used to compare the morphological variables of males with those of females and to compare observed age with estimated age. Regression formula was also calculated. Results: Present study was a comparative study of 497 subjects with a distribution between male and female, with their dental age assessed by using Panoramic radiograph, following the method described by Cameriere, which is widely accepted. Statistical analysis in our study indicated that gender does not have a significant influence on age estimation. (R2= 0.787). Conclusion: This infers that cameriere’s method can be effectively applied in north Indianpopulation.

Keywords: Forensic, Chronological Age, Dental Age, Skeletal Age

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Authors: Wajid Mumtaz, Aamir Saeed Malik, Syed Saad Azhar Ali, Mohd Azhar Mohd Yasin

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In this paper, a technique based on electroencephalogram (EEG) analysis is presented, aiming for diagnosing major depressive disorder (MDD) among a potential population of MDD patients and healthy controls. EEG is recognized as a clinical modality during applications such as seizure diagnosis, index for anesthesia, detection of brain death or stroke. However, its usability for psychiatric illnesses such as MDD is less studied. Therefore, in this study, for the sake of diagnosis, 2 groups of study participants were recruited, 1) MDD patients, 2) healthy people as controls. EEG data acquired from both groups were analyzed involving inter-hemispheric asymmetry and composite permutation entropy index (CPEI). To automate the process, derived quantities from EEG were utilized as inputs to classifier such as logistic regression (LR) and support vector machine (SVM). The learning of these classification models was tested with a test dataset. Their learning efficiency is provided as accuracy of classifying MDD patients from controls, their sensitivities and specificities were reported, accordingly (LR =81.7 % and SVM =81.5 %). Based on the results, it is concluded that the derived measures are indicators for diagnosing MDD from a potential population of normal controls. In addition, the results motivate further exploring other measures for the same purpose.

Keywords: major depressive disorder, diagnosis based on EEG, EEG derived features, CPEI, inter-hemispheric asymmetry

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Authors: Antoine Vikkey Hinson, Ranty Jolianelle Dassi, Menonli Adjobimey, Rose Mikponhoue, Paul Ayelo

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Introduction: The psychological hardship of the teaching profession maintains a chronic stress that inevitably evolves into burnout (BO) in the absence of adequate preventive measures. The objective of this study is to study the prevalence and factors associated with burnout among secondary school teachers in the city of Cotonou in 2022. Methods: This was a descriptive cross-sectional study with an analytical aim and prospective data collection that took place over a period of 2 months, from July 19 to August 19 and from October 1 to October 31, 2022. Sampling was done using a three-stage probability sampling technique. Data analysis was performed using R 4.1.1 software. Bivariate logistic regression was used to identify associated factors. The significance level chosen was 5% (p < 0.05). Results: A total of 270 teachers were included in the study, of whom 208 (77.00%) were men. The mean age of the workers was 38.03 ± 8.30 years. According to the Maslach Burnout Inventory, 58.51% of the teachers had burnout, with 41.10% of teachers in emotional exhaustion, 27.40% in depersonalization and 21.90% in loss of personal accomplishment. The severity of the syndrome was low to moderate in almost all teachers. The occurrence of BO was associated with), not practicing sports (ORa= 2,38 [1,32; 4,28]), jobs training (ORa= 1,86 [1,04; 3,34]) and an imbalance of effort/reward (ORa= 5,98 [2,24;15,98]). Conclusion: The prevalence of BO is high among secondary school teachers in the city of Cotonou. A larger scale study, including research on its consequences on the teacher and the learner, is necessary in order to act quickly to implement a prevention program.

Keywords: burnout, teachers, Maslach burnout inventory, associated factors, Benin

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Authors: Aref Aasi, Sahar Ebrahimi Bajgani, Erfan Aasi

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Breast cancer (BC) continues to be the most frequent cancer in females and causes the highest number of cancer-related deaths in women worldwide. Inspired by recent advances in studying the relationship between different patient attributes and features and the disease, in this paper, we have tried to investigate the different classification methods for better diagnosis of BC in the early stages. In this regard, datasets from the University Hospital Centre of Coimbra were chosen, and different machine learning (ML)-based and neural network (NN) classifiers have been studied. For this purpose, we have selected favorable features among the nine provided attributes from the clinical dataset by using a random forest algorithm. This dataset consists of both healthy controls and BC patients, and it was noted that glucose, BMI, resistin, and age have the most importance, respectively. Moreover, we have analyzed these features with various ML-based classifier methods, including Decision Tree (DT), K-Nearest Neighbors (KNN), eXtreme Gradient Boosting (XGBoost), Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM) along with NN-based Multi-Layer Perceptron (MLP) classifier. The results revealed that among different techniques, the SVM and MLP classifiers have the most accuracy, with amounts of 96% and 92%, respectively. These results divulged that the adopted procedure could be used effectively for the classification of cancer cells, and also it encourages further experimental investigations with more collected data for other types of cancers.

Keywords: breast cancer, diagnosis, machine learning, biomarker classification, neural network

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Authors: Sumanta Meher, Gaurav Shukla

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The torments of the victims of terrorism have not only confined to loss of life and limp but also includes the physiological trauma to the innocent victims. The physical wounds may heal, but the trauma remains in the mind and heart of the victims and their loved ones; however, one should not deny that these terrorist activities affect to a major extent to their livelihood. To protect their human rights and restore the shattered lives of the victims of terrorism all the Nations beyond their differences have to show solidarity and frame a comprehensive restorative policy with an effective implementing mechanism. The General Assembly of United Nations, through its several resolutions, has appealed Nations to show solidarity and also committed to helping the Members State to frame the law and policy to support the victims of terrorism. To achieve the objectives of the resolutions adopted by the United Nations, the Indian legislators in 2008 amended the Code of Criminal Procedure, 1973 and incorporated Section 357A to provide financial assistance to the victims of terrorism. In India, the contemporary developments in the victims’ oriented studies have increased the dimension of the traditional criminal justice systems to protect the rights of the victims. In this regard, the paper has ascertained the Indian legal framework in respect to the restorative justice to the victims of terrorism and also addressed the question as to whether the statutory provisions and enforcement mechanisms are efficient enough to protect the human rights of the victims of terrorism. For that purpose, the paper has analyzed the International instruments and the reports with regard to the compensation to the victims of terrorist attacks, with that, the article also evaluates the initiatives of United Nations to help Members State to frame the law and policies to support the victims of terrorism. The study also made an attempt to critically analyze the legal provisions of compensation and rehabilitation of the victims of terrorist attacks in India and whether they are in alignment with the International standards. While concluding, the paper has made an endeavor for a robust legal framework towards the restorative justice for the victims of terrorism in India.

Keywords: victims of terrorism, restorative justice, human rights, criminal justice system of India

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Authors: Ying Ge

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Wetlands are one of the essential types of ecosystems in the world. They are of great value to human society thanks to their special ecosystem functions and services, such as protecting biodiversity, adjusting hydrology and climate, providing essential habitats and, products and tourism resources. However, wetlands worldwide are degrading severely due to climate change, accelerated urbanization, and rapid economic development. Both nature and human factors drive wetland change, and the influences are variable from wetland types. Thus, the objectives of this study were to (1) to compare the changes in China’s wetland area during the three decades rapid growth period (1978-2008); (2) to analyze the effects of social economy and environmental factors on wetlands change (area loss and change of wetland types) in China during the high-speed economic development. The socio-economic influencing factors include population, income, education, development of agriculture, industry, infrastructure, wastewater amount, etc. Several statistical methods (canonical correlation analysis, principal component analysis, and regression analysis) were employed to analyze the relationship between socio-economic indicators and wetland area change. This study will determine the relevant driving socio-economic factors on wetland changes, which is of great significance for wetland protection and management.

Keywords: socioeconomic effects, China, wetland change, wetland type

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Authors: Isabel Moscol, Carlos J. Díaz, Ciro Rodríguez

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Hip replacement becomes necessary when a person suffers severe pain or considerable functional limitations and the best option to enhance their quality of life is through the replacement of the damaged joint. One of the main components in femoral prostheses is the stem which distributes the loads from the joint to the proximal femur. To preserve more bone stock and avoid weakening of the diaphysis, a short starting stem was selected, generated from the intramedullary morphology of the patient's femur. It ensures the implantability of the design and leads to geometric delimitation for personalized optimization with machine learning (ML) and metaheuristic algorithms. The present study attempts to design a cementless short stem to make the strain deviation before and after implantation close to zero, promoting its fixation and durability. Regression models developed to estimate the percentage change of maximum principal stresses were used as objective optimization functions by the metaheuristic algorithm. The latter evaluated different geometries of the short stem with the modification of certain parameters in oblique sections from the osteotomy plane. The optimized geometry reached a global stress shielding (SS) of 18.37% with a determination factor (R²) of 0.667. The predicted results favour implantability integration in the short stem optimization to effectively reduce SS in the proximal femur.

Keywords: machine learning techniques, metaheuristic algorithms, short-stem design, stress shielding, hip replacement

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Authors: Ali Muhammad

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The study investigates the effect of a number of contextual on the degree of trust within Kuwaiti business organizations. The model used in this study suggests that degree of trust within the organization is determined by four contextual variables, namely, centralization, formalization, role ambiguity, and procedural justice. Organizational trust refers to employee’ positive assumptions in regard to the goal and behaviors of other members in the organization according to organizational duties, relationships, experiences, and interrelatedness. According to the norm of reciprocity, individuals with high perceived organizational justice will be compelled to react positively to the organization in the form of higher degree of trust. The duty to exchange kindness for kindness. Based on the exchange theory, this research proposes that procedural justice, role clarity, and voice in the organization will lead to the perception of an organization’s discretionary positive treatment of employees and, in return enhances their trust in the organization. Survey data were collected from a sample of 206 employees working in Kuwaiti business organizations. Results of multiple regression analysis revealed that both organizational justice and formalization have positive effects on organizational trust. Furthermore, results indicate that lower degree of role ambiguity leads to higher degree of organizational trust. On the other hand, centralization was not found to have a significant effect on organizational trust. Implications of the findings and directions for future research are discussed.

Keywords: centralization, formalization, organizational justice, organizational trust, role ambiguity

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Authors: Ming Ma, Zhenyu Cai, Rui Li

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People are more vulnerable to health problems than ever before, such as overweight and obesity due to the change of built environment. In the high-rise buildings, the core-tube layout is closely associated with user’s physical activities which will affect human’s health in a long-term. As for the white-collars who spends the amount of time working in the office tower, using staircase seems to provide an opportunity for them to increase the physical activities in the workplaces. This paper is aiming to find out the specific relationship between health and core-tube in the office tower through analyzing the correlation between staircase’s layout and user’s health. The variables of staircase’s layout are consisted of two indicators: plan layout and space design, including nine factors while health variable is applying BIM as the only main factor. 14 office towers in downtown Shanghai are selected as the research samples because of its typical users’ pattern and similar core-tube layout. In the result, it is obvious that the users from these 14 cases have higher BMI than average partly because that the staircases are mainly designed for emergency and fire instead of daily use. After the regression and correlation analysis of the variables of health and staircases, it’s found that users’ BMI is significantly associated with the factors of floor guide-signs and distance from lobby to the staircase. In addition, the factors of comfort level of staircase such as width and daylighting have a certain correlation with users’ BMI.

Keywords: office tower, staircase, design, obesity, physical activity

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Authors: Cornelius Michael Ekenta

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The study determined institutional and technological factors that influence the adoption of tenera oil palm production practices with a gender dimension among smallholder farmers in Edo State, Nigeria. Primary data were generated with use of questionnaire administered to 155 males and 137 female respondents. Results show that the level of adoption of tenera oil palm production practices was low for both male and females. Tobi regression result shows that land ownership structure and affordability at 1% significance influenced male adoption of tenera oil palm production practices while age and level of income at 1% significance influenced female in the adoption. The major roles of male as reported in adopting process were purchase of seedlings, clearing of bush for planting and selling of cut bunches while the major roles of female were periodic weeding, gathering of cut bunches and mulching of palm field. The major constraint faced by male in adoption process were high cost of labour while for females is drudgery nature of the work. The study recommended that the Land Use Act of 1978 should be enforced to help women and non-indigenes to have sizeable farm lands, Government should empower Agricultural Development Programme (ADP) by employing more extension personnel to increase their contacts with the farmers.

Keywords: gender, adoption, variety, oil, tenera, Edo

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Authors: Shishay Kidanu, Abdullah Alhaj

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Sinkholes in the karst terrain of Greene County, Missouri, pose significant geohazards, imposing challenges on construction and infrastructure development, with potential threats to lives and property. To address these issues, understanding the influencing factors and modeling sinkhole susceptibility is crucial for effective mitigation through strategic changes in land use planning and practices. This study utilizes geographic information system (GIS) software to collect and process diverse data, including topographic, geologic, hydrogeologic, and anthropogenic information. Nine key sinkhole influencing factors, ranging from slope characteristics to proximity to geological structures, were carefully analyzed. The Frequency Ratio method establishes relationships between attribute classes of these factors and sinkhole events, deriving class weights to indicate their relative importance. Weighted integration of these factors is accomplished using the Analytic Hierarchy Process (AHP) and the Weighted Linear Combination (WLC) method in a GIS environment, resulting in a comprehensive sinkhole susceptibility index (SSI) model for the study area. Employing Jenk's natural break classifier method, the SSI values are categorized into five distinct sinkhole susceptibility zones: very low, low, moderate, high, and very high. Validation of the model, conducted through the Area Under Curve (AUC) and Sinkhole Density Index (SDI) methods, demonstrates a robust correlation with sinkhole inventory data. The prediction rate curve yields an AUC value of 74%, indicating a 74% validation accuracy. The SDI result further supports the success of the sinkhole susceptibility model. This model offers reliable predictions for the future distribution of sinkholes, providing valuable insights for planners and engineers in the formulation of development plans and land-use strategies. Its application extends to enhancing preparedness and minimizing the impact of sinkhole-related geohazards on both infrastructure and the community.

Keywords: sinkhole, GIS, analytical hierarchy process, frequency ratio, susceptibility, Missouri

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Authors: V. Jamuna, A. K. Chakravarty, C. S. Patil, Vijay Kumar, M. A. Mir, Rakesh Kumar

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Intense selection of buffaloes for milk production at organized herds of the country without giving due attention to fertility traits viz. pregnancy rate has lead to deterioration in their performances. Aim of study is to develop an optimum model for predicting pregnancy rate and to assess the level of pregnancy rate with respect to milk production Murrah buffaloes. Data pertaining to 1224 lactation records of Murrah buffaloes spread over a period 21 years were analyzed and it was observed that pregnancy rate depicted negative phenotypic association with lactation milk yield (-0.08 ± 0.04). For developing optimum model for pregnancy rate in Murrah buffaloes seven simple and multiple regression models were developed. Among the seven models, model II having only Service period as an independent reproduction variable, was found to be the best prediction model, based on the four statistical criterions (high coefficient of determination (R 2), low mean sum of squares due to error (MSSe), conceptual predictive (CP) value, and Bayesian information criterion (BIC). For standardizing the level of fertility with milk production, pregnancy rate was classified into seven classes with the increment of 10% in all parities, life time and their corresponding average pregnancy rate in relation to the average lactation milk yield (MY).It was observed that to achieve around 2000 kg MY which can be considered optimum for Indian Murrah buffaloes, level of pregnancy rate should be in between 30-50%.

Keywords: life time, pregnancy rate, production, service period, standardization

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Authors: Mary David, Denis Basila

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This study assessed the Impact of Board Composition on the Performance of Listed Deposit Money Banks in Nigeria. A sample of ten (10) deposit money banks formed the sample of this study. Board size, gender diversity, and board independence were used as the independent variables, and firm size as a control variable, whiles the bank performance was proxy with Tobin’s Q (TQ) as the dependent variable. Secondary data was collected from secondary source through the annual report and account of the banks and was analyzed through the support of STATA 14 versions. Descriptive statistics, correlation matrix, and OLS multiple regression model were adopted for the study. Breusch and pagan lagrangian multiplier test for random effect was conducted. The findings of the study reveal that board size has positive and significant impact on Tobin’s Q, gender diversity has positive and significant impact on Tobin’s Q, while board independent had a negative and nonsignificant influence on the Tobin’s Q, Similarly, firm size was found to have a negative and nonsignificant impact on Tobin’s Q of the study banks. This study recommended that policy makers, stakeholders, and corporate managers of deposit money banks of Nigeria and related industries are encouraged to adopt board sizes and gender diversity that impact positively on bank performance.

Keywords: board composition, performance, deposit money banks, nigeria

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Authors: R. Meier, M. Pander

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In accordance with the industry 4.0 concept, manufacturing process steps as well as the materials themselves are going to be more and more digitalized within the next years. The “digital twin” representing the simulated and measured dataset of the (semi-finished) product can be used to control and optimize the individual processing steps and help to reduce costs and expenditure of time in product development, manufacturing, and recycling. In the present work, two material characterization methods based on Lamb waves were evaluated and compared. For demonstration purpose, both methods were shown at a standard industrial product - copper ribbons, often used in photovoltaic modules as well as in high-current microelectronic devices. By numerical approximation of the Rayleigh-Lamb dispersion model on measured phase velocities second order elastic constants (Young’s modulus, Poisson’s ratio) were determined. Furthermore, the effective third order elastic constants were evaluated by applying elastic, “non-destructive”, mechanical stress on the samples. In this way, small microstructural variations due to mechanical preconditioning could be detected for the first time. Both methods were compared with respect to precision and inline application capabilities. Microstructure of the samples was systematically varied by mechanical loading and annealing. Changes in the elastic ultrasound transport properties were correlated with results from microstructural analysis and mechanical testing. In summary, monitoring the elastic material properties of plate-like structures using Lamb waves is valuable for inline and non-destructive material characterization and manufacturing process control. Second order elastic constants analysis is robust over wide environmental and sample conditions, whereas the effective third order elastic constants highly increase the sensitivity with respect to small microstructural changes. Both Lamb wave based characterization methods are fitting perfectly into the industry 4.0 concept.

Keywords: lamb waves, industry 4.0, process control, elasticity, acoustoelasticity, microstructure

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Authors: Chanjuan Sun, Shijie Hong, Jialing Zhang, Yuchao Guo, Zhijun Zou, Chen Huang

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Background: Studies on associations between air temperature and childhood respiratory diseases are lack in China. Objectives: We aim to analyze the relationship between air temperature and childhood respiratory diseases. Methods: We conducted the on-site inspection into 454 residences and questionnaires survey. Indoor air temperature were from field inspection and outdoor air temperature were from website. Multiple logistic regression analyses were used to investigate the associations. Results: Indoor extreme hot air temperature was positively correlated with duration of a common cold (>=2 weeks), and outdoor extreme hot air temperature was also positively related with pneumonia among children. Indoor and outdoor extreme cold air temperature was a risk factor for rhinitis among children. The biggest indoor air temperature difference (indoor maximum air temperature minus indoor minimum air temperature) (Imax minus Imin) (the 4th quartile, >4 oC) and outdoor air temperature difference (outdoor maximum air temperature minus outdoor minimum air temperature) (Omax minus Omin) (the 4th quartile, >8oC) were positively related to pneumonia among children. Meanwhile, indoor air temperature difference (Imax minus Imin) (the 4th quartile, >4 oC) was positively correlated with diagnosed asthma among children. Air temperature difference between indoor and outdoor was negatively related with the most childhood respiratory diseases. This may be partly related to the avoidance behavior. Conclusions: Improper air temperature may affect the respiratory diseases among children.

Keywords: air temperature, extreme air temperature, air temperature difference, respiratory diseases, children

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Authors: Girish Chavadappanavar

Abstract:

The impact of climate on agriculture could result in problems with food security and may threaten the livelihood activities upon which much of the population depends. In the present study, the development of a statistical yield forecast model has been carried out for sugarcane production over Belagavi district, Karnataka using weather variables of crop growing season and past observed yield data for the period of 1971 to 2010. The study shows that this type of statistical yield forecast model could efficiently forecast yield 5 weeks and even 10 weeks in advance of the harvest for sugarcane within an acceptable limit of error. The performance of the model in predicting yields at the district level for sugarcane crops is found quite satisfactory for both validation (2007 and 2008) as well as forecasting (2009 and 2010).In addition to the above study, the climate variability of the area has also been studied, and hence, the data series was tested for Mann Kendall Rank Statistical Test. The maximum and minimum temperatures were found to be significant with opposite trends (decreasing trend in maximum and increasing in minimum temperature), while the other three are found in significant with different trends (rainfall and evening time relative humidity with increasing trend and morning time relative humidity with decreasing trend).

Keywords: climate impact, regression analysis, yield and forecast model, sugar models

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Authors: Seung-Seok Bak, Chang-Mu Jung

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An enough amount supply of industrial complexes is an important national policy in South Korea, which is highly dependent on foreign trade. A development process of the industrial complex can distinguish between the planning stage and the construction stage. The planning stage consists of the process of consulting with many stakeholders on the contents of the development of industrial complex, feasibility study, compliance with the Regional policies, and so on. The industrial complex planning stage, including licensing procedure, usually takes about three years in South Korea. The government determined that the appropriate supply of industrial complexes have been delayed, due to the long licensing period and drafted a law to shorten the license period in 2008. The law was expected to shorten the period of licensing, which was about three years, to six months. This paper attempts to show that the shortening of the licensing period does not positively affect the appropriate supply of industrial complexes. To do this, we used Interrupted Time Series Designs. As a result, it was found that the supply of industrial complexes was influenced more by other factors such as actual industrial complex demand of private sector and macro-level economic variables. In addition, the specific provisions of the law conflict with local policy and cause some problems such as damage to nature and agricultural land, traffic congestion.

Keywords: development of industrial complexes, industrial complexes, interrupted time series designs, simplification of licensing procedures for industrial complexes, time series regression

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Authors: Viktor Müller, Ulman Lindenberger

Abstract:

Neurophysiological evidence suggests that the physiological states of the system are characterized by specific network structures and network topology dynamics, demonstrating a robust interplay between network topology and function. It is also evident that interpersonal action coordination or social interaction (e.g., playing music in duets or groups) requires strong intra- and interbrain synchronization resulting in a specific hyper brain network activity across two or more brains to support such coordination or interaction. Such complex hyper brain networks can be described as multiplex or multilayer networks that have a specific multidimensional or multilayer network organization characteristic for superordinate systems and their constituents. The aim of the study was to describe multilayer hyper brain networks and synchronization patterns of guitarists playing guitar in a quartet by using electroencephalography (EEG) hyper scanning (simultaneous EEG recording from multiple brains) and following time-frequency decomposition and multilayer network construction, where within-frequency coupling (WFC) represents communication within different layers, and cross-frequency coupling (CFC) depicts communication between these layers. Results indicate that communication or coupling dynamics, both within and between the layers across the brains of the guitarists, play an essential role in action coordination and are particularly enhanced during periods of high demands on musical coordination. Moreover, multilayer hyper brain network topology and dynamical structure of guitar sounds showed specific guitar-guitar, brain-brain, and guitar-brain causal associations, indicating multilevel dynamics with upward and downward causation, contributing to the superordinate system dynamics and hyper brain functioning. It is concluded that the neuronal dynamics during interpersonal interaction are brain-wide and frequency-specific with the fine-tuned balance between WFC and CFC and can best be described in terms of multilayer multi-brain networks with specific network topology and connectivity strengths. Further sophisticated research is needed to deepen our understanding of these highly interesting and complex phenomena.

Keywords: EEG hyper scanning, intra- and interbrain coupling, multilayer hyper brain networks, social interaction, within- and cross-frequency coupling

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Authors: Haifeng Wang, Haili Zhang

Abstract:

Most movie recommendation systems have been developed for customers to find items of interest. This work introduces a predictive model usable by small and medium-sized enterprises (SMEs) who are in need of a data-based and analytical approach to stock proper movies for local audiences and retain more customers. We used classification models to extract features from thousands of customers’ demographic, behavioral and social information to predict their movie genre preference. In the implementation, a Gaussian kernel support vector machine (SVM) classification model and a logistic regression model were established to extract features from sample data and their test error-in-sample were compared. Comparison of error-out-sample was also made under different Vapnik–Chervonenkis (VC) dimensions in the machine learning algorithm to find and prevent overfitting. Gaussian kernel SVM prediction model can correctly predict movie genre preferences in 85% of positive cases. The accuracy of the algorithm increased to 93% with a smaller VC dimension and less overfitting. These findings advance our understanding of how to use machine learning approach to predict customers’ preferences with a small data set and design prediction tools for these enterprises.

Keywords: computational social science, movie preference, machine learning, SVM

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Authors: Jiameng Wang, Yunfei Yin, Xiyu Deng

Abstract:

Parallel optimization is one of the important research topics of data mining at this stage. Taking Classification and Regression Tree (CART) parallelization as an example, this paper proposes a parallel data mining algorithm based on SSP-OGini-PCCP. Aiming at the problem of choosing the best CART segmentation point, this paper designs an S-SP model without data association; and in order to calculate the Gini index efficiently, a parallel OGini calculation method is designed. In addition, in order to improve the efficiency of the pruning algorithm, a synchronous PCCP pruning strategy is proposed in this paper. In this paper, the optimal segmentation calculation, Gini index calculation, and pruning algorithm are studied in depth. These are important components of parallel data mining. By constructing a distributed cluster simulation system based on SPARK, data mining methods based on SSP-OGini-PCCP are tested. Experimental results show that this method can increase the search efficiency of the best segmentation point by an average of 89%, increase the search efficiency of the Gini segmentation index by 3853%, and increase the pruning efficiency by 146% on average; and as the size of the data set increases, the performance of the algorithm remains stable, which meets the requirements of contemporary massive data processing.

Keywords: classification, Gini index, parallel data mining, pruning ahead

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Authors: Lina Najafian, Hamidreza Vakilifard

Abstract:

The present study focused on the relationship between the quality of accruals and non-systematic risk. The independent study variables included the ability of accruals, the information content of accruals, and amount of discretionary accruals considered as accruals quality measures. The dependent variable was non-systematic risk based on the Fama and French Three Factor model (FFTFM) and the capital asset pricing model (CAPM). The control variables were firm size, financial leverage, stock return, cash flow fluctuations, and book-to-market ratio. The data collection method was based on library research and document mining including financial statements. Multiple regression analysis was used to analyze the data. The study results showed that there is a significant direct relationship between financial leverage and discretionary accruals and non-systematic risk based on FFTFM and CAPM. There is also a significant direct relationship between the ability of accruals, information content of accruals, firm size, and stock return and non-systematic based on both models. It was also found that there is no relationship between book-to-market ratio and cash flow fluctuations and non-systematic risk.

Keywords: accruals quality, non-systematic risk, CAPM, FFTFM

Procedia PDF Downloads 152