Search results for: training symbol

Authors: Jennifer Gallup, Beverly Ray, Esther Ntuli

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Many students such as students from linguistically or culturally diverse backgrounds and those with a disability remain chronically underrepresented in higher level science and mathematics disciplines as well as many hands-on-lab-based activities due to the need for remedial reading and mathematics instruction. Makerspace labs can be a conduit for supporting inclusive learning for these students through hands-on active learning strategies that support equitable access to STEM disciplines. Makerspace is a physical space where individuals gather to create, invent, innovate, and learn while using hands-on materials such as 2D and 3D printers, software programs, electronics, and other tools and supplies. Makerspaces are emerging across many P-12 settings; however, many teachers enter the field not prepared to harness the power inherent in a makerspace, especially for those with disabilities and differing needs. This paper offers suggestions on teaching pre-service teachers and practicing teachers how to incorporate a makerspace into their professional practice through guided instruction and hands-on practice. Recommendations for interested stakeholders are included as well.

Keywords: STEM learning, technology, autism, students with disabilities, makerspace

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Authors: Myla Zenaida Cabrillas-Torio, Von Anthony G. Torio

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Homework is defined as an additional task that a student does outside of the school. This added activity is in recognition of the necessity to spend additional time for subjects such as Mathematics. The dilemma comes in the form of the advantages and disadvantages that can be derived from homework. Studies have revealed varying effects to students on academic and non-academic areas. Teachers are at the forefront of the decision towards the giving or not of homework. Pre-service teachers at the elementary level represent the future leaders of the educational system and should be acquainted and involved at the onset of the dilemma. The main objective of this study is to determine the perspective of pre-service elementary teachers towards homework. The anatomy of their belief can be key towards addressing the issue via teacher training. Salient results revealed that the subjects favor the giving homework on the following grounds: it helps add knowledge and confidence. Those who do not favor homework find it as an additional burden. Difficulties in complying with homework are usually associated with lack of references and performance of other household chores. Students usually spend late nights to comply with homework and are unable to perform at the best of their potentials.

Keywords: attitude, homework, pre-service teachers, mathematics education, Philippines

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Authors: Badar Dhehawi A. Aldhafeeri

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Objective: To explore the views and experiences of healthcare providers (HCPs) and their expectations toward pharmacists in mental healthcare, in addition to their acceptance of new pharmacist roles. Barriers and facilitators that are emerging in the process of developing enhanced pharmacist-related roles were also explored. Methods: Qualitative semi-structured face-to-face interviews were conducted with HCPs who had worked in mental health services in Saudi Arabia. The data were thematically analysed using a constant comparison with NVivo software to develop a series of key themes from the interviews. Results: Most HCPs indicated that they rarely interacted with pharmacists. They expected pharmacists to educate both patients and other healthcare workers in the future. Concerns were raised regarding inadequate pharmacy education and lack of clinical training for pharmacists. Conclusion: This study revealed that interactions between HCPs and pharmacists concerning mental health are still limited. A communication strategy for addressing mental health issues should be developed among pharmacists and other HCPs.

Keywords: pharmacist, pharmacy student, saudi arabia, qualitative research

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Authors: B. G. Abiona

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This study compared profitability analysis of integrated and non-integrated fish farming in Ogun State, Nigeria. Primary data were collected using interview guide. Random sampling techniques was used to select 133 non-integrated fish farmers (NIFF) and 216 integrated fish farmers (IFF) (n = 349) from the study area. Data were analyzed using Chi-square, T-test and Pearson Product moment correlation. Results showed that 92.5% of NIFF was male compared to IFF (90.7%). Also, 96.8% of IFF and 79.7% of NIFF were married. The mean ages of sampled farmers were 44 years (NIFF) and 46 years (IFF) while the mean fish farming experiences were 4 years (NIFF) and 5 years (IFF). Also, the average net profit per year of integrated fish farmers was ₦162,550 compared to NIFF (₦61,638). The chi-square analyses showed that knowledge of fish farming had significant relationship with respondents sex (χ2 = 9.44, df = 2, p < 0.05), age (r = 0.20, p< 0.05) and farming experience (r = p = 0.05). Significant differences exist between integrated and non-integrated fish farming, considering their knowledge of fish farming (t = 21.5, χ = 43.01, p < 0.05). The study concluded that IFF are more profitable compared to NIFF. It was recommended that private investors and NGOs should sponsor short training and courses which will enhance efficiency of fish farming to boost productivity among fish farmers.

Keywords: profitability analysis, farms, integration

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Authors: S. M. Anowarul Haque, Md. Asiful Islam

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Electrical load forecasting is considered to be one of the most indispensable parts of a modern-day electrical power system. To ensure a reliable and efficient supply of electric energy, special emphasis should have been put on the predictive feature of electricity supply. Artificial Neural Network-based approaches have emerged to be a significant area of interest for electric load forecasting research. This paper proposed an Artificial Neural Network model based on the particle swarm optimization algorithm for improved electric load forecasting for Mymensingh, Bangladesh. The forecasting model is developed and simulated on the MATLAB environment with a large number of training datasets. The model is trained based on eight input parameters including historical load and weather data. The predicted load data are then compared with an available dataset for validation. The proposed neural network model is proved to be more reliable in terms of day-wise load forecasting for Mymensingh, Bangladesh.

Keywords: load forecasting, artificial neural network, particle swarm optimization

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Authors: Md. Mazharul Anwar, Md. Samim Hossain Molla, Md. Akkas Ali, Mian Sayeed Hassan

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After 68 years, the agreement between Bangladesh and India was ratified on 6 June 2015 and Bangladesh received 111 Indian enclaves. Millions of farm household lived in these previous enclaves, being detached from the mainland of the country, they were socially, economically and educationally deprived people in the world. This study was undertaken to compare of the Stochastic Frontier Analysis (SFA) and the constant returns to scale (CRS) and variable returns to scale (VRS) output-oriented DEA models, based on a sample of 300 farms from the three largest enclaves of Bangladesh in 2017. However, the aim of the study was not only to compare estimates of technical efficiency obtained from the two approaches, but also to examine the determinants of inefficiency. The results from both the approaches indicated that there is a potential for increasing farm production through efficiency improvement and that farmers' age, educational level, new technology dissemination and training on crop production technology have a significant effect on efficiency. The detection and measurement of technical inefficiency and its determinants can be used as a basis of policy recommendations.

Keywords: DEA approach, previous enclaves, SFA approach, technical inefficiency

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Authors: Jawad Ul Haq, Evan Mazur, Ahmed Qureshi, Mohamed Al-Hussein

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The uses of wood in furniture, building, bridges and aluminum in transportation and construction, make aluminum and forest economy a prominent matter in North America. Machines available to date to cut the aforementioned materials are mostly industry oriented with complex structure and operations which require special training and skill. Furthermore, requirements such as pneumatics, 3-phase supply are associated with cost, maintenance, and safety hazards. Power saws are very useful tools used to cut and shape materials; however, they can cause serious hand injuries. Operator’s hands in table saw are vulnerable as they are used to guide pieces into the saw. Apart from hands, saw operator is also prone to material being kicked back out of the saw or sustain eye or respiratory injuries due to rapidly flying sawdust and other debris. In this paper, design of an automatic saw cutting machine has been proposed to ensure safety, portability, usage at domestic level and capability to cut both aluminum and wood. This paper demonstrates detailed Mechanical design in SOLIDWORKS and Control Systems using Programmable Logic Controller (PLC), based on the aforementioned design objectives.

Keywords: programmable logic controller, saw cutting, control, automation

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Authors: Shayan Mohajer Hamidi

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Federated Learning (FL) has emerged as a promising paradigm for training machine learning models on distributed data without the need for centralized data aggregation. In the realm of wireless communications, FL has the potential to leverage the vast amounts of data generated by wireless devices to improve model performance and enable intelligent applications. However, the fairness aspect of FL in wireless communications remains largely unexplored. This abstract presents an idea for fair federated learning in wireless communications, addressing the challenges of imbalanced data distribution, privacy preservation, and resource allocation. Firstly, the proposed approach aims to tackle the issue of imbalanced data distribution in wireless networks. In typical FL scenarios, the distribution of data across wireless devices can be highly skewed, resulting in unfair model updates. To address this, we propose a weighted aggregation strategy that assigns higher importance to devices with fewer samples during the aggregation process. By incorporating fairness-aware weighting mechanisms, the proposed approach ensures that each participating device's contribution is proportional to its data distribution, thereby mitigating the impact of data imbalance on model performance. Secondly, privacy preservation is a critical concern in federated learning, especially in wireless communications where sensitive user data is involved. The proposed approach incorporates privacy-enhancing techniques, such as differential privacy, to protect user privacy during the model training process. By adding carefully calibrated noise to the gradient updates, the proposed approach ensures that the privacy of individual devices is preserved without compromising the overall model accuracy. Moreover, the approach considers the heterogeneity of devices in terms of computational capabilities and energy constraints, allowing devices to adaptively adjust the level of privacy preservation to strike a balance between privacy and utility. Thirdly, efficient resource allocation is crucial for federated learning in wireless communications, as devices operate under limited bandwidth, energy, and computational resources. The proposed approach leverages optimization techniques to allocate resources effectively among the participating devices, considering factors such as data quality, network conditions, and device capabilities. By intelligently distributing the computational load, communication bandwidth, and energy consumption, the proposed approach minimizes resource wastage and ensures a fair and efficient FL process in wireless networks. To evaluate the performance of the proposed fair federated learning approach, extensive simulations and experiments will be conducted. The experiments will involve a diverse set of wireless devices, ranging from smartphones to Internet of Things (IoT) devices, operating in various scenarios with different data distributions and network conditions. The evaluation metrics will include model accuracy, fairness measures, privacy preservation, and resource utilization. The expected outcomes of this research include improved model performance, fair allocation of resources, enhanced privacy preservation, and a better understanding of the challenges and solutions for fair federated learning in wireless communications. The proposed approach has the potential to revolutionize wireless communication systems by enabling intelligent applications while addressing fairness concerns and preserving user privacy.

Keywords: federated learning, wireless communications, fairness, imbalanced data, privacy preservation, resource allocation, differential privacy, optimization

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Authors: Srinidhi. A, Naveed Ahmed, Twinkle Hareendran, Vriksha Prakash

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This work reviews some of the advanced image processing techniques for deep learning applications. Object detection by template matching, image denoising, edge detection, and super-resolution modelling are but a few of the tasks. The paper looks in into great detail, given that such tasks are crucial preprocessing steps that increase the quality and usability of image datasets in subsequent deep learning tasks. We review some of the methods for the assessment of image quality, more specifically sharpness, which is crucial to ensure a robust performance of models. Further, we will discuss the development of deep learning models specific to facial emotion detection, age classification, and gender classification, which essentially includes the preprocessing techniques interrelated with model performance. Conclusions from this study pinpoint the best practices in the preparation of image datasets, targeting the best trade-off between computational efficiency and retaining important image features critical for effective training of deep learning models.

Keywords: image processing, machine learning applications, template matching, emotion detection

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Authors: Vijaya Chandra Tenneti

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The juvenile justice delivery system in India suffers from many lacunae at the operational level and ignores many dimensions of human rights guaranteed to the juvenile delinquents. The present study begins with the hypothesis that the existing justice delivery system seemingly ignores the basic tenets of the fair trial and systemic support to the delinquent juveniles in integrating them into the mainstream of society. As per the designed methodology, data has been collected from the unit of the present study, and other stakeholders, namely, Juvenile Justice Board, Observation Homes etc., of Warangal district of Telangana state, India. The study shows that there is the overemphasis on procedural laws. The juvenile integration programs are not effective. The administrators lack training. Juveniles lack formal education. The study indicates the incidents of juvenile crimes is on the rise and that the majority of the juvenile delinquents hold a low socio-economic profile. Another significant observation of the study is that the juvenile justice system lacks a holistic and human rights-centric approach.

Keywords: delinquency, human rights, juvenile justice, rehabilitation

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Authors: Angely Sim Jia Wun, Sasa Arsovski

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Numerous studies have been conducted on the segmentation of medical images. Segmenting the lungs is one of the common research topics in those studies. Our research stemmed from the lack of solutions for automatic bone, airway, and vessel segmentation, despite the existence of multiple lung segmentation techniques. Consequently, currently, available software tools used for medical image segmentation do not provide automatic lung, bone, airway, and vessel segmentation. This paper presents segmentation techniques along with an interactive software tool architecture for segmenting bone, lung, airway, and vessel tissues. Additionally, we propose a method for creating binary masks from automatically generated segments. The key contribution of our approach is the technique for automatic image thresholding using adjustable Hounsfield values and binary mask extraction. Generated binary masks can be successfully used as a training dataset for deep-learning solutions in medical image segmentation. In this paper, we also examine the current software tools used for medical image segmentation, discuss our approach, and identify its advantages.

Keywords: lung segmentation, binary masks, U-Net, medical software tools

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Authors: Asra Yousuf, Ajaykumar Tannirkulum

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Inferring rural-urban migration trends can help design effective policies that promote better urban planning and rural development. In this paper, we describe how machine learning algorithms can be applied to predict internal migration decisions of people. We consider data collected from household surveys in Tamil Nadu to train our model. To measure the performance of the model, we use data on past migration from National Sample Survey Organisation of India. The factors for training the model include socioeconomic characteristic of each individual like age, gender, place of residence, outstanding loans, strength of the household, etc. and his past migration history. We perform a comparative analysis of the performance of a number of machine learning algorithm to determine their prediction accuracy. Our results show that machine learning algorithms provide a stronger prediction accuracy as compared to statistical models. Our goal through this research is to propose the use of data science techniques in understanding human decisions and behaviour in developing countries.

Keywords: development, migration, internal migration, machine learning, prediction

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Authors: Perminderjit Singh, Sukhvir Singh

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Total Quality Management (TQM) refers to management methods used to enhance quality and productivity in business organizations. Total Quality Management (TQM) has become a frequently used term in discussions concerning quality. Total Quality management has brought rise in demands on the organizations policy and the customers have gained more importance in the organizations focus. TQM is considered as an important management tool, which helps the organizations to satisfy their customers. In present research critical success factors includes management commitment, customer satisfaction, continuous improvement, work culture and environment, supplier quality management, training and development, employee satisfaction and product/process design are studied. A questionnaire is developed to implement these critical success factors in implementation of total quality management in Indian industry. Questionnaires filled by consulting different industrial organizations. Data collected from questionnaires is analyzed by descriptive and importance indexes.

Keywords: total quality management, critical success factor, employee satisfaction, supplier quality management, customer focus, quality information, quality measurement

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Authors: Soukaina Boutaky, Abdelhak Sahibeddine

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A study was carried out among students who have especially a scientific and technical educational background and who had opportunities to benefit from a program entrepreneurship course of 50 hours; at Higher School of Technology Khenifra, Morocco. This article has as a goal to explain the relationship between entrepreneurial education, entrepreneurial passion and entrepreneurial intention. The authors chose Bandura’s theory of social cognition as a theoretical framework. The modeling methods equation is adopted to analyze the hypotheses by SMART PLS for 188 students. The results show a strong positive relationship between entrepreneurial education and entrepreneurial passion. They also reveal that entrepreneurship education affects entrepreneurial intention through the effect of entrepreneurial passion, particularly among women than men. In addition, this study contributes in a theoretical way to the level of the relationship between entrepreneurial education and entrepreneurial passion, and these results provide educators and public decision-makers with advice on the importance of entrepreneurship training based on emotional traits such as passion; which constitutes a key and essential element to encourage young graduates to choose an entrepreneurial career as an alternative option or to develop entrepreneurial passion among the business leaders of tomorrow.

Keywords: entrepreneurship education, entrepreneurial passion, entrepreneurial intention, equation modeling methods

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Authors: Mei-Hui Liu

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This paper reports 20 preservice EFL teachers’ learning-to-teach experience when they were engaged in an online project-based practicum implemented on a Cloud Platform. This 10-month study filled in the literature gap by documenting the impact of online project-based instruction on preservice EFL teachers’ professional development. Data analysis showed that the online practicum was regarded as a flexible mechanism offering chances of teaching practices without geographical barriers. Additionally, this project-based practice helped the participants integrate the theories they had learned and further foster them how to create a self-directed online learning environment. Furthermore, these preservice teachers with experiences of technology-enabled practicum showed their motivation to apply technology and online platforms into future instructional practices. Yet, this study uncovered several concerns encountered by these participants during this online field experience. The findings of this study rendered meaning and lessons for teacher educators intending to integrate online practicum into preservice training courses.

Keywords: online teaching practicum, project-based learning, teacher preparation, English language education

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Authors: Esha Thakkar, Ina Liu, Kalynn Hosea, Shana Katz, Katie Marks, Sarah Hall, Cat Liu, Suzanne Harris

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Purpose: Inequities in mental healthcare accessibility are cited as an international public health concern by the World Health Organization (WHO) and National Alliance on Mental Illness (NAMI). These disparities are further exacerbated in racial and ethnic minority groups and are especially concerning in health professional training settings such as Doctor of Pharmacy (PharmD) programs and postgraduate residency training where mental illness rates are high. The purpose of the study was to determine baseline access to culturally sensitive mental healthcare and how to improve such access and communication for racially and ethnically minoritized pharmacy students and residents at one school of pharmacy and a partnering academic medical center in the United States. Methods: This IRB-exempt study included 60-minute focus groups conducted in person or online from November 2021 to February 2022. Eligible participants included PharmD students in their first (P1), second (P2), third (P3), or fourth year (P4) or pharmacy residents completing a postgraduate year 1 (PGY1) or PGY2 who identify as Black, Indigenous, or Person of Color (BIPOC). There were four core theme questions asked during the focus groups to lead the discussion, specifically on the core themes of personal barriers, identities, areas that are working well, and areas for improvement. Participant responses were transcribed and analyzed using an open coding system with two individual reviews, followed by collaborative and intentional discussion and, as needed, an external audit of the coding by a third research team member to reach a consensus on themes. Results: This study enrolled 26 participants, with eight P1, five P2, seven P3, two P4, and four resident participants. Within the four core themes of barriers, identities, areas working well, and areas for improvement, emerging subthemes included: lack of time, access to resources, and stigma under barriers; lack of representation, cultural and family stigma, and gender identities for identity barriers; supportive faculty, sense of community and culture supporting paid time off for areas going well; and wellness days, reduced workload and diversity of the workforce in areas of improvement. Subthemes sometimes varied within a core theme depending on the participant year. Conclusions: There is a gap in the literature in addressing barriers and disparities in mental health access for pharmacy trainees who identify as BIPOC. We identified key findings in regards to barriers, identities, areas going well and areas for improvement that can inform the School and the Residency Program in two priority initiatives of well-being and diversity equity and inclusion in creating actionable recommendations for trainees, program directors, and employers of our institutions, and also has the potential to provide insight for other organizations about the structures influencing access to culturally sensitive care in BIPOC trainees. These findings can inform organizations on how to continue building on communication with those who identify as BIPOC and improve access to care.

Keywords: mental health, disparities, minorities, wellbeing, identity, communication, barriers

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Authors: Mohamed Gomaa Mohamed

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Type of study: Observational cross section study. Background and purpose: Forward head posture (FHP) is a common sagittal faulty posture with anterior head translation relative to vertical posture line. FHP related to temporomandibular joint dysfunctions, neck pain and headache. Sports persons usually overuse one side of the body in training and playing leading to postural imbalance, yet the effect of playing football or bodybuilding on measurements of FHP has never been studied. Participants: Thirty six subjects divided into 3 groups of 12 football players, 12 body builders and 12 students. Method: FHP severity was assessed by measuring the craniovertebral (CVA) and gaze angles, using the photogrammetric method. Photos were taken from right side of subjects while assuming standing position. Analysis of variance was used to assess angles difference between the three groups. Results: No significant differences were found in CVA and gaze angles between the three groups (P > 0.05). Conclusion: Playing football or body building doesn't impose significant FHP.

Keywords: craniovertebral angle, gaze angle, football, body building

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Authors: H. Shola Adeosun, D. Adeoye Odedeji, F. Ajoke Adebiyi

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This study examines the impact of media on the electoral process in Nigeria and the roles played by the media in the reform process. Survey research method was adopted as research methodology, and this enables the researcher to use questionnaire, and oral interview to elicit primary data from the respondents was interpreted, analysed and interpreted with statistical tools such as tables, figures, and percentages. The hypothesis formulated were tested with chi-square. The findings revealed that there is significant relationship between the media and electoral reform process in the 2011 and 2015 general elections in Nigeria. The study recommends that electoral committee should implement virile electoral system with the peaceful voting environment. The media should intensify efforts to expose violation of electoral laws; media should play an advocacy role for dialogue and debate on the reform recommendations. The study recommends that media should unite the nation through their reports on peace, national security, national integration and ethnoreligious tolerance and that adequate training should be given to media practitioners on how to report issues relating to elections.

Keywords: evaluation, impact, media, electoral reform process

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Authors: Monica Racha, Siva Chandrasekaran, Alex Stojcevski

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The engineering programs focus on promoting students' personal and professional development by ensuring that students acquire technical and professional competencies during four-year studies. The traditional engineering laboratory provides an opportunity for students to "practice by doing," and laboratory facilities aid them in obtaining insight and understanding of their discipline. Due to rapid technological advancements and the current COVID-19 outbreak, the traditional labs were transforming into virtual learning environments. Aim: To better understand the limitations of the physical laboratory, this research study aims to use a Machine Learning (ML) algorithm that interfaces with the Augmented Reality HoloLens and predicts the image behavior to classify and detect the electronic components. The automated electronic components error classification and detection automatically detect and classify the position of all components on a breadboard by using the ML algorithm. This research will assist first-year undergraduate engineering students in conducting laboratory practices without any supervision. With the help of HoloLens, and ML algorithm, students will reduce component placement error on a breadboard and increase the efficiency of simple laboratory practices virtually. Method: The images of breadboards, resistors, capacitors, transistors, and other electrical components will be collected using HoloLens 2 and stored in a database. The collected image dataset will then be used for training a machine learning model. The raw images will be cleaned, processed, and labeled to facilitate further analysis of components error classification and detection. For instance, when students conduct laboratory experiments, the HoloLens captures images of students placing different components on a breadboard. The images are forwarded to the server for detection in the background. A hybrid Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) algorithm will be used to train the dataset for object recognition and classification. The convolution layer extracts image features, which are then classified using Support Vector Machine (SVM). By adequately labeling the training data and classifying, the model will predict, categorize, and assess students in placing components correctly. As a result, the data acquired through HoloLens includes images of students assembling electronic components. It constantly checks to see if students appropriately position components in the breadboard and connect the components to function. When students misplace any components, the HoloLens predicts the error before the user places the components in the incorrect proportion and fosters students to correct their mistakes. This hybrid Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) algorithm automating electronic component error classification and detection approach eliminates component connection problems and minimizes the risk of component damage. Conclusion: These augmented reality smart glasses powered by machine learning provide a wide range of benefits to supervisors, professionals, and students. It helps customize the learning experience, which is particularly beneficial in large classes with limited time. It determines the accuracy with which machine learning algorithms can forecast whether students are making the correct decisions and completing their laboratory tasks.

Keywords: augmented reality, machine learning, object recognition, virtual laboratories

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Authors: Ika Wulansari, Yati Afiyanti, Indang Trihandini

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Sleeping disorder experienced by patients with breast cancer can affect the physical, mental, health, and well-being. This study examines the effect of progressive muscle relaxation training and sleep hygiene education to change sleep quality scores of the patient with breast cancer. The study design using quasi-experiment with pre-post test within the control group, involving 62 breast cancer patients using consecutive sampling method in Jakarta. Statistical test results with independent t-test showed a significant difference in score of sleep quality between in intervention group and the control group (6,66±3,815; 9,30±3,334, p-value = 0,005). Progressive muscle relaxation exercise and sleep hygiene education proven to be affective to change the patients sleeping quality, so that it can be an alternative therapeutic option to overcome sleeping disorders.

Keywords: sleeping disorders, breast cancer, progressive muscle relaxation, sleep hygiene education

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Authors: Zainal Abidin Akasah, Maizam Alias, Azuin Ramli

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The safety and health performances aspects of a building are the most challenging aspect of facility management. It requires a deep understanding by the building managers on the factors that contribute to health and safety performances. This study attempted to develop an explanatory architectural safety performance model for stratified low-cost housing in Malaysia. The proposed Building Safety and Health Performance (BSHP) model was tested empirically through a survey on 308 construction practitioners using Partial Least Squares (PLS) and Structural Equation Modelling (SEM) tool. Statistical analysis results supports the conclusion that architecture, building services, external environment, management approaches and maintenance management have positive influence on safety and health performance of stratified low-cost housing in Malaysia. The findings provide valuable insights for construction industry to introduce BSHP model in the future where the model could be used as a guideline for training purposes of managers and better planning and implementation of building management.

Keywords: building management, stratified low-cost housing, safety, health model

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Authors: Qamar Ul Haq, Talat Hussain, Mufti Fahad Ahmed Qureshi

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From the Islamic perspective, managing human resource meets various challenges, especially in the modern organizations. The adoption of Western practices in various aspects of management have caused gaps in justice, trustworthy, responsibility and other values of workers in Muslim countries. Thus, the interference of Islamic principles in human resource management (HRM) can be considered as a great solution for treating employees fairly and justly. This research aims to examine the level of Islamic practices in HRM, in which includes recruitment and selection, training and development, career development, performance management and rewards. The paper will analyze the relationships between HRM practices and organizational justice which focus on three elements, which are distributive justice, procedure justice and interactional justice. The data will be collected from selected Malaysian Government-Linked Company (GLC). Convenience sampling will be used to select the respondents for completing questionnaires. This conceptual paper essentially provides organizations with effective ways of understanding and implementing HRM by using Islamic principles. It also can be used as guidance for decision-making and day-today HR activities and will help organization to face uncertainties in the business world as well.

Keywords: human resource management, organizational justice, Islam, Islamic banking

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Authors: Harjyot Khosa

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In India and south Asia, stigma and discrimination against transgender community remain disproportionately high. Lack of mental health care restricts effective treatment and care for both physical and mental health. Knowledge assessment of 80 counsellors across India reflected that only 28% counsellors knew about the transgender community. Whereas, only 6% of them felt, that transgender community require a specific mental health support, considering the stigma they face in day to day life. Lastly, 62% did agree that they require specific training to address unmet needs of transgender community. A robust counselling module was developed with focus on technical counselling skills and strategies, specific counselling issues, identity and sexuality, disclosure, hormone therapy and sex reassignment surgery. Mental health related support should be an integral part of government and non-government programs for the overall well-being of transgender community who face stigma and discrimination at every level. Needs based capacity building and technical assistance is required towards providing mental health support for transgender populations and their partners.

Keywords: identity and sexuality, mental health, stigma, transgender

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Authors: Meltem Işık, Fatma Gür, İbrahim Kılıç

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This study aims to explore the effects of hemsball shooting techniques on the fine motor skill level of children with hearing disabilities. A total number of 26 children with hearing disabilities, ages ranging between 7 and 11 and which were equally divided into experimental group and control group participated in the study. In this context, an exercise training program dedicated to hemsball shooting techniques was introduced to the experimental group 3 days a week in one hour sessions for a period of 10 weeks. BOT-2 fine motor skills test which includes three dimensions (fine motor accuracy, fine motor task completion, and dexterity) was selected as the data collection method. Descriptive statistics along with two-factor ANOVA which was focused on repetitive measurements of the differences between pretest and posttest scores of both groups were used in the analysis of the data collected. The results of this study showed that hemsball shooting techniques have a statistically significant effect on the fine motor skill level.

Keywords: hemsball shooting techniques, BOT-2 test, fine motor skills, hearing disabilities

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Authors: Vasudeva Murthy Challakere Ramaswamy

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Background: Conventionally forensic pathology is learnt through autopsy demonstrations which carry various limitations such as unavailability of cases in the mortuary, medico-legal implication and infection. Over the years forensic pathology and science has undergone significant evolution in this digital world. Forensic imaging is a technology which can be effectively utilized for overcoming the current limitations in the undergraduate learning of forensic curriculum. Materials and methods: demonstration of forensic imaging was done using a novel technology of autopsy which has been recently introduced across the globe. Three sessions were conducted in international medical university for a total of 196 medical students. The innovative educational tool was evacuated by using quantitative questionnaire with the scoring scales between 1 to 10. Results: The mean score for acceptance of new tool was 82% and about 74% of the students recommended incorporation of the forensic imaging in the regular curriculum. 82% of students were keen on collaborative research and taking further training courses in forensic imaging. Conclusion: forensic imaging can be an effective tool and also a suitable alternative for teaching undergraduate students. This feedback also supports the fact that students favour the use of contemporary technologies in learning medicine.

Keywords: forensic imaging, forensic pathology, medical students, learning tool

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Authors: Kathryn Watson

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In 2020 North Carolina passed legislation mandating all educators be trained in identifying, referring, and supporting students showing signs of mental health issues, drug use, suicidal ideation, and sex trafficking. This study collected survey responses from 226 educators in North Carolina to better understand their perspectives on the legislation and their self-efficacy in supporting student mental health needs. Key findings of the study reveal that the mandated trainings increased educator awareness of student mental health, and higher awareness was linked to higher self-efficacy in supporting student mental health needs. Additionally, the results showed that educators who identify as Black had lower levels of self-efficacy in supporting student mental health. Additionally, rural educators were least likely to support the legislation in comparison to their urban and suburban counterparts. These findings can help inform policymakers in evaluating the policy and district decision-makers in selecting and implementing school-based mental health training.

Keywords: school-based mental health, education policy, student health, North Carolina, K-12 education

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Authors: Lucie Litvack, Malaz Elsaddig, Kevin Jones

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There is strong evidence to support the efficacy of the World Health Organization (WHO) Surgical Safety Checklist in improving patient safety; however, its use can be associated with difficulties. This study uses qualitative data collected in Kitovu Healthcare Complex, a rural Ugandan hospital, to identify factors that may influence the use of the checklist in a low-income setting. Potential barriers to and motivators for the hospital’s use of this checklist are identified and explored through observations of current patient safety practices; semi-structured interviews with theatre staff; a focus group with doctors; and trial implementation of the checklist. Barriers identified include the institutional context; knowledge and understanding; patient safety culture; resources and checklist contents. Motivators for correct use include prior knowledge; team attitudes; and a hospital advocate. Challenges are complex and unique to this socioeconomic context. Stepwise change to improve patient safety practices, local champions, whole team training, and checklist modification may assist the implementation and sustainable use of the checklist in an effective way.

Keywords: anaesthesia, patient safety, Uganda, WHO surgical safety checklist

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Authors: Aamar Ilyas

Abstract:

Value of firm does not only depend upon its financial and material resources rather human resource is also a significant contributor in success of organizations by achieving competitive advantage. Human resource is an important asset so it is the main responsibility of employers to get the best use of this resource. Hence, this paper will explore the human resource practices used by entrepreneurs in the informal economy in Lahore, the provincial capital of Punjab, Pakistan. In this study three major sectors are randomly selected. Snowball sampling technique was applied to collect data. Survey was conducted through interviews of 45 respondents working in the informal sector. The results show that informal sector in Pakistan is not using any formal human resource practices as done by formal enterprises. Findings suggest that there should be the implementation of the human resource practices that help the firm to increase its productivity and ensure the betterment of the employees. The main limitation of the study was short time period to cater all sectors of informal economy of Pakistan which limits the extent of its generalizability. The rationale behind this study is to uncover the facts regarding management practices of human capital in the informal sector.

Keywords: HR practices, informal economy, working condition, recruitment, training, employee motivation, welfare

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Authors: Tahir Nawaz Cheema, Dumitru Baleanu, Ali Raza

Abstract:

In this research work, we design intelligent computing through Bayesian Regularization artificial neural networks (BRANNs) introduced to solve the mathematical modeling of infectious diseases (Covid-19). The dynamical transmission is due to the interaction of people and its mathematical representation based on the system's nonlinear differential equations. The generation of the dataset of the Covid-19 model is exploited by the power of the explicit Runge Kutta method for different countries of the world like India, Pakistan, Italy, and many more. The generated dataset is approximately used for training, testing, and validation processes for every frequent update in Bayesian Regularization backpropagation for numerical behavior of the dynamics of the Covid-19 model. The performance and effectiveness of designed methodology BRANNs are checked through mean squared error, error histograms, numerical solutions, absolute error, and regression analysis.

Keywords: mathematical models, beysian regularization, bayesian-regularization backpropagation networks, regression analysis, numerical computing

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Authors: Ashlesha Khanapure, Harsh Kashyap, Abhinav Anand, Sanjana Habib, Anupama Bidargaddi

Abstract:

Technologies that are developing quickly are being developed daily in a variety of disciplines, particularly the medical field. For the purpose of detecting bone fractures in X-ray pictures of different body segments, our work compares the ResNet-50 and MobileNetV3 architectures. It evaluates accuracy and computing efficiency with X-rays of the elbow, hand, and shoulder from the MURA dataset. Through training and validation, the models are evaluated on normal and fractured images. While ResNet-50 showcases superior accuracy in fracture identification, MobileNetV3 showcases superior speed and resource optimization. Despite ResNet-50’s accuracy, MobileNetV3’s swifter inference makes it a viable choice for real-time clinical applications, emphasizing the importance of balancing computational efficiency and accuracy in medical imaging. We created a graphical user interface (GUI) for MobileNet V3 model bone fracture detection. This research underscores MobileNetV3’s potential to streamline bone fracture diagnoses, potentially revolutionizing orthopedic medical procedures and enhancing patient care.

Keywords: CNN, MobileNet V3, ResNet-50, healthcare, MURA, X-ray, fracture detection

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