Sultan Idris Education University

Abstract The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-19 1,2 , host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases 3–7 . They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease.

Abstract Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for many applications dismissing the use of DL. Having sufficient data is the first step toward any successful and trustworthy DL application. This paper presents a holistic survey on state-of-the-art techniques to deal with training DL models to overcome three challenges including small, imbalanced datasets, and lack of generalization. This survey starts by listing the learning techniques. Next, the types of DL architectures are introduced. After that, state-of-the-art solutions to address the issue of lack of training data are listed, such as Transfer Learning (TL), Self-Supervised Learning (SSL), Generative Adversarial Networks (GANs), Model Architecture (MA), Physics-Informed Neural Network (PINN), and Deep Synthetic Minority Oversampling Technique (DeepSMOTE). Then, these solutions were followed by some related tips about data acquisition needed prior to training purposes, as well as recommendations for ensuring the trustworthiness of the training dataset. The survey ends with a list of applications that suffer from data scarcity, several alternatives are proposed in order to generate more data in each application including Electromagnetic Imaging (EMI), Civil Structural Health Monitoring, Medical imaging, Meteorology, Wireless Communications, Fluid Mechanics, Microelectromechanical system, and Cybersecurity. To the best of the authors’ knowledge, this is the first review that offers a comprehensive overview on strategies to tackle data scarcity in DL.

Purpose – The purpose of this paper is to examine the relationship between Bass’s (1985) leadership dimensions (transformational, transactional, and laissez-faire) and several outcome variables (employee extra effort, employee satisfaction with leader, leadership effectiveness) and organizational commitment. Design/methodology/approach – This is a systematic literature review. Findings – This review briefly discusses the conceptual framework and the Full Range Leadership Model (Bass, 1985) which include transformational leadership, transactional leadership, and laissez-faire leadership. Also discussed in this section were the abilities and the characteristics of transformational leaders. The leadership section was concluded with discussion on previous researches on transformational leadership. This review also provides a literature review on organizational commitment. Originality/value – Described in this paper are the various definitions of organizational commitment and the three-component model of commitment. This paper also described the antecedents and outcomes of organizational commitment obtained from previous researches. This paper concluded with a discussion on the impact of transformational leadership on employee organizational commitment.

The metaverse is a kind of imagined world with immersive digital spaces that increase, allowing a more interactive environment in educational settings. The metaverse is an expansion of the synchronous communication that embraces an effective number of users to share different experiences. The study aims to investigate the students' perceptions towards metaverse system for educational purposes in the Gulf area. The conceptual model comprises the adoption properties, namely trialability, observability, compatibility, and complexity, users' satisfaction, personal innovativeness, and Technology Acceptance Model (TAM) constructs. The novelty of the paper lies in its conceptual model that correlates both personal-based characteristics and technology-based features. In addition, the novel approach of hybrid analysis will be used in the current study to perform deep-learning-based analysis of structural equation modelling (SEM) and artificial neural network (ANN). Moreover, the importance-performance map analysis (IPMA) is used in the current study to evaluate the involved factors for their importance and performance. The study identified Perceived Usefulness (PU) to be an essential predictor of the factor of Users’ Intention to Use the Metaverse System (MS). The fact was discovered during ANN and IPMA analysis. Furthermore, this study is practically significant, as it helped the concerned authorities in educational sector in understanding the significance of each factor and allowed them to make efforts and plans according to the order of significance of factors. Another important implication of the study is methodological in nature. It validates that deep ANN architecture can offer deep insight into non-linear relationships shared by various factors of a theoretical model.

This study presents a systematic review of artificial intelligence (AI) techniques used in the detection and classification of coronavirus disease 2019 (COVID-19) medical images in terms of evaluation and benchmarking. Five reliable databases, namely, IEEE Xplore, Web of Science, PubMed, ScienceDirect and Scopus were used to obtain relevant studies of the given topic. Several filtering and scanning stages were performed according to the inclusion/exclusion criteria to screen the 36 studies obtained; however, only 11 studies met the criteria. Taxonomy was performed, and the 11 studies were classified on the basis of two categories, namely, review and research studies. Then, a deep analysis and critical review were performed to highlight the challenges and critical gaps outlined in the academic literature of the given subject. Results showed that no relevant study evaluated and benchmarked AI techniques utilised in classification tasks (i.e. binary, multi-class, multi-labelled and hierarchical classifications) of COVID-19 medical images. In case evaluation and benchmarking will be conducted, three future challenges will be encountered, namely, multiple evaluation criteria within each classification task, trade-off amongst criteria and importance of these criteria. According to the discussed future challenges, the process of evaluation and benchmarking AI techniques used in the classification of COVID-19 medical images considered multi-complex attribute problems. Thus, adopting multi-criteria decision analysis (MCDA) is an essential and effective approach to tackle the problem complexity. Moreover, this study proposes a detailed methodology for the evaluation and benchmarking of AI techniques used in all classification tasks of COVID-19 medical images as future directions; such methodology is presented on the basis of three sequential phases. Firstly, the identification procedure for the construction of four decision matrices, namely, binary, multi-class, multi-labelled and hierarchical, is presented on the basis of the intersection of evaluation criteria of each classification task and AI classification techniques. Secondly, the development of the MCDA approach for benchmarking AI classification techniques is provided on the basis of the integrated analytic hierarchy process and VlseKriterijumska Optimizacija I Kompromisno Resenje methods. Lastly, objective and subjective validation procedures are described to validate the proposed benchmarking solutions.

Following the global outbreak of COVID-19 in March 2020, individuals report psychological distress associated with the “new normal”—social distancing, financial hardships, and increased responsibilities while working from home. Given the interpersonal nature of stress and coping responses between romantic partners, based on the systemic transactional model this study posits that perceived partner dyadic coping may be an important moderator between experiences of COVID-19 psychological distress and relationship quality. To examine these associations, self-report data from 14,020 people across 27 countries were collected during the early phases of the COVID-19 pandemic (March–July, 2020). It was hypothesized that higher symptoms of psychological distress would be reported post-COVID-19 compared to pre-COVID-19 restrictions (Hypothesis 1), reports of post-COVID-19 psychological distress would be negatively associated with relationship quality (Hypothesis 2), and perceived partner DC would moderate these associations (Hypothesis 3). While hypotheses were generally supported, results also showed interesting between-country variability. Limitations and future directions are presented.

This study investigates the impact of self-efficacy, achievement motivation, and learning strategies on students’ academic achievement. Conducting this research is important since there is a lack of educational research on the above research components as an integrated motivational model. Selected undergraduateHstudentsEtsarticipatedHniahelStudy.EirhelStructuralH equationHnodellingl^SEMlll/vas&ppliedHo&nswer the following research Question: What is the impact of self-efficacy beliefs, achievement motivation, and self- learning strategies on academic achievement of the UKM undergraduate students? Scientifically,&esults&ffflirectandHndirectaechnique indicated the effects of self-efficacy beliefs, achievement motivation, and elf learning strategies on academic achievement. Self-efficacy beliefs were significantly enhanced learning attainment.Tel: +60 5 450 5522; Fax: +60 173058930.

Loss of the ability to speak or hear exerts psychological and social impacts on the affected persons due to the lack of proper communication. Multiple and systematic scholarly interventions that vary according to context have been implemented to overcome disability-related difficulties. Sign language recognition (SLR) systems based on sensory gloves are significant innovations that aim to procure data on the shape or movement of the human hand. Innovative technology for this matter is mainly restricted and dispersed. The available trends and gaps should be explored in this research approach to provide valuable insights into technological environments. Thus, a review is conducted to create a coherent taxonomy to describe the latest research divided into four main categories: development, framework, other hand gesture recognition, and reviews and surveys. Then, we conduct analyses of the glove systems for SLR device characteristics, develop a roadmap for technology evolution, discuss its limitations, and provide valuable insights into technological environments. This will help researchers to understand the current options and gaps in this area, thus contributing to this line of research.

Emerging longitudinal research has highlighted poor sleep as a risk factor of a range of adverse health outcomes, including disabling pain conditions. In establishing the causal role of sleep in pain, it remains to be clarified whether sleep deterioration over time is a driver of pain and whether sleep improvement can mitigate pain-related outcomes. A systematic literature search was performed using PubMed MEDLINE, Ovid EMBASE, and Proquest PsycINFO, to identify 16 longitudinal studies involving 61,000 participants. The studies evaluated the effect of sleep changes (simulating sleep deterioration, sleep stability, and sleep improvement) on subsequent pain-related outcomes in the general population. A decline in sleep quality and sleep quantity was associated with a two- to three-fold increase in risk of developing a pain condition, small elevations in levels of inflammatory markers, and a decline in self-reported physical health status. An exploratory meta-analysis further revealed that deterioration in sleep was associated with worse self-reported physical functioning (medium effect size), whilst improvement in sleep was associated with better physical functioning (small effect size). The review consolidates evidence that changes in sleep are prospectively associated with pain-related outcomes and highlights the need for further longitudinal investigations on the long-term impact of sleep improvements.

Permanent magnet synchronous motors (PMSMs) are known as highly efficient motors and are slowly replacing induction motors in diverse industries. PMSM systems are nonlinear and consist of time-varying parameters with high-order complex dynamics. High performance applications of PMSMs require their speed controllers to provide a fast response, precise tracking, small overshoot and strong disturbance rejection ability. Sliding mode control (SMC) is well known as a robust control method for systems with parameter variations and external disturbances. This paper investigates the current status of implementation of sliding mode control speed control of PMSMs. Our aim is to highlight various designs of sliding surface and composite controller designs with SMC implementation, which purpose is to improve controller’s robustness and/or to reduce SMC chattering. SMC enhancement using fractional order sliding surface design is elaborated and verified by simulation results presented. Remarkable features as well as disadvantages of previous works are summarized. Ideas on possible future works are also discussed, which emphasize on current gaps in this area of research.

Artificial intelligence (AI)-based chatbots have received considerable attention during the last few years. However, little is known concerning what affects their use for educational purposes. This research, therefore, develops a theoretical model based on extracting constructs from the expectation confirmation model (ECM) (expectation confirmation, perceived usefulness, and satisfaction), combined with the knowledge management (KM) factors (knowledge sharing, knowledge acquisition, and knowledge application) to understand the sustainable use of chatbots. The developed model was then tested based on data collected through an online survey from 448 university students who used chatbots for learning purposes. Contrary to the prior literature that mainly relied on structural equation modeling (SEM) techniques, the empirical data were analyzed using a hybrid SEM-artificial neural network (SEM-ANN) approach. The hypotheses testing results reinforced all the suggested hypotheses in the developed model. The sensitivity analysis results revealed that knowledge application has the most considerable effect on the sustainable use of chatbots with 96.9% normalized importance, followed by perceived usefulness (70.7%), knowledge acquisition (69.3%), satisfaction (61%), and knowledge sharing (19.6%). Deriving from these results, the study highlighted a number of practical implications that benefit developers, designers, service providers, and instructors.

The learning environment usually raises various types of anxiety based on the student’s abilities to use technology and their abilities to overcome the negative feelings of an individual being watched all the time and criticized. Hence, learners still feel anxious while using computers and socializing in an e-learning environment. Learners who are faced with computer and AI tools are confused and frustrated. The uneasiness stems from anxiety or uneasiness, which is highly evident in daily interaction with computers and artificial intelligence tools or devices in e-learning contexts. The uneasiness stems from anxiety or uneasiness, which is highly evident in the daily interaction with computers and artificial intelligence tools or devices in e-learning contexts. To investigate this phenomenon empirically, a questionnaire was distributed among a group of undergraduate students who are studying different majors. This study aims to investigate the role of social anxiety and computer anxiety in an e-learning environment at the university level. Universities in the Gulf area are among those implementing e-learning systems. In spite of this, recent studies have shown that most students at Gulf universities are still resistant to using online systems; hence, it is necessary to determine the type of anxiety that creates such resistance and their relationship with other external variables such as motivation, satisfaction and self-efficacy. Students would be more likely to use e-learning tools and participate more effectively in their courses using the accessible electronic channels when the degree of anxiety is low. In this study, we have proposed a theoretical framework to investigate the role of social anxiety and computer anxiety in e-learning environments in the Gulf region. We examined how different variables such as satisfaction, motivation and self-efficacy can negatively or positively affect these two types of anxiety.

The efficacy of coconut tree sawdust (CTS), eggshell (ES) and sugarcane bagasse (SB) as alternative low-cost biosorbents for the removal of Cu(II), Pb(II) and Zn(II) ions from aqueous solutions was investigated. Batch adsorption studies were carried out to evaluate the effects of solution pH and initial metal concentration on adsorption capacity. The optimum biosorption condition was found at pH 6.0, 0.1 g biomass dosage and at 90 min equilibrium time. The adsorption data were fitted to the Freundlich and Langmuir isotherm models. The adsorption capacity and affinity of CTS, ES and SB were evaluated. The Freundlich constant (n) and separation factor (RL) values suggest that the metal ions were favourably adsorbed onto biosorbents. The maximum adsorption capacities (Q) estimated from the Langmuir isotherm model for Cu(II), Pb(II) and Zn(II) were 3.89, 25.00 and 23.81 mg/g for CTS, 34.48, 90.90 and 35.71 mg/g for ES, and 3.65, 21.28 and 40.00 mg/g for SB, respectively. The characterisation studies were performed using Scanning Electron Microscope (SEM), Energy Dispersive X-ray Spectrometer (EDX) and Fourier Transform Infrared Spectrometer (FTIR). Interaction with metal ions led to the formation of discrete aggregates on the biosorbents surface. The metal ions bound to the active sites of the biosorbents through either electrostatic attraction or complexation mechanism.

Coronavirus outbreak is the latest world tragedy that have affected all sectors in economy. The lockdown, confinement, limited movement order and social distancing are amongst the preemptive governments’ effort to safeguard the public health. While recognizing the importance of the national order in preventing the immense spread of the virus, the authors contend that there are certain undiscovered impacts of the control order policy on SMEs in Malaysia. The objectives of this article are to scrutinize the implications of the Covid-19 Movement Control Order (MCO) on SMEs businesses and to identify survival strategies based on the owners’ perspectives. The study applies qualitative approach conducted through phone-based interviews with six selected SMEs’ owners during the first phase of control order from March 18, 2020 to March 31, 2020. In summary, the impacts of MCO on SMEs are classified into the operational problems (i.e. operation distruption; supply chain distruption; foresighting the future business direction) and the financial problems (i.e. cash flow imbalance; access to stimulus packages; risk of bankcruptcy). Meanwhile, the major themes of current survival strategies fall under the financial and marketing strategies. The paper recommends few suggestions for future research work, business development agencies and entrepreneurs. Keywords: Covid-19, movement control order (MCO), small and medium enterprises (SMEs), business, strategy

The study was conducted to produce empirical data on the reliability and validity of the Teaching Framework for Mathematics (TF@Maths) questionnaire. A survey was conducted in one public university and one institution of teacher education in Northern Zone of Malaysia towards 436 students from the Mathematics Education. The reliability and validity of the TF@Maths questionnaire were tested with the Cronbach’s alpha and Exploratory Factor Analysis (EFA) respectively using the Statistical Package for the Social Sciences (SPSS) software version 23. The TF@Maths questionnaire is a 7 point Likert- scale survey consisted of 86 items. The Cronbach’s alpha test conducted shows that the overall score was 0.939 indicating high reliability of the items in the instrument. For validity, EFA was then conducted with the items using principal component analysis extraction and Varimax rotation. There were 62 items retaining with the factor loadings that was above 0.4. The factor analysis shows that the TF@Maths produced six factors, namely: mathematics content knowledge, mathematical pedagogical knowledge, general pedagogical knowledge, classroom management skill, mathematics disposition and quality mathematics teacher. The findings of TF@Maths will bebefit educational practitioners in designing a Teaching Mathematics Framework.

Artificial intelligence applications (AIA) increase innovative interaction, allowing for a more interactive environment in governmental institutions. Artificial intelligence is user-friendly and embraces an effective number of features among the different services it offers. This study aims to investigate users’ experiences with AIA for governmental purposes in the Gulf area. The conceptual model comprises the adoption properties (namely trialability, observability, compatibility, and complexity), relative advantage, ease of doing business, and technology export. The novelty of the paper lies in its conceptual model that correlates with both personal characteristics and technology-based features. The results show that the variables of diffusion theory have a positive impact on the two variables of ease of doing business and technology export. The practical implications of the current study are significant. We urge the concerned authorities in the governmental sector to understand the significance of each factor and encourage them to make plans, according to the order of significance of the factors. The managerial implications provide insights into the implementation of AIA in governmental systems to enhance the development of the services they offer and to facilitate their use by all users.

Purpose The objective of this paper is to explore the critical success factors (CSFs) for Lean Six Sigma (LSS) in the Malaysian automotive industry. Design/methodology/approach Structural equation modeling (SEM) was employed to test the model drawing on a sample of 252 Malaysian automotive organisations. Exploratory factor analyses (EFA), confirmatory factor analysis (CFA), and reliability analysis empirically verified and validated the underlying items of CSFs of LSS. Findings The results of EFA, CFA, and reliability analysis show that two items for supplier relationship are recommended to be excluded from the analysis. The result indicates that LSS has identified 40 items as compared to the original questionnaire which had 42 items. Based on the survey of empirical data, the two factors of leadership and customer focus have been shown to be the extremely important factors for LSS implementation in the Malaysian automotive industry. Research limitations/implications Firstly, this survey is based only on the automotive industry in Malaysia, and therefore it is not generalisable to other industries. Secondly, there may be other CSFs for LSS such as culture change, project management skill, and employee involvement, which were not included in this study. Finally, for future research agenda, the authors are looking at the structural relationship between LSS practices and organizational performance in the Malaysian automotive industry. Originality/value The developed and tested content of this study fills the research gap by providing reliable and useful reference material on the CSFs of LSS. On top of that, the contribution for academic researchers and practitioners is to provide important guidelines for automotive and related companies to implement LSS strategic practices to improve organizational performance.

Fake news and disinformation (FNaD) are increasingly being circulated through various online and social networking platforms, causing widespread disruptions and influencing decision-making perceptions. Despite the growing importance of detecting fake news in politics, relatively limited research efforts have been made to develop artificial intelligence (AI) and machine learning (ML) oriented FNaD detection models suited to minimize supply chain disruptions (SCDs). Using a combination of AI and ML, and case studies based on data collected from Indonesia, Malaysia, and Pakistan, we developed a FNaD detection model aimed at preventing SCDs. This model based on multiple data sources has shown evidence of its effectiveness in managerial decision-making. Our study further contributes to the supply chain and AI-ML literature, provides practical insights, and points to future research directions.

The increasing rate of unemployed graduates is one of the issues that triggers world's concerns lately. Consequently, this research aims to investigate factors that lead to the unemployment problem among Malaysian graduates from three aspects, which are graduates’ attributes, lecturers’ competency and quality of education. This qualitative research adopted an interview method, which was conducted to seven respondents who have the experience in teaching and working in the industry. The majority of the respondents agreed that the graduates’ attributes, lecturers’ competency and the quality of education, which is referred to the curriculum of a study field, are among the factors that contribute to the unemployment problem among the Malaysian graduates nowadays. Therefore, it is reasonable for the parties who are involved in the construction and the enhancement of the curriculum of the related to the field of study to conduct an in-depth study. This should be done in order to identify the problems that occur throughout the implementation of the study area. It is important to evaluate the suitability of the implementation of a curriculum of the study area in order to ensure the implemented curriculum can produce human resources, which are qualified, skilful and can fulfil the need of the industries and the current market.

Generating of idea is thinking skills activity which require high level of creative thinking and actions. Hence, the purpose of this research was to analyse the needs of learning higher order thinking skills for generating ideas among technical students based on the opinions of academic staffs. The findings indicated that deadlock of ideas is the most important factor in the difficulty in generating ideas among these students. The difficulty of generating ideas is a key factor in affecting the achievements of the students’ assignments. Thus, students need to learn higher order thinking skills to address the difficulty in generating ideas.