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The purpose of this paper is to determine the effect of clothing fabrics, sizes and air ventilation rate on the volume and thickness of the air gap under the air ventilation garments (AVGs).

The geometric models of the human body and clothing were obtained by using a 3D body scanner. Then the distribution of the volume and thickness of the air gap for four clothing fabrics and three air ventilation rates (0L/S, 12L/S and 20L/S) were calculated by Geomagic software. Finally, a more suitable fabric was selected from the analysis to compare the distribution of the air gap entrapped for four clothing sizes (S, M, L and XL) and the three air ventilation rates.

The results show that the influence of air ventilation rate on the air gap volume and thickness is more obvious than that of the clothing fabrics and sizes. The higher is the air ventilation rate, the thicker is the air gap entrapped, and more evenly distributed is the air gap. It can be seen that the thickness of the air gap in the chest does not change significantly with the changes of the air ventilation rates, clothing fabrics and sizes, while the air gap in the waist is affected significantly.

This research provides a better understanding of the distribution of the air gap entrapped in ventilated garments, which can help in designing the optimal air gap dimensions and thus provide a basis and a reference for the design of the AVGs.

The purpose of this study is to understand the public value of the government of Kuwait using blockchain technology to develop the capabilities of smart cities.

Research was conducted in Kuwait, where the increased use of blockchain technology has been evidenced in both the private and public sectors. A total of seven IT managers were interviewed to gauge their responses to blockchain and its use in Kuwait ministries.

Blockchain technology offers many benefits for the development of smart cities in Kuwait. This is a statement that received almost mutual agreement amongst all the IT managers interviewed. However, as regards wider acceptance, the majority mentioned that a framework is necessary to better articulate the public value of using blockchain in smart cities in Kuwait.

This paper develops research hypotheses and a framework for articulating the public value of blockchain technology for smart cities in Kuwait.

Knowledge is an important resource that becomes obsolete if not shared in time. Therefore, this study aims to examine how employees’ perceived fun at work affects their psychological empowerment to evoke knowledge-sharing behavior. The study further explored the conditional role of inclusive leadership between the same.

Using the time lag approach, the data for the current study was collected from 355 employees working in IT firms. The study used convenience sampling, and structural equation modeling was used for hypotheses testing.

The results showed that perceptions of fun have a favorable impact on employees' knowledge sharing behavior and psychological empowerment mediate this relationship. Furthermore, there was no empirical evidence of the conditional role of inclusive leadership between fun and psychological empowerment. However, the results of the alternative model indicated that inclusive leadership moderates the relationship between psychological empowerment and knowledge sharing.

The findings reveal that management should nurture fun at work as it can lead to psychological empowerment and knowledge sharing. The study also reveals that the role of leadership is important for empowerment and knowledge sharing relationships.

Based on the conservation of resources and self-determination perspectives, this study proposes and empirically investigates the mechanism of psychological empowerment and inclusive leadership which has largely been an unattended area of research in knowledge sharing literature.

Drawing on conservation of resource (COR) theory, the authors investigated relationships between bullying during work from home, turnover intention and meaningfulness of work among home-based teleworkers.

The authors used three-wave data from 212 home-based teleworkers to investigate the authors' hypotheses.

Findings reveal that bullying during home-based teleworking exacerbates teleworkers' emotional exhaustion and intention to quit. Further, the authors also found that the interrelationship between bullying during work from home and the intention to leave was positively moderated by the meaningfulness of work.

The authors' research helps understand how bullying stimulates teleworkers' turnover intention. Further, the authors find a counterintuitive impact of the meaningfulness of work on the relationship between bullying during work and turnover intention. The findings will help managers better manage home-based teleworkers.

This study aims to discuss the behavioral economics and Islamic economic joint criticisms against the conceptual and economic political view of the mainstream.

The purpose of this study is to examine the effectiveness of mainstream economic policies in addressing unemployment. Furthermore, it critically assesses the mainstream perspective on unemployment within the contexts of Islamic economics and behavioral economics, separately. The commonalities and disparities between the approaches of Islamic economics and behavioral economics regarding unemployment are evaluated. Subsequently, the conventional viewpoint on unemployment is scrutinized from the combined standpoint of Islamic economics and behavioral economics. This article employs a theoretical approach to address these concerns.

Although there are some differences, the recommendations and values of Islamic Economics and behavioral economics in the context of unemployment are almost the same. And, more importantly, both approaches are similar in their emphasis on the ineffectiveness and distance from human values of mainstream economic policies.

This article is the first to examine unemployment from the joint perspectives of Islamic economics and behavioral economics. It is also the first article to criticize the mainstream view of unemployment from the common framework of these two approaches.

In the current study based on the resource-based view (RBV), a three-way interaction model tests the relationships among human and social capital resources, innovation orientation (IO) and innovation capability in the context of new ventures.

Hierarchical linear regression modeling presents the linear relations at two decision layers of start-ups, their founders and managers. Data is collected and analyzed from 233 new ventures in Turkey.

Findings of the two and three-way interaction analyses indicate a positive relationship between human capital and innovation capability when social capital and IO are high; however, the relation turns off when low.

The study extends the previous works on the proposed link between intellectual capital (IC) resources and innovation, by confirming the moderating role of social capital and IO on the positive association between human capital resources and innovation capability.

The results show that for start-up companies, the co-existence of strong social capital and the strategic orientation towards innovation is required for the effective utilization of human capital for generating innovation capability within the organization. Thus, this study highlights the importance of networks, alliances and social relationships, together with the unification of strategic thinking, organizational learning and a culture of innovation for attaining innovation goals, which are crucial for the survival and success of these units.

This study presents the first model in the literature which examines the moderating effects of IO and social capital on the human capital-innovation capability relationship.

Given the decisive role of knowledge-oriented leadership (KOL) in boosting organizational innovation capacities, the research intends to investigate the effect of KOL on ambidextrous innovation with the mediating effect of knowledge management capability (KMC). Furthermore, technological turbulence (TT) is regarded as a moderator in the relationship between KMC and ambidextrous innovation.

The data obtained from 122 Pakistani manufacturing firms were used to evaluate the proposed relationships using the partial least square structural equation modeling approach.

The empirical findings demonstrate that KOL positively affects both aspects of ambidextrous innovation, namely exploitative innovation (EII) and exploratory innovation (ERI), with a higher effect on EII. Additionally, knowledge management process capability (KMPC) partially mediates the association between KOL and both dimensions of ambidextrous innovation (EII and ERI). Similarly, knowledge management infrastructure capability (KMIC) mediates the link between KOL and ERI but does not mediate the relationship between KOL and EII. The impacts of the KMPC and KMIC on EII are also significantly moderated by TT, although the link between the two components of the KMC (KMPC and KMIC) and ERI is unaffected.

The research provides useful knowledge and a novel strategy for policymakers to foster KOL and invest in KMC to improve the capabilities of Pakistani manufacturing firms in terms of innovation.

The research has contributed significantly to the resources-based view and knowledge-based view (KBV) literature by examining the various mediation moderation mechanisms and offering greater insights into the relationship between KOL and firms, KMC, and ambidextrous innovations.

This study aims to explore how information technology (IT) professionals perceive work-life balance (WLB) in a work-from-home (WFH) setup. Additionally, it explores what emotions one may associate with such changing work environments, which have high implications for organisational success.

The two primary research questions guided this research. An online questionnaire-based survey was conducted to collect the data so that respondents’ both subjective and objective perceptions were documented. Purposive cum snowball sampling was used to collect data from 262 IT professionals. However, the data was analysed using both qualitative (content analysis) and quantitative (chi-square) techniques.

The findings of this study are interesting in nature and reported the work-life experiences at various socio-demographic levels (age, gender, educational qualification, designation, work experience, income, type of family and the number of children). The comprehensive examination of the data obtained from diverse aspects related to remote work environments has shed light on crucial facets impacting IT professionals. A predominant observation derived from the study reveals a significant disparity in working hours between male and female respondents during remote work. This discrepancy is notable, with male employees tending to work longer hours (i.e. 10 or more hours daily) than their female counterparts. The investigation into respondents’ sleep patterns revealed that the majority slept between 5 h and 7 h daily, underscoring reduced sleep hours for IT professionals during remote work. This comprehensive study thus emphasises the multifaceted nature of gender-associated influences on work patterns, health and well-being during remote work scenarios among IT professionals. As remote work is the new normal, this study has high implications for future work arrangements and organisational success.

The findings of the study will assist managers in dealing with the work conflict issue of remote workers. Importantly, these managers should try eliminating or reducing workplace conflict, emotional exhaustion and social overload associated with remote work.

This study is a humble attempt to highlight the employee’s WLB in the context of WFH in an emerging market (i.e. India). Furthermore, emphasises practical issues associated with changing work paradigms and concludes with interesting recommendations for future work arrangements.

Pervasive health-care computing applications in medical field provide better diagnosis of various organs such as brain, spinal card, heart, lungs and so on. The purpose of this study is to find brain tumor diagnosis using Machine learning (ML) and Deep Learning(DL) techniques. The brain diagnosis process is an important task to medical research which is the most prominent step for providing the treatment to patient. Therefore, it is important to have high accuracy of diagnosis rate so that patients easily get treatment from medical consult. There are many earlier investigations on this research work to diagnose brain diseases. Moreover, it is necessary to improve the performance measures using deep and ML approaches.

In this paper, various brain disorders diagnosis applications are differentiated through following implemented techniques. These techniques are computed through segment and classify the brain magnetic resonance imaging or computerized tomography images clearly. The adaptive median, convolution neural network, gradient boosting machine learning (GBML) and improved support vector machine health-care applications are the advance methods used to extract the hidden features and providing the medical information for diagnosis. The proposed design is implemented on Python 3.7.8 software for simulation analysis.

This research is getting more help for investigators, diagnosis centers and doctors. In each and every model, performance measures are to be taken for estimating the application performance. The measures such as accuracy, sensitivity, recall, F1 score, peak-to-signal noise ratio and correlation coefficient have been estimated using proposed methodology. moreover these metrics are providing high improvement compared to earlier models.

The implemented deep and ML designs get outperformance the methodologies and proving good application successive score.

In pursuit of good governance and better allocation of resources, corruption and informal economy are of interest to policymakers and citizens alike. The impacts of military spending on the informal economy are scant. Moreover, the effects of an external factor, such as corruption that moderates this relationship, have largely been neglected in previous studies. Hence, this paper investigates how corruption moderates the effects of military spending on the informal economy in 30 Asian countries from 1995 to 2017.

This paper utilizes the GMM estimation technique, which allows cross-sectional dependence and slope homogeneity in panel data analysis, to examine the moderating role of corruption on the relationship between military spending and the informal economy.

Empirical findings from this paper indicate that an increase in military spending declines the informal economy while corruption increases it. Interestingly, the negative effects of military spending on the informal economy will mitigate with a greater degree of corruption in the Asian region. We also find that enhancing economic growth and attracting more FDI has reduced the informal economy in Asian countries.

To the best of the authors' knowledge, this is the first empirical study conducted to examine the moderating role of corruption on the military spending – informal economy nexus. Thus far, this approach has not been investigated in the existing literature, particularly for Asian countries.