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This research outlines the practices that managers believe are critical for achieving quality management in applied research and development (e.g. R&D, process development, product development, and engineering) departments of manufacturing companies. Using these practices, comments from R&D managers, and the existing quality in R&D research literature, a model is developed that highlights the need for broad management practices, specific management practices, awareness of the R&D external environment, and a quality culture to achieve quality management in the R&D departments of manufacturing organizations.

The current era of the fourth industrial revolution has attracted significant research on the use of digital technologies in improving construction project delivery. However, less emphasis has been placed on how these digital tools will influence the management of the construction workforce. To this end, using a review of existing works, this chapter explores the fourth industrial revolution and its associated technologies that can positively impact the management of the construction workforce when implemented. Also, the possible challenges that might truncate the successful deployment of digital technologies for effective workforce management were explored. The chapter submitted that implementing workforce management-specific digital platforms and other digital technologies designed for project delivery can aid effective workforce management within construction organisations. Technologies such as cloud computing, the Internet of Things, big data analytics, robotics and automation, and artificial intelligence, among others, offer significant benefits to the effective workforce management of construction organisations. However, several challenges, such as resistance to change due to fear of job loss, cost of investment in digital tools, organisational structure and culture, must be carefully considered as they might affect the successful use of digital tools and by extension, impact the success of workforce management in the organisations.

With the advent of Big Data, the ability to store and use the unprecedented amount of clinical information is now feasible via Electronic Health Records (EHRs). The massive collection of clinical data by health care systems and treatment canters can be productively used to perform predictive analytics on treatment plans to improve patient health outcomes. These massive data sets have stimulated opportunities to adapt computational algorithms to track and identify target areas for quality improvement in health care.

According to a report from Association of American Medical Colleges, there will be an alarming gap between demand and supply of health care work force in near future. The projections show that, by 2032 there is will be a shortfall of between 46,900 and 121,900 physicians in US (AAMC, 2019). Therefore, early prediction of health care risks is a demanding requirement to improve health care quality and reduce health care costs. Predictive analytics uses historical data and algorithms based on either statistics or machine learning to develop predictive models that capture important trends. These models have the ability to predict the likelihood of the future events. Predictive models developed using supervised machine learning approaches are commonly applied for various health care problems such as disease diagnosis, treatment selection, and treatment personalization.

This chapter provides an overview of various machine learning and statistical techniques for developing predictive models. Case examples from the extant literature are provided to illustrate the role of predictive modeling in health care research. Together with adaptation of these predictive modeling techniques with Big Data analytics underscores the need for standardization and transparency while recognizing the opportunities and challenges ahead.

This study examines and describes the experiences and perceptions of women and men associate professors from various academic disciplines as they chart and navigate their academic career trajectories.

Using a case study approach, we interviewed 11 purposively selected mid-career faculty members and five department heads.

Through the Social Cognitive Career Theory (SCCT), we identified issues of clarity, climate, self-efficacy, and gender disparity as major concerns for mid-career faculty.

This research is limited to a research-intensive university in the southeastern United States. The small study population and unique context limit the generalizability of the study.

Findings of the study provide a lens for university and college administrators, human resources professionals, and other institutional leaders to view professional development programs for mid-career faculty members at their own institutions. The findings also suggest a need for improvements to current family-friendly policies to reduce gender bias and retain women faculty members.

This paper offers practical recommendations to higher education administrators and human resources professionals on how to positively cultivate a better work climate and culture for mid-career faculty members. It also offers suggestions on how to be sensitive to and improve gender equity among mid-career faculty in higher education.

Employee involvement (EI) is a major part of high-performance work systems (HPWS) that have successfully transformed a large number of organizations and have become standard practice in many new organizations. Despite the proven benefits of EI, however, it is still not as widely utilized as it could be even when accounting for industry and organization differences in its applicability. We suggest that EI implementation is limited in part by the change management challenges it presents. We review the recent research on EI and HPWS, and suggest ways in which change research and theory can inform our understanding of why EI practices have fallen short of their potential and how they can be effectively implemented.

One of the techniques for improving the performance of distributed systems is data replication, wherein new replicas are created to provide more accessibility, fault tolerance and lower access cost of the data. In this paper, the authors propose a community-based solution for the management of data replication, based on the graph model of communication latency between computing and storage nodes. Communities are the clusters of nodes that the communication latency between the nodes are minimum values. The purpose of this study if to, by using this method, minimize the latency and access cost of the data.

This paper used the Louvain algorithm for finding the best communities. In the proposed algorithm, by requesting a file according to the nodes of each community, the cost of accessing the file located out of the applicant’s community was calculated and the results were accumulated. On exceeding the accumulated costs from a specified threshold, a new replica of the file was created in the applicant’s community. Besides, the number of replicas of each file should be limited to prevent the system from creating useless and redundant data.

To evaluate the method, four metrics were introduced and measured, including communication latency, response time, data access cost and data redundancy. The results indicated acceptable improvement in all of them.

So far, this is the first research that aims at managing the replicas via community detection algorithms. It opens many opportunities for further studies in this area.

Cloud eases information processing, but it holds numerous risks, including hacking and confidentiality problems. It puts businesses at risk in terms of data security and compliance. This paper aims to maximize the covered human resource (HR) vulnerabilities and minimize the security costs in the enterprise cloud using a fuzzy-based method and firefly optimization algorithm.

Cloud computing provides a platform to improve the quality and availability of IT resources. It changes the way people communicate and conduct their businesses. However, some security concerns continue to derail the expansion of cloud-based systems into all parts of human life. Enterprise cloud security is a vital component in ensuring the long-term stability of cloud technology by instilling trust. In this paper, a fuzzy-based method and firefly optimization algorithm are suggested for optimizing HR vulnerabilities while mitigating security expenses in organizational cloud environments. MATLAB is employed as a simulation tool to assess the efficiency of the suggested recommendation algorithm. The suggested approach is based on the firefly algorithm (FA) since it is swift and reduces randomization throughout the lookup for an optimal solution, resulting in improved performance.

The fuzzy-based method and FA unveil better performance than existing met heuristic algorithms. Using a simulation, all the results are verified. The study findings showed that this method could simulate complex and dynamic security problems in cloud services.

The findings may be utilized to assist the cloud provider or tenant of the cloud infrastructure system in taking appropriate risk mitigation steps.

Using a fuzzy-based method and FA to maximize the covered HR vulnerabilities and minimize the security costs in the enterprise cloud is the main novelty of this paper.

This paper aims to improve the grease thermal elastohydrodynamic lubrication (TEHL) properties of the tripod sliding universal coupling (TSUC) under automotive practical conditions. For this purpose, the effect of effective radius was theoretically investigated.

Based on the simplified geometric model, the effect of effective radius on the pressure distribution, film thickness and temperature distribution of the TSUC was theoretically investigated using the multigrid and stepping methods. The TEHL properties were compared with the results obtained using the isothermal calculation method.

The results show that the thermal effect has a great impact on the film thickness and the pressure distribution of grease lubrication properties. Moreover, larger effective radius results in a wider but lower pressure distribution, a wider and thicker lubricating film and a lower temperature distribution.

The TSUC can be widely used in the front drive automotive transmission because it can transmit larger torque than before. The effect of effective radius on the thermal grease lubrication properties under automotive practical conditions provides a new direction for designing it.

Traditional Chinese Medicine (TCM) is a complete medical healthcare system that encompasses acupuncture, acupressure, moxibustion, herbal medicine, diet, tui na massage, and exercises (tai chi and qigong) among other traditional therapies. It uses herbs and natural resources to produce the traditional medicines and focuses on maintaining the balance between body and mind. As such, many aspects of TCM can be considered as green and sustainable. While there is market demand for TCM in some countries and among some communities, some others are still not aware of TCM. Moreover, there are fewer discussions in the academic platforms on TCM. This case highlights the scenario of TCM based on Malaysia’s perspective and discusses its challenges and prospects.