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The goal of this chapter is to explore the requirements modern companies expect of HR professionals’ competences.

Departing from the widely acknowledged HR competence studies of Ulrich and associates, we extended them with the continuous learning competence profile and HR professionals’ individual job performance. The empirical study is built on open interviews with HR leaders of ten large Dutch companies.

The study offers a new set of HRM competences. This set includes six HRM profiles: Business Focus, Learning Focus, Strategic Focus, HR Technology, HR Delivery, and Personal Credibility. Several contingency factors are thought to play a role in supporting these HRM competences: company culture, strategy, size, sector, scope, and position of HR professionals.

Based on these contributions, we recommended conducting a quantitative study to gain understanding of the relevance of the individual HRM job performance and to find associations between the HRM competences and the individual HRM job performance.

The focus of this chapter is a combination of HRM competences and the individual job performance of HR professionals.

Background: Artificial intelligence (AI) is a booming sector that has profoundly influenced every walk of life, and the education sector is no exception. In education, AI has helped to develop novel teaching and learning solutions that are currently being tested in various contexts. Businesses and governments across the globe have been pouring money into a wide array of implementations, and dozens of EdTech start-ups are being funded to capitalise on this technological force. The penetration of AI in classroom teaching is also a profound matter of discussion. These have garnered massive amounts of student big data and have a significant impact on the life of both students and educators alike.

Purpose: The prime focus of this chapter is to extensively review and analyse the vast literature available on the utilities of AI in health care, learning, and development. The specific objective of thematic exploration of the literature is to explicate the principal facets and recent advances in the development and employment of AI in the latter. This chapter also aims to explore how the EdTech and healthcare–education sectors would witness a paradigm shift with the advent and incorporation of AI.

Design/Methodology/Approach: To provide context and evidence, relevant publications were identified on ScienceDirect, PubMed, and Google Scholar using keywords like AI, education, learning, health care, and development. In addition, the latest articles were also thoroughly reviewed to underscore recent advances in the same field.

Results: The implementation of AI in the learning, development, and healthcare sector is rising steeply, with a projected expansion of about 50% by 2022. These algorithms and user interfaces economically facilitate efficient delivery of the latter.

Conclusions: The EdTech and healthcare sector has great potential for a spectrum of AI-based interventions, providing access to learning opportunities and personalised experiences. These interventions are often economic in the long run compared to conventional modalities. However, several ethical and regulatory concerns should be addressed before the complete adoption of AI in these sectors.

Originality/Value: The value in exploring this topic is to present a view on the potential of employing AI in health care, medical education, and learning and development. It also intends to open a discussion of its potential benefits and a remedy to its shortcomings.

This study aims to investigate the literature concerning the five basic tastes and find out who contributed to these publications, where they were published and what concepts were investigated.

The Web of Science was searched to identify the relevant articles. For each paper, the full record and cited references were analyzed.

Sweetness received the most attention, with 6,445 publications, 144,648 citations and h-index of 137. It was followed by bitterness (5,606 publications and 125,525 citations), sourness (1,841 publications and 40,696 citations), umami (1,569 publications and 39,120 citations) and saltiness (1,547 publications and 33,627 citations). Though umami taste had similar publication number as salty and sour tastes, it had the highest number of average citations per publication (24.9). The USA, Japan, Germany and England were major contributors to research on every basic taste. Chemical Senses was the major outlet of taste papers. Terms from the titles suggested that multiple tastes were often co-investigated. Ikeda (1909) and Kodama (1913) were identified as the seminal references that laid the foundation of umami research.

Umami, although only added relatively late to the family of basic tastes, is a highly investigated principle that receives similar amount of attention as some other basic tastes, such as sourness and saltiness.

Three decades of academic and professional discourse on HR technologies (HRTs) have produced continued disagreement over construct definitions and research streams that are highly fragmented. These realities suggest that greater consistency in meanings is sorely needed if we are to integrate and upgrade knowledge in this area. This chapter draws on the findings of a systematic research review to properly define the content domains of human resource information systems (HRIS), virtual human resources (virtual HR), electronic human resource management (e-HRM), and business-to-employee (B2E) systems. An integrative synthesis was performed on 242 system-level writings that appeared in the literature from 1983 to 2017. The weight of the evidence strongly supports treating HRIS, virtual HR, e-HRM, and B2E systems as independent, complimentary constructs. While the first three comprise a firm’s HRT system, the fourth construct is more appropriately positioned in the business-collaborative system. The sample was further evaluated with an analytic framework to detect patterns of practice in research designs. This revealed that much more attention has been focused on system actions and outcomes than on attitudes and system characteristics. Different units of analysis were well represented aside from trans-organizational studies. Finally, a case is made for better contextualizing HRT research by recognizing differences in assimilation stage, functional penetration, and collective proficiency. These factors are rarely mentioned, let alone studied, raising additional concerns about measurement error. Detailed suggestions are offered on ways to incorporate them. Together, these materials should promote more sophisticated and generalizable assessments of technology, improving our ability to understand its impacts.

As social networking services are popular, the need for recognizing reliable people has become the main concern. Quantifying the relationships between the users of social networks is also one of the important challenges with the increasing number of users. So, trust plays an important role in social networks in order to recognize trustworthy people. The purpose of this paper is to propose an approach to recognize trustworthy users and protect users from abuse by untrustable users.

In this paper, the authors suggest a method to measure the trust value between users of social networks based on call log histories such as abundance, novelty, and sincerity and quality of service (QoS) requirements such as accessibility, response ability, tend to respond, and agility. After that, the authors calculate error-hit, precision, and recall to evaluate the trust value.

The results indicate that the proposed approach has better performance in terms of error-hit, precision, and recall than FIFO, combined, QoS-based, and call log-based method.

In this paper, the trust issue in social networks is pointed out and a new approach to evaluate the trust value is proposed based on call log histories and QoS requirements.

This paper aims to combine Eringen’s micromorphic and nonlocal theories and thus develop a comprehensive size-dependent beam model capable of capturing the effects of micro-rotational/stretch/shear degrees of freedom of material particles and nonlocality simultaneously.

To consider nonlocal influences, both integral (original) and differential versions of Eringen’s nonlocal theory are used. Accordingly, integral nonlocal-micromorphic and differential nonlocal-micromorphic beam models are formulated using matrix-vector relations, which are suitable for implementing in numerical approaches. A finite element (FE) formulation is also provided to solve the obtained equilibrium equations in the variational form. Timoshenko micro-/nano-beams with different boundary conditions are selected as the problem under study whose static bending is addressed.

It was shown that the paradox related to the clamped-free beam is resolved by the present integral nonlocal-micromorphic model. It was also indicated that the nonlocal effect captured by the integral model is more pronounced than that by its differential counterpart. Moreover, it was revealed that by the present approach, the softening and hardening effects, respectively, originated from the nonlocal and micromorphic theories can be considered simultaneously.

Developing a hybrid size-dependent Timoshenko beam model including micromorphic and nonlocal effects. Considering the nonlocal effect based on both Eringen’s integral and differential models proposing an FE approach to solve the bending problem, and resolving the paradox related to nanocantilever.

As a world leader in the textiles and clothing trade, Hong Kong has played an important role in world denim products trade. To obtain a sound understanding of the current status and position of Hong Kong in the world denim trade, the authors carried out a comprehensive statistical survey of the Hong Kong denim industry. This paper reports detailed analyses on the production, sales and trade of denim products in Hong Kong. The analysis on trade covers imports, domestic exports and re‐exports of denim fabric and apparel products.

This paper aims to investigate the use of artificial neural networks (ANN) to predict the sewing performance of fabrics. The purpose of this study is to verify the ANN techniques that could be emulated as human decision in the prediction of sewing performance of fabrics.

In order to verify the ANN techniques that could be emulated as human decision in the prediction of sewing performance of fabrics, 109 data sets of fabrics were tested by using fabric assurance by simple testing system and the sewing performance of each fabric's specimen was assessed by the domain experts. Of these 109 input‐output data pairs, 94 were used to train the proposed backpropagation (BP) neural network for the prediction of the unknown sewing performance of a given fabric, and 15 were used to test the proposed BP neural network.

After 10,000 iterations of training of BP neural network, the neural network converged to the minimum error level. The experimental results reveal the great potential of the proposed approach in predicting the sewing performance of fabrics for apparel production.

Generally, the fabric's performance in the manufacturing process is judged subjectively by the operators and/or their supervisors. Current methodologies of acquiring fabric property information and predicting fabric sewing performance are still incapable of providing a means for efficient planning and control for the sewing operation. Further, development of techniques to predict the sewing performance of fabric is essential for the current apparel production environment. In this paper, the use of ANN to predict the sewing performance of fabrics in garment manufacturing is investigated.

The lack of a good planning system in preventing operational problems occurring in garment manufacture was of concern to garment manufacturers. Neither mathematical nor statistical approaches have proved to be very effective in tackling this problem. The goal of this research is to establish a model of measuring operational problems by the use of a proven inductive learning technique known as automatic pattern analysis and classification system (APACS). To be effective in this particular application domain, real data on garment production were used. The accuracy of the resulting system is nearly 95 per cent compared with real performance, possibly significantly achieving the goal.