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The purpose of this paper is to present a multi‐expert system that can provide designers with suggestions for improvement. The multi‐expert system can analyse a design and provide designers with ideas for changes to designs at an early stage in order to improve assembly later in the manufacturing process.

The whole system consists of four expert systems: computer‐aided design (CAD) expert, automated assembly expert, manual assembly expert and design analysis expert. The design analysis expert includes a sub‐system to collate the information from the assembly experts and to provide costs and advice.

The paper finds that the approach and the systems can reduce manufacturing costs and lead times.

A knowledge‐based reckoning approach to design‐for‐assembly automation is used. The approach and systems can reduce manufacturing costs and lead times. The system can estimate assembly time and cost for manual or automatic assembly and select suitable assembly techniques.

The system can estimate assembly time and cost for manual or automatic assembly and select a suitable assembly technique.

The new system models assembly, product and process design using a natural approach for capturing intelligence. The new approach categorised automated assembly and manual assembly into separate individual experts. Intelligence and knowledge from each is captured and embedded within the individual expert that represented the process. This approach enabled greater flexibility and made the sub‐systems easier to modify, upgrade, extend and reuse.

The purpose of this paper is to understand restaurant operators’ perception towards key areas of green practices that could be adopted in the future operations of the restaurant business in Malaysia.

The paper used a qualitative approach through face-to-face interviews. Five restaurant operators who operated their independently owned casual upscale restaurants in the Klang Valley were selected as the respondents for the interviews.

Eight areas of green practices adapted from Hu et al. (2010) have been commented and proposed to be served as a guideline to design the Environmental Management System (EMS) for restaurants to go green in Malaysia. Although the feedback given on the feasibility of implementing these green practices provided by the restaurant operators were generally positive, challenges lies ahead in getting them to adopt these green practices. It required understanding of the difficulties and motivations of implementing these practices.

An in-depth understanding from the voices of restaurant operators was essential for further policy formulation and implementation in fostering the green practices which in turn can serve as a “win-win” situation for all parties.

There seems to be a dearth of studies conducted on the perceptions of restaurant operators about the feasibility of green practices adoption in the operation of the restaurants in Malaysia. The implementation of EMS or the adoption of green practices was a vital missing-link among the restaurants. The outcome of this paper was expected to provide new ideas and knowledge on the areas of green practices to be adopted as the environmental guidelines to operate restaurants in Malaysia.

The purpose of this paper is to assess the safety and efficiency of, and acceptance by, patients for an express refill service (ERS).

A pilot uncontrolled, cross-sectional, single-centred study was conducted at the outpatient pharmacy of a tertiary acute care hospital. Under ERS, prescriptions were dispensed without clinical review and counselling for patients refilling prescription medications. Efficiency was assessed by comparing processing times of ERS prescriptions with regular prescriptions. Safety was assessed by independent review of prescriptions by two pharmacists. Patient acceptance was assessed by a survey.

ERS reduced processing time of prescriptions by more than 30 per cent compared to the regular fill process. ERS was generally safe for patients, with drug-related problems identified in only one prescription which may have warranted closer monitoring. It was accepted by patients who opted for the service, as 91.4 per cent agreed or strongly agreed that they were satisfied with the service.

The study was conducted on a small convenience sample of patients in a single centre, with no control group.

Results showed that ERS was efficient, safe and well-accepted for select patients refilling their prescriptions. This leads to shorter waiting times and greater patient satisfaction.

This is the first published study that has explored the feasibility of an express prescription refill service. Despite some limitations, this study showed that omitting prospective clinical review and patient counselling for a defined population segment is safe, and can reduce processing time and improve patient satisfaction.

The paper aims to tackle the classification of Remote Sensing Images (RSIs), which presents a significant challenge for computer algorithms due to the inherent characteristics of clustered ground objects and noisy backgrounds. Recent research typically leverages larger volume models to achieve advanced performance. However, the operating environments of remote sensing commonly cannot provide unconstrained computational and storage resources. It requires lightweight algorithms with exceptional generalization capabilities.

This study introduces an efficient knowledge distillation (KD) method to build a lightweight yet precise convolutional neural network (CNN) classifier. This method also aims to substantially decrease the training time expenses commonly linked with traditional KD techniques. This approach entails extensive alterations to both the model training framework and the distillation process, each tailored to the unique characteristics of RSIs. In particular, this study establishes a robust ensemble teacher by independently training two CNN models using a customized, efficient training algorithm. Following this, this study modifies a KD loss function to mitigate the suppression of non-target category predictions, which are essential for capturing the inter- and intra-similarity of RSIs.

This study validated the student model, termed KD-enhanced network (KDE-Net), obtained through the KD process on three benchmark RSI data sets. The KDE-Net surpasses 42 other state-of-the-art methods in the literature published from 2020 to 2023. Compared to the top-ranked method’s performance on the challenging NWPU45 data set, KDE-Net demonstrated a noticeable 0.4% increase in overall accuracy with a significant 88% reduction in parameters. Meanwhile, this study’s reformed KD framework significantly enhances the knowledge transfer speed by at least three times.

This study illustrates that the logit-based KD technique can effectively develop lightweight CNN classifiers for RSI classification without substantial sacrifices in computation and storage costs. Compared to neural architecture search or other methods aiming to provide lightweight solutions, this study’s KDE-Net, based on the inherent characteristics of RSIs, is currently more efficient in constructing accurate yet lightweight classifiers for RSI classification.

This chapter reports on an ethnographic case study of how a group of elementary school teachers designed technology enhanced learning with mobile technology (e.g. notebooks, tablet computers and mobile phones) to facilitate students’ development of literacy and twenty-first century competencies. These teachers designed the school’s literacy pedagogical approach, leveraging the use of technology, namely digital storytelling. The school in this case study is one of the eight Future Schools in Singapore under the FutureSchools@Singapore program. The school has been providing one-to-one mobile computing learning devices and wireless Internet access for its students. The introduction of technology in the classroom makes it possible for twenty-first century competencies to be integrated into literacy development. The conversational framework is used as a framework to examine how the design of the digital storytelling pedagogical approach brings about the various teaching–learning activities – acquisition, inquiry, practice, production, discussion and collaboration. The use of digital storytelling as an approach to integrate information communication technology (ICT) into the classroom has not only modified how ICT is being used in the school, it has redefined how ICT could be used to engage young learners. It has fundamentally transformed conventional storytelling with the use of current state-of-the-shelf (i.e. easily and widely available) technologies. This case study also outlines the processes involved in improving the design of digital storytelling over the years by the teachers. The ecosystem of the school is also taken into consideration and described in detail. Findings suggest the importance of the collaborative efforts of the teachers in the continuous improvements made to this design. The adoption of a learning design framework, such as the one used in this study, can be beneficial to ensure a systematic approach to the design of learning. In addition, the availability of technological infrastructure and computing devices are necessary for the seamless use of technology in the classroom. The just-in-time learning approach is adopted for students to learn technology as they are developing their digital stories.

This paper investigates the endogenous relationships between banking performance and social welfare in Southern African Development Community (SADC) countries.

A comprehensive three-stage multi-criteria decision-making (MCDM) approach based on alternative informational assumptions is applied.

Results indicate that banking performance is paradoxically associated with stagnant economic activity and higher wealth concentration for the minority. The authors found that SADC banking performance promotes higher Human Development Index (HDI) standards possibly via efficient financial intermediation, dissemination of best managerial practices and other forms of positive spillovers in these countries.

This paper contributes to the MCDM literature by simultaneously exploring the key concepts of “utility functions” (using COPRAS) and “distance to ideal solutions” (using TOPSIS) in mapping and explaining the feedback and cause-effect processes between banking performance and social welfare that may exist. Another distinctive aspect is related to the computation of bias-free criteria weights, using a robust SWARA order-rank based on information entropy. Finally, this paper is concerning the endogeneity measurement, using a novel stochastic structural relationship non-linear programme.

Gives a bibliographical review of the finite element methods (FEMs) applied for the linear and nonlinear, static and dynamic analyses of basic structural elements from the theoretical as well as practical points of view. The range of applications of FEMs in this area is wide and cannot be presented in a single paper; therefore aims to give the reader an encyclopaedic view on the subject. The bibliography at the end of the paper contains 2,025 references to papers, conference proceedings and theses/dissertations dealing with the analysis of beams, columns, rods, bars, cables, discs, blades, shafts, membranes, plates and shells that were published in 1992‐1995.

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.