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Abstract In today's world, remote-controlled robots are widely used across various industries due to their ability to enhance working efficiency in various applications. Learning about robot operation and human–computer interaction has emerged as a popular topic in recent times. Indeed, learning robotics can be challenging for many students as it requires knowledge of programming, control systems, electronics, etc. Collaborative learning in a physical robotics setting is common in higher education and has received significant attention for its potential to enhance individual learning outcomes. However, the effectiveness of learning robotics in a remote setting is still a matter of debate. In this study, we establish a remote laboratory environment to teach undergraduate students in the engineering discipline. Students are required to utilize a robotic arm to grasp designated objects collaboratively among students through synchronous interactions online. To compare students’ performance under different pedagogical teaching approaches, students are divided into two groups. They each perform the task individually and collaboratively, albeit in a different order. Our study adopts a quantitative method to measure students' learning outcomes based on the assessment of performing the laboratory tasks and completion time. The results indicate a noteworthy improvement in the individual performance of the group of students who engage in collaborative work prior to the individual tasks. These findings have implications for other remote laboratory setups and highlight the effectiveness of collaborative learning in higher education.

This research aims to explore the influential factors that promote and impede Chinese customers’ adoption intention of electric vehicles (EVs). An extended version of the Unified Theory of Acceptance and Use of Technology (UTAUT) was developed to gauge customers’ willingness to adopt EVs. 348 responses and structural equation modelling were employed for analysis. This paper contributes to this research area mainly through two aspects: First of all, it provides more robust evidence of the positive moderating effects on price value since few research studies on it. Secondly, the positive moderating effects of facilitating conditions between perceived risk and adoption intention of EVs were found for the first time, which fills the gap in this research area. Except these, the results also showed that performance expectations, social influence, and price value positively influence EV adoption intention, while perceived risk has a negative impact. Additionally, age and gender were found to significantly moderate adoption intention. These findings offer insights for policymakers to craft more effective policies, ultimately advancing sustainability and accelerating the achievement of long-term carbon neutrality goals.

Fuzzy association rule mining (FARM) is a well-known data mining algorithm to identify frequently occurring patterns from datasets, in which the fuzzy set theory is applied to consider linguistic variables for building an explainable reasoning system. In this study, an improved algorithm, namely ck-FARM, is proposed as an R package. Differing from the typical FARM mechanism, the statistical significance of the parameter correspondence is examined, while the fuzzy membership functions are autonomously built and adapted to the given datasets. Consequently, the adaptability and reliability of building fuzzy association rules can be enhanced.

Although blockchain is regarded as a promising and revolutionary technology, not everyone, particular non-technical students, can effectively understand and appraise its mechanism. To spread knowledge of blockchain technology, an online educational game for learning blockchain concepts named ‘BlockTrainHK’ is designed and developed from the React Native framework. Users from multi-disciplinary backgrounds can experience the block mining process, where the whole journey of building the blockchain and other advanced concepts, including immutability, Merkle tree, and zero knowledge proof, can be revealed, such that technical barriers of learning blockchain can be overcome.

Contribution: This research explores the effectiveness of a proposed teaching strategy in blockchain education, finding that it enhances learning outcomes, cognitive well-being, and student engagement in tertiary education, ultimately resulting in a shallower learning curve for STEM knowledge. Background: In the context of Industry 4.0, blockchain technology has emerged as a key driver of transformation in data management and system automation across a range of industrial applications. Despite its significance, the intricate theories and concepts associated with blockchain often serve as a deterrent for novice learners, inhibiting their ability to appreciate the value of industrial blockchain. Consequently, there is a pressing need to develop interactive teaching content that alleviates the steep learning curve. Intended Outcomes: The teaching strategy for the gamification in blockchain education is proposed, which positively influence students' cognitive well-being in terms of knowledge retention, cognitive curiosity, and heightened enjoyment. Application Design: Based on the experimental learning theory, the gamification of blockchain education, namely "BlockTrainHK", is implemented in the experimental learning cycle. Therefore, the gamified learning in experimental learning (GEL) strategy is proposed to examine the effectiveness of concrete experience, reflective observation, abstract conceptualization and active experimentation by two case studies. Findings: The results of the two-year study on the gamified blockchain education are encouraging: test groups using the GEL strategy were better in the cognitive well-being, and students' cognitive well-being is positively proportional to the level of individual technical knowledge and skills.

In view of increasing supply chain disruption events, for example the China–United States trade war, the COVID-19 pandemic, and the Russia–Ukraine war, the complexity and dynamicity of global freight management keeps increasing. To build a resilient and sustainable supply chain, industrial practitioners are eager to systematically revamp the freight management decision process related to the selection of carriers, shipping lanes, and third-party logistics service providers. Therefore, this study aims at strengthening decision-making capabilities for global freight management, in which an industrial blockchain-based global freight decision framework (IB-GFDF) is proposed to incorporate consortium blockchain technology with the Bayesian best-worst method. Through the blockchain technology, pairwise comparisons can be conducted over the international freight network in a decentralized and immutable manner, and thus, a secure and commonly agreed-on pairwise comparison dataset is acquired. Subsequently, the pairwise comparison dataset with multi-stakeholder opinions is analyzed using the Bayesian best-worst method in order to prioritize the selection decision criteria related to carriers, shipping lanes, and 3PL service providers for global freight management. To verify the methodological feasibility, a case study of an Australian agricultural supply chain firm was conducted to support the development end-to-end (E2E) supply chain solutions originated from Australia. It was found that port infrastructure, ports of call and communication effectiveness were the major criteria for the selection decision, which can be emphasized in future global freight collaboration. In addition, an immutable and append-only record of pairwise comparisons can be established to support the visibility of time-varying stakeholders’ preferences.

Food traceability has been one of the emerging blockchain applications in recent years, for improving the areas of anti-counterfeiting and quality assurance. Existing food traceability systems do not guarantee a high level of system reliability, scalability, and information accuracy. Moreover, the traceability process is time-consuming and complicated in modern supply chain networks. To alleviate these concerns, blockchain technology is promising to create a new ontology for supply chain traceability. However, most consensus mechanisms and data flow in blockchain are developed for cryptocurrency, not for supply chain traceability; hence, simply applying blockchain technology to food traceability is impractical. In this paper, a blockchain-IoT-based food traceability system (BIFTS) is proposed to integrate the novel deployment of blockchain, IoT technology, and fuzzy logic into a total traceability shelf life management system for managing perishable food. To address the needs for food traceability, lightweight and vaporized characteristics are deployed in the blockchain, while an integrated consensus mechanism that considers shipment transit time, stakeholder assessment, and shipment volume is developed. The data flow of blockchain is then aligned to the deployment of IoT technologies according to the level of traceable resource units. Subsequently, the decision support can be established in the food supply chain by using reliable and accurate data for shelf life adjustment, and by using fuzzy logic for quality decay evaluation.

New product development (NPD) is essential to most business organizations to create new values and protect existing values for maintaining high profitability and sustainability. However, the success of NPD projects is deemed to be difficult and challenging owing to high organizational complexity, uncertain business environment, and time-critical innovation. Under the smart manufacturing paradigm, NPD is an active research area to establish effective measures through the adoption of systematic approaches so as to facilitate idea management in the fuzzy front end for the product innovation. In this paper, the domain of new product idea selection is focused on and enhanced by means of the multi-criteria decision-making (MCDM) approach, in which multiple criteria and sub-criteria can be considered in the selection process. Among a number of MCDM approaches, the fuzzy set theory and best-worst method (BWM) are integrated as the fuzzy BWM in this study to structure the new product idea selection process under a group decision-making process. The hierarchy structure for the new product idea selection is also established to consider the perspectives of finance, marketing, engineering, manufacturing, and sustainability. Overall speaking, this study contributes to the field of NPD through overcoming the new product idea selection problem, while the group decision-making process is incorporated into the fuzzy BWM.

The development of electric vehicles (EVs) has drawn considerable attention to the establishment of sustainable transport systems to enable improvements in energy optimization and air quality. EVs are now widely used by the public as one of the sustainable transportation measures. Nevertheless, battery charging for EVs create several challenges, for example, lack of charging facilities in urban areas and expensive battery maintenance. Among various components in EVs, the battery pack is one of the core consumables, which requires regular inspection and repair in terms of battery life cycle and stability. The charging efficiency is limited to the power provided by the facilities, and therefore the current business model for EVs is not sustainable. To further improve its sustainability, plug-in electric vehicle battery pack standardization (PEVBPS) is suggested to provide a uniform, standardized and mobile EV battery that is managed by centralized service providers for repair and maintenance tasks. In this paper, a fuzzy-based battery life-cycle prediction framework (FBLPF) is proposed to effectively manage the PEVBPS in the market, which integrates the multi-responses Taguchi method (MRTM) and the adaptive neuro-fuzzy inference system (ANFIS) as a whole for the decision-making process. MRTM is formulated based on selection of the most relevant and critical input variables from domain experts and professionals, while ANFIS takes part in time-series forecasting of the customized product life-cycle for demand and electricity consumption. With the aid of the FPLCPF, the revolution of the EV industry can be revolutionarily boosted towards total sustainable development, resulting in pro-active energy policies in the PEVBPS eco-system.

In digital and green city initiatives, smart mobility is a key aspect of developing smart cities and it is important for built-up areas worldwide. Double-parking and busy roadside activities such as frequent loading and unloading of trucks, have a negative impact on traffic situations, especially in cities with high transportation density. Hence, a real-time internet of things (IoT)-based system for surveillance of roadside loading and unloading bays is needed. In this paper, a fully integrated solution is developed by equipping high-definition smart cameras with wireless communication for traffic surveillance. Henceforth, this system is referred to as a computer vision-based roadside occupation surveillance system (CVROSS). Through a vision-based network, real-time roadside traffic images, such as images of loading or unloading activities, are captured automatically. By making use of the collected data, decision support on roadside occupancy and vacancy can be evaluated by means of fuzzy logic and visualized for users, thus enhancing the transparency of roadside activities. The CVROSS was designed and tested in Hong Kong to validate the accuracy of parking-gap estimation and system performance, aiming at facilitating traffic and fleet management for smart mobility.