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Gesture recognition plays an important role in many fields such as human–computer interaction, medical rehabilitation, virtual and augmented reality. Gesture recognition using wearable devices is a common and effective recognition method. This study aims to combine the inverse magnetostrictive effect and tunneling magnetoresistance effect and proposes a novel wearable sensing glove applied in the field of gesture recognition.

A magnetostrictive sensing glove with function of gesture recognition is proposed based on Fe-Ni alloy, tunneling magnetoresistive elements, Agilus30 base and square permanent magnets. The sensing glove consists of five sensing units to measure the bending angle of each finger joint. The optimal structure of the sensing units is determined through experimentation and simulation. The output voltage model of the sensing units is established, and the output characteristics of the sensing units are tested by the experimental platform. Fifteen gestures are selected for recognition, and the corresponding output voltages are collected to construct the data set and the data is processed using Back Propagation Neural Network.

The sensing units can detect the change in the bending angle of finger joints from 0 to 105 degrees and a maximum error of 4.69% between the experimental and theoretical values. The average recognition accuracy of Back Propagation Neural Network is 97.53% for 15 gestures.

The sensing glove can only recognize static gestures at present, and further research is still needed to recognize dynamic gestures.

A new approach to gesture recognition using wearable devices.

This study has a broad application prospect in the field of human–computer interaction.

The sensing glove can collect voltage signals under different gestures to realize the recognition of different gestures with good repeatability, which has a broad application prospect in the field of human–computer interaction.

Complementing the importance of adequate relief supplies and transportation capacity in the first two weeks of post-disaster logistics, efficient communication, information sharing, and informed decision making play a crucial yet often underestimated role in reducing wasted material resources and loss of human life. The purpose of this paper is to provide a method of quantifying these effects.

A mathematical discrete dynamical system is used to model transportation of different commodities from multiple relief suppliers to disaster sites across a network of limited capacity. The physical network is overlaid with the communication network to model information delays and communication breakdowns between agents. The cost in human lives and the monetary cost are measured separately.

Simulations results highlight quantitatively how communication deficiencies and indiscriminate shipping of resources result in material convergence and shortage of urgent supplies observed in actual emergencies.

The model provides an example of a simple, objective, quantitative tool for decision making and training volunteer managers in the importance of a smart response protocol.

Understanding electricity use behaviour is considered one of the strategies to achieve sustained electricity management in buildings. The lack of understanding of occupants’ electricity use behaviour has been found to cause various environmental and ecological issues. This paper aims to investigate the factors influencing occupants’ inefficient use of electricity in buildings becomes a vital area of study to achieve maximum benefit in the area of electricity management.

The study adopted a quantitative survey and questionnaire as instruments for gathering relevant information from end-users in the study area, and the data collected were analysed using descriptive and inferential statistics.

The major factors influencing the electricity use behaviour of students in the study area were attributed to their level of awareness, personal beliefs and attitude towards electricity, managerial influences and economic factors.

The threats to the environment and ecology necessitate immediate attention to the elements that impact students’ electricity use habits. This research explains the key elements that might impact students’ electricity consumption habits in buildings. Understanding these key characteristics will provide policymakers with vital knowledge of its prevalence.

Driven by the dual-carbon target and the widespread digital transformation, leveraging digital technology (DT) to facilitate sustainable, green and high-quality development in heavy-polluting industries has emerged as a pivotal and timely research focus. However, existing studies diverge in their perspectives on whether DT’s impact on green innovation is synergistic or leads to a crowding-out effect. In pursuit of optimizing the synergy between DT and green innovation, this paper aims to investigate the mechanisms that can be harnessed to render DT a more constructive force in advancing green innovation.

Drawing from the theoretical framework of resource orchestration, the authors offer a comprehensive elucidation of how DT intricately influences the green innovation efficiency of enterprises. Given the intricate interplay within the synergistic relationship between DT and green innovation, the authors use the fuzzy-set qualitative comparative analysis method to explore diverse configurations of antecedent conditions leading to optimal solutions. This approach transcends conventional linear thinking to provide a more nuanced understanding of the complex dynamics involved.

The findings reveal that antecedent configurations fostering high green innovation efficiency actually differ across various stages. First, there are three distinct configuration patterns that can enhance the green technology research and development (R&D) efficiency of enterprises, namely, digitally driven resource integration (RI), digitally driven resource synergy (RSy) and high resource orchestration capability. Then, the authors also identify three configuration patterns that can bolster the high green achievement transfer efficiency of enterprises, including a digitally optimized resource portfolio, digitally driven RSy and efficient RI. The findings not only contribute to advancing the resource orchestration theory in the digital ecosystem but also provide empirical evidence and practical insights to support the sustainable development of green innovation.

The findings can offer valuable insights for enterprise managers, providing decision-making guidance on effectively harnessing the innovation-driven value of internal and external resources through resource restructuring, bundling and leveraging, whether with or without the support of DT.

The research findings contribute to heavy-polluting enterprises addressing the paradoxical tensions between digital transformation and resource constraints under environmental regulatory pressures. It aims to facilitate the simultaneous achievement of environmental and commercial success by enhancing their green innovation capabilities, ultimately leading to sustainability across profit and the environment.

Compared with previous literature, this research introduces a distinctive theoretical perspective, the resource orchestration view, to shed light on the paradoxical relationship on resource-occupancy between DT application and green innovation. It unveils the “black box” of how digitalization impacts green innovation efficiency from a more dynamic resource-based perspective. While most studies regard green innovation activities as a whole, this study delves into the impact of digitalization on green innovation within the distinct realms of green technology R&D and green achievement transfer, taking into account a two-stage value chain perspective. Finally, in contrast to previous literature that predominantly analyzes influence mechanisms through linear impact, the authors use configuration analysis to intricately unravel the complex influences arising from various combinatorial relationships of digitalization and resource orchestration behaviors on green innovation efficiency.

Music sentiment analysis helps to promote the diversification of music information retrieval methods. Traditional music emotion classification tasks suffer from high manual workload and low classification accuracy caused by difficulty in feature extraction and inaccurate manual determination of hyperparameter. In this paper, the authors propose an optimized convolution neural network-random forest (CNN-RF) model for music sentiment classification which is capable of optimizing the manually selected hyperparameters to improve the accuracy of music sentiment classification and reduce labor costs and human classification errors.

A CNN-RF music sentiment classification model is designed based on quantum particle swarm optimization (QPSO). First, the audio data are transformed into a Mel spectrogram, and feature extraction is conducted by a CNN. Second, the music features extracted are processed by RF algorithm to complete a preliminary emotion classification. Finally, to select the suitable hyperparameters for a CNN, the QPSO algorithm is adopted to extract the best hyperparameters and obtain the final classification results.

The model has gone through experimental validations and achieved a classification accuracy of 97 per cent for different sentiment categories with shortened training time. The proposed method with QPSO achieved 1.2 and 1.6 per cent higher accuracy than that with particle swarm optimization and genetic algorithm, respectively. The proposed model had great potential for music sentiment classification.

The dual contribution of this work comprises the proposed model which integrated two deep learning models and the introduction of a QPSO into model optimization. With these two innovations, the efficiency and accuracy of music emotion recognition and classification have been significantly improved.

This paper aims to discuss characteristics of Korea’s system responses with a research framework of the structure, conduct and performance theory and explain the role of information, communication technologies (ICT) and Big Data from a technology-mediated control (TMC) perspective.

This study examines the contextual nature of Korea’s diagnostic, preventive and treatment efforts. Particular attention is paid to issues related to the effective use of Big Data analytics and its applications, reporting mechanisms and public safety measures. The research model defines key factors in assessing the effectiveness of Korea’s responses.

Findings of this research suggest: effective strategic planning and operational execution use well-tested and designed crisis-responsive manuals; linkage role of ICT/Big Data is prominent in trace, test and treat and participation (3T + P); and aggressive epidemic investigations require synergistic efforts of national and local government units, broad societal support and participation and contribution of global firms offering their domestic and global supply chain network capabilities.

The Korean Government's effective response experiences suggest the synergy of political, social, cultural and technological factors. Future studies may explore how personal privacy and public safety are both achieved in different social–cultural–political contexts (Ahn et al., 2020; Delgado et al., 2020; Sharma and Bashir, 2020). Other emerging organizational issues and international comparative studies are worth further investigation in future studies.

This case study suggests how to apply ICT capabilities for organizing a national response to the coronavirus pandemic (COVID-19) pandemic. Public and private partnership in the framework of sociotechnological synergy (i.e. integration of ICT and social orchestration) is essential for the 3T process. In support of public policy initiatives, global firms share their IT infrastructure and supply chain integration experiences to accommodate global-level crises like the COVID-19 pandemic.

This study extends the TMC framework to a national level. In the adapted TMC framework, the control source, control target and linkage mechanism are specified. Using TMC, this shows the dynamic roles of ICT/Big Data in Korea’s COVID-19 response experiences.

The impacts of the COVID-19 are rapid and enormous. Despite the controversial early policy decisions and the rapid rise of confirmed patients, the world has recognized Korea’s effective responses to the COVID-19 pandemic.