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The purpose of this study is to introduce the top-level design ideas and the overall architecture of earthquake early-warning system for high speed railways in China, which is based on P-wave earthquake early-warning and multiple ways of rapid treatment.

The paper describes the key technologies that are involved in the development of the system, such as P-wave identification and earthquake early-warning, multi-source seismic information fusion and earthquake emergency treatment technologies. The paper also presents the test results of the system, which show that it has complete functions and its major performance indicators meet the design requirements.

The study demonstrates that the high speed railways earthquake early-warning system serves as an important technical tool for high speed railways to cope with the threat of earthquake to the operation safety. The key technical indicators of the system have excellent performance: The first report time of the P-wave is less than three seconds. From the first arrival of P-wave to the beginning of train braking, the total delay of onboard emergency treatment is 3.63 seconds under 95% probability. The average total delay for power failures triggered by substations is 3.3 seconds.

The paper provides a valuable reference for the research and development of earthquake early-warning system for high speed railways in other countries and regions. It also contributes to the earthquake prevention and disaster reduction efforts.

A real-time production scheduling method for semiconductor back-end manufacturing process becomes increasingly important in industry 4.0. Semiconductor back-end manufacturing process is always accompanied by order splitting and merging; besides, in each stage of the process, there are always multiple machine groups that have different production capabilities and capacities. This paper studies a multi-agent based scheduling architecture for the radio frequency identification (RFID)-enabled semiconductor back-end shopfloor, which integrates not only manufacturing resources but also human factors.

The architecture includes a task management (TM) agent, a staff instruction (SI) agent, a task scheduling (TS) agent, an information management center (IMC), machine group (MG) agent and a production monitoring (PM) agent. Then, based on the architecture, the authors developed a scheduling method consisting of capability & capacity planning and machine configuration modules in the TS agent.

The authors used greedy policy to assign each order to the appropriate machine groups based on the real-time utilization ration of each MG in the capability & capacity (C&C) planning module, and used a partial swarm optimization (PSO) algorithm to schedule each splitting job to the identified machine based on the C&C planning results. At last, we conducted a case study to demonstrate the proposed multi-agent based real-time production scheduling models and methods.

This paper proposes a multi-agent based real-time scheduling framework for semiconductor back-end industry. A C&C planning and a machine configuration algorithm are developed, respectively. The paper provides a feasible solution for semiconductor back-end manufacturing process to realize real-time scheduling.

Recognizing novel entrepreneurial opportunities arising from a crisis is of paramount importance for firms. Hence, understanding the pivotal factors that facilitate firms in this endeavor holds significant value. This study delves into such factors within a representative empirical context impacted by a crisis, drawing insights from existing literature on opportunity recognition during such tumultuous periods.

The authors conducted a qualitative inspection of 14 Italian firms during the COVID-19 pandemic crisis. The authors collected a rich body of multi-source qualitative data, including 34 interviews (with senior managers and entrepreneurs) and secondary data (press releases, videos, web interviews, newspapers, reports and academic articles) in two phases (March–August 2020 and September–December 2020).

The results suggest the existence of a process model of opportunity recognition during crises based on five entrepreneurial influencing factors (entrepreneurial knowledge, entrepreneurial alertness, entrepreneurial proclivity, entrepreneurial personality and entrepreneurial purpose).

Various scholars have highlighted that, in times of crises, it is not easy and indeed very challenging for entrepreneurs to identify novel entrepreneurial opportunities. However, recent research has shown that crises can also positively impact entrepreneurs and their capacity to identify new entrepreneurial opportunities. Given these findings, not much research has analyzed the process by which entrepreneurs identify novel entrepreneurial opportunities during crises. This study shows that some entrepreneurial influencing factors are very important to identify new entrepreneurial opportunities during crises.

This study aims to explore the human resource management (HRM) practices related to training and feedback in the app work industry, specifically in online food delivery service, and investigate the emotional and behavioral responses of gig workers.

This study adopts a qualitative approach by interviewing 19 gig workers from six food delivery firms operating in different countries.

The results show limited training and feedback opportunities are provided to app workers, although the complexity of training and delivery methods differ across platforms. To address this shortage, app workers developed response strategies relying on social interaction.

This study adds to the research on HRM practices in the gig economy by portraying the way in which training and feedback unfold in the food delivery app ecosystem and by disclosing the gig workers’ emotional and behavioral responses to it.

This study shows that the way training activities are currently designed may provide little value to the ecosystem and are likely to produce negative emotional responses in gig workers. Thus, platform providers may make use of these findings by introducing more transparent feedback and social learning opportunities.

To the best of the authors’ knowledge, this study is among the first empirical studies on online delivery gig workers addressing specific HRM practices. It reveals significant insights for training and feedback, suggesting app economy characteristics strongly affect training and feedback practices for app workers.

The paper aims to solve the problem of personnel intrusion identification within the limits of high-speed railways. It adopts the fusion method of millimeter wave radar and camera to improve the accuracy of object recognition in dark and harsh weather conditions.

This paper adopts the fusion strategy of radar and camera linkage to achieve focus amplification of long-distance targets and solves the problem of low illumination by laser light filling of the focus point. In order to improve the recognition effect, this paper adopts the YOLOv8 algorithm for multi-scale target recognition. In addition, for the image distortion caused by bad weather, this paper proposes a linkage and tracking fusion strategy to output the correct alarm results.

Simulated intrusion tests show that the proposed method can effectively detect human intrusion within 0–200 m during the day and night in sunny weather and can achieve more than 80% recognition accuracy for extreme severe weather conditions.

(1) The authors propose a personnel intrusion monitoring scheme based on the fusion of millimeter wave radar and camera, achieving all-weather intrusion monitoring; (2) The authors propose a new multi-level fusion algorithm based on linkage and tracking to achieve intrusion target monitoring under adverse weather conditions; (3) The authors have conducted a large number of innovative simulation experiments to verify the effectiveness of the method proposed in this article.

In this study, the authors examine artificial knowledge as a fundamental stream of knowledge management for sustainable and resilient business models in supply chain management (SCM). The study aims to provide a comprehensive overview of artificial knowledge and digitalization as key enablers of the improvement of SCM accountability and sustainable performance towards the UN 2030 Agenda.

Using the SCOPUS database and Google Scholar, the authors analyzed 135 English-language publications from 1990 to 2022 to chart the pattern of knowledge production and dissemination in the literature. The data were collected, reviewed and peer-reviewed before conducting bibliometric analysis and a systematic literature review to support future research agenda.

The results highlight that artificial knowledge and digitalization are linked to the UN 2030 Agenda. The analysis further identifies the main issues in achieving sustainable and resilient SCM business models. Based on the results, the authors develop a conceptual framework for artificial knowledge and digitalization in SCM to increase accountability and sustainable performance, especially in times of sudden crises when business resilience is imperative.

The study results add to the extant literature by examining artificial knowledge and digitalization from the resilience theory perspective. The authors suggest that different strategic perspectives significantly promote resilience for SCM digitization and sustainable development. Notably, fostering diverse peer exchange relationships can help stimulate peer knowledge and act as a palliative mechanism that builds digital knowledge to strengthen and drive future possibilities.

This research offers valuable guidance to supply chain practitioners, managers and policymakers in re-thinking, re-formulating and re-shaping organizational processes to meet the UN 2030 Agenda, mainly by introducing artificial knowledge in digital transformation training and education programs. In doing so, firms should focus not simply on digital transformation but also on cultural transformation to enhance SCM accountability and sustainable performance in resilient business models.

This study is, to the authors' best knowledge, among the first to conceptualize artificial knowledge and digitalization issues in SCM. It further integrates resilience theory with institutional theory, legitimacy theory and stakeholder theory as the theoretical foundations of artificial knowledge in SCM, based on firms' responsibility to fulfill the sustainable development goals under the UN's 2030 Agenda.

This paper investigates the focal role of close co-worker friendship in reducing incivility. Furthermore, this paper examines negative workplace gossip as a mediator and gender and promotion focus as moderators.

Using a time-lagged approach, 553 full-service restaurant front-line co-workers in Greater Cairo responded. Further, the data were analyzed using SmartPLS v.4.

Promotion focus weakened close co-workers’ friendships, causing them to speak negatively about each other with other co-workers. Multi-group analysis showed that males were more likely to spread negative gossip about their close co-workers and thus were subjected to incivility-related behaviors by their co-workers.

This paper is an early attempt to explore the focal role of promotion focus in the full-service restaurant context. This paper adds to affective events theory (AET) with a limited understanding of explaining and predicting co-worker incivility.

本文擬探討同僚間緊密的友好關係在減少不文明行為方面所扮演的重要角色。此外、本文擬把職場的流言蜚語看作是調解員而對其加以探索; 本文亦把性別和對晉升的關注看作是仲裁人而進行探究。

研究人員使用時間差距法進行研究和探討。數據來自553名於大開羅提供整套服務的餐館內工作的一線員工所給予的回應。研究人員以SmartPLS 結構方程建模軟體第四版 (SmartPLS v.4) 對數據進行分析。

研究人員發現，僱員對晉升的關注削弱了同僚間緊密的友好關係，並驅使他們在其他同事中對同僚作負面的評價。另外，多組分析顯示了男性員工更有可能散播關於其要好同僚的閒言閒語，因此，他們會遭受同僚不文明的待遇。

本研究是早期的嘗試，去探索在提供整套服務餐館的背景下，僱員對晉升的關注所扮演的重要角色。另外，本研究的結果將會添加至情感事件理論 - 該理論就解釋和預測同僚不文明行為所提供的闡釋似有點不足。

In the context of the Industry 4.0, this paper aims to investigate the state of the art of Italian manufacturing, focusing the attention on the implementation of intelligent predictive maintenance (IPdM) and 4.0 key enabling technologies (KETs), analyzing advantages and limitations encountered by companies.

A survey has been developed by the University of Parma in cooperation with the Italian Workers' Compensation Authority (INAIL) and was submitted to a sample of Italian companies. Overall, 70 answers were collected and analyzed.

Results show that the 54% of companies implemented smart technologies, increasing quality and safety, reducing the operating costs and sometimes improving the process' sustainability. However, IPdM was implemented only by the 37% of respondents: thanks to big data collection and analytics, Internet of Things, machine learning and collaborative robots, they reduced downtime and maintenance costs. These changes were implemented mainly by large companies, located in northern Italy. To spread the use of IPdM in Italian manufacturing, the high initial investment, lack of skilled labor and difficulties in the integration of new digital technologies with the existing infrastructure are the main obstacles to overcome.

The article gives an overview on the current state of the art of 4.0 technologies implementation in Italy: it is useful not only for companies that want to discover the implementations' advantages but also for institutions or research centres that could help them to solve the encountered obstacles.

Drawing on COR theory and based on a person-centered approach, this study aims to explore profiles of both leadership behavior (transformational leadership, abusive supervision) and well-being indicators (cognitive irritation, emotional exhaustion). Additionally, we consider whether certain resource-draining (work intensification) and resource-creating factors (leader autonomy, psychological contract fulfillment) from the leaders' work context are related to profile membership.

The profiles are built using LPA on data from 153 leaders and their 1,077 followers. The relationship between profile membership and correlates from the leaders' work context is examined using multinomial logistic regression analyses.

LPA results in an interpretable four-profile solution with the profiles named (1) Good health – constructive leading, (2) Average health – inconsistent leading, (3) Impaired health – constructive leading and (4) Impaired health – destructive leading. The two groups with the highest sample share – Profiles 1 and 3 – both show highly constructive leadership behavior but differ significantly in their well-being indicators. The regression analyses show that work intensification and psychological contract fulfillment are significantly related to profile membership.

The person-centered approach provides a more nuanced view of the leadership behavior – leader well-being relationship, which can address inconsistencies in previous research. In terms of practical relevance, the person-centered approach allows for the identification of risk groups among leaders for whom organizations can provide additional resources and health-promoting interventions.

Many manufacturers are exploring adopting smart technologies in their operations, also referred to as the shift towards “Industry 4.0”. Employees' contribution to high-tech initiatives is key to successful Industry 4.0 technology adoption, but few studies have examined the determinants of employee acceptance. This study, therefore, aims to explore how managers affect employees' acceptance of Industry 4.0 technology, and, in turn, Industry 4.0 technology adoption.

Rooted in the unified theory of acceptance and use of technology model and social exchange theory, this inductive research follows an in-depth comparative case study approach. The two studied Dutch manufacturing firms engaged in the adoption of Industry 4.0 technologies in their primary processes, including cyber-physical systems and augmented reality. A mix of qualitative methods was used, consisting of field visits and 14 semi-structured interviews with managers and frontline employees engaged in Industry 4.0 technology adoption.

The cross-case comparison introduces the manager's need to adopt a transformational leadership style for employees to accept Industry 4.0 technology adoption as an organisational-level factor that extends existing Industry 4.0 technology user acceptance theorising. Secondly, manager's and employee's recognition and serving of their own and others' emotions through emotional intelligence are proposed as an additional individual-level factor impacting employees' acceptance and use of Industry 4.0 technologies.

Synthesising these insights with those from the domain of Organisational Behaviour, propositions were derived from theorising the social aspects of effective Industry 4.0 technology adoption.