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The development and presentation of a framework that integrates modern methods for detecting, assessing and mitigating mental health issues in the context of dynamic and adverse changes in social networks.

This viewpoint is based on a literature review of current advancements in the field. The use of causal discovery and causal inference methods forms the foundation for applying all the techniques included in the framework (machine learning, deep learning, explainable AI as well as large language models and generative AI). Additionally, an analysis of network effects and their influence on users’ emotional states is conducted.

The synergy of all methods used in the framework, combined with causal analysis, opens new horizons in predicting and diagnosing mental health disorders. The proposed framework demonstrates its applicability in providing additional analytics for the studied subjects (individual traits and factors that worsen mental health). It also proves its ability to identify hidden factors and processes.

The proposed framework offers a novel perspective on addressing mental health issues in the context of rapidly evolving digital platforms. Its flexibility allows for the adaptation of tools and methods to various scenarios and user groups. Its application can contribute to the development of more accurate algorithms that account for the impact of negative (including hidden) external factors affecting users. Furthermore, it can assist in the diagnostic process.

Mega construction projects (megaprojects) require technological innovation cooperation (TIC) to address complex construction demands and the interests of multiple stakeholders. Although TIC has been extensively discussed at the firm level, a significant gap remains in understanding megaprojects at the project level. This paper aims to identify TIC’s influencing factors and transmission paths and discuss stakeholders’ TIC mechanisms at the project level.

Based on case analysis, expert interviews, literature analysis and the Delphi method, this paper identifies the influencing factors of TIC in megaprojects at the project level. A structural system of these influencing factors is constructed by interpretive structural modeling (ISM), developing various mechanisms for TIC from bottom to top. The Matriced’ Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) method validates the driving forces and dependencies of the influencing factors, clarifying their roles and positions within the system. Additionally, the TIC mechanism is constructed.

The research findings identify 26 influencing factors categorized into four hierarchical levels: cooperative relationships, cooperative behavior, cooperative performance and technological innovation risks. Regarding direct factors, resource sharing affects goal congruence and communication effectiveness in megaprojects, affecting TIC’s satisfaction and trust. Most factors exist in the middle layer, and bridging the upper and lower levels depends on stakeholder collaboration. The root factors in the independent group significantly impact TIC, including policy circumstances, high technical requirements and limited site conditions. Addressing these issues influences improvements in other factors. The development of a digital resource-sharing platform, the enhancement of innovation incentives, the optimization of benefit distribution mechanisms and the improvement of risk-sharing mechanisms are essential for the effective operation of the TIC mechanism.

This study contributes to identifying and classifying challenges and opportunities in TIC. It explores transmission paths for enhancing TIC and presents strategies for successfully implementing and delivering megaprojects.

With recent advances in artificial intelligence, the hospitality industry has introduced the concept of unmanned smart hotels staffed by service robots instead of human employees. Research is needed to understand consumers’ receptivity to such an innovation. This paper examines factors associated with consumers’ potential resistance to using automated service hotels via two sequential studies. Given that younger generations of consumers are typically early adopters of advanced technology and innovative services, our sampling approach focused on this consumer group.

Two studies were conducted. Study 1 proposed and empirically tested a theoretical model. Results revealed that attitude, subjective norms and perceived behavioral control each positively influenced individuals’ intentions to use unmanned smart hotels. In Study 2, we further investigated aspects informing perceived security, a key variable in the use of unmanned smart hotels.

Findings showed how people’s beliefs about unmanned smart hotels and security control assurances led to perceived security. These perceptions were shaped by perceived physical risks, privacy concerns, website design and hotel reputation. Overall, this research provides theoretical and practical implications for various stakeholders associated with unmanned smart hotels.

Findings of this study suggested that managers of unmanned smart hotels should design user-friendly, secure processes and offer comprehensive support resources to enhance customer experience and usage.

The findings provide a holistic understanding of consumers’ receptivity to unmanned smart hotels.

Achieving accurate trajectory tracking control of robot manipulators is challenging due to dynamic model errors and uncertain payloads. This paper aims to enhance trajectory tracking performance for robots with n degrees of freedom (DOF).

This study proposes a robust motion control framework that combines uncertainty and disturbance estimator with model-based compensation. The proposed framework ensures precise trajectory tracking in robot manipulators. In addition, uncertainties in the high-DOF robot dynamics are estimated through a simple model-based compensation for system error dynamics. The stability of the closed-loop system of the proposed framework is analyzed and proved.

The results indicate that the proposed framework can significantly reduce tracking errors and increase disturbance resistance. The simulation results of a two-link robotic arm verify the effectiveness of the proposed method. The results of the experiments conducted on a seven-DOF torque-controlled Flexiv4S manipulator demonstrate the superior trajectory tracking performance and robustness of the proposed algorithm.

This study introduces a highly efficient, robust motion control framework for high-DOF robots, which can improve the trajectory tracking performance in the presence of model uncertainties.

The purpose of this study is to investigate how manufacturers make collaborative decisions in the ecosystem in the context of digital servitization.

Constructing an inframarginal analysis mathematical model to transform the ideal ecological niche choice of firms into solving the problem of maximizing the utility of firms, exploring the service boundary conditions of digital service business under the ideal ecological niche choice and focusing on the effects of niche factor, digital technology input and transaction efficiency on the boundary decision.

The boundaries of digital services business are in dynamic change, and changes are influenced by a combination of niche factor, digital technology input and transaction efficiency. Businesses focusing on a single niche and fully collaborative business delivery models are more in line with the trends under the digital service transformation.

Analyzing ecosystem participants’ cooperative decision-making on digital services from the perspective of ecological niche strategy selection. The study enriches the research on firm niche theory as well as service boundary decision theory in servitization transformation and provides theoretical guidance and management suggestions for firms to construct appropriate service boundaries to achieve synergistic and stable development of service ecosystems in the process of digital servitization transformation.

The brake pipe system was an essential braking component of the railway freight trains, but the existing E-type sealing rings had problems such as insufficient low-temperature resistance, poor heat stability and short service life. To address these issues, low-phenyl silicone rubber was prepared and tested, and the finite element analysis and experimental studies on the sealing performance of its sealing rings were carried out.

The low-temperature resistance and thermal stability of the prepared low-phenyl silicone rubber were studied using low-temperature tensile testing, differential scanning calorimetry, dynamic thermomechanical analysis and thermogravimetric analysis. The sealing performance of the low-phenyl silicone rubber sealing ring was studied by using finite element analysis software abaqus and experiments.

The prepared low-phenyl silicone rubber sealing ring possessed excellent low-temperature resistance and thermal stability. According to the finite element analysis results, the finish of the flange sealing surface and groove outer edge should be ensured, and extrusion damage should be avoided. The sealing rings were more susceptible to damage in high compression ratio and/or low-temperature environments. When the sealing effect was ensured, a small compression ratio should be selected, and rubbers with hardness and elasticity less affected by temperature should be selected. The prepared low-phenyl silicone rubber sealing ring had zero leakage at both room temperature (RT) and −50 °C.

The innovation of this study is that it provides valuable data and experience for the future development of the sealing rings used in the brake pipe flange joints of the railway freight cars in China.

This paper aims to explore how thermal activation enhances the oxidation complexation of the titanium alloy, aiming to enhance surface quality and processing efficiency.

The titanium alloys were chemically mechanically polished under various temperatures. The removal rate and surface roughness were characterized using a three-dimensional topography tester. The surface composition, content and valence state were characterized by X-ray photoelectron spectroscopy. The abrasion performance of the surface reaction layers was conducted using a friction wear testing machine.

The thermal activation temperature can enhance the chemical-mechanical polishing effect of titanium alloy. The thermal activation temperature can enhance the oxidation complexation synergistic effect of K₂S₂O₈ and KF on titanium alloy, thereby improving the polishing effect. With the increase in temperature, the wear resistance of titanium alloy decreases after oxidation corrosion, making it more susceptible to removal through friction. By promoting the oxidation and corrosion of K₂S₂O₈ and KF on the titanium alloy, higher temperatures can facilitate the formation of easily removable film layers on the surface, thereby enhancing the polishing effect.

This research contributes to enriching the theoretical framework of precision machining of titanium alloy and enhancing surface quality and machining efficiency.

With this statement, the authors hereby certify that the manuscript is the result of their own effort and ability. They have indicated all quotes, citations and references. Furthermore, the authors have not submitted any essay, paper or thesis with similar content elsewhere. No conflict of interest exists in the submission of this manuscript.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0167/

COVID-19 has aggregated the need for a non-contact medical logistic system. A non-contact robot with self-navigation ability has greatly enhanced the efficiency of the medical logistic system. This paper aims to design a new medical logistics robot system for the complex environment of hospitals with dynamic obstacles.

Targeting a medical logistics robot system for a large-scale hospital environment, this study proposed a dynamic obstacle avoidance system to reduce the robot’s delay time as well as frequent route switching. In the algorithm, this study proposed a new loop closure detection with an artificial correction factor. Moreover, this study presented enhanced 3D object detection, improving detection accuracy in hospital environments.

Experimental results confirm that the robot can move along its global path and reach its destination without colliding with stationary or moving obstacles.

The medical logistics robot system has safe and stable performance in real hospital scenarios. The implementation verifies that the robot has effectiveness and reliability in both hardware and software design.

The purpose of this study is to map the intellectual structure of sustainable finance and accounting (SFA) literature by identifying the influential aspects, main research streams and future research directions in SFA.

The results are obtained using bibliometric citation analysis and content analysis to conduct a bibliometric review of the intersection of sustainable finance and sustainable accounting using a sample of 795 articles published between 1991 and November 2023.

The most influential factors in the SFA literature are identified, highlighting three primary areas of research: corporate social responsibility and environmental disclosure; financial and economic performance; and regulations and standards.

SFA has experienced rapid development in recent years. The results identify the current research domain, guide potential future research directions, serve as a reference for SFA and provide inspiration to policymakers.

SFA typically encompasses sustainable corporate business practices and investments. This study contributes to broader social impacts by promoting improved corporate practices and sustainability.

This study expands on previous research on SFA. The authors identify significant aspects of the SFA literature, such as the most studied nations, leading journals, authors and trending publications. In addition, the authors provide an overview of the three major streams of the SFA literature and propose various potential future research directions, inspiring both academic research and policymaking.

This study explores the role of human resource management (HRM) practices in advancing sustainable development goals (SDGs) within emerging markets. Specifically, we examine how HRM practices in financial institutions in the emerging markets of India and China promote SDGs 8 (decent work and economic growth), 10 (reduced inequalities) and 13 (climate action). We also propose a framework integrating these key SDG goals with core HRM functions.

Secondary data analysis was employed using data from sustainability reports of the top five Indian and Chinese banks listed in Forbes – the Global (2000) ranking for 2022–2023. These sustainability reports were analysed based on their reporting of indicators from the Global Reporting Initiative GRI 400 series, aligned with the SDGs 8, 10 and 13.

The result of the comparative analysis indicates that both Indian and Chinese banks use HRM practices of recruitment and selection, rewards and payments, workplace health and safety, and training and development to meet SDGs 8 (decent work and economic growth), 10 (reduced inequalities) and 13 (climate action). Regarding the reporting and disclosure of HRM practices in diversity, equity and inclusion, Indian banks outperform Chinese banks, and these practices contribute significantly to SDGs 8, 10 and 13. The dominance of state-owned initiatives in China dictates the alignment of HRM strategies with economic priorities at the national level, highlighting the challenge of balancing global sustainability initiatives with a centralised management system.

The study provides a comprehensive examination of sustainability reports with a specific focus on HRM practices and their role in advancing SDGs. It applies institutional theory to understand the differences in the reporting and implementation of HRM practices that contribute to the achievement of SDGs.