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This article attempts to contribute to medical dispute resolution by examining the adoption of medical judicial expertise opinions in determining medical malpractice responsibility and its coordination with the judge’s legal opinions.

This article examines the legal basis and empirical data to demonstrate the decisive effect of medical judicial experts’ opinions in allocating medical malpractice responsibility and corresponding dispute resolution effectiveness.

High reliance on medical judicial expertise in medical dispute litigation not only unifies the judicial standards but also limits judges’ discretion, which brings the risk of contradiction between factual and legal findings, which currently ends in judges’ compromise.

The current medical malpractice provisions neglect the divergence of medical judicial expertise and judges’ opinions in determining medical malpractice responsibility, which produces difficulties in harmonizing awarded compensations and parties’ expectations, leading to problematic medical dispute litigation in Mainland China.

This paper aims to conduct work to obtain high-quality brazed joint of YAG ceramic and kovar alloy.

Wetting and spreading behavior of AgCuTi filler alloy on YAG ceramic and kovar alloy under vacuum (2∼3 × 10–4 Pa) and argon conditions was investigated and compared. Then, YAG ceramic was brazed to kovar alloy under a high vacuum of 2∼3 × 10–4 Pa; the influence of holding time on the interface structure of the joint was investigated.

The wettability of AgCuTi on YAG is poor in the argon atmosphere, the high oxygen content in the reaction layer hinders the formation of the TiY2O5 reaction layer, thereby impeding the wetting of AgCuTi on YAG; in the vacuum, a contact angle (?=16.6°) is obtained by wetting AgCuTi filler alloy on the YAG substrate; the microstructure of the YAG/AgCuTi/kovar brazed joint is characterized to be YAG/Y2O3/(Fe, Ni)Ti/Ag(s, s) + Cu(s, s)/Fe2Ti + Ni3Ti/Fe2Ti/kovar; at 870 °C for the holding time of 10 min, a (Fe, Ni) Ti layer of approximately 1.8 µm is formed on the YAG side.

Wetting and spreading behavior of the brazing filler alloy under different conditions and the influence of the holding time on the interface microstructure of the joint were studied to provide references for obtaining high-quality brazed joints.

This study aims to deepen our understanding of how chatbots’ empathy influences humans–AI relationship in frontline service encounters. The authors investigate the underlying mechanisms, including perceived anthropomorphism, perceived intelligence and psychological empowerment, while also considering variations between different stages of the customer journey (before and after purchase).

Data collection was conducted through an online survey distributed among 301 customers who had experience using AI-based service chatbot in frontline service encounters in China. The hypotheses were examined through structural equation modeling and multi-group analysis.

The findings of this study revealed the positive impacts of emotional and cognitive empathy on humans–AI relationship through perceived anthropomorphism, perceived intelligence and psychological empowerment. Furthermore, this study verified the moderating effect of the customer journey stages, such that the impacts of anthropomorphism and intelligence on humans–AI relationship displayed more strength during the pre- and post-purchase phases, respectively.

This research offers practical implications for companies: recognize and enhance empathy dimensions in AI-based service chatbot to empower human–AI relationships; boost customer empowerment in human–AI interactions; and tailor anthropomorphic features in the pre-purchase stage and improve problem-solving capability in the post-purchase stage to enrich user experiences.

This study extends relationship marketing theory and human–AI interaction frameworks by investigating the underlying mechanisms of the effect of two-dimensional empathy on human–AI relationship. This study also enriches service design theories by revealing the moderating effect of customer journey stages.

Humans now enjoy a better life because of Artificial Intelligence (AI). AI has a significant impact on the creation of smart cities. Modern applications based on big data, Internet of Things (IoT) systems, and deep learning require extensive use of complex computational solutions. Thus, the following problems arise: (1) what are smart cities? (2) what is AI? (3) How is AI used in smart cities? To respond to this problem, the following objective was set: to map how AI is used in smart cities. For this purpose, a qualitative methodology based on a narrative analysis of the literature was used. It is concluded that AI and smart cities are complementary technologies that can assist cities in tackling difficult issues including public safety, transportation, energy management, environmental monitoring, and predictive maintenance. This chapter’s findings, while broadly applicable, offer valuable insights into the Gulf region’s unique context, where rapid urbanization and technological adoption intersect with cultural and environmental considerations. The integration of AI in smart cities presents a promising avenue for the Gulf region to address its specific challenges and leverage its economic and infrastructural strengths, thereby contributing to the broader goals of innovation, development, prosperity, and well-being as envisioned in the region’s Vision 2040 initiatives.

As after-hours technology-mediated work (ATW) becomes common in organizations, the increased workload and interference to life caused by ATW has induced employee turnover. This research develops a mediated moderation model to explain how employees' intrinsic and extrinsic motivations for ATW affect their turnover intention through work–life conflict.

A survey was conducted to collect data of 484 employees from Chinese companies. Partial Least Square was used to perform data analysis.

The results show that intrinsic motivation for ATW has an indirect negative impact on turnover intention via work–life conflict, whereas extrinsic motivation for ATW has both a positive direct impact and a positive indirect impact (via work–life conflict) on turnover intention. This study also helps find that time spent on ATW can strengthen the positive impact of extrinsic motivation for ATW on turnover intention but has no moderation effect on the impact of intrinsic motivation for ATW. Furthermore, this study reveals that the interaction effect of time spent on ATW and extrinsic motivation on turnover intention is mediated by employees' perceived work–life conflict.

By discovering the distinct impact of employees' intrinsic and extrinsic motivations for ATW on turnover intention, this research provides a contingent view regarding the impact of ATW and offers guidance to managers regarding how to mitigate ATW-induced turnover intention through fostering different motivations.

This paper aims to study the problem of starvation lubrication of high-speed ball bearings due to temperature rise during operation and to avoid thermal failure of bearing lubrication.

Under the quasi-statics model of grease lubrication, both the oil film dragging force and the rolling friction between the balls and raceways collectively counteract the gyroscopic torque. Initially, the static model for grease lubrication is solved, followed by calculating the generated heat using the local heat generation method and ultimately the multinodal thermal network model is solved, and the solved results of the quasi-statics are updated by the temperatures of the grease nodes based on the relationship of the grease temperature and viscosity, as well as the relationship of the viscosity and the film thickness.

By comparing the numerical calculation results of bearings under different working conditions, the influence of starvation lubrication on the oil film thickness, oil film drag force and rolling friction of bearings is discussed, and it is found that the numerical calculation results of the outer ring temperature of bearings under the starvation lubrication due to the consideration of temperature rise are closer to the experimental values.

This study reveals the dynamic characteristics of bearings under starvation lubrication, which is more practical and engineering guiding significance for the design of bearings, and introduces a new method and basis for the calculation of temperature rise of rolling bearings.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-06-2024-0208/

Explore the impact of Party organization embedding on firm green governance.

The regression analysis method.

The findings show that Party organization embedding significantly enhances the green governance effects of firms, with this effect being more pronounced in environments with high-quality internal control. Moreover, the study reveals that Party organization embedding facilitates green governance through mechanisms such as reducing agency costs and optimizing management decisions. Agency costs have a negative transmission effect, while management decisions have a positive transmission effect, with the quality of internal control playing a crucial moderating role.

Most existing studies on firm green governance have focused on aspects such as the heterogeneity of management teams (Liu, 2019; Wu et al., 2019), executive green cognition (Fineman and Clarke, 1996; Huang and Wei, 2023), organizational structure and the involvement of controlling families (Bertrand and Schoar, 2006; Symeou et al., 2019), with limited attention to the unique role of Party organizations’ incentive and restraint mechanisms, supervisory power and management functions in firm green governance. Additionally, while scholars have examined the impact of political embedding in firms, including Party organization embedding as a specific form of political embedding, and find that it affects various aspects of business performance (Chang and Wong, 2004; Gu and Yang, 2023), governance quality (Li et al., 2020; Huang and Yang, 2024), agency costs (Qian, 2000; Wang and Ma, 2014), excessive management compensation (Chang and Wong, 2004; Chen et al., 2014), social externalities and audit needs (Faccio, 2006; Cheng, 2022), there is still insufficient discussion on how Party organization embedding promotes firm green governance. Particularly in the context of China’s unique system and using Chinese data, there is a need for more in-depth research on the impact of Party organization embedding on firm green governance. This paper addresses this research gap by empirical analysis.

Overall, this study has significant theoretical and practical implications. Theoretically, it enriches the literature on Party organization embedding and firm green governance, filling a gap in the intersection research of firm governance and green governance. Practically, on the one hand, this paper’s findings demonstrate that the involvement of Party organizations in firm governance plays a significant role in enhancing green governance. This supports the modernization of firm governance in China, establishes a micro-level foundation for achieving the strategic goals of “carbon peaking and carbon neutrality” and offers empirically-backed insights into green transformation for policymakers. The research also provides practical policy recommendations for strengthening Party building efforts within firms and optimizing government-business relations, thereby facilitating the deep integration of Party building with business operations. On the other hand, this study highlights that the unique feature of China’s corporate governance system, Party organization embedding, can effectively enhance green governance. This offers empirical support for leveraging the strengths of China’s firm governance model and provides valuable governance strategies for firms in other countries and regions to improve their green governance practices.

This study’s social implications are significant as it highlights the broader societal benefits that arise from integrating Party organization involvement into firm governance structures, especially within the context of green governance. By improving the green governance practices of firms, Party organization embedding helps to address pressing environmental issues such as pollution, carbon emissions and resource depletion, which ultimately contributes to healthier living environments and a more sustainable society. The emphasis on green governance supports China’s national strategy for sustainable development and demonstrates a governance model that balances economic growth with environmental stewardship. Additionally, the study underscores the role of Party organizations in fostering social responsibility, equity and cohesion by ensuring that firm decision-making aligns with both economic and social welfare goals. This model of governance provides a framework that can serve as a reference for other countries and regions looking to enhance environmental protection efforts while maintaining social stability and economic progress.

This study offers original insights by exploring the distinctive role of Party organization embedding in enhancing firm green governance within the unique context of China’s political and economic systems. Unlike previous research, which has primarily focused on conventional governance structures, this paper delves into the underexplored area of how Party organizations influence firm-level green governance. By examining the direct and indirect effects of Party organization embedding, this study expands current understanding of corporate governance models that integrate political structures, providing a novel perspective on how firms can achieve both economic and environmental objectives. The findings not only contribute to the literature on green governance but also present a valuable model for emerging economies that are pursuing sustainable development. This research thus provides a meaningful addition to the dialogue on corporate governance innovation and environmental responsibility.

Ultracapacitors find extensive applications in various fields because of their high energy density and long cycling periods. However, due to the movement of ions and the arrangement patterns on rough/irregular electrode surfaces during the charge and discharge process of ultracapacitors, the parameters of ultracapacitors usually change with the variation of operating conditions. The purpose of this study is to accurately and quickly identify the parameters of ultracapacitors.

A variable forgetting factor recursive least square (VFFRLS) algorithm is proposed in this paper for online identifying the equivalent series resistance and capacitance C of ultracapacitors. In this work, a real-time error-based strategy is developed to adaptively regulate the value of the forgetting factor of traditional forgetting factor recursive least square (FFRLS) algorithm. The strategy uses the square of the average time autocorrelation estimation of the prior error and the posterior error between the predicted output and the actual output as the adjustment basis of forgetting factors.

Experiments were conducted using the proposed scheme, and the results were compared with the estimation results obtained by the recursive least squares (RLS) algorithm and the traditional FFRLS algorithm. The maximum root mean square error between the estimated values and actual values for VFFRLS is 3.63%, whereas for FFRLS it is 9.61%, and for RLS it is 19.33%.

By using the proposed VFFRLS algorithm, a relatively high precision can be achieved for the online parameter estimation of ultracapacitors. Besides, the dynamic balance between parameter stability and tracking performance can be validated by dynamically adjusting the forgetting factor.

Community question answering (CQA) platforms play a significant role in knowledge dissemination and information retrieval. Expert recommendation can assist users by helping them find valuable answers efficiently. Existing works mainly use content and user behavioural features for expert recommendation, and fail to effectively leverage the correlation across multi-dimensional features.

To address the above issue, this work proposes a multi-dimensional feature fusion-based method for expert recommendation, aiming to integrate features of question–answerer pairs from three dimensions, including network features, content features and user behaviour features. Specifically, network features are extracted by first learning user and tag representations using network representation learning methods and then calculating questioner–answerer similarities and answerer–tag similarities. Secondly, content features are extracted from textual contents of questions and answerer generated contents using text representation models. Thirdly, user behaviour features are extracted from user actions observed in CQA platforms, such as following and likes. Finally, given a question–answerer pair, the three dimensional features are fused and used to predict the probability of the candidate expert answering the given question.

The proposed method is evaluated on a data set collected from a publicly available CQA platform. Results show that the proposed method is effective compared with baseline methods. Ablation study shows that network features is the most important dimensional features among all three dimensional features.

This work identifies three dimensional features for expert recommendation in CQA platforms and conducts a comprehensive investigation into the importance of features for the performance of expert recommendation. The results suggest that network features are the most important features among three-dimensional features, which indicates that the performance of expert recommendation in CQA platforms is likely to get improved by further mining network features using advanced techniques, such as graph neural networks. One broader implication is that it is always important to include multi-dimensional features for expert recommendation and conduct systematic investigation to identify the most important features for finding directions for improvement.

This work proposes three-dimensional features given that existing works mostly focus on one or two-dimensional features and demonstrate the effectiveness of the newly proposed features.

How does the regional institutional environment of China’s transitional economy influence the relationship between a firm’s R&D investment intensity and innovation performance? Based on the resource-based view and institution-based view, an empirical study was executed to identify the moderating effects of institutional environment variables from the Marketization Index of China’s Provinces: National Economic Research Institute (NERI) Report on the relationship between a firm’s R&D investment intensity and innovation performance. This paper aims to study how effectively improve the impact of R&D investment intensity on innovation performance under the influence of the institutional environment.

Against the background of China’s transitional economy, the authors present empirical evidence from panel data covering 374 Chinese A-share listed high-tech manufacturing firms on the Shanghai and Shenzhen Stock Exchange to examine the relationship between R&D investment intensity and innovation performance.

Empirical results illustrate the following: The R&D investment intensity and innovation performance displayed an inverse U-shaped relationship, and R&D investment intensity had a lagged effect on R&D output according to the uncertainty and industrialization period of R&D activities. The level of financial market development can intensify the effects of R&D investment intensity on innovation performance. The degree of government intervention weakens the effect of R&D investment intensity on innovation performance.

Based on the background of China’s institutional environment during the transition period, combined with previous research and the Marketization Index of China’s Provinces: NERI Report, selecting financial market development, government intervention level and legalization level as moderating variables to study how effectively improve the impact of R&D investment intensity on innovation performance under the influence of the institutional environment. Due to the different ownership of firms during the transition period, the appropriate impact of the institutional environment on the relationship between R&D investment intensity and innovation performance will vary. Moreover, the level of legalization would impact on innovation insignificantly.