Search results

The purpose of this study is to investigate the effectiveness of a composite lubrication system combining polytetrafluoroethylene (PTFE) film and oil lubrication in steel–steel friction pairs.

A PTFE layer was sintered on the surface of a steel disk, and a lubricant with additives was applied to the surface of the steel disk. A friction and wear tester was used to evaluate the tribological properties and insulation capacity. Fourier transform infrared spectrometer was used to analyze the changes in the composition of the lubricant, and X-ray photoelectron spectroscopy was used to analyze the chemical composition of the worn surface.

It was found that incorporating the PTFE film with PSAIL 2280 significantly enhanced both the friction reduction and insulation capabilities at the electrical contact interface during sliding. The system consistently achieved ultra-low friction coefficients (COF < 0.01) under loads of 2–4 N and elucidated the underlying lubrication mechanisms.

This work not only confirm the potential of PTFE films in insulating electrical contact lubrication but also offer a viable approach for maintaining efficient and stable low-friction wear conditions.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-06-2024-0222/

This paper introduces a novel method to improve building safety management by leveraging building information modeling (BIM) and adaptive information retrieval techniques. The integration aims to overcome the limitations of traditional safety management methods in connecting construction processes with risk management efficiently.

The proposed method involves developing industry foundation classes (IFC) ontologies and integrating them with a safety document ontology to form a comprehensive BIM-based safety context framework. Custom reasoning rules and an inference engine are constructed to enable automatic context-aware safety information retrieval. The methodology is demonstrated through an adaptive information retrieval system using job hazard analysis (JHA) documents.

The implementation of the BIM-based adaptive information retrieval system shows significant improvements in identifying and managing construction risks. By mapping job-specific risks to corresponding safety measures, the system enhances risk detection and management tailored to particular construction tasks. The results indicate a marked improvement in the precision and accuracy of safety assessments and recommendations, aligning them closely with planned construction activities and conditions.

This paper offers an innovative approach to construction safety management through the development of a BIM-facilitated context-aware information retrieval system. This approach provides a more intelligent and automated framework for identifying and managing risks in construction projects. By focusing on specific job steps and related risks, the system enhances the effectiveness and accuracy of safety measures, contributing to better overall building safety management.

Nurse turnover in critical care units (CCU) significantly affects patient outcomes and health systems worldwide. To safeguard patient care quality, hospitals must address the underlying reasons for turnover and strategize to retain their skilled nursing workforce. The study proposes a prescriptive framework to reduce nurse turnover in CCUs.

In this study, the integrated methodology of Delphi-AHP-Entropy was used for the comparative prioritization of factors and subfactors that influence nursing staff turnover in CCUs.

Study findings reveal that “Organizational factors” and “Individual factors” dictate critical care nurse attrition rate. At the subfactor level, staffing policy, chronic fatigue, and perceived career are the leading concerns for the decision of nurses whether to work or leave.

This study is valuable for both researchers and healthcare professionals. It examines whether actions related to nurse retention align with existing theory and identifies areas requiring further theoretical or applied studies to enhance understanding in this area. This insight can bolster the field’s knowledge base and integrate theoretical and applied knowledge effectively. Additionally, for healthcare professionals, the study provides an overview of key factors conducive to retaining nursing staff in the CCU, offering valuable guidance for implementing effective strategies.

This study uniquely positions itself by presenting a comprehensive and prescriptive framework for critical care nurse retention in the UAE.

This study aims to examine the impact of board characteristics on firm performance while also exploring the influential mechanisms that help Chinese listed companies establish effective boards of directors and strengthen their corporate governance mechanisms.

This paper uses machine learning methods to investigate the predictive ability of the board of directors' characteristics on firm performance based on the data from Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges in China during 2008–2021. This study further analyzes board characteristics with relatively strong predictive ability and their predictive models on firm performance.

The results show that nonlinear machine learning methods are more effective than traditional linear models in analyzing the impact of board characteristics on Chinese firm performance. Among the series characteristics of the board of directors, the contribution ratio in prediction from directors compensation, director shareholding ratio, the average age of directors and directors' educational level are significant, and these characteristics have a roughly nonlinear correlation to the prediction of firm performance; the improvement of the predictive ability of board characteristics on firm performance in state-owned enterprises in China performs better than that in private enterprises.

The findings of this study provide valuable suggestions for enriching the theory of board governance, strengthening board construction and optimizing the effectiveness of board governance. Furthermore, these impacts can serve as a valuable reference for board construction and selection, aiding in the rational selection of boards to establish an efficient and high-performing board of directors.

The study findings unequivocally demonstrate the superiority of nonlinear machine learning approaches over traditional linear models in examining the relationship between board characteristics and firm performance in China. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. The study reveals that the predictive performance of board attributes is generally more robust for state-owned enterprises in China in comparison to their counterparts in the private sector.

Following the recent rise in generative artificial intelligence (GenAI) tools, fundamental questions about their wider impacts have started to reverberate around various disciplines. This study aims to track the unfolding landscape of general issues surrounding GenAI tools and to elucidate the specific opportunities and limitations of these tools as part of the technology-assisted enhancement of mechanical engineering education and professional practices.

As part of the investigation, the authors conduct and present a brief scientometric analysis of recently published studies to unravel the emerging trend on the subject matter. Furthermore, experimentation was done with selected GenAI tools (Bard, ChatGPT, DALL.E and 3DGPT) for mechanical engineering-related tasks.

The study identified several pedagogical and professional opportunities and guidelines for deploying GenAI tools in mechanical engineering. Besides, the study highlights some pitfalls of GenAI tools for analytical reasoning tasks (e.g., subtle errors in computation involving unit conversions) and sketching/image generation tasks (e.g., poor demonstration of symmetry).

To the best of the authors’ knowledge, this study presents the first thorough assessment of the potential of GenAI from the lens of the mechanical engineering field. Combining scientometric analysis, experimentation and pedagogical insights, the study provides a unique focus on the implications of GenAI tools for material selection/discovery in product design, manufacturing troubleshooting, technical documentation and product positioning, among others.

With the rapid low-carbon transformation in China, the industrial approach and labor structure of mining enterprises are undergoing constant changes, leading to an increasing psychological dilemma faced by coal miners. This study aims to reveal the relationship and mechanism of factors influencing the psychological dilemma of miners, and to provide optimal intervention strategies for the safety and sustainable development of employees and enterprises.

To effectively address the complex issue of the psychological dilemma faced by miners, this study identifies and constructs five-dimensional elements, comprising 20 indicators, that influence psychological dilemmas. The relational mechanism of action of factors influencing psychological dilemma was then elucidated using an integration of interpretive structural modeling and cross-impact matrix multiplication.

Industry dilemma perception is a “direct” factor with dependent attributes. The perceptions of management response and relationship dilemmas are “root” factors with driving attributes. Change adaptation dilemma perception is a “susceptibility” factor with linkage attributes. Work dilemma perception is a “blunt” factor with both dependent and autonomous attributes.

The aforementioned findings offer a critical theoretical and practical foundation for developing systematic and cascading intervention strategies to address the psychological dilemma mining enterprises face, which contributes to advancing a high-quality coal industry and efficient energy development.

Information from secondary sources was used to develop this case study. The sources of the data included the organization’s website, yearly reports, news releases, reports that have been published and documents that are accessible online.

As of 2023, Kenya generated around 0.5–1.3 million tons of plastic waste per year, of which only 8% was recycled. The remaining waste was either dumped into landfills, burned or released back into the environment. In addition to the plastic problem, a deforestation crisis was looming large in the country. Despite the country’s efforts to improve recycling, banning the use of single-use plastic to reduce plastic pollution, plastic waste continued to be a major issue. Growing up in the Kaptembwa slums of rural Kenya, Lorna saw the adverse impact that plastic waste had on the local ecosystem. Also, she was perturbed by the widespread cutting down of trees for construction of buildings, etc., which had resulted in deforestation. Lorna’s concern for the environment and her desire to address these issues motivated her to found EcoPost, a business that promoted a circular economy by gathering and recycling plastic waste.

With the common goal of enhancing circularity, EcoPost and Austria-based chemical company Borealis collaborated to stop waste from seeping into the environment and to make a positive socioeconomic and environmental impact. The funding from Borealis would help EcoPost in increasing its capabilities, providing training and recruiting more waste collectors. The funds were also supposed to help formalize the work of the waste pickers (mostly youth and women from marginalized communities) by financing the entrepreneurial start-up kits. Lorna aimed to create a business model that would not only solve the plastic waste problem but would also contribute to the social and economic development of local communities. Amidst these gigantic problems of plastic waste and deforestation that Kenya was facing, how will Lorna achieve her ambitious goal of reducing plastic waste and save trees? How will EcoPost pave the way to a cleaner, healthier and more sustainable future?

This case is intended for use in MBA, post-graduate/executive level programs as part of entrepreneurship and sustainability courses.

Despite the importance of foreign market entry mode (FMEM) decisions for the internationalisation of small and medium-sized enterprises (SMEs), there is insufficient understanding of the knowledge types and sources necessary for such decisions. This study addresses this issue by investigating the knowledge configurations that underpin FMEM initial choices and subsequent changes in SMEs.

This study adopted an interpretive approach and analysed empirical data from 37 in-depth interviews with decision-makers in internationalised SMEs from the United Kingdom.

The findings reveal that different knowledge configurations drive FMEM decisions in SMEs. Based on the analysis conducted for this study, initial FMEM choices draw on prior experiential knowledge combined with knowledge from desk research and knowledge acquired from peers, competitors and international partners. However, unlike many previous contributions, this research shows that foreign market experiential knowledge does not influence mode changes. Within-mode changes rely mainly on mode-specific knowledge and on knowledge about exploiting the benefits of the internet and digital platform ecosystems. Conversely, between-mode changes draw on diverse knowledge that is frequently created in interaction with international stakeholders or acquired externally.

This study contributes to the SME internationalisation literature by highlighting the knowledge configurations that inform not only initial choices but also between- and within-mode changes. Moreover, it reveals the importance of distinct types of digital technology-based knowledge for facilitating mode changes. It also adds to the knowledge-based perspective by underscoring that dynamic and heterogenous knowledge configurations, often created in interaction with international stakeholders, promote firm internationalisation.

The pedestrian dead reckoning (PDR) based on smartphones has been widely applied in continuous indoor positioning. However, when the position of the mobile phone and the walking patterns of the pedestrian are mixed, traditional PDR tends to become confused and thus degrade performance. To address this issue, this paper aims to propose an improved PDR scheme by focusing on gait pattern recognition and the impact of short-period but negative transitions on tracking.

The overall solution uses the inertial sensor integrated within the phone for positioning. A binary classifier-based change point detection algorithm is used to identify the transition points in pedestrian gait. Additionally, to enhance the accuracy of gait recognition, this paper presents a combined CNN-attention-based bi-directional long short-term memory(ABiLSTM) model, integrating convolutional neural networks (CNN), bi-directional long short-term memory (Bi-LSTM) and an attention mechanism, to recognize the current gait pattern. The outcomes of this gait pattern recognition are then applied to PDR. Based on distinct gait patterns, corresponding PDR strategies are devised to enable continuous tracking and positioning of pedestrians.

Through experimental verification, the CNN-ABiLSTM model achieves a gait recognition accuracy of 99.52% on the self-constructed data set. The pedestrian navigation estimation method proposed in this paper, which is based on gait recognition assistance, demonstrates a 32.56% improvement in accuracy over traditional positioning algorithms in multi-gait scenarios.

The improved PDR scheme algorithm significantly enhances the robustness and smoothness of pedestrian tracking, particularly during multiple gait transitions. This, in turn, provides strong support for the utilization of low-cost inertial sensors integrated within mobile phones for indoor positioning applications.

The success of online health communities (OHCs) depends on maintaining long-term relationships with physicians and preventing churn. Even so, the reasons for physician churn are poorly understood. In this study, an empirical model was proposed from a social influence perspective to explore the effects of online social influence and offline social influence on physician churn, as well as the moderating effect of their online returns.

The empirical data of 4,145 physicians from a Chinese OHC, and probit regression models were employed to verify the proposed theoretical model.

The results suggest that physicians' churn intention is influenced by online and offline social influences, and the offline social influence is more powerful. Physicians' reputational and economic returns could weaken the effect of online social influence on churn intention. However, physicians' economic returns could strengthen the effect of offline social influence on churn intention.

This research study is the first attempt to explore physician churn and divides the social influence into online and offline social influences according to the source of social relationship. The findings contribute to the literature on e-Health, user churn and social influence and provide management implications for OHC managers.