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This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

Besides ensuring traditional project objectives, expatriate construction professionals (EXCPs) working on international projects face challenges adapting to unfamiliar environments with varying construction standards, work practices and cultural values. This puts them at a high risk of job burnout. Thus, this study aims to investigate the antecedents and outcomes of EXCPs' job burnout in the international construction industry.

Based on the Job demands-resource model (JD-R), a theoretical framework was developed. Industry-specific stressors and expatriate management practices were identified using a literature review and interviews. The authors then used a questionnaire survey to collect data from Chinese EXCPs. Exploratory factor analysis, confirmatory factor analysis and structural equation modeling were then utilized to test hypotheses.

The findings indicate that early-career EXCPs experience the most severe levels of job burnout. The paths analysis proved the direct and indirect mitigating effects of expatriate management practices on job burnout, and EXCP's job burnout was associated with poor job performance and decreased intention to stay in the international assignment.

While prior research has explored job burnout among construction professionals working on domestic projects, little attention has been given to EXCPs and their unique challenges. This study aims to fill this critical gap in the literature by offering a unique perspective on the antecedents and outcomes of job burnout among EXCPs in international contexts and presents a significant contribution to understanding and addressing occupational health issues faced by EXCPs.

Young construction project management practitioners (CPMPs) have unique, complex, and notable occupational mental health problems. However, there were few studies about the intervention for occupational mental health of CPMPs, and traditional intervention modes often failed to achieve significant effects. Therefore, this study aims to propose a new and effective intervention method.

Job crafting intervention was used to design an intervention experiment. A total of 72 CPMPs participated in a 10-week randomized controlled trial in China. Descriptive statistics and repeated measures analysis of variance were used to verify the effectiveness of job crafting intervention (JCI) on job crafting behaviors, job burnout, and work engagement with consideration of the impact of time.

Results showed that the intervention increased social resources, thus effectively reducing job burnout and promoting work engagement. Time also had a significant impact on cynicism, dedication, and social resources.

The authors should promote the habit of job crafting in CPMPs. Furthermore, in order to facilitate their job crafting, the authors should increase structural and social resources for them, and the authors can also encourage them to undertake challenging demands to increase their self-efficacy and the sense of achievement.

The authors bring into light a new psychological intervention approach among CPMPs, which integrates the advantages of the guidance in traditional organized intervention methods and the proactivity in individual spontaneous job crafting. The authors verify the efficacy of the JCI among CPMPs and help propose countermeasures and suggestions to improve the occupational mental health of CPMPs.

This paper aims to study the influence of cylindrical texture parameters on the lubrication performance of static and dynamic pressure thrust bearings (hereinafter referred to as thrust bearings) and to optimize their lubrication performance using multiobjective optimization.

The influence of texture parameters on the lubrication performance of thrust bearings was studied based on the modified Reynolds equation. The objective functions are predicted through the BP neural network, and the texture parameters were optimized using the improved multiobjective ant lion algorithm (MOALA).

Compared with smooth surface, the introduction of texture can improve the lubrication properties. Under the optimization of the improved algorithm, when the texture diameter, depth, spacing and number are approximately 0.2 mm, 0.5 mm, 5 mm and 34, respectively, the loading capacity is increased by around 27.7% and the temperature is reduced by around 1.55°C.

This paper studies the effect of texture parameters on the lubrication properties of thrust bearings based on the modified Reynolds equation and performs multiobjective optimization through an improved MOALA.

Despite carbon reduction commitments, many constraints hinder the delivery of low-carbon buildings (LCBs) in high-rise high-density cities. The collaborative commitment of relevant stakeholders is vital to effectively address and mitigate these constraints. Hence, this study aims to comprehensively explore the required stakeholder collaboration attributes to address and mitigate the “common” constraints of delivering LCBs by focussing on several high-rise high-density cities.

A list of 21 “significant and common” constraints was identified through a systematic literature review followed by a questionnaire survey covering five economies (Hong Kong, Singapore, Australia, Qatar and the UAE). Nineteen influential stakeholders/stakeholder categories were identified through the literature, and their ability to influence the 21 constraints was mapped and identified through a two-round Delphi survey of 15 experienced professionals. The Delphi survey findings were analysed through social network analysis (SNA) methods to assess the stakeholder engagement and collaboration attributes.

The SNA results revealed the ability of stakeholders to influence the constraints, required collaborative stakeholder networks to address the constraints, significance of stakeholders according to the SNA centrality measures, core and periphery stakeholders and individual co-affiliation networks of core stakeholders.

While achieving the planned primary target of exploring stakeholder collaboration and their significance through SNA, this study also presents a useful sequential methodological approach for future researchers to conduct similar studies in different contexts. The findings also provide a foundation for accelerating the delivery of LCBs by strengthening stakeholder collaboration.

Firms may suffer differently from the patent thickets in a particular technology field. This paper explores how patent thickets affect the financial performance of firms with different patent propensities and technological leadership.

From the perspective of patent strategy, the authors study how patent propensity, the possibility that a firm applies for patents, affects the patent thickets and financial performance. Additionally, this paper uses patent stock to measure technological leadership, the degree to which a firm can develop, maintain and enhance technology and product innovation, to study the impact of patent propensity on firms. A three-way interaction model is used to explore the relationship among patent thickets, patent propensity, technological leadership and financial performance based on an unbalanced panel of 69 Chinese telecommunication equipment firms from 2008 to 2019.

The authors find that patent propensity positively moderates patent thickets and financial performance. Notably, technological leadership negatively moderates the moderating effect of patent propensity.

This paper enriches the heterogeneous literature of patent thickets and financial performance. It sheds light on the fact that firms with different technological leadership may use different patent strategies to cut through patent thickets.

Message stream advertising (MSA) has become an increasingly popular option for advertising on mobile social media. However, MSA is often avoided by consumers, and this avoidance deserves more research attention. The purpose of this study is therefore to identify the underlying mechanism and key variables that affect consumer avoidance of MSA in the context of mobile social media.

A face-to-face survey was administered to current mobile users of WeChat (N = 438). Structural equation modeling was conducted to test the relationships in the research model.

Results revealed that mobile consumers employ mechanical avoidance methods (i.e. zipping, muting and zapping) against MSA. The findings also demonstrated that advertising intrusiveness (stimulus) is directly linked to negative emotions, perceived entertainment and sense of control (organism), which, in turn, relate to MSA avoidance (response).

The study contributes to the MSA avoidance literature by using the stimulus-organism-response model to deepen the understanding of consumers' MSA avoidance on mobile social media, and it suggests important managerial implications for advertising practitioners and platform operators.

This study explores how chatbot gender and symbolic service recovery may improve the satisfaction of angry customers in the context of service failures. It provides a strategy for companies to deploy chatbots effectively in customer anger.

This research relies upon a systematic literature review to propose three hypotheses, and we recruit 826 participants to examine the effect of chatbot gender on angry customers through one lab study and one field study.

This research shows that female chatbots are more likely to increase the satisfaction of angry customers than male chatbots in service failure scenarios. In addition, symbolic recovery (apology vs. appreciation) moderates the effect of chatbot gender on angry customers. Specifically, male (vs. female) chatbots are more effective in increasing the satisfaction of angry customers when using the apology method, whereas female (vs. male) chatbots are more effective when using the appreciation method.

The rapid advancements in artificial intelligence technology have significantly enhanced the effectiveness of chatbots as virtual agents in the field of interactive marketing. Previous research has concluded that chatbots can reduce negative customer feedback following a service failure. However, these studies have primarily focused on the level of chatbot anthropomorphism and the design of conversational texts, rather than the gender of chatbots. Therefore, this study aims to bridge that gap by examining the effect of chatbot gender on customer feedback, specifically focusing on angry customers following service failures.

Higher education plays an essential role in achieving the United Nations sustainable development goals (SDGs). However, there are only scattered studies on monitoring how universities promote SDGs through their curriculum. The purpose of this study is to investigate the connection of existing common core courses in a university to SDG education. In particular, this study wanted to know how common core courses can be classified by machine-learning approach according to SDGs.

In this report, the authors used machine learning techniques to tag the 166 common core courses in a university with SDGs and then analyzed the results based on visualizations. The training data set comes from the OSDG public community data set which the community had verified. Meanwhile, key descriptions of common core courses had been used for the classification. The study used the multinomial logistic regression algorithm for the classification. Descriptive analysis at course-level, theme-level and curriculum-level had been included to illustrate the proposed approach’s functions.

The results indicate that the machine-learning classification approach can significantly accelerate the SDG classification of courses. However, currently, it cannot replace human classification due to the complexity of the problem and the lack of relevant training data.

The study can achieve a more accurate model training through adopting advanced machine learning algorithms (e.g. deep learning, multioutput multiclass machine learning algorithms); developing a more effective test data set by extracting more relevant information from syllabus and learning materials; expanding the training data set of SDGs that currently have insufficient records (e.g. SDG 12); and replacing the existing training data set from OSDG by authentic education-related documents (such as course syllabus) with SDG classifications. The performance of the algorithm should also be compared to other computer-based and human-based SDG classification approaches for cross-checking the results, with a systematic evaluation framework. Furthermore, the study can be analyzed by circulating results to students and understanding how they would interpret and use the results for choosing courses for studying. Furthermore, the study mainly focused on the classification of topics that are taught in courses but cannot measure the effectiveness of adopted pedagogies, assessment strategies and competency development strategies in courses. The study can also conduct analysis based on assessment tasks and rubrics of courses to see whether the assessment tasks can help students understand and take action on SDGs.

The proposed approach explores the possibility of using machine learning for SDG classifications in scale.

This study aims to empirically examine the impact of internal corporate governance mechanisms (ICGM) related to the size of the board, board composition, CEO duality and audit committee independence as a single metric on a firm’s investment decisions.

This study attempts to develop an internal corporate governance quality index comprising 10 items under four main ICGMs – size and independence of the board, CEO duality and audit committee independence – employing panel data analysis to investigate its impact on the investment decisions in 301 nonfinancial firms listed in six emerging capital markets in the Gulf Cooperation Council (GCC) member countries for the years 2015–2020. Data were extracted from sample companies' websites, stock markets, annual reports and Refinitiv database.

This study provides convincing evidence that effective ICGMs minimize inefficient investment and ultimately boost investment efficiency. The findings remain consistent even after considering the potential endogeneity bias.

This study provides empirical evidence on investment efficiency in the GCC region and emphasizes the importance of high-quality ICGMs in reducing inefficient investment. By examining the impact of ICGMs on investment inefficiencies, this study contributes to the corporate governance literature. The GCC region's unique economic and social contexts, with its growing economies, are considered to shed light on this issue.