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A transmission tower usually experiences bolt loosening under long-term alternating cyclic load, which may lead to collapse of the tower in extreme operating conditions. The paper aims to propose a data-driven identification method for bolt looseness of complicated tower structures based on reduced-order models and numerical simulations to perceive and evaluate the health state of a tower in operation.

The equivalent stiffnesses of three types of bolt joints under various loosening scenarios are numerically determined by three-dimensional finite element (FE) simulations. The order of the FE model of a tower structure with bolt loosening is reduced by means of the component modal synthesis method, and the dynamic responses of the reducer-order model under calibration loads are simulated and used to create the dataset. An identification model for bolt looseness of the tower structure based on convolutional neural networks driven by the acceleration sensors is constructed.

An identification model for bolt looseness of the tower structure based on convolutional neural networks driven by the acceleration sensors is constructed and the applicability of the model is investigated. It is shown that the proposed method has a high identification accuracy and strong robustness to data noise and data missing. Meanwhile, the method is less dependent on the number and location of sensors and is easier to apply in real transmission lines.

This paper proposes a data-driven identification method for bolt looseness of a complicated tower structure based on reduced-order models and numerical simulations. Non-linear relationships between equivalent stiffness of bolted joints and bolt preload depicting looseness are obtained and reduced-order model of tower structure with bolt looseness is established. Finally, this paper investigates applicability of identification model for bolt looseness.

This study is dedicated to systematically collating the distribution and utilization circumstances of geothermal resources in China. Moreover, it endeavors to formulate a comprehensive utilization scheme for geothermal resources during the construction and operation phases of the railway, thereby furnishing robust support and valuable reference for the holistic utilization of geothermal resources along the railway corridor.

Through an in-depth analysis of the extant utilization of geothermal resources in China, it is discerned that the current utilization modalities are relatively rudimentary, bereft of rational planning and characterized by a low utilization rate. Concurrently, by integrating the practical requisites of railway construction and operation and conducting theoretical dissections, a comprehensive utilization plan for the construction and operation periods of railway is proffered.

In light of the railway’s construction and operation characteristics, geothermal utilization models are categorized. During construction, comprehensive modalities include tunnel illumination power generation, construction area heating, tunnel antifreeze using shallow geothermal energy, tunnel pavement antifreeze and construction concrete maintenance. During operation, they comprise operation tunnel antifreeze, railway roadbed antifreeze, railway switch snow melting and deicing, geothermal power station establishment and railway hot spring health tourism planning.

According to the characteristics and actual needs of railway construction and operation, it is of great significance to rationally utilize geothermal resources to promote the construction and operation of green railways.

This paper aims to present the development of a holistic campus facility management (CFM) performance assessment framework that incorporates a fuzzy logic approach and integrates a comprehensive set of key factors for successful management of campus facilities. The devised framework aims to cater to the needs of campus facilities management firms and departments for the purpose of gauging and assessing their performance across different management domains. Through this approach, facility management organizations can detect potential areas of enhancement and adopt preemptive steps to evade issues, foster progress and ensure success.

After a comprehensive analysis of the literature, conducting in-depth interviews with industry experts and employing the Delphi technique in two rounds, a total of 45 indicators critical to CFM success were identified and subsequently sorted into seven distinct groups. Through an online questionnaire, 402 subject-matter experts proficiently assessed the significance of the critical success indicators and their groups. A fuzzy logic framework was developed to evaluate and quantify a firm's compliance with the critical success indicators and groups of indicators. The framework was subsequently weighted using computations of the relative importance index (RII) based on the responses received from the questionnaire participants. The initial section of the framework involved a comprehensive analysis of the firm's performance vis-à-vis the indicators, while the latter part sought to evaluate the impact of the indicators groups on the overall firm's performance.

The utilization of fuzzy logic has uncovered the significant effects each effective CFM key indicator on indicators groups, as well as the distinct effects of each CFM indicators group on the overall performance of CFM. The results reveal that financial management, communications management, sustainability and environment management and workforce management are the most impactful indicators groups on the CFM performance. This suggests that it is imperative for management to allocate increased attention to these specific areas.

This study contributes to the advancement of current knowledge by revealing vital indicators of effective CFM and utilizing them to construct a thorough fuzzy logic framework that can assist in evaluating the effectiveness of CFM firms worldwide. This has the potential to provide crucial assistance to facility management organizations, facility managers and policymakers in their quest for informed decision-making.

This paper aims to study the impact of the interaction effects between live-streaming marketing and clothing type on consumers' intention to purchase clothing, and the mediating effect of internalization and identification on the relationship between them.

This paper conducts a scenario experiment to 486 consumers who had experience in purchasing clothing on the live-streaming platform and employs the analysis of variance, structural equation model and multivariate regression model.

Our findings reveal that professional live-streaming marketing (PLSM) can better stimulate consumers' intention to purchase formal clothing than entertainment live-streaming marketing (ELSM) does. Compared with PLSM, ELSM can better stimulate consumers' intention to purchase casual clothing. When PLSM promotes formal clothing, it triggers the internalization mechanism of consumers, so as to improve their purchase intention. When ELSM promotes casual clothing, it triggers consumers' identification mechanism, so as to improve their purchase intention.

This paper helps to identify the differences in the impact of different types of live-streaming marketing on consumers' intention to purchase different types of clothing, as well as the mediating role of internalization and identification mechanisms. This paper provides a theoretical reference for clothing firms to strategically select the appropriate type of live-streaming marketing.

The purpose of this study is to explore the relationship between digital transformation and excess employees. This research investigates the questions of when human−machine synergy can be achieved after a firm goes through digital transformation and whether there will be excess employees in the interim.

This paper takes A-share listed companies as research object in the period of 2011−2020 and a total of 24,718 samples are obtained. Hypothesis testing and regression analysis are performed in STATA.

This paper finds a human−machine mismatch in the short term, as evidenced by an increase in the rate of excess employees; however, with the progress of digital transformation, it can drive the achievement of human−machine synergy in the long term, and management efficiency plays a mediating role in this process. Further research showed that the effects of digital transformation on the number of employees, revenue generation per capita and profit generation per capita varied in both the short and long term. In addition, the characteristics of the company affect the relationship between digital transformation and excess employees.

This paper contributes to the understanding of the impacts of digital transformation on the human capital structure of companies at a micro level. It also provides insights into how to improve human capital demand structure through digitalization, thus providing insights into labor market changes.

This study aims to test the relationship between virtual knowledge sharing (VKS) and team effectiveness (TE) during the COVID-19 pandemic. The study also explores the moderating role of collaborative technologies.

This is a cross-sectional study conducted in the service sector of India. A purposive sample of 321 knowledge workers from National Capital Region of India was used. Questionnaires were distributed to knowledge workers working in a virtual environment. The hypotheses were tested with confirmatory factor analysis and structural equation modeling (SEM) using partial least square-SEM.

The study reveals that, amid the COVID-19 pandemic, virtual knowledge sharing (VKS) positively affects team effectiveness (TE). Furthermore, the impact of VKS on TE is contingent upon the utilization of collaborative technologies.

The study contributes to the existing literature by exploring the impact of VKS on TE during the COVID-19 pandemic and the importance of collaborative technologies in facilitating virtual team collaboration, which has practical implications for organizations seeking to enhance TE in virtual environments.

The study aimed to examine the impact of COVID-19-related governments’ interventions on the volatility in stock returns in several Asian countries following the COVID-19 outbreak.

Using a battery of conditional volatility models, we first investigate the dynamic behavior of the stock return volatility for selected Asian stock markets during the pandemic period. Second, we wish to find out how these volatilities overlap with a wide range of governments’ interventions related to COVID-19 and whether a relationship can be established between two types of uncertainty and the volatility of the considered stock returns.

We confirm an asymmetric pattern in the volatility of selected Asian stock markets. In addition, the result shows that the effects of governments’ interventions vary significantly across countries. The “Containment and Health” and “Economic Support” indices appear to have a significant and negative impact on the volatility of the overwhelming majority of stock markets. Further, all Asian stock markets are experiencing a significant positive effect of “Stringency measures” on the return volatilities.

This research could have implications for investors and policymakers in terms of portfolio diversification to maintain active and gainful investment strategies during the pandemic crisis.

The purpose of this study is to explore the impact of knowledge management (KM) practices (knowledge creation, knowledge storage, knowledge transfer and knowledge application) and demographic and occupational factors on team learning (TL) in public enterprises in Jordan.

A convenient random sample of 389 employees working in 52 various functional teams in the Jordanian public enterprises completed a self-administrated questionnaire. Descriptive statistics, confirmatory factor analysis and hierarchical regression analysis were used to analyze the data and test the proposed hypotheses.

The results of this study showed that KM practices explained an additional 53% of the variance in TL above the 9% variance explained by the demographic and occupational factors (i.e. gender, work experience, age, education, occupational position, team size and participation in training on KM and TL). Notably, in the absence of the effects of KM dimensions, work experience, age, team size and “participation in training on KM and TL” were significant predictors of TL. However, after including the effects of KM dimensions in the regression analysis, only the participation in training variable, along with the KM dimensions, remained significant predictors of TL.

This study contributes to public enterprise administration by highlighting the importance of KM practices in nurturing a healthy TL climate that can ultimately enhance job performance and organizational success.

To the best of the authors’ knowledge, this study is one of the few studies in the Arab world that examines real functional teams to understand the role of KM in enhancing the practice of TL.

Supply chain risk management (SCRM) is a multi-stage process that handles the adverse impact of disruptions in the supply chain network (SCN), and various SCRM techniques have been widely developed in the literature. As artificial intelligence (AI) techniques advance, they are increasingly applied in SCRM to enhance risk management’s capabilities.

In the current, systematic literature review (SLR), which is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method, we analysed the existing literature on AI-based SCRM methods without any time limit to categorise the papers’ focus in four stages of the SCRM (identification, assessment, mitigation and monitoring). Three research questions (RQs) consider different aspects of an SCRM method: interconnectivity, external events exposure and explainability.

For the PRISMA process, 715 journal and conference papers were first found from Scopus and Web of Science (WoS); then, by automatic filtering and screening of the found papers, 72 papers were shortlisted and read thoroughly, our review revealed research gaps, leading to five key recommendations for future studies: (1) Attention to considering the ripple effect of risks, (2) developing methods to explain the AI-based models, (3) capturing the external events impact on the SCN, (4) considering all stages of SCRM holistically and (5) designing user-friendly dashboards.

The current SLR found research gaps in AI-based SCRM and proposed directions for future studies.

The development of regional public brands for agricultural products necessitates compelling narratives that resonate deeply with consumers. Given the distinctiveness of agricultural products, consumers prioritize the inherent connection to roots and heritage when making purchasing decisions. Therefore, crafting brand narratives must emphasize this root appeal, namely, consumers’ information appeal preference, to positively influence consumers’ brand perceptions and underscore the value of regional public brands. This study investigates this phenomenon through the lens of cue utilization theory.

Four experiments were conducted for this purpose. Study 1 examined the stimulus materials for brand story type (typical vs atypical). The purpose of study 2 was to verify whether the experimental material could be used to categorize participants' information appeal preferences (geographic vs cultural). Study 3 employed a between-subjects design with a 2 (brand story: typical vs atypical) × 2 (consumers’ information appeal preferences: cultural vs geographic) factorial design. Study 4 used a between-subjects design of 2 (brand story: typical and atypical) × 2 (consumers’ information appeal preferences: cultural vs geographic) × 2 (culturally derived power perception: individual and social).

The findings indicated that the type of brand story and consumers’ information appeal preferences interact with consumers’ brand attitudes toward regional public brands for agricultural products. In addition, a sense of place was found to mediate the interaction between the type of brand story and consumers’ information appeal preferences. Furthermore, culturally derived power perceptions moderated this mechanism.

This study offers valuable insights into marketing regional public brands for agricultural products by categorizing their brand stories into typical and non-typical narratives.