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This study aims to propose a method for monitoring bearing health in the time–frequency domain, termed the Lock-in spectrum, to track the evolution of bearing faults over time and frequency.

The Lock-in spectrum uses vibration signals captured by vibration sensors and uses a lock-in process to analyze specified frequency bands. It calculates the distribution of signal amplitudes around fault characteristic frequencies over short time intervals.

Experimental results demonstrate that the Lock-in spectrum effectively captures the degradation process of bearings from fault inception to complete failure. It provides time-varying information on fault frequencies and amplitudes, enabling early detection of fault growth, even in the initial stages when fault signals are weak. Compared to the benchmark short-time Fourier transform method, the Lock-in spectrum exhibits superior expressive ability, allowing for higher-resolution, long-term monitoring of bearing condition.

The proposed Lock-in spectrum offers a novel approach to bearing health monitoring by capturing the dynamic evolution of fault frequencies over time. It surpasses traditional methods by providing enhanced frequency resolution and early fault detection capabilities.

Under the rural revitalization, the effect of China's implementation of rural prefabricated housing is not obvious. Cost has become the biggest obstacle to its development. Therefore, it is necessary to study the factors influencing the cost of prefabricated buildings in villages and clarify the focus of cost control.

This paper focuses on the whole process of prefabricated housing construction in villages in China and uses grounded theory to identify and screen out 27 related factors that affect the construction cost of prefabricated buildings. A system dynamics model is used to dynamically analyze the influencing factors. The engineering examples in rural areas of southern Shaanxi are simulated. Finally, five key factors that influence cost are obtained. Based on this, cost control countermeasures are proposed for rural prefabricated housing in southern Shaanxi.

The results show that: the key factors affecting the cost of prefabricated buildings in villages include the selection of production methods, the degree of design standardization, the quality of construction personnel, the level of construction technology and the circulation cycle of molds. The cost of prefabricated housing in villages can be controlled through five aspects: mass production of components, design exchange and feasibility analysis, improvement of employee professionalism, strict selection of construction schemes and technologies and improvement of mold turnover rate.

The system dynamics model applied in this paper is based on the idealized state. The system boundary is narrow and has a certain subjectivity. It needs further detailed research to make it closer to the engineering practice. In addition, this paper applies the rural engineering example in southern Shaanxi to carry out a single case study, and the universality of the research results needs to be further tested. There are many village construction projects and building types, so the research results can be further enriched through large sample research.

Rural construction is an important step in the implementation of rural revitalization. Exploring the factors that affect the key costs of prefabricated buildings in villages and towns in view of the particularity of rural areas will help provide a reference for their cost control and help the rural development of prefabricated houses.

The research results of this paper can provide a reference for the development of prefabricated buildings in other rural revitalization areas.

Different from the traditional research on urban prefabricated buildings, this paper focuses on rural areas and explores the core factors affecting the cost of prefabricated buildings from the micro level. This study establishes a system dynamics model suitable for the cost control of prefabricated housing at the village level and provides methods for its cost control. Based on the identified key factors affecting costs, cost control measures were proposed for prefabricated housing tailored to the unique characteristics of villages.

Free knowledge sharing in the online health community has been widely documented. However, whether free knowledge sharing can help physicians accumulate popularity and further the accumulated popularity can help physicians attract patients remain unclear. To unveil these gaps, this study aims to examine how physicians' popularity are affected by their free knowledge sharing, how the relationship between free knowledge sharing and popularity is moderated by professional capital, and how the popularity finally impacts patients' attraction.

The authors collect a panel dataset from Hepatitis B within an online health community platform with 10,888 observations from April 2020 to August 2020. The authors develop a model that integrates free knowledge sharing, popularity, professional capital, and patients' attraction. The hierarchical regression model is used to for examining the impact of free knowledge sharing on physicians' popularity and further investigating the impact of popularity on patients' attraction.

The authors find that the quantity of articles acted as the heuristic cue and the quality of articles acted as the systematic cue have positive effect on physicians' popularity, and this effect is strengthened by physicians' professional capital. Furthermore, physicians' popularity positively influences their patients' attraction.

This study reveals the aggregation of physicians' popularity and patients' attraction within online health communities and provides practical implications for managers in online health communities.

Research on the impact of the engagement of online medical teams (OMTs) on patient evaluation, for example, satisfaction, remains insufficient. This study attempts to recognize the underlying mechanism of how OMTs’ engagement influences patient satisfaction by adopting social support as the mediator. This study also scrutinizes the moderating effects of the transactive memory system (TMS) on the link between OMTs’ engagement and social support.

We utilized a linear model that had fixed effects controlled at the team level for analysis. A bootstrapping approach using 5,000 samples was employed to test the mediation effect.

Our results reveal that OMTs’ engagement improves informational and emotional support, thereby promoting patient satisfaction. Specialization and credibility strengthen the impact of OMTs’ engagement on informational and emotional support. Simultaneously, coordination has an insignificant influence on the link between OMTs’ engagement and social support.

This study contributes to the literature on OMTs, social support, and TMS, providing insights into patients’ perceptions of OMTs’ engagement during online team consultation. This study also generates several implications for the practice of online health communities and OMTs.

Online medical teams (OMTs) have emerged as an innovative healthcare service mode that relies on the collaboration of doctors to produce comprehensive medical recommendations. This study delves into the relationship between knowledge collaboration and team performance in OMTs and examines the complex effects of participation patterns.

The analysis uses a dataset that consists of 2,180 OMTs involving 8,689 doctors. Ordinary least squares regression with robust standard error is adopted for data analysis.

Our findings demonstrate a positive influence of knowledge collaboration on OMT performance. Leader participation weakens the relationship between knowledge collaboration and team performance, whereas multidisciplinary participation strengthens it. Passive participation and chief doctor participation have no significant effect on the association between knowledge collaboration and OMT performance.

This study provides valuable insights into how knowledge collaboration shapes OMTs' performance and reveals how the participation of different types of members affects outcomes. Our findings offer important practical implications for the optimization of online health platforms and for enhancing the effectiveness of collaborative healthcare delivery.

Polyaniline (PANI) has garnered attention for its potential applications in anticorrosion fields because of its unique properties. Satisfactory outcomes have been achieved when using PANI as a functional filler in organic coatings. More recently, research has extensively explored PANI-based organic coatings with self-healing properties. The purpose of this paper is to provide a summary of the active agents, methods and mechanisms involved in the self-healing of organic coatings.

This study uses specific doped acids and metal corrosion inhibitors as active and self-healing agents to modify PANI using the methods of oxidation polymerization, template synthesis, nanosheet carrier and nanocontainer loading methods. The anticorrosion performance of the coatings is evaluated using EIS, LEIS and salt spray tests.

Specific doped acids and metal corrosion inhibitors are used as active agents to modify PANI and confer self-healing properties to the coatings. The coatings’ active protection mechanism encompasses PANI’s own passivation ability, the adsorption of active agents and the creation of insoluble compounds or complexes.

This paper summarizes the active agents used to modify PANI, the procedures used for modification and the self-healing mechanism of the composite coatings. It also proposes future directions for developing PANI organic coatings with self-healing capabilities. The summaries and proposals presented may facilitate large-scale production of the PANI organic coatings, which exhibit outstanding anticorrosion competence and self-healing properties.

This study aims to investigate the influence of the reviewed establishment’s price level and the user’s social network size and reputation status on consumers’ tendency to post more visual imagery content. Furthermore, it explores the moderating effects of user experiences and geographic distance on these dynamics.

This study adopts a multi-method approach to explore both the determinants behind the sharing of user-generated photos in online reviews and their internal mechanisms. Using a comprehensive secondary data set from Yelp.com, the authors focused on restaurant reviews from a prominent tourist destination to construct econometric models incorporating time-fixed effects. To enhance the robustness of the authors’ findings, the authors complemented the big data analysis with a series of controlled experiments.

The reviewed establishments price level and the users reputation status and social network size incite corresponding motivations conspicuous display “reputation seeking” and social approval motivating users to incorporate more images in reviews. “User experiences can amplify the influence of these factors on image sharing.” An increase in the users geographical distance lessens the impact of the price level on image sharing, but it heightens the influence of the users reputation and social network size on the number of shared images.

As a result of this study, high-end establishments can increase their online visibility by leveraging user-generated visual content. A structured rewards program could significantly boost engagement by incentivizing photo sharing, particularly among users with elite status and extensive social networks. Additionally, online review platforms can enhance users’ experiences and foster more dynamic interactions by developing personalized features that encourage visual content production.

This research, anchored in trait activation theory, offers an innovative examination of the determinants of photo-posting behavior in online reviews by enriching the understanding of how the intricate interplay between users’ characteristics and situational cues can shape online review practices.

This study aims to explore the potential role of supply chain digital transformation on collaborative knowledge creation, supply chain innovation, and value co-creation in new norms. It also examines the impact of collaborative knowledge creation and supply chain innovation on value co-creation. Furthermore, the study examines the impact of collaborative knowledge creation on supply chain innovation. Finally, it investigates the possible mediating role of knowledge absorptive capacity and relationship quality in shaping these interactions.

To establish the empirical part of this study, the collection of data involved distributing a questionnaire to 247 managers working in manufacturing companies. The measurement model assessment and hypothesis testing were performed employing the PLS-SEM approach.

The findings indicate that supply chain digital transformation significantly impacts collaborative knowledge creation, supply chain innovation, and value co-creation. This study also confirms the significant impact of collaborative knowledge creation on supply chain innovation and value co-creation. Furthermore, it reveals that knowledge absorptive capacity mediates the impact of supply chain digital transformation on collaborative knowledge creation. It also shows that the impact of collaborative knowledge creation on supply chain innovation and value co-creation is mediated by relationship quality among participants.

The findings of this study make significant contributions to academic theory, existing literature, and the scholarly community within the realms of supply chain management, innovation, knowledge management, and value co-creation. It also offers practical implications for managers to strategically navigate the evolving norms of supply chain management. Companies can use these insights to improve their innovation processes and knowledge management, while policymakers can consider the study's findings when developing supportive frameworks for the manufacturing sector.

This paper aims to explore the influencing factors of public perceived efficacy of emergency infrastructure projects based on the triadic interactive determinism, and analyze the relationship among these factors.

Based on the triadic interactive determinism, we explored the factors influencing public perceived efficacy of emergency infrastructure project and empirically verified the relationship among these factors and perceived efficacy by using data drawn from a questionnaire survey of 491 residents near Leishenshan Hospital, Jiangxia District, Wuhan, China.

Prior experience, emotional response, personal expectation, public trust, context message and interactivity level, namely behavior, individual and environment, affect the perceived efficacy of public emergency infrastructure projects.

The results offer an insight into public perceived efficacy of emergency infrastructure project from the perspective of antecedents in a triadic reciprocal determinism, which provides a reference basis for the sustainable development of the emergency infrastructure projects. This study also suggests valuable practical implications to government departments to improve the quality of administrative decision-making effectively.

Although existing studies have found some influencing factors of public perceived efficacy in general infrastructure, there is still a lack of systematic carding and quantitative description of influencing factors of public perceived efficacy of emergency infrastructure projects. This study bridges this gap by exploring the determinants and their influencing relationship of public perceived efficacy especially for emergency infrastructure projects.

This research aims to evaluate the accuracy of several Value-at-Risk (VaR) approaches for determining the Minimum Capital Requirement (MCR) for Islamic stock markets during the pandemic health crisis.

This research evaluates the performance of numerous VaR models for computing the MCR for market risk in compliance with the Basel II and Basel II.5 guidelines for ten Islamic indices. Five models were applied—namely the RiskMetrics, Generalized Autoregressive Conditional Heteroskedasticity, denoted (GARCH), fractional integrated GARCH, denoted (FIGARCH), and SPLINE-GARCH approaches—under three innovations (normal (N), Student (St) and skewed-Student (Sk-t) and the extreme value theory (EVT).

The main findings of this empirical study reveal that (1) extreme value theory performs better for most indices during the market crisis and (2) VaR models under a normal distribution provide quite poor performance than models with fat-tailed innovations in terms of risk estimation.

Since the world is now undergoing the third wave of the COVID-19 pandemic, this study will not be able to assess performance of VaR models during the fourth wave of COVID-19.

The results suggest that the Islamic Financial Services Board (IFSB) should enhance market discipline mechanisms, while central banks and national authorities should harmonize their regulatory frameworks in line with Basel/IFSB reform agenda.

Previous studies focused on evaluating market risk models using non-Islamic indexes. However, this research uses the Islamic indexes to analyze the VaR forecasting models. Besides, they tested the accuracy of VaR models based on traditional GARCH models, whereas the authors introduce the Spline GARCH developed by Engle and Rangel (2008). Finally, most studies have focus on the period of 2007–2008 financial crisis, while the authors investigate the issue of market risk quantification for several Islamic market equity during the sanitary crisis of COVID-19.