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Although building information modeling (BIM) has brought competitive advantages and many new jobs, the BIM-related job market is still confusing in China, which will undermine the adoption of BIM. This paper aims to show what kinds of BIM-related jobs are there in China, what employers require and whether all BIM engineers are the same kind.

A text mining approach, structural topic model, was used to process the job descriptions of 1,221 BIM-related online job advertisements in China, followed by a cluster analysis based on it.

First, 10 topics of requirements with the impact of experience and educational background to them were found, namely, rendering software, international project, design, management, personal quality, experience, modeling, relation and certificate. Then, six types were clustered, namely, BIM modeler, BIM application engineer, BIM consultant, BIM manager, BIM developer and BIM designer. Finally, different kinds of BIM engineers proved this title was an expediency leading to confusion.

This paper can provide a clear and insightful look into the confusing and unheeded BIM-related job market in China and might help to cope with the abuse of job titles. It could also benefit both employers and candidates in their recruitment for better matching.

The rapid development of digital reading has made it a mainstream reading method for the public, and scholars have conducted research on its effectiveness.The purpose of this study is to systematically summarize and generalize the factors that affect the effectiveness of digital reading in current practical research.

Retrieved the search results from the Web of Science database and the China National Knowledge Infrastructure database, collected the relevant literature in both Chinese and English on the effectiveness of digital reading, qualitatively coded the relevant literature, and conducted a systematic literature review analysis on the factors affecting the effectiveness of digital reading.

There are 37 factors that influence the effectiveness of digital reading, forming five factor themes, namely, the reading subject, reading environment, organizational support, technical support and reading text. The five influencing factor themes are further divided into three types of functional mechanisms, namely, driving, supportive and assurance mechanisms. Based on this, a research framework is proposed, providing a comprehensive approach for the research positioning of digital reading effectiveness.

A research framework is proposed, providing a comprehensive approach for the research positioning of digital reading effectiveness.

This paper takes stock of developments in, and the state of, the field of comparative and international education at the beginning of the 21st century, using as data base articles published in the journal Comparative Education Review during the second decade of the 21st century and to compare results with a content analysis done on the first 50 years of the existence of the Review and which was published in 2008. The 246 articles that were published in the Comparative Education Review during the decade 2010–2019 were analyzed under the following metrics: levels of analysis of articles; number of units covered by articles; research methods; narrative basis; phase of education articles cover; and mode of education articles deal with. Compared to the first 50 years of the existence of the Review, single-unit national-level studies still dominate the field, though less so. A case can be made out for a deconcentration to allow more space for research at geographic levels both larger and smaller than the nation-state. The most prominent narrative in which articles are framed is that of the social justice narrative. The neo-liberal economic narrative stands strong too, while the poor standing of the human rights narrative is disappointing. Turning to modes and phases of education is concerned, the shadow education system has registered on the comparative and international education research agenda, while there seems to be a modest upswing in interest in pre-primary education. Thoughts about the future trajectory of the field are suggested.

This study analyzes the evolution of topics related to COVID-19 on Chinese social media platforms with the aim of identifying changes in netizens' concerns during the different stages of the pandemic.

In total, 793,947 posts were collected from Zhihu, a Chinese Question and Answer website, and Dingxiangyuan, a Chinese online healthcare community, from 31 December, 2019, to 4 August, 2021. Topics were extracted during the prodromal and outbreak stages, and in the abatement–resurgence cycle.

Netizens' concerns varied in different stages. During the prodromal and outbreak stages, netizens showed greater concern about COVID-19 news, the impact of COVID-19 and the prevention and control of COVID-19. During the first round of the abatement and resurgence stage, netizens remained concerned about COVID-19 news and the prevention and control of the pandemic, however, less attention was paid to the impact of COVID-19. During later stages, popularity grew in topics concerning the impact of COVID-19, while netizens engaged more in discussions about international events and the raising of spirits to fight the global pandemic.

This study contributes to the practice by providing a way for the government and policy makers to retrospect the pandemic and thereby make a good preparation to take proper measures to communicate with citizens and address their demands in similar situations in the future.

This study contributes to the literature by applying an adapted version of Fink's (1986) crisis life cycle to create a five-stage evolution model to understand the repeated resurgence of COVID-19 in Mainland China.

Productivity is often cited as a key barrier to the adoption of metal laser-based powder bed fusion (ML-PBF) technology for mass production. Newer generations of this technology work to overcome this by introducing more lasers or dramatically different processing techniques. Current generation ML-PBF machines are typically not capable of taking on additional hardware to maximise productivity due to inherent design limitations. Thus, any increases to be found in this generation of machines need to be implemented through design or adjusting how the machine currently processes the material. The purpose of this paper is to identify the most beneficial existing methodologies for the optimisation of productivity in existing ML-PBF equipment so that current users have a framework upon which they can improve their processes.

The review method used here is the preferred reporting items for systematic review and meta-analysis (PRISMA). This is complemented by using an artificial intelligence-assisted literature review tool known as Elicit. Scopus, WEEE, Web of Science and Semantic Scholar databases were searched for articles using specific keywords and Boolean operators.

The PRIMSA and Elicit processes resulted in 51 papers that met the criteria. Of these, 24 indicated that by using a design of experiment approach, processing parameters could be created that would increase productivity. The other themes identified include scan strategy (11), surface alteration (11), changing of layer heights (17), artificial neural networks (3) and altering of the material (5). Due to the nature of the studies, quantifying the effect of these themes on productivity was not always possible. However, studies citing altering layer heights and processing parameters indicated the greatest quantifiable increase in productivity with values between 10% and 252% cited. The literature, though not always explicit, depicts several avenues for the improvement of productivity for current-generation ML-PBF machines.

This systematic literature review provides trends and themes that aim to influence and support future research directions for maximising the productivity of the ML-PBF machines.

Balancing economic development with social challenges such as gender inequality has become critical in the current times. Structural barriers persist as obstacles to women's professional opportunities despite evolving gender roles in the modern era. This chapter investigates the ‘motherhood penalty’, which is a concept couched in sociology and describes the disadvantages that working mothers face including lower wages and fewer opportunities of career advancement. This chapter explores economic, physical and mental health impacts of this penalty and sheds light on its detrimental effects on women's well-being and organisational success. Potentially, virtual reality (VR) technology is a solution through which working mothers can alleviate stress and improve well-being. In this chapter, existing literature on the motherhood penalty has been reviewed, and the innovative use of VR in workplace settings has been examined. There is limited research on the nexus between VR, motherhood penalty and working mothers' well-being, and therefore, this chapter seeks to provide a comprehensive overview of this intersection and proposes VR as a tool to mitigate the motherhood penalty, promoting gender equality and enhancing overall workplace well-being. Future research should explore VR's long-term benefits and ensure inclusive applications for diverse demographic groups.

When recommending products to consumers, it is important to be able to accurately predict how consumers will rate them. However, existing collaborative filtering models are difficult to achieve a balance between rating prediction accuracy and complexity. Therefore, the purpose of this paper is to propose an accurate and effective model to predict users’ ratings of products for the accurate recommendation of products to users.

First, we introduce an attention mechanism that dynamically assigns weights to user preferences, highlighting key interaction information and enhancing the model’s understanding of user behavior. Second, a fold embedding strategy is employed to segment user interaction data, increasing the information density of each subset while reducing the complexity of the attention mechanism. Finally, a masking strategy is integrated to mitigate overfitting by concealing portions of user-item interactions, thereby improving the model’s generalization ability.

The experimental results demonstrate that the proposed model significantly minimizes prediction error across five real-world datasets. On average, the evaluation metrics root mean square error (RMSE) and mean absolute error (MAE) are reduced by 9.11 and 13.3%, respectively. Additionally, the Friedman test results confirm that these improvements are statistically significant. Consequently, the proposed model more accurately captures the intrinsic correlation between users and products, leading to a substantial reduction in prediction error.

We propose a novel collaborative filtering model to learn the user-item interaction matrix effectively. Additionally, we introduce a fold embedding strategy to reduce the computational resource consumption of the attention mechanism. Finally, we implement a masking strategy to encourage the model to focus on key features and patterns, thereby mitigating overfitting.

The high-load operation of the engine crankshaft causes severe wear and fatigue. This study aims to prepare in situ textures with effective density and study their wear mechanism on the surface of ductile cast iron, which optimizes the tribological properties of engine crankshafts and reduces wear.

A new method was proposed based on the hardness difference in graphite removal to form an in situ texture. The friction performance was evaluated using a combination of computational fluid dynamics and tribological testings. The influence of the texture characteristic parameters on the bearing capacity of the oil film was analyzed. The surface wear morphology was studied by scanning electron microscopy.

The texture density significantly affected the oil film bearing capacity. The surface texture can reduce the average friction coefficient (COF) by more than 35% owing to the oil film bearing and storage capacity. Specifically, the 13% texture density exhibited the lowest wear rate and COF under all three experimental conditions. The reduction in abrasive particles in the wear area of the textured surface indicates that the surface texture can improve the lubrication mechanism.

This study systematically explored the influence of the weight of each model parameter on tribological properties. Subsequently, focusing on the critical parameter (texture density), detailed tribological testings were carried out to reveal the specific effect of texture density on the wear mechanism under different working conditions, and the optimal texture density to achieve the optimal tribological performance was determined accordingly.

This paper aims to develop an optimal maintenance and spare parts policy for an urban micro wind power system, focusing on two urban micro wind farms (UMWF). The reliability and efficiency of these systems are sought to be enhanced by considering the relationship between urban wind parameters and wind turbine degradation.

A proportional hazards (PH) model is utilized to describe how urban wind conditions impact turbine degradation. The maintenance strategy includes preventive maintenance (PM), corrective maintenance (CM) and opportunistic maintenance (OM). A multi-objective optimization algorithm is developed to optimize the joint policy of OM plans and spare parts resource allocation.

The proposed maintenance and spare parts policy effectively balances the trade-offs between PM, CM and OM strategies. Numerical experiments demonstrate that the policy improves the reliability of UMWF, reducing downtime and maintenance costs while ensuring the availability of spare parts when needed. The results show a significant enhancement in system performance compared to traditional maintenance approaches.

A novel maintenance policy and spare parts management approach for urban micro wind power systems is proposed. A multi-objective optimization algorithm is developed to optimize the OM schedule and maintenance spare parts resource management strategy for wind farms in urban wind environments.

The purpose of this research is to examine the potential impact of technologies on enhancing the efficiency and effectiveness of supply chain performance inside healthcare organizations, with a particular focus on cost and quality improvement.

The present investigation employs the survey method to examine the research hypothesis and objective. A total of 630 surveys were collected using an online platform, all of which were deemed to be valid. The gathered data were analyzed using SPSS version 20.0 and Smart-PLS version 3.0 software.

The finding represents a holistic investigation into Industry 4.0 technologies, quality management practices, supply chain performance and organizational performance is essential for the healthcare industry’s evolution. Embracing these elements collectively has the potential to redefine healthcare delivery, improve patient outcomes and drive operational excellence. The results seek to shed light on the broader implications for enhancing patient care, optimizing resources and improving organizational effectiveness within the evolving landscape of Industry 4.0-driven healthcare environments.

Exploration of the incorporation of Industry 4.0 technologies within the healthcare domain has the potential to augment operational efficacy, patient care and data administration. Examination of the repercussions of these technologies on quality management procedures in healthcare environments imparts an understanding of the enhancement of healthcare service quality and patient outcomes.

Implementing Industry 4.0 technologies, which encompass Internet of Things devices and analytics driven by artificial intelligence, within the healthcare sector has the potential to streamline operational procedures, minimize errors and optimize resource distribution. This, in turn, may result in heightened precision of diagnostic procedures, refined treatment strategies and an overall enhancement in the quality of care provided to patients.

There exist certain constraints inherent to this study. In the initial instance, the data were gathered from moderately sizable medical institutions situated within India. As the present investigation was conducted in India, it is possible to examine other countries in order to identify potential disparities in social conditions. Future research should consider, cross-cultural and longitudinal studies of organizational performance.

In the present investigation, the writer presents innovative research that may assist the healthcare industry in identifying the most crucial component of Industry 4.0 technologies for the relevant personnel. There is a notable relationship between the technologies of Industry 4.0 and the supply chain of healthcare, which was formerly the central focus. With a specific emphasis on big data, the Internet of things, cloud computing, blockchain, artificial intelligence and 3D printing, the authors of the current study have showcased a connection between the practice of quality management and the performance of the supply chain by employing industry 4.0 technologies. This paves the way for the healthcare sector to place a heightened emphasis on organizational performance.