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This study aims to offer a comprehensive exploration of the potential and challenges associated with sensor fusion-based virtual reality (VR) applications in the context of enhanced physical training. The main objective is to identify key advancements in sensor fusion technology, evaluate its application in VR systems and understand its impact on physical training.

The research initiates by providing context to the physical training environment in today’s technology-driven world, followed by an in-depth overview of VR. This overview includes a concise discussion on the advancements in sensor fusion technology and its application in VR systems for physical training. A systematic review of literature then follows, examining VR’s application in various facets of physical training: from exercise, skill development and technique enhancement to injury prevention, rehabilitation and psychological preparation.

Sensor fusion-based VR presents tangible advantages in the sphere of physical training, offering immersive experiences that could redefine traditional training methodologies. While the advantages are evident in domains such as exercise optimization, skill acquisition and mental preparation, challenges persist. The current research suggests there is a need for further studies to address these limitations to fully harness VR’s potential in physical training.

The integration of sensor fusion technology with VR in the domain of physical training remains a rapidly evolving field. Highlighting the advancements and challenges, this review makes a significant contribution by addressing gaps in knowledge and offering directions for future research.

When the emergency transition started in the spring of 2020 in the USA, teachers had to quickly switch from traditional in-person teaching to distance and remote teaching, regardless of their level of preparation. The distance and remote learning environments and contexts were different from traditional classrooms, which significantly changed the way teachers communicated and engaged with students in learning. The purpose of this study was to explore the workplace learning experience of K-12 educators during their work transition due to the COVID-19 pandemic in the USA.

In total, 30 qualitative, in-depth, semi-structured, one-on-one interviews were conducted with K-12 educators in Arkansas in the USA and synthesized their experiences.

This study identified four major themes in the workplace learning experiences of K-12 teachers during the COVID-19 pandemic: major challenges in workplace learning, including limited time, information overload, lack of relevance and customization and balancing priorities; challenges specific to different subgroups, such as age differences, prior experience and access to technology; strategies of workplace learning, notably collaborative learning, on-the-job training and professional development; and support for workplace learning, encompassing both formal support from schools and districts and informal support from family, friends and peers.

The paper provides original insights into K-12 teachers’ workplace learning during the COVID-19 pandemic by understanding their adaptation strategies. It fills a research gap by highlighting both the challenges and support mechanisms in educational transitions during a crisis.

Wine tourism is gaining increasing popularity among Chinese tourists, making it necessary to thoroughly examine tourist behavior. While online reviews posted by wine tourists have been extensively studied from the perspectives of destinations and wineries, the perspective of the tourists themselves has been overlooked. To address this gap, this study aims to identify significant attributes intrinsic to the tourism experiences of Chinese wine tourists by adopting a text-mining approach from a tourist-centric perspective.

The authors use topic modeling to extract these attributes, calculate topic intensity to understand tourists’ attention distribution across these attributes and conduct topical sentiment analysis to evaluate tourists’ satisfaction levels with each attribute. The authors perform importance-performance analyses (IPAs) using topic intensity and sentiment scores. Furthermore, the authors conduct semistructured in-depth interviews with Chinese wine tourists to gain insights into the underlying reasons behind the key findings.

The study identifies eleven attributes for domestic wine tourists and seven attributes for outbound wine tourists. From the reviews of both domestic and outbound tourists, three common attributes have been identified: “scenic view”, “wine tasting and purchase” and “wine knowledge”.

According to the results of the IPAs, there is a pressing need for enhancements in the wine tasting and purchasing experience at domestic wine attractions. Additionally, managers of domestic wine attractions should continue to prioritize the positive aspects of the family trip experience and scenic views. On the other hand, for outbound wine attractions, it is crucial for managers to maintain their efforts in providing opportunities for wine knowledge acquisition, ensuring scenic views and upholding the reputation of wine regions.

First, this study breaks new ground by adopting a tourist-centric perspective to extract significant attributes from real wine tourism reviews. Second, the authors conduct a comparative analysis between Chinese wine tourists who travel domestically and those who travel abroad. The third novel aspect of this study is the application of IPA based on textual review data in the context of wine tourism. Fourth, by integrating topic modeling with qualitative interviews, the authors use a mixed-method approach to gain deeper insights into the experiences of Chinese wine tourists.

The interruption event will seriously affect the normal operation of urban rail transit lines,causing a large number of passengers to be stranded in the station and even making the train stranded in the interval between stations. This study aims to reduce the impact of interrupt events and improve service levels.

To address this issue, this paper considers the constraints of train operation safety, capacity and dynamic passenger flow demand. It proposes a method for adjusting small loops during interruption events and constructs a train operation adjustment model with the objective of minimizing the total passenger waiting time. This model enables the rapid development of train operation plans in interruption scenarios, coordinating train scheduling and line resources to minimize passenger travel time and mitigate the impact of interruptions. Regarding the proposed train operation adjustment model, an improved genetic algorithm (GA) is designed to solve it.

The model and algorithm are applied to a case study of interruption events on Beijing Subway Line 5. The results indicate that after solving the constructed model, the train departure intervals can be maintained between 1.5 min and 3 min. This ensures both the safety of train operations on the line and a good match with passengers’ travel demands, effectively reducing the total passenger waiting time and improving the service level of the urban rail transit system during interruptions. Compared to the GA algorithm, the algorithm proposed in this paper demonstrates faster convergence speed and better computational results.

This study explicitly outlines the adjustment method of using short-turn operation during operational interruptions, with train departure times and station stop times as decision variables. It takes into full consideration safety constraints on train operations, train capacity constraints and dynamic passenger demand. It has constructed a train schedule optimization model with the goal of minimizing the total waiting time for all passengers in the system.

This study aims to explain the relationship between employer branding, social media, online reviews and intention to apply for a job vacancy (IAJV), which organizations should ponder upon while designing branding campaigns.

The sample belongs to 385 final-year management graduates and postgraduates enrolled in central universities in the state of Uttar Pradesh, India. The dual mediation model is tested by regression and PROCESS macro.

Out of five employer branding dimensions, three (corporate social responsibility, healthy work atmosphere and training and development) were found to be significant predictors of IAJV. On the other hand, the dimensions of compensation and benefits and work-life balance did not influence candidates’ intention to apply for a job. The findings indicate that social recruiting could act as an effective tool for leveraging an organization’s image as an employer and could communicate unique brand values to the target market. Moreover, review whether positive, negative or neutral attributes could help job seekers affirm and reaffirm employer branding attributes before applying for a job.

Studies in social media and employer branding areas lag far behind in practice, and the present research attempts to fill this research gap. A further contribution of this research work will be to assess the role of reviews for a meaningful analysis of potential employees’ intentions to apply in an organization.

In the quest for better construction workforce management, this chapter explored the background of workforce management and related theories, models, and practices. Through a review, the chapter provided meaning to the concept of construction and workforce management. The chapter concluded that while the construction industry worldwide is important to the economic growth of the countries where it operates, the industry’s management of its workforce is challenged by several problems. These problems include the nature of the industry, skill shortage, unhealthy working environment, and poor image of the industry, among others. Also, while the construction industry is rich in diversity, this has been a major source of problems for workforce management. The chapter further revealed that to improve workforce management and attain better-performing construction organisations, careful recruitment, effective training, providing a safe working environment, putting policies to promote diversity, and ensuring innovativeness, among others, are essential.

The purpose of this research is to achieve multi-task autonomous driving by adjusting the network architecture of the model. Meanwhile, after achieving multi-task autonomous driving, the authors found that the trained neural network model performs poorly in untrained scenarios. Therefore, the authors proposed to improve the transfer efficiency of the model for new scenarios through transfer learning.

First, the authors achieved multi-task autonomous driving by training a model combining convolutional neural network and different structured long short-term memory (LSTM) layers. Second, the authors achieved fast transfer of neural network models in new scenarios by cross-model transfer learning. Finally, the authors combined data collection and data labeling to improve the efficiency of deep learning. Furthermore, the authors verified that the model has good robustness through light and shadow test.

This research achieved road tracking, real-time acceleration–deceleration, obstacle avoidance and left/right sign recognition. The model proposed by the authors (UniBiCLSTM) outperforms the existing models tested with model cars in terms of autonomous driving performance. Furthermore, the CMTL-UniBiCL-RL model trained by the authors through cross-model transfer learning improves the efficiency of model adaptation to new scenarios. Meanwhile, this research proposed an automatic data annotation method, which can save 1/4 of the time for deep learning.

This research provided novel solutions in the achievement of multi-task autonomous driving and neural network model scenario for transfer learning. The experiment was achieved on a single camera with an embedded chip and a scale model car, which is expected to simplify the hardware for autonomous driving.

The objective of this paper is to evaluate whether the data from consignors, logistics service providers (LSPs) and consignees contribute to the prediction of air transport shipment delays in a machine learning application.

The research database contained 2,244 air freight intercontinental shipments to 4 automotive production plants in Latin America. Different algorithm classes were tested in the knowledge discovery in databases (KDD) process: support vector machine (SVM), random forest (RF), artificial neural networks (ANN) and k-nearest neighbors (KNN).

Shipper, consignee and LSP data attribute selection achieved 86% accuracy through the RF algorithm in a cross-validation scenario after a combined class balancing procedure.

These findings expand the current literature on machine learning applied to air freight delay management, which has mostly focused on weather, airport structure, flight schedule, ground delay and congestion as explanatory attributes.

Purpose of This Chapter: This study explores the positive chain effects of Employee-Centered CSR (ECCSR) in harmonizing the current challenges of The Great Resignation from the perspective of employees’ well-being.

Design / Methodology / Approach: The quantitative approach was used to test the proposed research model by using a self-responded questionnaire. Purposive judgemental sampling was applied to qualify the respondents based on the criteria that they are gainfully employed now and during the pandemic. The responses gathered were analyzed using structural equation modelling (SEM).

Findings: The findings show that ECCSR significantly and positively influences employees’ well-being, specifically workplace well-being (β = 0.793), social well-being (β = 0.761), psychological well-being (β = 0.712), and subjective well-being (β = 0.611). The PLSpredict results reveal that the proposed research model possesses the predictive relevance of ECCSR in reflecting the reality of employees’ well-being.

Research Limitations: The data were collected in the post-pandemic phase to capture the employees’ state of mind. Hence, the findings may not represent the normal business cycle challenges.

Practical Implications: The empirical evidence suggests that depressing organizations to consider implementing ECCSR for employees’ well-being which in turn enables the organizations to navigate through turbulent times a little easier.

Originality: The novelty of this study is attributed to the positive and detailed findings of ECCSR in the context of employee well-being for organizational resilience.