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Activity sampling is a technique to monitor onsite labourers' time utilisation, which can provide helpful information for the management level to implement suitable labour productivity improvement strategies continuously. However, there needs to be a review paper that compiles research on activity sampling studies to give readers a thorough grasp of the research trend. Hence, this paper aims to investigate the activity sampling techniques applied in earlier research from the angles of activity categories formation, data collection methods and data analysis.

The method used in this paper is a systematic review guided by the PRISMA framework. The search was conducted in Scopus and Web of Science. The inclusion and exclusion criteria were applied, selecting 70 articles published between 2011 and 2022 for data extraction and analysis. The analysis method involved a qualitative synthesis of the findings from the selected articles.

Activity sampling is broadly divided into four stages: targeting trade, determining activity categories, data collection and data analysis. This paper divides the activity categories into three levels and classifies the data collection methods into manual observation, sensor-based activity sampling and computer vision-based activity sampling. The previous studies applied activity sampling for two construction management purposes: labour productivity monitoring and ergonomic safety monitoring. This paper also further discusses the scientific research gaps and future research directions.

This review paper contributes to the body of knowledge in construction management by thoroughly understanding current state-of-the-art activity sampling techniques and research gaps.

By introducing Stacking algorithm to solve the underfitting problem caused by insufficient data in traditional machine learning, this paper provides a new solution to the cold start problem of entrepreneurial borrowing risk control.

The authors introduce semi-supervised learning and integrated learning into the field of migration learning, and innovatively propose the Stacking model migration learning, which can independently train models on entrepreneurial borrowing credit data, and then use the migration strategy itself as the learning object, and use the Stacking algorithm to combine the prediction results of the source domain model and the target domain model.

The effectiveness of the two migration learning models is evaluated with real data from an entrepreneurial borrowing. The algorithmic performance of the Stacking-based model migration learning is further improved compared to the benchmark model without migration learning techniques, with the model area under curve value rising to 0.8. Comparing the two migration learning models reveals that the model-based migration learning approach performs better. The reason for this is that the sample-based migration learning approach only eliminates the noisy samples that are relatively less similar to the entrepreneurial borrowing data. However, the calculation of similarity and the weighing of similarity are subjective, and there is no unified judgment standard and operation method, so there is no guarantee that the retained traditional credit samples have the same sample distribution and feature structure as the entrepreneurial borrowing data.

From a practical standpoint, on the one hand, it provides a new solution to the cold start problem of entrepreneurial borrowing risk control. The small number of labeled high-quality samples cannot support the learning and deployment of big data risk control models, which is the cold start problem of the entrepreneurial borrowing risk control system. By extending the training sample set with auxiliary domain data through suitable migration learning methods, the prediction performance of the model can be improved to a certain extent and more generalized laws can be learned.

This paper introduces the thought method of migration learning to the entrepreneurial borrowing scenario, provides a new solution to the cold start problem of the entrepreneurial borrowing risk control system and verifies the feasibility and effectiveness of the migration learning method applied in the risk control field through empirical data.

Work and leisure, as important activity domains, play important roles in the lives of individuals. However, most previous studies focused on only the interference and negative effects of work on leisure, with little focus on the facilitation of work and the positive effects of work on leisure. In view of the shortcomings of previous studies, this study focuses on the facilitation effect of work on leisure and its impact on individual psychology. This study aims to explore the relationship between work–leisure facilitation (WLF) and turnover intention and the role of positive emotions and perceived supervisor support in this relationship.

In this study, the method of multipoint data collection was adopted to measure the subjects; 180 employees were sampled for 5 consecutive working days, and a multilevel structural equation model was established for analysis.

The results show that WLF is negatively related to turnover intention, and positive emotions play a mediating role in this relationship. Perceived supervisor support significantly positively moderates not only the relationship between WLF and positive emotions but also the indirect effect of WLF on turnover intention through positive emotions.

Based on affective events theory, this study explored the relationship between WLF and turnover intention and its mechanism by using the daily diary sampling method for the first time, to the best of the authors’ knowledge. The results not only deepen the understanding of affective events theory but also provide management suggestions for reducing employees’ turnover intentions.

The purpose of this paper is to develop optimal estimation procedures for the stress-strength reliability (SSR) parameter R = P(X > Y) of an inverse Pareto distribution (IPD).

We estimate the SSR parameter R = P(X > Y) of the IPD under the minimum risk and bounded risk point estimation problems, where X and Y are strength and stress variables, respectively. The total loss function considered is a combination of estimation error (squared error) and cost, utilizing which we minimize the associated risk in order to estimate the reliability parameter. As no fixed-sample technique can be used to solve the proposed point estimation problems, we propose some “cost and time efficient” adaptive sampling techniques (two-stage and purely sequential sampling methods) to tackle them.

We state important results based on the proposed sampling methodologies. These include estimations of the expected sample size, standard deviation (SD) and mean square error (MSE) of the terminal estimator of reliability parameters. The theoretical values of reliability parameters and the associated sample size and risk functions are well supported by exhaustive simulation analyses. The applicability of our suggested methodology is further corroborated by a real dataset based on insurance claims.

This study will be useful for scenarios where various logistical concerns are involved in the reliability analysis. The methodologies proposed in this study can reduce the number of sampling operations substantially and save time and cost to a great extent.

This study aims to explore and conceptualize metaverse adoption using a systematic literature review (SLR). It also aims to propose a conceptual model that identifies significant factors affecting metaverse adoption in the entertainment, education, tourism and health sectors.

A SLR was conducted using the “preferred reporting items for systematic reviews and meta-analyses” report protocol and the “theory, context, characteristics, methods” framework to include all relevant articles published up to March 2023, which were sourced from the Scopus and Web of Science databases.

The reviewed literature revealed that the countries with the highest publications in the field of metaverse were China and the USA. It was also found that the technology acceptance model was the most used theoretical framework. Survey-based research using purposive and convenience sampling techniques emerged as the predominant method for data collection, and partial least square-structural equation modeling was the most used analytical technique. The review also identified the top six journals and the variables that help to develop a proposed model.

This review presents a novel contribution to the literature on metaverse adoption by forming a conceptual model that incorporates the most used variables in the entertainment, education, tourism and health sectors. The possible directions for future research with identified research gaps were also discussed.

This paper aims to study the agent learning from expert demonstration data while incorporating reinforcement learning (RL), which enables the agent to break through the limitations of expert demonstration data and reduces the dimensionality of the agent’s exploration space to speed up the training convergence rate.

Firstly, the decay weight function is set in the objective function of the agent’s training to combine both types of methods, and both RL and imitation learning (IL) are considered to guide the agent's behavior when updating the policy. Second, this study designs a coupling utilization method between the demonstration trajectory and the training experience, so that samples from both aspects can be combined during the agent’s learning process, and the utilization rate of the data and the agent’s learning speed can be improved.

The method is superior to other algorithms in terms of convergence speed and decision stability, avoiding training from scratch for reward values, and breaking through the restrictions brought by demonstration data.

The agent can adapt to dynamic scenes through exploration and trial-and-error mechanisms based on the experience of demonstrating trajectories. The demonstration data set used in IL and the experience samples obtained in the process of RL are coupled and used to improve the data utilization efficiency and the generalization ability of the agent.

To improve the speed and accuracy of turbine blade film cooling design process, the most advanced deep learning models were introduced into this study to investigate the most suitable define for prediction work. This paper aims to create a generative surrogate model that can be applied on multi-objective optimization problems.

The latest backbone in the field of computer vision (Swin-Transformer, 2021) was introduced and improved as the surrogate function for prediction of the multi-physics field distribution (film cooling effectiveness, pressure, density and velocity). The basic samples were generated by Latin hypercube sampling method and the numerical method adopt for the calculation was validated experimentally at first. The training and testing samples were calculated at experimental conditions. At last, the surrogate model predicted results were verified by experiment in a linear cascade.

The results indicated that comparing with the Multi-Scale Pix2Pix Model, the Swin-Transformer U-Net model presented higher accuracy and computing speed on the prediction of contour results. The computation time for each step of the Swin-Transformer U-Net model is one-third of the original model, especially in the case of multi-physics field prediction. The correlation index reached more than 99.2% and the first-order error was lower than 0.3% for multi-physics field. The predictions of the data-driven surrogate model are consistent with the predictions of the computational fluid dynamics results, and both are very close to the experimental results. The application of the Swin-Transformer model on enlarging the different structure samples will reduce the cost of numerical calculations as well as experiments.

The number of U-Net layers and sample scales has a proper relationship according to equation (8). Too many layers of U-Net will lead to unnecessary nonlinear variation, whereas too few layers will lead to insufficient feature extraction. In the case of Swin-Transformer U-Net model, incorrect number of U-Net layer will reduce the prediction accuracy. The multi-scale Pix2Pix model owns higher accuracy in predicting a single physical field, but the calculation speed is too slow. The Swin-Transformer model is fast in prediction and training (nearly three times faster than multi Pix2Pix model), but the predicted contours have more noise. The neural network predicted results and numerical calculations are consistent with the experimental distribution.

This paper creates a generative surrogate model that can be applied on multi-objective optimization problems. The generative adversarial networks using new backbone is chosen to adjust the output from single contour to multi-physics fields, which will generate more results simultaneously than traditional surrogate models and reduce the time-cost. And it is more applicable to multi-objective spatial optimization algorithms. The Swin-Transformer surrogate model is three times faster to computation speed than the Multi Pix2Pix model. In the prediction results of multi-physics fields, the prediction results of the Swin-Transformer model are more accurate.

This paper aims to investigate the role-play of Protean Career Attitude (PCA) and Career Success (CS) in Affective Organizational Commitment (AOC).

A cross-sectional study on 376 employees from 55 hotels in Malaysia were conducted. The co-variance-based structural equation modeling was employed to analyze the data to test the direct and indirect relationships of PCA and CS with AOC.

The findings reveal that self-directed career attitude (SDCA) has a positive direct influence on AOC as well as indirect influence through the mediation of OCS and SCS. However, the value-driven career attitude (VDCA) neither influences AOC nor the OCS.

This is a first paper to body of knowledge in Asian context which identify mediating role of career success (SCA and OCS) to PCA and AOC. The findings of this research are the workplace learning in hospitality management. The authors argue that hotels should not assume spontaneously PCA with diminishing AOC, but rather hotels' attention is required to identify the most important preferences of these butterfly career attitudes such as OCS and SCS. Most importantly the research negates many negative labels of PCA and adds new perception to the contemporary career literature. Higher education institutions, government, and primary, secondary, and post-secondary education departments can play a significant role in developing PCA dispositions like SDCA and VDCA toward career success. Therefore, further study should examine PCA and their relevance to career outcome like job searching and employability of students in Malaysia. The paper is the first, to one's knowledge, to assess organizational commitment with specific measures of PCA. While the results are simple, they refute many stereotypes of the new career and, in that sense, add an important perspective to the career literature.

The purpose of this study is to investigate the influences of the characteristics of procurement strategies, in this instance labour-only, on project performance concerning health and safety (H&S), a project performance indicator.

Using non-probability purposeful and snowballing sampling methods, questionnaires were used to collect data from construction professionals in Nigeria. This was then analysed using descriptive (frequency and mean scores) and inferential statistics (Mann–Whitney-U and Kendall's Tau_b tests).

The findings indicate a statistically significant negative correlation between ‘the level of client involvement and ‘fatalities' and a positive one with ‘conducting of health and safety risk assessment' and ‘conducting employee surveys on health and safety attitude’. Poor hygiene is found to be the worst lagging indicator, while conducting of inspection is the most adopted leading indicator of project health and safety performance. It also emerged that there is no significant difference in the health and safety performance of projects procured through the procurement strategy in urban and rural areas.

The study provides valuable insight into the complexities in H&S management due to the high level of client involvement in labour-only procurement system (LoPS) projects and the level of diversity in their responsibilities therein. It creates a fundamental direction for developing a detailed framework or guidance notes for client involvement in the integration of H&S into LoPS projects.

This is the first study that examines the influence of the characteristics of procurement strategy on project health and safety performance. Evidence in the literature shows that project delivery outcomes significantly improve if procurement is strategically used, including when it is considered early in projects. However, integrating H&S into procurement strategies has received little attention.

The study investigates the mediating effect of HR flexibility in the relationship between capacity-enhancing HR practices and job performance in small and medium-sized enterprises (SMEs) operating in Egypt.

On the basis of the literature review, the results imply a quantitatively tested conceptual model. The model is empirically validated using the partial least squares method to structural equation modelling (PLS-SEM) with survey data from 270 SME owners and managers in Egypt. The sample was selected using a quota sampling approach for small and medium-sized businesses and a proportionate stratification sampling method for the industry and region.

Findings for the sample revealed that capacity-enhancing HR practices affected job performance positively and significantly. The findings also revealed a direct, positive and significant impact of capacity-enhancing HR practices on HR flexibility and HR flexibility on job performance. Functional flexibility was identified as a significant mediator of the capacity-enhancing HR practices-job performance link, whereas behavioural and skill flexibility were not significant mediators for such a relationship.

The study's cross-sectional design is an evident weakness. All variables were self-reported; this may raise issues regarding method bias. Other limitations include the generalisability of the study's findings outside the setting in which it was conducted. The accuracy of the field study results would have been enhanced if they had not been limited exclusively to the geographical confines of Egypt.

The paper proposes many implications emphasising the role of HR flexibility in enhancing the performance of SMEs. The study developed a mediation model to understand how SMEs boost the performance of human resources by focusing on flexibility dimensions. Accordingly, companies may strategically employ flexible practices and provide an environment that encourages skill and behavioural development.