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This paper aims to propose a novel wheel-based multiaxis force sensor designed to detect the interaction forces and moments between the planetary rover’s wheel and the terrain, thereby assisting the rover in environmental perception.

The authors’ design approach encompasses the mechanical structure design, decoupling methods and component integration techniques, effectively incorporating multiaxis sensors into the forward-sensing wheel. This enables high-precision and high-reliability detection of wheel–terrain interaction forces and torques.

The designed wheel-based multiaxis force sensor exhibits a nonlinearity error of 0.45%, a hysteresis error of 0.56% and a repeatability error of 0.49%, meeting the requirements for practical applications. Furthermore, the effectiveness and stability of the designed wheel-based multidimensional force sensor have been validated through hardware-in-the-loop experiments and full-vehicle model testing.

Unlike previous methods that directly integrate multiaxis sensors into the forward-sensing wheel, the authors have designed the force sensing wheel with consideration of its limited design space and the need for high measurement accuracy. The effectiveness of the designed wheel-based multidimensional force sensor was ultimately validated through static calibration, hardware-in-the-loop experiments and full-vehicle model experiments.

The COVID-19 pandemic and the corresponding control measures have harmed the mental health of professionals working in the construction industry. Existing research has also indicated that demographic characteristics are leading variables causing differences in individual’s perceptions on mental health and psychosocial hazardous factors. Combining these, this research aims to compare the differences and similarities of the perceived mental health outcomes and psychosocial hazards among construction professionals with different demographic characteristics during the pandemic.

Using a questionnaire survey, data were collected from 531 construction professionals working in Chinese construction companies, which were analyzed quantitatively using mean score comparative analysis, Mann–Whitney U test and Kruskal–Wallis H test, as well as Spearman’s correlation analysis.

The results indicate that construction professionals with different ages, years of working experience and positions are exposed to different psychosocial hazards, resulting in different mental health conditions during the pandemic. Age and years of working experience are also strong predictors of the level of depression and anxiety experienced by construction professionals; that is, mental ill health tends to decrease with the increase of age and experience. Male department/unit heads, working in a company office environment for a private company and aged 31–40 years old with 11–20 years of working experience, tend to have the best mental health condition. In contrast, psychosocial hazards are more likely to produce the most serious impact on male site-based construction professionals working for a state-owned company, either with less than one year of working experience or in a senior management position.

Despite the significant contribution of the construction industry to the global economy, the differences and similarities of the mental health outcomes and psychosocial hazards among construction professionals with different demographic characteristics during the pandemic remain unknown. This research, therefore, reveals the mental health outcomes and psychosocial hazard impacts among different types of construction professionals during the pandemic. Specifically, this research unveils the important personal characteristics which are closely associated with poor mental health and the stronger impacts of psychosocial hazards on the mental health of construction professionals during the pandemic. The results are valuable for governments and construction companies to formulate targeted mental health intervention strategies during future public health emergencies.

Prefabricated building (PB) has high technical requirements and is susceptible to safety accidents, and its construction occupational health and safety (OHS) problems should not be ignored. To promote the better development of PB, this study aims to measure their construction safety management level and propose corresponding countermeasures.

By systematically combing the relevant literature, this study extracts the influencing factors that appear frequently in several studies and categorizes them according to six dimensions: people, materials and components, technology, mechanical equipment, environment and system. Combining expert opinions, the measurement index system, including 6 primary indexes and 24 secondary indexes, is constructed. The structural entropy weight (SEW) method is applied to calculate the index weights. The cloud matter element (CME) model based on the weights is constructed to determine the level of construction occupational health and safety management (COHSM). A project case of a training building is used to verify it. The results obtained from the model are compared with those from other measurement models to verify the feasibility of the model in measuring the level of COHSM for PB.

The calculated weights show that technology is the most important for the COHSM of PB. The management level of the project in terms of people, materials and components, technology, machinery and equipment, environment and system is Level II good. The overall safety management level is also Level II, which is good. The model of this study is consistent with other model measurements. The methodology of this study yields reasonable and realistic results.

This study is the first to include occupational health dimensions in the research on the construction safety management of PB, which not only covers the key elements in traditional construction safety management but also considers the impact of the construction process, material use and technology of PB on safety management, making the measurement index system more scientific. Meanwhile, the introduction of the CME model based on the SEW method effectively solves the deficiencies of the traditional method in dealing with ambiguity and uncertainty and provides practitioners with more accurate and comprehensive measurement results. It helps practitioners formulate a more scientific management plan in combination with the actual situation and provides a guiding idea and practical path for the COHSM of similar projects.

Over the past decade, artificial intelligence (AI) technologies have rapidly advanced organizational management, with many organizations adopting AI-based algorithms to enhance employee management efficiency. However, there remains a lack of sufficient empirical research on the specific impacts of these algorithmic management practices on employee behavior, particularly the potential negative effects. To address this gap, this study constructs a model based on the psychological ownership theory, aiming to investigate how algorithmic management affects employees’ knowledge hiding.

This study validates the model through a situational experiment and a multi-wave field study involving full-time employees in organizations implementing algorithmic management. Various analytical methods, including analysis of variance, regression analysis and path analysis, were used to systematically test the hypotheses.

The study reveals that algorithmic management exerts a positive indirect influence on knowledge hiding through the psychological ownership of personal knowledge. This effect is particularly pronounced when employees have lower organizational identification, highlighting the critical role of organizational culture in the effectiveness of technological applications.

This study is among the first empirical investigations to explore the relationship between algorithmic management and employee knowledge hiding from an individual perception perspective. By applying psychological ownership theory, it not only addresses the current theoretical gap regarding the negative effects of algorithmic management but also provides new theoretical and empirical support for the governance and prevention of knowledge hiding within organizations in the context of AI algorithm application. The study highlights the importance of considering employee psychology (i.e. psychological ownership of personal knowledge) and organizational culture (i.e. organizational identification) under algorithmic management. This understanding aids organizations in better managing knowledge risks while maximizing technological advantages and effectively designing organizational change strategies.

A 3D finite element (FE) model based on the double ellipsoidal heat source was developed to investigate the evolution of temperature and stress fields during the multilayer and multi-pass wire and arc additive manufacturing (WAAM) process. This paper aims to investigate the evolution of temperature and stress fields during the multilayer and multi-pass wire and arc additive manufacturing (WAAM) process by developing a 3D finite element (FE) model based on the double ellipsoidal heat source.

Experimental thermal cycle curves and residual stresses were obtained by thermocouples and X-ray diffraction, respectively. The validity of the model was verified by the corresponding experimental results.

The deposition process of the upper pass led to the partial remelting of the lower deposited pass. The thermal process of the current-deposited pass alleviated the stress concentration in the previous-formed passes. A more uniform temperature distribution could be obtained by using the reciprocating deposition path. Compared to the reciprocating deposition path, the peak values of the transverse and longitudinal tensile residual stresses of the deposited sample under the unidirectional deposition path were reduced by 15 MPa and increased by 13 MPa, respectively. The heat conduction in the deposited passes could be improved by extending the inter-pass cooling time appropriately. With an increase in the inter-pass cooling time, the longitudinal residual stress in the middle region of sample along longitudinal and transverse directions showed increase and decrease–increase trends, respectively, while the transverse residual stress exhibited decrease trend.

This study enhances the understanding of temperature and stress fields evolution during the multilayer and multi-pass cold metal transfer-WAAM processes of magnesium alloy and provides the reference for parameter optimization.

Blockchain technology (BCT), since its emergence touted to be disruptive, is gaining momentum, especially in the agri-food system owing to its multiple benefits.

The authors attempted to conduct a systematic bibliometric visualization analysis of the BCT in the agri-food system. The analysis investigated the list of countries and institutions that conducted research on BCT in agriculture, growth trend analysis in research publications, bibliographic coupling of journals using the VOSviewer tool, and the countries and institutions researching BCT.

The authors discovered that China, the USA and India were the highly active countries in BCT research and publication. However, India has only limited research collaboration with other countries as compared to China and the USA. The keyword analysis indicates the role of BCT in order to maintain the transparency of the supply chain by means of protecting the privacy of the personal data of the stakeholders.

More research related to the implementation of BCT in livestock, fishery and agro-forestry sector is recommended.

The case examined is of particular interest as it is concerned with efficient supply chain management.

This study adds value and evidence to the scope and benefits of BCT by providing a comprehensive literature review, with a special focus on the opportunities and challenges concerned with implementation of BCT in the Indian agri-food system.

1. Blockchain technology (BCT) – a promising tool to resolve issues in agriculture supply chain.

2. BCT ensures transparency and protection of information along the supply chain transactions.

3. China, the USA and India are the highly active countries in BCT research and publication.

4. Multiple potential benefits to stakeholders are attributed to the BCT in the agri-food system.

5. The key challenge is to bridge the digital gap between developed and developing nations.

6. Future research on BCT should aim at easing and undistorted competition among stakeholders.

Blockchain technology (BCT) – a promising tool to resolve issues in agriculture supply chain.

BCT ensures transparency and protection of information along the supply chain transactions.

China, the USA and India are the highly active countries in BCT research and publication.

Multiple potential benefits to stakeholders are attributed to the BCT in the agri-food system.

The key challenge is to bridge the digital gap between developed and developing nations.

Future research on BCT should aim at easing and undistorted competition among stakeholders.

With the development of Web information systems, steel e-commerce platforms have accumulated a large number of quality objection texts. These texts reflect consumer dissatisfaction with the dimensions, appearance and performance of steel products, providing valuable insights for product improvement and consumer decision-making. Currently, mainstream solutions rely on pre-trained models, but their performance on domain-specific data sets and few-shot data sets is not satisfactory. This paper aims to address these challenges by proposing more effective methods for improving model performance on these specialized data sets.

This paper presents a method on the basis of in-domain pre-training, bidirectional encoder representation from Transformers (BERT) and prompt learning. Specifically, a domain-specific unsupervised data set is introduced into the BERT model for in-domain pre-training, enabling the model to better understand specific language patterns in the steel e-commerce industry, enhancing the model’s generalization capability; the incorporation of prompt learning into the BERT model enhances attention to sentence context, improving classification performance on few-shot data sets.

Through experimental evaluation, this method demonstrates superior performance on the quality objection data set, achieving a Macro-F1 score of 93.32%. Additionally, ablation experiments further validate the significant advantages of in-domain pre-training and prompt learning in enhancing model performance.

This study clearly demonstrates the value of the new method in improving the classification of quality objection texts for steel products. The findings of this study offer practical insights for product improvement in the steel industry and provide new directions for future research on few-shot learning and domain-specific models, with potential applications in other fields.

This paper aims to investigate the crucial roles of textured surfaces on oil-impregnated polytetrafluoroethylene (PTFE) created by a facile tattoo strategy in improving tribological properties.

Pored PTFE (PPTFE) was prepared by mixing powder PTFE and citric acid and experienced a cold-press sintering molding process. Subsequently, textured surfaces were obtained with using a tattoo strategy. Surface-textured PPTFE was thus impregnated with polyethylene glycol 200, yielding oil-impregnated and pore-connected PPTFE.

This study found that oil-impregnated and surface-textured PPTFE exhibited excellent tribological performances with an 82% reduction in coefficient of friction and a 72.5% lowering in wear rate comparing to PPTFE.

This study shows an efficient strategy to improve the tribological property of PTFE using a tattoo-inspired surface texturing method.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2024-0378/

Early termination of public–private partnerships (PPPs) in China is caused by various risk factors, resulting in significant losses. This study aimed to clarify the key factors and identify the causal relationships among these factors.

Social network analysis (SNA) was used to analyze 37 risk factors that were summarized from 97 early terminated PPP cases and to identify the relationships among these key risk factors. Interpretive structural modeling (ISM) was conducted to explore the causal relationships. Data were collected from case documents, questionnaires and interviews.

A total of 17 key risk factors were identified and distributed in a hierarchical structure with six tiers. Among these key risk factors, the root causes affecting the early termination of PPP projects were government oversight in decision-making, local government transition, policy and law changes and force majeure. The direct cause was insufficient returns. Furthermore, local government and private sector defaults were essential mediating factors. Local government transition and the low willingness of the private sector were highlighted as potential key risks.

The cases and experts were all from China, and outcomes in other countries or cultures may differ from those of this study. Therefore, further studies are required.

This research provides knowledge regarding the key risk factors leading to the early termination of PPP projects and guidance on avoiding these factors and blocking the factors' transmission in the project lifecycle.

This study contributes to the knowledge of risk management by emphasizing the importance of local government transition, the low willingness of the private sector and project cooperation and operation, whose significance is ignored in the existing literature. The proposed ISM clarifies the role of risk factors in causing early termination and explains their transmission patterns.

The objective of this study is to examine how perceived augmentation of virtual makeup influences consumers’ perceived value (utilitarian and hedonic value).

This research conducts an empirical study, and investigates 474 respondents.

The results show that perceived augmentation positively influences utilitarian and hedonic value. The wow-effect mediates the impact of perceived augmentation on utilitarian value. Immersion mediates the impact of perceived augmentation on two types of perceived value. Perceived ease of use moderates the influence of perceived augmentation on utilitarian value. Recreational shopper does not moderate the effect of perceived augmentation on hedonic value.

The study provides practical implications for beauty e-retailers.

This study examines the effect of perceived augmentation, supplements the literature on virtual makeup and AR technology application and enriches the literature on consumer experience of using AR technology.