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This study aims to obtain methods to identify and find the place of damage, which is one of the topics that has always been discussed in structural engineering. The cost of repairing and rehabilitating massive bridges and buildings is very high, highlighting the need to monitor the structures continuously. One way to track the structure's health is to check the cracks in the concrete. Meanwhile, the current methods of concrete crack detection have complex and heavy calculations.

This paper presents a new lightweight architecture based on deep learning for crack classification in concrete structures. The proposed architecture was identified and classified in less time and with higher accuracy than other traditional and valid architectures in crack detection. This paper used a standard dataset to detect two-class and multi-class cracks.

Results show that two images were recognized with 99.53% accuracy based on the proposed method, and multi-class images were classified with 91% accuracy. The low execution time of the proposed architecture compared to other valid architectures in deep learning on the same hardware platform. The use of Adam's optimizer in this research had better performance than other optimizers.

This paper presents a framework based on a lightweight convolutional neural network for nondestructive monitoring of structural health to optimize the calculation costs and reduce execution time in processing.

This paper aims to investigate the impact of gender diversity in the composition of engagement auditors on the disclosure of key audit matters (KAMs) in a dual-signature environment.

The authors used the unique institutional setup of Taiwan, where the law requires that audit reports be signed by two audit partners. The authors examined the effect of gender diversity composition among engagement auditors on KAM disclosure, considering behavioral differences between female and male auditors.

The empirical results indicate that gender diversity composition in the dual-signature environment is associated with the number of disclosed KAM items (KAMIT) and the length of the explanations for each KAMIT. Furthermore, the authors found that gender diversity composition, particularly when led by female audit partners, has a more pronounced impact on the explanation of each KAMIT rather than on the disclosure of KAMIT. The authors also noted that the moderating effect of audit firm specialization does not influence the gender diversity composition of audit partners in disclosing KAMs.

This study’s empirical findings demonstrate that the interaction between different gender compositions in a dual-signature environment influences KAM disclosure.

Despite a growing body of research focusing on the dark side of sharing economy development, arguments are fragmented and incomplete. This study aims to address the gap by integrating existing viewpoints based on a provider’s perspective.

This study conducted a bibliometric analysis using text mining and clustering algorithm techniques to measure the scope of scientific output on this topic and identify the main research themes.

Through the bibliometric analysis, this study developed an integrative framework based on the platform providers’ internal management issues and external conflicts with consumers, society, government regulations and traditional business. It also identified significant gaps within each research theme and proposed a future research agenda.

Sharing economy development has not yet been fully understood and regulated, leading to unprecedented challenges to existing business systems. The study addresses knowledge gaps and advances the understanding of the dark side of the sharing economy based on the provider’s internal management and interplay with external forces. It offers a roadmap for future research to advance understanding of the “hidden” dark side of the sharing economy.

Limited by the types of sensors, the state information available for musculoskeletal robots with highly redundant, nonlinear muscles is often incomplete, which makes the control face a bottleneck problem. The aim of this paper is to design a method to improve the motion performance of musculoskeletal robots in partially observable scenarios, and to leverage the ontology knowledge to enhance the algorithm’s adaptability to musculoskeletal robots that have undergone changes.

A memory and attention-based reinforcement learning method is proposed for musculoskeletal robots with prior knowledge of muscle synergies. First, to deal with partially observed states available to musculoskeletal robots, a memory and attention-based network architecture is proposed for inferring more sufficient and intrinsic states. Second, inspired by muscle synergy hypothesis in neuroscience, prior knowledge of a musculoskeletal robot’s muscle synergies is embedded in network structure and reward shaping.

Based on systematic validation, it is found that the proposed method demonstrates superiority over the traditional twin delayed deep deterministic policy gradients (TD3) algorithm. A musculoskeletal robot with highly redundant, nonlinear muscles is adopted to implement goal-directed tasks. In the case of 21-dimensional states, the learning efficiency and accuracy are significantly improved compared with the traditional TD3 algorithm; in the case of 13-dimensional states without velocities and information from the end effector, the traditional TD3 is unable to complete the reaching tasks, while the proposed method breaks through this bottleneck problem.

In this paper, a novel memory and attention-based reinforcement learning method with prior knowledge of muscle synergies is proposed for musculoskeletal robots to deal with partially observable scenarios. Compared with the existing methods, the proposed method effectively improves the performance. Furthermore, this paper promotes the fusion of neuroscience and robotics.

Based on social influence theory, this paper aims to explore the influence of academic entrepreneurs on team innovation activities. The innovation behavior of academic team members is the key behavior in academic entrepreneurial activities. As a special entrepreneurial group, academic entrepreneurs' political skills play an important role in stimulating team innovative behaviors.

This paper adopts a multi-level study design and takes as samples the paired data of 91 academic entrepreneurial teams (n = 475). Based on team cognition, it constructs a model of the influence mechanism of academic entrepreneurs' political skills on team innovation behavior and explores the mechanism of transactive memory system in this influence effect. The authors use HLM and PROCESS macro to test our multilevel model.

The results show that academic entrepreneurs' political skills positively impact team innovation behavior, and a transactive memory system plays a mediating role between them. Team psychological safety significantly enhances the positive relationship of both academic entrepreneurs' political skills and a transactive memory system with team innovation behavior. Moreover, with enhanced perceptions of team psychological safety, academic entrepreneurs' political skills are more likely to improve team innovation behavior through the transactive memory system.

The study explores the influence of transactive memory system on the relationship between academic entrepreneurs' political skills and team innovation behavior, with the team cognitive perspective derived from social influence theory. This provides authors with new insights on the complex dynamics at place in the team innovation process and offers implications for how we can fruitfully manage this process.

Modern subway transportation systems need to satisfy increasing safety demands to rapidly evacuate passengers under hazardous emergency circumstances, such as fires, accidents or terrorist attacks, to reduce passenger injuries or life losses. The emergency evacuation capacity (EEC) of a subway station needs to be revised timely, in case passenger demand increases or the evacuation route changes in the future. However, traditional ways of estimating EEC, e.g. fire drills are time- and resource-consuming and are difficult to revise from time to time. The purpose of this study is to establish an intuitive modelling approach to increase the EEC of subway stations in a stepwised manner.

This study develops an approach to combine agent-based evacuation modelling and building information modelling (BIM) technology to estimate the total evacuation time of a subway station.

Evacuation time can be saved (33% in the studied case) from iterative improvements including stopping escalators running against the evacuation flow and modifying the geometry around escalator exits. Such iterative improvements rely on integrating agent-based modelling and BIM.

The agent-based model can provide a more realistic simulation of intelligent individual movements under emergency circumstances and provides precise feedback on locations of evacuation bottlenecks. This study also examined the effectiveness of two rounds of stepwise improvements in terms of operation or design to increase the EEC of the station.

The purpose of this paper is to present the influence of inter-turn short circuit faults (ITSF) on electromagnetic vibration in high-voltage line-starting permanent magnet synchronous motor (HVLSPMSMS).

In this paper, the ampere–conductor wave model of HVLSPMSM after ITSF is established. Second, a mathematical model of the magnetic field after ITSF is established, and the influence law of the ITSF on the air-gap magnetic field is analyzed. Further, the mathematical expression of the electromagnetic force density is established based on the Maxwell tensor method. The impact of HVLSPMSM torque ripple frequency, radial electromagnetic force spatial–temporal distribution and rotor unbalanced magnetic tension force by ITSF is revealed. Finally, the electromagnetic–mechanical coupling model of HVLSPMSM is established, and the vibration spectra of the motor with different degrees of ITSF are solved by numerical calculation.

In this study, it is found that the 2np order flux density harmonics and (2 N + 1) p order electromagnetic forces are not generated when ITSF occurs in HVLSPMSM.

By analyzing the multi-harmonics of HVLSPMSM after ITSF, this paper provides a reliable method for troubleshooting from the perspective of vibration and torque fluctuation and rotor unbalanced electromagnetic force.

People usually try to avoid uncertainty. Recently, however, uncertainty has become an emerging marketing tool in the hedonic product industry. In the case of blind box consumption, for example, the consumers become addicted to the uncertainty created by businesses, leading to repeat purchases and even indulgences. Previous research has, yet, to focus on the impact of uncertainty on indulgence and the role of emotions.

This paper constructs and validates a chain mediation model of uncertainty triggering indulgent consumption based on the information gap theory, positive emotion theory and uncertainty resolution theory and examines the difference between resolved and unresolved uncertainty. This study also explores differences in the impact of whether uncertainty is resolved on emotions. The uncertainty-resolved group elicited a more positive emotional response than the uncertainty-unresolved group, leading to a more indulgent consumption.

The results of three studies show that uncertainty influences indulgent consumption through curiosity and positive emotion, and that curiosity and positive emotion play separate and chain mediating roles between uncertainty and indulgent consumption, respectively. We validate our central hypothesis with questionnaires among blind box consumer groups, examining the moderating role of perceived luck and risk preferences.

The findings shed new light on firms' use of uncertainty to promote consumer purchases.

Social responsibility (SR) has become critical in facilitating the sustainability of new infrastructure construction (NIC) and is also a nonnegligible aspect in its management. Although studies attempting to explore this issue from various and disparate perspectives have become increasingly popular, no consensus has yet been reached regarding what SR factors affect NIC management. This paper aims to establish an inventory of SR factors for NIC and reveal a comprehensive framework for SR of NIC (NIC-SR) management through an in-depth analysis of the relationships among factors.

This article proposes a mixed-review method that combines the preferred reporting items for systematic reviews and meta-analyses and content analysis methods as a solution.

From 62 chosen publications on NIC-SR published in peer-reviewed journals between 2010 and 2022, a total of 44 SR factors were found. These 44 SR factors were divided into 4 interconnected categories: political, ethics-environmental, legal and economic. Based on the interactions among SR factors and incorporating the impact of the four categories of SR factors on NIC management, an integrated framework from micro to macro was developed.

This paper educates researchers and practitioners about the SR factors that must be considered to improve the sustainability of NIC management and provides practical implications for architectural, engineering and construction (AEC) practices. Furthermore, it serves as an impetus for governments to improve their programs and policies and fulfill social responsibilities.

In order to analyze the level of independent controllability and its evolution of high-end equipment manufacturing industry from Jiangsu Province, this article introduces the dual-excitation control line method to construct a comprehensive evaluation model for independent controllability.

Through the collection of information of high-end equipment manufacturing industry's independent and controllable capabilities on different indicators, the three aspects of advancement, autonomy and controllability, an empirical evaluation of 10 enterprises in the high-end equipment cluster in Jiangsu Province was conducted in terms of advancement, autonomy and controllability.

It effectively reveals the area and evolution characteristics of the “reward” and “punishment” of different indicators of each representative enterprise and reflects the development status and different characteristics of each representative enterprise on the three indicators. The research results provide decision-making guidance for enterprises in the management and control of advanced manufacturing systems with independent and controllable capabilities.

Existing research focuses on the evaluation of enterprises' independent controllability only on a single angle or index. This paper maps the dynamic evaluation problem of multiple time-point data to the evaluation problem of single time-point multi-index data and investigates the fluctuation of the performance of the same enterprise under different indexes, so as to comprehensively evaluate the independent controllable level of high-end equipment manufacturing industry and analyze the reasons. Further, this paper first establishes an evaluation index system of independent controllable level of high-end equipment manufacturing industry and quantitatively measures the advanced, independent, controllable and other aspects of typical enterprises in this industry by constructing a double incentive control line evaluation model.