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Drawing on conservation of resources theory, this study aims to examine the relationship between temporal leadership (TL) and employees’ thriving at work (TW) by focusing on the positive mediating role of perception of work-goal progress (PWP), the negative mediating roles of job-based psychological ownership (JPO) and the moderating role of synchrony preference (SP).

We employed a dedicated data collection platform called Credamo for two waves of online questionnaires in China between March 2022 and April 2022. A total of 326 questionnaires were collected and analyzed to test the hypotheses.

(1) TL directly and positively affects TW. (2) TL indirectly and positively affects TW via PWP. (3) TL indirectly and negatively affects TW via JPO. (4) SP positively moderates the positive mediating effect of PWP on the relationship between TL and TW. (5) SP negatively moderates the negative mediating effect of JPO on the relationship between TL and TW.

Supervisors in organizations ought to discreetly practice TL and try to maximize the positive role of PWP and minimize the negative role of JPO.

The findings simultaneously discuss the effects of TL on TW from dark and bright perspectives. The influence of interaction between contextual and individual features on TW is also specified.

Because of the various advantages of reinforcement learning (RL) mentioned above, this study uses RL to train unmanned aerial vehicles to perform two tasks: target search and cooperative obstacle avoidance.

This study draws inspiration from the recurrent state-space model and recurrent models (RPM) to propose a simpler yet highly effective model called the unmanned aerial vehicles prediction model (UAVPM). The main objective is to assist in training the UAV representation model with a recurrent neural network, using the soft actor-critic algorithm.

This study proposes a generalized actor-critic framework consisting of three modules: representation, policy and value. This architecture serves as the foundation for training UAVPM. This study proposes the UAVPM, which is designed to aid in training the recurrent representation using the transition model, reward recovery model and observation recovery model. Unlike traditional approaches reliant solely on reward signals, RPM incorporates temporal information. In addition, it allows the inclusion of extra knowledge or information from virtual training environments. This study designs UAV target search and UAV cooperative obstacle avoidance tasks. The algorithm outperforms baselines in these two environments.

It is important to note that UAVPM does not play a role in the inference phase. This means that the representation model and policy remain independent of UAVPM. Consequently, this study can introduce additional “cheating” information from virtual training environments to guide the UAV representation without concerns about its real-world existence. By leveraging historical information more effectively, this study enhances UAVs’ decision-making abilities, thus improving the performance of both tasks at hand.

The purpose of this paper is to exploit the multiple-Toeplitz matrices reconstruction method combined with quadratic spatial smoothing processing to improve the direction-of-arrival (DOA) estimation performance of coherent signals at low signal-to-noise ratio (SNRs).

An improved multiple-Toeplitz matrices reconstruction method is proposed via quadratic spatial smoothing processing. Our proposed method takes advantage of the available information contained in the auto-covariance matrices of individual Toeplitz matrices and the cross-covariance matrices of different Toeplitz matrices, which results in a higher noise suppression ability.

Theoretical analysis and simulation results show that, compared with the existing Toeplitz matrix processing methods, the proposed method improves the DOA estimation performance in cases with a low SNR. Especially for the cases with a low SNR and small snapshot number as well as with closely spaced sources, the proposed method can achieve much better performance on estimation accuracy and resolution probability.

The study investigates the possibility of reusing pre-existing designs for the DOA estimation of the coherent signals. The proposed technique enables achieve good estimation performance at low SNRs.

The paper includes implications for the DOA problem at low SNRs in communication systems.

The proposed method proved to be useful for the DOA estimation at low SNR.

Despite the growing body of empirical research on leader anger expressions, the issue of how and when leader anger expressions shape newcomers’ proactive career behavior and work alienation in the construction industry has been largely overlooked. Building upon social information processing theory, this research identifies newcomers’ organization-based self-esteem as a mediator, and suggests that newcomers’ performance goal orientation could moderate the relationship.

A questionnaire study was conducted on the construction industry in China, and the PROCESS program developed by Hayes was used to test the hypothetical model with 215 valid cases.

The results suggest that leader anger expressions are negatively associated with newcomers’ organization-based self-esteem, and organization-based self-esteem mediated the link between leader anger expressions and newcomers’ proactive career behavior and work alienation. Furthermore, the newcomers’ performance goal orientation moderates the negative impact of leader anger expressions on newcomers’ organization-based self-esteem.

Due to the cross-sectional nature of this study, causal implications are difficult to draw. Moreover, all data we received was based on participant self-reports, which may raise concerns about common method variance.

In this paper, we contribute to a deeper understanding of the mediating mechanisms and boundary conditions by which leader anger expressions influence newcomers’ proactive career behavior and work alienation from social information processing perspective, in addition to providing valuable insights for management of newcomers in the construction industry.

Technological innovation is crucial for businesses to achieve development and profitability through enhancing core capabilities and differentiating competitive advantages. The key to organisational survival is boosting innovation performance focused on technological innovation, as SMEs lack resources and competencies compared to large companies. Entrepreneurship is a topic of active research to overcome SMEs’ resource and size limits. This is because entrepreneurs’ capabilities are considered more important in small and medium-sized enterprises closely related to corporate success than in large enterprises that can receive organisational support. In addition, a company’s holding capacity is a direct driver of creating differentiated competitiveness because it can pursue product differentiation through high levels of market capabilities and technology capabilities. Therefore, this study attempts to demonstrate entrepreneurship and technological innovation for SMEs. Reviewing previous studies, the authors derive the organisational capabilities needed by the organisation for innovation and examine how these organisational capabilities (technological, market, and operational capabilities) relate to entrepreneurship and technological innovation.

Despite the increasing importance of boundary-spanning behaviors in construction projects, the research on how leader anger expressions impact employees’ boundary-spanning behaviors remains unclear. This study aims to investigate the impact of leader anger expressions on employees’ boundary-spanning behaviors in construction projects while exploring the mediating effect of work hope and the moderating effect of power distance orientation through the lens of social information processing theory.

The empirical data were collected from a questionnaire survey of 235 employees in construction projects, and the hypotheses were tested using the PROCESS program developed by Hayes.

This study demonstrated that leader anger expressions negatively affect employees’ work hope, and work hope mediates the relationship between leader anger expressions and employees’ boundary-spanning behaviors. Besides, the stronger the employees’ power distance orientation, the stronger the negative relationship between leader anger expressions and employees’ work hope, and the above mediating effect was also stronger.

The use of cross-sectional data was unable to entirely infer a causal relationship between leader anger expressions and employees’ boundary-spanning behaviors. Additionally, the reliance on self-reported data inevitably introduced some common method bias.

Applying social information processing theory, this paper enriches the discourse on factors influencing employees’ boundary-spanning behaviors in construction projects. It also offers valuable insights for leaders in construction projects to manage their teams effectively.

Purpose: Industry 5.0 is characterized by a revolution in the industrial field where humans collaborate with machines. This study aims to highlight the role of the concept of the “Digital Twin” (DT) within Industry 5.0, aiming to predict the effects of natural disaster scenarios in advance and to take preventive measures more effectively.

Need for the study: The innovations brought by Industry 5.0 demonstrate the possibility of creating DTs of cities to predict and minimize the effects of natural disasters. This is of great importance in terms of preparation for future natural disasters and risk management.

Methodology: This study was conducted by analyzing the fundamental principles of Industry 5.0 and the concept of DTs. Scientific literature and industry reports were examined to explore how DTs can be used in the field of risk management related to natural disasters.

Findings: The use of DTs has significant potential in simulating natural disaster scenarios in advance and predicting potential damages. For example, through DTs of cities, the effects of disaster scenarios such as earthquakes, tsunamis, and floods can be analyzed in advance, and necessary measures can be taken accordingly.

Practical implications: These findings offer important practical implications for decision-makers working in areas such as urban planning and infrastructure management. The use of DTs can assist in the development of preparation and risk management strategies for natural disasters, thereby minimizing the impact of disasters and ensuring the safety of individuals.

This paper aims to explore the influencing factors of public perceived efficacy of emergency infrastructure projects based on the triadic interactive determinism, and analyze the relationship among these factors.

Based on the triadic interactive determinism, we explored the factors influencing public perceived efficacy of emergency infrastructure project and empirically verified the relationship among these factors and perceived efficacy by using data drawn from a questionnaire survey of 491 residents near Leishenshan Hospital, Jiangxia District, Wuhan, China.

Prior experience, emotional response, personal expectation, public trust, context message and interactivity level, namely behavior, individual and environment, affect the perceived efficacy of public emergency infrastructure projects.

The results offer an insight into public perceived efficacy of emergency infrastructure project from the perspective of antecedents in a triadic reciprocal determinism, which provides a reference basis for the sustainable development of the emergency infrastructure projects. This study also suggests valuable practical implications to government departments to improve the quality of administrative decision-making effectively.

Although existing studies have found some influencing factors of public perceived efficacy in general infrastructure, there is still a lack of systematic carding and quantitative description of influencing factors of public perceived efficacy of emergency infrastructure projects. This study bridges this gap by exploring the determinants and their influencing relationship of public perceived efficacy especially for emergency infrastructure projects.

Because of the advantages of high deposition efficiency and low manufacturing cost compared with other additive technologies, robotic wire arc additive manufacturing (WAAM) technology has been widely applied for fabricating medium- to large-scale metallic components. The additive manufacturing (AM) method is a relatively complex process, which involves the workpiece modeling, conversion of the model file, slicing, path planning and so on. Then the structure is formed by the accumulated weld bead. However, the poor forming accuracy of WAAM usually leads to severe dimensional deviation between the as-built and the predesigned structures. This paper aims to propose a visual sensing technology and deep learning–assisted WAAM method for fabricating metallic structure, to simplify the complex WAAM process and improve the forming accuracy.

Instead of slicing of the workpiece modeling and generating all the welding torch paths in advance of the fabricating process, this method is carried out by adding the feature point regression branch into the Yolov5 algorithm, to detect the feature point from the images of the as-built structure. The coordinates of the feature points of each deposition layer can be calculated automatically. Then the welding torch trajectory for the next deposition layer is generated based on the position of feature point.

The mean average precision score of modified YOLOv5 detector is 99.5%. Two types of overhanging structures have been fabricated by the proposed method. The center contour error between the actual and theoretical is 0.56 and 0.27 mm in width direction, and 0.43 and 0.23 mm in height direction, respectively.

The fabrication of circular overhanging structures without using the complicate slicing strategy, turning table or other extra support verified the possibility of the robotic WAAM system with deep learning technology.