The sustainable development of infrastructure construction projects heavily depends on favorable cooperation of all parties and ethical code of conduct, while Un-ethical pro-organizational behavior (UPB) may undermine the mutual efforts and cause serious consequences. UPB has aroused wide interest of researchers, but what may trigger construction employees to engage in UPB at team-level has not been elucidated completely. With information asymmetry and huge uncertainty, the behaviors of employees in temporary project teams are marked by environmental and personal characters. The study discusses the influences of collective moral judgement focus on self (CMJS) and Machiavellianism on UPB. Through a moderated mediation analysis conducted on a set of survey data from Chinese construction projects, the empirical results of the two-level hierarchical linear model indicate that CMJS positively impacts UPB directly, and meanwhile Machiavellianism acts as a partial mediator in the relationship between CMJS and UPB. The findings also reveal that performance-avoidance goal orientation (PAGO) and motivation to learn (MTL) moderate and strengthen the relationship between Machiavellianism and UPB. The study offers practical suggestions for both project managers and policymakers of construction projects.

Heterogeneous domain adaptation (HDA) utilizes the knowledge of the source domain to model the target domain. Although the two domains are semantically related, the problem of feature and distribution differences in heterogeneous data still needs to be solved. Most of the existing HDA methods only consider the feature or distribution problem but do not consider the geometric semantic information similarity between the domain structures, which leads to a weakened adaptive performance. In order to solve the problem, a centroid connected structure matching network (CCSMN) approach is proposed, which firstly maps the heterogeneous data into a shared public feature subspace to solve the problem of feature differences. Secondly, it promotes the overlap of domain centers and nodes of the same category between domains to reduce the positional distribution differences in the internal structure of data. Then, the supervised information is utilized to generate target domain nodes, and the geometric structural and semantic information are utilized to construct a centroid-connected structure with a reasonable inter-class distance. During the training process, a progressive and integrated pseudo-labeling is utilized to select samples with high-confidence labels and improve the classification accuracy for the target domain. Extensive experiments are conducted in text-to-image and image-to-image HDA tasks, and the results show that the CCSMN outperforms several state-of-the-art baseline methods. Compared with state-of-the-art HDA methods, in the text-to-image transfer task, the efficiency has increased by 8.05%; and in the image-to-image transfer task, the efficiency has increased by about 2%, which suggests that the CCSMN benefits more from domain geometric semantic information similarity.

With the popularity of Internet of things technology and intelligent devices, the application prospect of accurate step counting has gained more and more attention. To solve the problems that the existing algorithms use threshold to filter noise, and the parameters cannot be updated in time, an intelligent optimization strategy based on deep reinforcement learning is proposed. In this study, the counting problem is transformed into a serialization decision optimization. This study integrates the noise recognition and the user feedback to update parameters. The end-to-end processing is direct, which alleviates the inaccuracy of step counting in the follow-up step counting module caused by the inaccuracy of noise filtering in the two-stage processing and makes the model parameters continuously updated. Finally, the experimental results show that the proposed model achieves superior performance to existing approaches.