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This paper aims to develop a Mini-Tribometer for in-situ observation of subsurface.

To observe the change of the microstructure during wear in real time, an in-situ observation mini-tribometer was developed according to the requirements of the basic frictional experiments and carried out the verification experiments.

The subsurface images and the tribological data obtained from the mini-tribometer clearly show that the graphite in the matrix moves to the surface and takes part in lubrication mainly in the form of extrusion and peeling off, and the migration of graphite in the copper-based composite to the frictional interface to act as lubricant and to result in the decrease of the friction coefficient. The experimental results of the developed tribometer are accurate, which can provide important references for further research on the wear mechanism of materials.

The developed in-situ observation mini-tribometer can be used to observe the dynamic wear mechanism of the frictional pairs, which is very important for optimization of material design and tribological performances.

In traditional Chinese medicine (TCM), the mechanism of disease (MD) constitutes an essential element of syndrome differentiation and treatment, elucidating the mechanisms underlying the occurrence, progression, alterations and outcomes of diseases. However, there is a dearth of research in the field of intelligent diagnosis concerning the analysis of MD.

In this paper, we propose a supervised Latent Dirichlet Allocation (LDA) topic model, termed MD-LDA, which elucidates the process of MDs identification. We leverage the label information inherent in the data as prior knowledge and incorporate it into the model’s training. Additionally, we devise two parallel parameter estimation algorithms for efficient training. Furthermore, we introduce a benchmark MD identification dataset, named TMD, for training MD-LDA. Finally, we validate the performance of MD-LDA through comprehensive experiments.

The results show that MD-LDA is effective and efficient. Moreover, MD-LDA outperforms the state-of-the-art topic models on perplexity, Kullback–Leibler (KL) and classification performance.

The proposed MD-LDA can be applied for the MD discovery and analysis of TCM clinical diagnosis, so as to improve the interpretability and reliability of intelligent diagnosis and treatment.

The replenishment of construction materials heavily relies on the functioning of heavy machinery, which often leads to confusion and negotiations among construction work groups regarding the allocation rights of these materials. When multiple groups require the same construction materials, they often struggle to determine whether the delivered materials are intended for their own use or if they have encroached upon supplies designated for others. Such uncertainties and negotiations frequently result in delays in construction progress and have the potential to escalate into conflicts. To minimize misunderstandings among work groups and mitigate the risk of severe safety consequences, it is crucial to understand the decision-making processes involved in the interaction between work groups.

This paper adopts a game theory approach to examine the interactions among work groups from a safety perspective. Quantum response equilibrium (QRE), as a specialized form of game with incomplete information, is assumed to govern the behavior of work groups in this study. By conducting a questionnaire survey, interactive scenarios were simulated. A resource overlap scenario for high-altitude construction is established, with the key factors being the importance of construction materials, the time required to supplement materials, whether managers are present and the climate within the groups. The model parameters were estimated using the expectation–maximization algorithm. Additionally, individual traits and safety awareness are surveyed in the questionnaire, further explaining the results of the game.

The findings indicate that the likelihood of conflicts between work groups under resource overlap can be quantified. The radical behavior of construction work groups exhibits a positive correlation with the importance of construction materials and the time required for material replenishment. Furthermore, the presence of a safety climate and the oversight of management personnel play a significant role in maintaining the composure of construction work groups. The expanded results of the questionnaire demonstrate that there is considerable room for improvement in workers' safety awareness, and management approaches can be further enhanced to prevent unsafe behaviors from occurring.

A novel game theory model was developed to evaluate the behavior of construction groups in situations of resource overlap. This model offers practical suggestions to improve safety performance and efficiency in construction projects.

This research is aimed to mainly be applicable to expediting engineering projects, uses the method of inverse optimization and the double-layer nested genetic algorithm combined with nonlinear programming algorithm, study how to schedule the number of labor in each process at the minimum cost to achieve an extremely short construction period goal.

The method of inverse optimization is mainly used in this study. In the first phase, establish a positive optimization model, according to the existing labor constraints, aiming at the shortest construction period. In the second phase, under the condition that the expected shortest construction period is known, on the basis of the positive optimization model, the inverse optimization method is used to establish the inverse optimization model aiming at the minimum change of the number of workers, and finally the optimal labor allocation scheme that meets the conditions is obtained. Finally, use algorithm to solve and prove with a case.

The case study shows that this method can effectively achieve the extremely short duration goal of the engineering project at the minimum cost, and provide the basis for the decision-making of the engineering project.

The contribution of this paper to the existing knowledge is to carry out a preliminary study on the relatively blank field of the current engineering project with a very short construction period, and provide a path for the vast number of engineering projects with strict requirements on the construction period to achieve a very short construction period, and apply the inverse optimization method to the engineering field. Furthermore, a double-nested genetic algorithm and nonlinear programming algorithm are designed. It can effectively solve various optimization problems.

Hazard warning schemes provide efficient hazard recognition and promote project safety. Nevertheless, these schemes perform poorly because the warning information is calibrated for individual characters and is not prioritized for the entire system. This study proposes a hazard warning scheme that prioritizes hazard characters from the inspection process based on the inspectors' experience.

First, hazard descriptions were decomposed into their characters, forming a double-layer network. Second, warning schemes based on cascading effects were proposed. Third, character-based warning schemes were simulated for various experiences.

The results show that when a specific hazard is detected, the degree centrality is the most effective parameter for prioritization, and hazard characters should be prioritized based on betweenness centrality for experienced inspectors, whereas degree centrality is preferred for novice inspectors.

The warning scheme theoretically supplements the information-processing theory in construction hazard warnings and provides a practical warning scheme with priority for the development of automated hazard navigation systems.