The factors affecting the performance of the tunnel wall drilling task and their priority.

Clarifying the relationship between the man-machine environment and its impact on the tunnel wall drilling task performance (TWDTP) is crucial for enhancing the task performance. Based on a questionnaire survey, indicators of the man-machine environment that affect the TWDTP were proposed in this study, and exploratory factor analysis and a structural equation model were employed to examine the potential factors influencing the task performance and their degrees of influence. By comparing the discrepancy between the perceived performance and importance, the satisfaction of potential factors was evaluated, and the priority order for optimizing these factors was determined by considering the degree of influence and dissatisfaction. The results of survey data analysis based on actual tunnel drilling operation scenarios indicated that tools had the greatest impact on the TWDTP, followed by the quality of the physical environment, while human factors had the least influence on the task performance. Convenient functional maintenance is the key to improving the TWDTP, along with enhancing the quality of the working environment. Once these main aspects are optimized, it is important to consider additional factors such as availability of spare tools, efficient personnel organization, man-tool matching, and safety and health assurance. This research approach provides significant guidance in understanding the relationships between the man-machine environmental factors affecting the performance of complex engineering tasks and identifying key influencing factors, thus providing essential insights for optimizing the TWDTP.

Transformable Superisostatic Crystals Self-Assembled from Segment Colloidal Rods.

Transformable mechanical structures can switch between distinct mechanical states. Whether this kind of structure can be self-assembled from simple building blocks at microscale is a question to be answered. In this work, we propose a self-assembly strategy for these structures based on a nematic monolayer of segmented colloidal rods with lateral cutting. By using Monte Carlo simulation, we find that rods with different cutting degrees can self-assemble into different crystals characterized by bond coordination z that varies from 3 to 6. Among these, we identify a transformable superisostatic structure with pgg symmetry and redundant bonds (z = 5). We show that this structure can support either soft bulk modes or soft edge modes depending on its Poisson's ratio, which can be tuned from positive to negative through a uniform soft deformation. We also prove that the bulk soft modes are associated with states of self-stress along the direction of zero strain during uniform soft deformation. The self-assembled transformable structures may act as mechanical metamaterials with potential applications in micromechanical engineering.

Duality, Hidden Symmetry, and Dynamic Isomerism in 2D Hinge Structures.

Recently, a new type of duality was reported in some deformable mechanical networks that exhibit Kramers-like degeneracy in phononic spectrum at the self-dual point. In this work, we clarify the origin of this duality and propose a design principle of 2D self-dual structures with arbitrary complexity. We find that this duality originates from the partial central inversion (PCI) symmetry of the hinge, which belongs to a more general end-fixed scaling transformation. This symmetry gives the structure an extra degree of freedom without modifying its dynamics. This results in dynamic isomers, i.e., dissimilar 2D mechanical structures, either periodic or aperiodic, having identical dynamic modes, based on which we demonstrate a new type of wave guide without reflection or loss. Moreover, the PCI symmetry allows us to design various 2D periodic isostatic networks with hinge duality. At last, by further studying a 2D nonmechanical magnonic system, we show that the duality and the associated hidden symmetry should exist in a broad range of Hamiltonian systems.