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This article presents a new data-driven reliability analysis framework through combining the sample convolution and interaction network (SCINet) with Bayesian probability recursion to predict the time-variant reliability of bridge girders. The structural response predicted by SCINet and the state parameter (i.e., the variance of normal distribution) estimated by Bayesian dynamic linear model (BDLM) are used to form the limit state function to predict time-variant reliability. The results with a practical bridge show that the proposed method can predict future structural responses and time-variant reliability more accurately than BDLM, long short-term memory (LSTM), and long- and short-term time-series network (LSTNet) methods. [ABSTRACT FROM AUTHOR]

The high‐speed impact‐resistanct materials are of great significance while their development is hindered by the intrinsic tradeoff between mechanical strength and energy dissipation capability. Herein, the new chemical system of molecular granular material (MGM) is developed for the design of impact‐resistant materials from the supramolecular complexation of sub‐nm molecular clusters (MCs) and hyper‐branched polyelectrolytes. Their hierarchical aggregation provides the origin of the decoupling of mechanical strengths and structural relaxation dynamics. The MCs' intrinsic fast dynamics afford excellent high‐speed impact‐resistance, up to 5600 s−1 impact in a typical split‐Hopkinson pressure bar test while only tiny boundary cracks can be observed even under 7200 s−1 impact. The high loadings of MCs and their hierarchical aggregates provide high‐density sacrificial bonding for the effective dissipation of the impact energy, enabling the protection of fragile devices from the direct impact of over 200 m s−1 bullet. Moreover, the MGMs can be conveniently processed into protective coatings or films with promising recyclability due to the supramolecular interaction feature. The research not only reveals the unique relaxation dynamics and mechanical properties of MGMs in comparison with polymers and colloids, but also develops new chemical systems for the fabrication of high‐speed impact‐resistant materials. [ABSTRACT FROM AUTHOR]

Although the finite element head model (FEHM) has been widely utilized to analyze injury locations and patterns in traumatic brain injury, significant controversy persists regarding the selection of a mechanical injury variable and its corresponding threshold. This paper aims to determine an objective injury threshold for maximum principal strain (MPS) through a novel data-driven method, and to validate and apply it. We extract the peak responses from all elements across 100 head impact simulations to form a dataset, and then determine the objective injury threshold by analyzing the relationship between the combined injury degree and the threshold according to the stationary value principle. Using an occipital impact case from a clinical report as an example, we evaluate the accuracy of the injury prediction based on the new threshold. The results show that the injury area predicted by finite element analysis closely matches the main injury area observed in CT images, without the issue of over- or underestimating the injury due to an unreasonable threshold. Furthermore, by applying this threshold to the finite element analysis of designed occipital impacts, we observe, for the first time, supra-tentorium cerebelli injury, which is related to visual memory impairment. This discovery may indicate the biomechanical mechanism of visual memory impairment after occipital impacts reported in clinical cases. [ABSTRACT FROM AUTHOR]

Conductive hydrogels combine the properties of both hydrogels and conductors, making them soft, flexible, and biocompatible. These properties enable them to conform to irregular surfaces, stretch and bend without losing their electrical conductivity, and interface with biological systems. Conductive hydrogels can be utilized as conductive traces, electrodes, or as a matrix for flexible electronics. Exciting applications in sensors, tissue engineering, and human-machine interaction have been demonstrated worldwide. This review comprehensively covers the progress in this field, focusing on several main aspects: functional materials, performance improvement strategies, and wearable applications in human-related areas. Furthermore, the major approaches and challenges for improving their mechanical properties, conductivity, and long-term stability are systematically summarized. [ABSTRACT FROM AUTHOR]

Smart wearable electronic textiles integrate sensing, perception, and control modules, which enhance human adaptability to environmental stimuli and concurrently serve as extensions for limb capabilities. The flexible and programmable nature of soft actuators makes them an indispensable part of smart wearable electronic textiles. These textiles seamlessly combine various components, such as sensors and actuators, through a comprehensive integration of materials manufacturing, circuit control, and transmission design. Exciting applications in various fields such as healthcare, sports, the Internet of Things, and human‐machine interaction have been demonstrated globally. However, there is still a persistent challenge in enhancing the actuation capabilities of soft actuators while maintaining their wearability. A timely and comprehensive review of the progress of this field is provided. Several main aspects are covered: functional materials, stimulus mechanisms, performance improvement strategies, and wearable applications in human‐related areas. Furthermore, the major approaches and challenges for improving the performance of actuators are systematically summarized. [ABSTRACT FROM AUTHOR]

Rice blast caused by the pathogenic fungus Magnaporthe oryzae poses a significant threat to rice cultivation. The identification of robust resistance germplasm is crucial for breeding resistant varieties. In this study, we employed functional molecular markers for 10 rice blast resistance genes, namely Pi1, Pi2, Pi5, Pi9, Pia, Pid2, Pid3, Pigm, Pikh, and Pita, to assess blast resistance across 91 indica rice backbone varieties in South China. The results showed a spectrum of resistance levels ranging from highly resistant (HR) to highly susceptible (HS), with corresponding frequencies of 0, 19, 40, 27, 5, and 0, respectively. Yearly correlations in blast resistance genes among the 91 key indica rice progenitors revealed Pid2 (60.44%), Pia (50.55%), Pita (45.05%), Pi2 (32.97%), Pikh (4.4%), Pigm (2.2%), Pi9 (2.2%), and Pi1 (1.1%). Significant variations were observed in the distribution frequencies of these 10 resistance genes among these progenitors across different provinces. Furthermore, as the number of aggregated resistance genes increased, parental resistance levels correspondingly improved, though the efficacy of different gene combinations varied significantly. This study provides the initial steps toward strategically distributing varieties of resistant indica rice genotypes across South China. [ABSTRACT FROM AUTHOR]