Walking and cycling for transportation provide immense benefits (e.g., health, environmental, social). However, pedestrians and bicyclists are the most vulnerable segment of the traveling public due to the lack of protective structure and difference in body mass compared with motorized vehicles. Numerous studies are dedicated to enhancing active transportation modes, but very few studies are devoted to the safety analysis of the transit stops, which serve as the important modal interface for pedestrians and bicyclists. This study bridges the gap by developing joint models based on the multivariate conditional autoregressive (MCAR) priors with distance-oriented neighboring weight matrix. For this purpose, transit-oriented design (TOD) related data in Los Angeles County were used for model development. Feature selection relying on both random forest (RF) and correlation analysis was employed, which leads to different covariates inputs to each of the two joint models, resulting in increased model flexibility. An integrated nested Laplace approximation (INLA) algorithm was adopted due to its fast, yet robust, analysis. For a comprehensive comparison of the predictive accuracy of models, different evaluation criteria were utilized. The results demonstrate that models with correlation effect perform much better than the models without a correlation of pedestrians and bicyclists. The joint models also aid in the identification of the significant covariates contributing to the safety of each of the two active transportation modes. The findings show that population density, employment density, and bus stop density positively influence bicyclist-involved crashes, suggesting that an increase in population, employment, or the number of bus stops leads to more active modes involved collisions. The findings of this study may prove helpful in the development and implementation of the safety management process to improve the roadway environment for the active modes in the long run.

Surface electromyography (sEMG) signals are the sum of action potentials emitted by many motor units; they contain the information of muscle contraction patterns and intensity, so they can be used as a simple and reliable source for grasping mode recognition. This paper introduces the InRes-ACNet (inception–attention–ACmix-ResNet50) model, a novel deep-learning approach based on ResNet50, incorporating multi-scale modules and self-attention mechanisms. The proposed model aims to improve gesture recognition performance by enhancing its ability to extract channel feature information within sparse sEMG signals. The InRes-ACNet model is evaluated on the NinaPro DB1 and NinaPro DB5 datasets; the recognition accuracy for these datasets can reach 87.94% and 87.04%, respectively, and recognition accuracy can reach 88.37% in the grasping mode prediction of an electromyography manipulator. The results show that the fusion of multi-scale modules and self-attention mechanisms endows a strong ability for the task of gesture recognition based on sparse sEMG signals.

The probabilistic fuzzy set (PFS) is designed for handling uncertainties with both fuzzy and stochastic nature, so the probabilistic fuzzy logic system (PFLS) has the ability to handle more complex uncertainties in process. In this paper, the general probabilistic fuzzy set is proposed, and the convergence analyses of its secondary probability density function (PDF) are conducted. It discloses the distribution regularity of membership degree in general PFS, which improves the information and interpretability of PFS. Then, according to convergence, a new method to tuning parameters for PFLS is proposed. This method avoids the parameters into local inefficiency, and also reduces the number of learning parameters in PFLS. Last, the new tuning method is applied to the electromyography (EMG) robots modeling problem. The comparison shows that the probabilistic fuzzy logic system based on general PFS (GPFLS) can achieve a simple modeling process, and also, it improves the learning speed compared to PFLS. The work presented will improve the potential application of probabilistic fuzzy logic system.

This study aims to reveal the spatiotemporal evolution patterns of the global frozen meat trade network from 2003 to 2023 and assess both its static and dynamic structural resilience. Based on UN commodity trade data, directed weighted networks for beef, pork, mutton, and poultry are constructed. By combining complex network theory and simulation models, the static resilience is analyzed from four dimensions: transmissibility, clustering, hierarchy, and assortativity. The impact of single node failures on network performance is simulated to assess dynamic resilience. The results indicate that the global frozen meat trade network shows a trend of polarization, with China emerging as the largest import hub and Brazil becoming the dominant exporter of beef and poultry, reflecting the coexistence of regionalization and globalization. The static resilience analysis reveals that the weighted network exhibits prominent hierarchy and increased assortativity. The poultry network has the highest transmissibility, while the beef network demonstrates the strongest resilience. Interruption simulation results show that the failure of core nodes causes more significant damage to the weighted network, but the network’s overall invulnerability to disruptions has increased over time. The conclusion emphasizes that the resilience of the frozen meat trade network is driven by both weights and topological structure. Reducing dependence on key nodes through diversified trade partners and optimizing regional cooperation is crucial for enhancing trade stability. The weighted directed network model and dynamic resilience evaluation framework proposed in this study provide new methods for global food trade risk management and supply chain optimization.