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This paper investigates the finite-time synchronization for inertial neural networks with stochastic switching parameters based on dynamic event-triggered protocol. Due to the complexity of network environment, semi-Markovian process is introduced into the modeling of inertial neural networks, in which the transition rates vary with the operating time. The dynamic event-triggered protocol is developed to determine whether the signal is transmitted, in which Zeno phenomenon is eliminated under limited bandwidth resources. The objective is to construct an appropriate dynamic event-triggered control law such that the drive-response system maintains finite-time synchronization under generally uncertain transition rates. Based on the Lyapunov functional theory, finite-time synchronization criterion is proposed for the related inertial neural networks. Furthermore, a dynamic event-triggered controller is constructed in a finite-time interval. A numerical example and an image encryption process are given to show the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

The multidrug resistance‐associated protein (MRP) ABCC4 facilitates substrate transport across the cytoplasmic membrane, crucial for normal physiology and mediating multidrug resistance in tumor cells. Despite intensive studies on MRPs, ABCC4's transport mechanism remains incompletely understood. In this study, we unveiled an inward‐open conformation with an ATP bound to degenerate NBD1. Additionally, we captured the structure with both ATP and substrate co‐bound in the inward‐open state. Our findings uncover the asymmetric ATP binding in ABCC4 and provide insights into substrate binding and transport mechanisms. ATP binding to NBD1 is parallel to substrate binding to ABCC4, and is a prerequisite for ATP‐bound NBD2‐induced global conformational changes. Our findings shed new light on targeting ABCC4 in combination with anticancer therapy. [ABSTRACT FROM AUTHOR]

In view of the existing traffic signal detection algorithms, which require a large amount of computation and model, are difficult to deploy at the embedded end, and are difficult to detect long-distance traffic signals with a high miss rate, this paper designs a lightweight traffic signal detection algorithm for the embedded end, which aims at lightweight and real-time requirements. GhostNet network Ghost module and Ghost bottleneck layer structure are adopted to reduce the number of model parameters and improve the detection speed. To solve the feature similarity problem, the weighted bidirectional feature pyramid network is used to make the algorithm more sensitive to the target. The dense void space pyramid pool is used to optimize the extraction of global context information. Aiming at the problem of small target recognition, the information extraction of small target is enhanced through the improvement of multi-scale detection. Finally, the learning ability of the model is improved through knowledge distillation, and the detection performance is improved. Experimental results show that the recognition accuracy and recall rate of traffic lights by this detection algorithm reach 97.0% and 99% respectively, 2.7 and 3 percentage points higher than that of YOLOv5s. The model size is reduced to 8.06 MB, 58% of that of YOLOv5s, and the recognition rate is increased from 51 frames per second to 56 frames per second. Through testing in the embedded terminal, the improved algorithm can recognize the traffic signal in real time and accurately. [ABSTRACT FROM AUTHOR]

Background: Little is known about internal carotid artery (ICA) hemodynamics in patients with moyamoya angiopathy (MMA) and its role in cerebrovascular events. Purpose: To characterize ICA hemodynamics in MMA patients by 4D flow MRI and investigate its relationship with cerebrovascular events. Study Type: Prospective. Subjects: Seventy MMA patients (50 years old ± 9, 30 males). Field Strength/Sequence: Time‐resolved three‐directional velocity encoded fast field echo sequence (4D flow) MRI, T1‐weighted fast field echo sequence, T2 weighted turbo spin echo sequence, diffusion weighted echo planar imaging; T2‐weighted fluid‐attenuated inversion recovery turbo spin echo sequence, susceptibility weighted fast field echo sequence, and time‐of‐flight MR angiography fast field echo sequence at 3.0T. Assessment: ICA hemodynamics (maximum and average velocity [Vmax, Vavg], average blood flow [Flowavg], and wall shear stress) were analyzed based on 4D flow data. Cerebral infarction, defined as the occurrence of events, in 124 brain hemispheres was determined according to clinical symptoms and conventional brain MR imaging. Statistical Tests: The independent‐samples T test was used to evaluate differences in ICA hemodynamics between infarcted and non‐infarcted hemispheres. Binary logistic regression was performed to investigate the relationship between ICA hemodynamics and events. A P value < 0.05 was considered statistically significant. Results: Sixty‐one infarcted hemispheres (eight hemispheres with acute ischemic damage, 30 with chronic ischemic damage, and 23 with chronic hemorrhagic damage) had cerebrovascular events and 63 non‐infarcted hemispheres did not. The hemodynamic parameters in the infarcted hemispheres (Vmax: P < 0.001; Vavg: P = 0.003; and Flowavg: P = 0.004) were significantly lower than those in the non‐infarcted hemispheres. However, Vmax (P = 0.052), Vavg (P = 0.107), and Flowavg (P = 0.074) were not significantly different among hemispheres with acute ischemic lesions, chronic ischemic lesions and chronic hemorrhagic lesions. Vmax (odds ratio 3.033, 95% CI: 1.075–8.562) was independently associated with cerebrovascular events. Data Conclusion: Vmax maybe a higher risk factor of cerebrovascular events in MMA patients. Evidence Level: 2 Technical Efficacy Stage: 3 [ABSTRACT FROM AUTHOR]