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With the proliferation of Internet of Things (IoT) devices and edge nodes, edge computing has taken on much of the real-time data processing and low-latency response tasks which were previously managed by cloud computing. However, edge computing often encounters challenges such as network instability and dynamic resource variations, which can lead to task interruptions or failures. To address these issues, developing a fault-tolerant scheduling mechanism is crucial to ensure that a system continues to operate efficiently even when some nodes experience failures. In this paper, we propose an innovative fault-tolerant scheduling model based on asynchronous graph reinforcement learning. This model incorporates a deep reinforcement learning framework built upon a graph neural network, allowing it to accurately capture the complex communication relationships between computing nodes. The model generates fault-tolerant scheduling actions as output, ensuring robust performance in dynamic environments. Additionally, we introduce an asynchronous model update strategy, which enhances the model's capability of real-time dynamic scheduling through multi-threaded parallel interactions with the environment and frequent model updates via running threads. The experimental results demonstrate that the proposed method outperformed the baseline algorithms in terms of quality of service (QoS) assurance and fault-tolerant scheduling capabilities. [ABSTRACT FROM AUTHOR]

The advancement of 6G (6th Generation Mobile Networks) communication technology has posed challenges for traditional communication network architectures in meeting the demands for communication efficiency and quality. Semantic communication technology, characterized by its "understand before transmit" approach, has emerged as a pivotal technology driving the progress of 6G due to its ability to enhance communication efficiency and quality. The Wireless Image Transmission Transformer (WITT) model, which operates as a semantic communication system leveraging vision transformer technology for the transmission of semantic images, has shown efficacy in transmitting input images through processes of feature extraction and channel adaptation. This study introduces an advanced channel adaptive module that is informed by deep learning methodologies and the adaptive modulation principles of the Variational Information Bottleneck (VIB). This innovation enhances the original WITT model, resulting in the development of the Adaptive Wireless Image Transmission Transformer (ADWITT) architecture. Comprehensive experimental results have unequivocally shown that the transmission performance of the ADWITT architecture substantially surpasses that of the conventional WITT (Wavelet Image Transmission Technique) model, particularly in scenarios characterized by harsh and detrimental channel conditions. These findings underscore the robustness and adaptability of the ADWITT approach, which is poised to improve the field of image transmission by offering superior performance and resilience in environments where traditional methods falter. [ABSTRACT FROM AUTHOR]

(1) Background: This study aims to compare the effects of AI-based exercise feedback and standard training on the physical and mental health outcomes of older adults participating in a 4-week tai chi training program. (2) Methods: Participants were divided into three groups: an AI feedback group received real-time movement accuracy feedback based on AI and inertial measurement units (IMUs), a conventional feedback group received verbal feedback from supervisors, and a control group received no feedback. All groups trained three times per week for 8 weeks. Outcome measures, including movement accuracy, balance, grip strength, quality of life, and depression, were assessed before and after the training period. (3) Results: Compared to pre-training, all three groups showed significant improvements in movement accuracy, grip strength, quality of life, and depression. Only the AI feedback group showed significant improvements in balance. In terms of movement accuracy and balance, the AI feedback group showed significantly greater improvement compared to the conventional feedback group and the control group. (4) Conclusions: Providing real-time AI-based movement feedback during tai chi training offers greater health benefits for older adults compared to standard training without feedback. [ABSTRACT FROM AUTHOR]

Low-loss, high-birefringence, bend-resistant hollow-core anti-resonant fibers for infrared wavelengths have important applications in the fields of precision interferometric sensing, laser systems, and optical communications. In this paper, an eight-tube cladding quasi-symmetric hollow-core anti-resonant fiber is proposed, and two other anti-resonant fibers are designed based on this fiber structure. The finite element analysis method is used to numerically analyze the limiting loss, birefringence coefficient, bending resistance, and other properties of the three optical fibers after the optimized design. The results show that the limiting loss of the three optical fibers at λ = 1.55 μm is lower than 10−4 magnitude, and all of them obtain a birefringence coefficient of 10−4 magnitude; at the same time, the three optical fibers have their own characteristics and advantages, and the first optical fiber can reach a birefringence coefficient of 9.25 × 10−4 at λ = 1.52 μm.The limiting loss at λ = 1.55 μm is 3.42 × 10−5 dB/km. The minimum bending radius of the three types of anti-resonant fibers is less than 40 mm, which represents good bending characteristics, and the eight-tube cladding quasi-symmetric optical fiber has a bending loss of less than 2.10 × 10−3 dB/km when the bending radius is 28 mm. The three types of optical fibers have obtained good results in improving the mutual constraints between low limiting loss and high birefringence, with better results than the other two types. The obtained results have high development potential. [ABSTRACT FROM AUTHOR]

Simple Summary: Tilapia is the second-most-farmed fish worldwide which plays a critical role in providing high-quality proteins to human. However, infections caused by S. agalactiae result in huge economic losses. Moreover, the spreads of antibiotic resistance restrict the use of antibiotics in treating S. agalactiae infections. Here, we tried to develop anti-S. agalactiae drugs by inhibiting SrtA to overcome infections caused by resistant bacterial strains. Sortase A (SrtA) is responsible for anchoring surface proteins to the cell wall, and has been identified as a promising target developing anti-infective drugs of Gram-positive bacteria. The aim of the study was to identify inhibitors of Streptococcus agalactiae (S. agalactiae) SrtA from natural compounds to overcome the spread of antibiotic resistance in aquaculture. Here, we found that the MIC of fraxetin against S. agalactiae was higher than 256 μg/mL, indicating that fraxetin had no anti- S. agalactiae activity. But fraxetin could dose-dependently decrease the activity of SrtA in vitro at concentrations ranging between 4–32 μg/mL by a fluorescence resonance energy transfer (FRET) assay. Moreover, the inhibition of SrtA by fraxetin decreased the anchoring of surface proteins with the LPXTG motif to the cell wall by detecting the immunofluorescence change of serine-rich repeat protein 1 (Srr1) on the bacterial cell surface. The results of fibronectin binding and cell adhesion assays indicated that fraxetin could significantly decrease the adhesion ability of S. agalactiae in a dose-dependent manner. The results were further proven by immunofluorescence staining. Animal challenge results showed that treatment with fraxetin could reduce the mortality of tilapia infected with S. agalactiae to 46.67%, indicating that fraxetin could provide a significant amount of protection to tilapia by inactivating SrtA. Taken together, these findings provided a novel inhibitor of S. agalactiae SrtA and a promising candidate for treating S. agalactiae infections in aquaculture. [ABSTRACT FROM AUTHOR]

This study presents a novel method for assessing landslide hazards along highways using remote sensing and machine learning. We extract geospatial features such as slope, aspect, and rainfall over Guangxi, China, and apply an extreme gradient boosting model pre-trained on contiguous United States datasets. The model produces susceptibility maps that indicate landslide probability at different scales. However, the lack of accurate data on historical landslides in Guangxi challenges the model evaluation and comparison between regions. To overcome this, we calibrate the model to fit the local conditions in Guangxi. The calibrated model agrees with the observed landslide locations, implying its capability to capture regional variations in landslide mechanisms. We apply the model at a 30 m resolution along the Heba Expressway and validate it against reports from July 2021 to March 2022. The model correctly predicts five of seven landslide events in this period with a reasonable alarm rate. This framework has the potential for large-scale landslide risk management by informing transportation planning and infrastructure maintenance decisions. More data on landslide timing and human disturbance events may improve the model's accuracy across diverse geographical areas and terrains. [ABSTRACT FROM AUTHOR]

The global pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become an urgent public health problem. Spike (S) protein mediates the fusion between the virus and the host cell membranes, consequently emerging as an important target of drug design. The lack of comparisons of in situ full-length S homotrimer structures in different states hinders understanding the structures and revealing the function, thereby limiting the discovery and development of therapeutic agents. Here, the steady-state structures of the in situ full-length S trimer in closed and open states (Sclosed and Sopen) were modeled with the constraints of density maps, associated with the analysis of the dynamic structural differences. Subsequently, we identified various regions with structure and property differences as potential binding pockets for ligands that promote the formation of inactive trimeric protein complexes. By using virtual screening strategy and a newly defined druggable cavity, five ligands were screened with potential bioactivities. Then molecular dynamic (MD) simulations were performed on apo protein structures and ligand bound complexes to reveal the conformational changes upon ligand binding. Our simulation results revealed that sulforaphane (SFN), which has the best binding affinity, could inhibit the conformational changes of S homotrimer that would occur during the viral membrane fusion. Our results could aid in the understanding of the regulation mechanism of S trimer aggregation and the structure-activity relationship, facilitating the development of potential antiviral agents. [ABSTRACT FROM AUTHOR]