The deep integration of Internet of Vehicles (IoV) and edge computing technologies brings new requirements for the Vehicular Edge Computing (VEC) information system security evaluation. Facing the two core problems of resource-constrained scenarios and dynamic security evaluation, the GFCIV-CGTOPSIS model for VEC information system dynamic security evaluation is proposed. In the model, subjective and objective evaluation indexes are considered and calculated separately to improve operability. An improved grey correlation F-statistics clustering and index validity combination index screening (GFCIV) method is proposed in order to improve the operational efficiency of the traditional grey F-statistics rough set (GFRS) index screening method. The CRITIC method is used to determine the subjective and objective comprehensive index set weights, and the grey TOPSIS method is used to realize the dynamic security evaluation. Experimental results demonstrate that the GFCIV-CGTOPSIS model, compared to the pre-improved GFRS-CGTOPSIS model, achieves reduced index tree redundancy and efficient dynamic security evaluation while exhibiting less information loss and lower evaluation result deviation due to index screening.

Objective: To provide evidence and a theoretical basis for the rational clinical use of toosendan fructus (Chinese name, Chuan lian zi), this study retrospectively analyzed its clinical application in inpatients and adopted network formulaology analysis to identify its core herbs combinations and diagnostic combinations. Methods: All the inpatient medical information and clinical data of toosendan fructus were collected from His system from January 2021 to Dec 2024. The specific situation of clinical use of toosendan fructus was analyzed, and relevant cases were consulted. The analysis of Network Formulaology applied in doctor's advice／instructions of toosendan fructus were also done to study how to use toosendan fructus more safely. Results: A total of 1783 doctor's advice/instructions data from 617 inpatients was collected. The specific analysis of the drug use and clinical diagnosis of toosendan fructus was found. There were 61 doctor's advice/instructions (3.42 %)in which the toosendan fructus may have potential interaction with other drugs. It were found that the dose of toosendan fructus in 370 doctor's advice/instructions (20.75%) exceeded the 12 g. The core drug combination and diagnostic combination of toosendan fructus were also found. Conclusion: The clinical use of toosendan fructus is reasonable. No adverse reactions due to drug interactions are found. No adverse reactions caused by overdose are found. Most of them are a combination of attenuated compatibility, indicating that clinical use is safe and reasonable.

Accurate and efficient localization of root cause instances in large-scale microservice systems is of paramount importance. Unfortunately, prevailing methods face several limitations. Notably, some recent methods rely on supervised learning which necessitates a substantial amount of labeled data. However, labeling root cause instances is time-consuming and laborious, especially with multiple modalities of data including logs, traces, metrics, and so on. Moreover, some approaches favor deep learning for localization but lack interpretability and continuous improvement mechanisms. To address the above challenges, we propose DeepHunt , a novel root cause localization method based on multimodal data analysis. Firstly, DeepHunt introduces root cause score (RCS) by integrating reconstruction errors and failure propagation patterns (upstream–downstream relationships), imparting interpretability to the localization of root causes. Then, it embraces graph autoencoder (GAE) to address the limitation imposed by scarce labeled data. It employs data augmentation to mitigate the adverse effects of insufficient historical training samples. We evaluate DeepHunt on two open source datasets, and it outperforms existing methods when facing a zero-label cold start. DeepHunt can be further improved by continuously fine-tuning through a feedback mechanism.

Aim: The study aims to explore the synergistic effects between different herbal pairs and the original traditional Chinese medicine prescriptions by developing a model for analyzing compatibility correlation and synergy in prescriptions. Method: We have applied TCMNPAS, a comprehensive analysis platform that integrates network formularology and network pharmacology. This platform is designed to investigate in-depth the compatibility characteristics of TCM formulas and their potential molecular mechanisms. Liuwei Dihuang prescription(LDP), formulated following the principle of "Three Tonics and Three Purgatives," was used as an example. An analysis model built via association network was applied to evaluate the relationship and synergistic impacts between the original prescription and three distinct drug pairs related to specific diseases. Results: Node similarities measured by the nearest-neighbor expansion network and shortest path expansion network between LDP and kidney yin deficiency disease network were 0.873 and 0.685, respectively. The average random walk correlation score between LDP and diseases showed a significant difference, suggesting a strong correlation with diseases. Among these, the herbal pair of Corni FructusMoutan Cortex(SZY-MDP) demonstrated node similarities of 0.742 and 0.433 with kidney yin deficiency on the nearest-neighbor expansion network and shortest path expansion network, respectively. Also, a significant difference was noted in the average random walk correlation score of this pair with diseases, signifying that this herb pair is most closely related to the original prescription and diseases. Simultaneously, within LDP, the SZY-MDP herbal pair showed the closest association with the original prescription concerning shared targets, active compounds, and disease targets. Notably, the most prominent KEGG signaling pathway for LDP intervention in hyperthyroidism was the regulation of adipocyte lipolysis, indicating its regulatory effect on body energy metabolism and lipid metabolism. Conclusion: Based on the analysis model focused on compatibility correlation and synergy, the "tonifying and purgative" drugs in LDP exhibit specific "correlation-synergy" effects on kidney yin deficiency.

Microservices improve the scalability and flexibility of monolithic architectures to accommodate the evolution of software systems, but the complexity and dynamics of microservices challenge system reliability. Ensuring microservice quality requires efficient failure diagnosis, including detection and triage. Failure detection involves identifying anomalous behavior within the system, while triage entails classifying the failure type and directing it to the engineering team for resolution. Unfortunately, current approaches reliant on single-modal monitoring data, such as metrics, logs, or traces, cannot capture all failures and neglect interconnections among multimodal data, leading to erroneous diagnoses. Recent multimodal data fusion studies struggle to achieve deep integration, limiting diagnostic accuracy due to insufficiently captured interdependencies. Therefore, we proposeUniDiag, which leverages temporal knowledge graphs to fuse multimodal data for effective failure diagnosis.UniDiagapplies a simple yet effective stream-based anomaly detection method to reduce computational cost and a novel microservice-oriented graph embedding method to represent the state of systems comprehensively. To assess the performance ofUniDiag, we conduct extensive evaluation experiments using datasets from two benchmark microservice systems, demonstrating its superiority over existing methods and affirming the efficacy of multimodal data fusion. Additionally, we have publicly made the code and data available to facilitate further research.

Purpose: We explored the mechanism effects of “The Same Treatment for Different metabolic Diseases” concept in traditional Chinese medicine (TCM).Methods: Network pharmacology, molecular docking technology and KATZ score were employed to predict the targets and signaling pathways affected by Shugan Quzhi Capsules (SGQZ), which are used to treat obesity, hyperlipidemia, and non-alcoholic fatty liver disease.Results: A total of 492 active ingredients in SGQZ were found to act on 666 potential targets. They were involved in GO biological processes related to lipid metabolism, oxidative stress, anti-inflammatory effects, and calcium ion channel effects; the three most significantly enriched KEGG pathways were endocrine resistance, advanced glycation end-product (AGE)/receptor for AGEs signaling pathway in diabetic complications, and peroxisome proliferator-activated receptor (PPAR) signaling. The correlation between SGQZ and each metabolic disease was ranked as NAFLD>hyperlipidemia>obesity.Conclusions: Our results clarify the mechanisms of action of SGQZ on three conditions, demonstrating the utility of “The Same Treatment for metabolic Different Diseases” from both the target and pathway perspectives. Among them, leptin, apolipoprotein B, and PPARG and PPAR-/AMP-activated protein kinase-related signal pathways are the key targets and pathways for SGQZ. In TCM terminology, the spleen and liver meridians are the key routes by which quercetin and apigenin in SGQZ exert their effects.