Reliability Model Based on Hypergraph for Dependent Failure System

AbstractIn order to describe the mechanism of system parts occurring dependent failure, extending the definition of stress and strength to wider range which based on the component's failure stress-strength interference theory. Using hypergragh theory to carry on the modeling of related failure system and calculate its component's related failure rate, then obtaining the real failure rate of the system. Using component's failure data of RIAC Automated Databook to calculate system failure rate, then comparing with corresponding system failure data of OREDA to demonstrate the efficiency of this method

Urban planning and planning education under economic reform in China

Includes bibliographical references.Selection of papers from the China Urban Planning and Planning Education Conference (on 13-17 December 1993 in Zhongshan University, Guangzhou) and invited papers.Some text in Chinese.published_or_final_versio

Transient Thermal and Structural Analysis of Cylinder and Bolted Joints on BOG Compressor During Starting Process

BOG (Boil off Gas) compressor deals with the gas evaporated from the LNG storage tank. Its working temperature is lower than -120℃. There are two types of starting process for BOG compressor: Direct Starting and No-load Starting. During the starting process, the temperature on cylinder will change rapidly and result in additional thermal stress to some parts on the cylinder, especially to the bolted joints on the cylinder head. In this paper, transient Finite Element thermal analysis is proposed on the cylinder with some improvement of the boundary condition settings, such as the consideration of the ice increase on the cylinder wall. Then, the theoretical and transient FE analysis are proposed subject to the bolted joints of the cylinder head in two starting process. Result shows that the maximum temperature difference on the cylinder is 81.5℃ during direct starting; while it could decrease to 60.9℃in No-Load Starting. Along the bolted joints in the cylinder head, the maximum temperature difference will be up to 52℃ in direct starting and 45℃ in No-Load Starting. The preload increases rapidly over 60% and its mean tensile stress on the bolt is near to the yield strength. Besides, the preload are distributed unevenly during the starting process. The maximum uneven rate is 20% in Direct Starting and 15% in No-Load Starting. It shows that No-Load Starting could offer a more stable starting process. Finally, based on the theoretical analysis, a preload adjustment method is proposed to ensure the safety and validity of bolted joints on BOG compressor

Advancing Federated Learning in 6G: A Trusted Architecture with Graph-based Analysis

Integrating native AI support into the network architecture is an essential objective of 6G. Federated Learning (FL) emerges as a potential paradigm, facilitating decentralized AI model training across a diverse range of devices under the coordination of a central server. However, several challenges hinder its wide application in the 6G context, such as malicious attacks and privacy snooping on local model updates, and centralization pitfalls. This work proposes a trusted architecture for supporting FL, which utilizes Distributed Ledger Technology (DLT) and Graph Neural Network (GNN), including three key features. First, a pre-processing layer employing homomorphic encryption is incorporated to securely aggregate local models, preserving the privacy of individual models. Second, given the distributed nature and graph structure between clients and nodes in the pre-processing layer, GNN is leveraged to identify abnormal local models, enhancing system security. Third, DLT is utilized to decentralize the system by selecting one of the candidates to perform the central server's functions. Additionally, DLT ensures reliable data management by recording data exchanges in an immutable and transparent ledger. The feasibility of the novel architecture is validated through simulations, demonstrating improved performance in anomalous model detection and global model accuracy compared to relevant baselines.Comment: Accepted by IEEE Global Communications Conference (GLOBECOM) 202

Resisting Backdoor Attacks in Federated Learning via Bidirectional Elections and Individual Perspective

Existing approaches defend against backdoor attacks in federated learning (FL) mainly through a) mitigating the impact of infected models, or b) excluding infected models. The former negatively impacts model accuracy, while the latter usually relies on globally clear boundaries between benign and infected model updates. However, model updates are easy to be mixed and scattered throughout in reality due to the diverse distributions of local data. This work focuses on excluding infected models in FL. Unlike previous perspectives from a global view, we propose Snowball, a novel anti-backdoor FL framework through bidirectional elections from an individual perspective inspired by one principle deduced by us and two principles in FL and deep learning. It is characterized by a) bottom-up election, where each candidate model update votes to several peer ones such that a few model updates are elected as selectees for aggregation; and b) top-down election, where selectees progressively enlarge themselves through picking up from the candidates. We compare Snowball with state-of-the-art defenses to backdoor attacks in FL on five real-world datasets, demonstrating its superior resistance to backdoor attacks and slight impact on the accuracy of the global model

Metabolic Profiling Study of Yang Deficiency Syndrome in Hepatocellular Carcinoma by H

This study proposes a 1H NMR-based metabonomic approach to explore the biochemical characteristics of Yang deficiency syndrome in hepatocellular carcinoma (HCC) based on serum metabolic profiling. Serum samples from 21 cases of Yang deficiency syndrome HCC patients (YDS-HCC) and 21 cases of non-Yang deficiency syndrome HCC patients (NYDS-HCC) were analyzed using 1H NMR spectroscopy and partial least squares discriminant analysis (PLS-DA) was applied to visualize the variation patterns in metabolic profiling of sera from different groups. The differential metabolites were identified and the biochemical characteristics were analyzed. We found that the intensities of six metabolites (LDL/VLDL, isoleucine, lactate, lipids, choline, and glucose/sugars) in serum of Yang deficiency syndrome patients were lower than those of non-Yang deficiency syndrome patients. It implies that multiple metabolisms, mainly including lipid, amino acid, and energy metabolisms, are unbalanced or weakened in Yang deficiency syndrome patients with HCC. The decreased intensities of metabolites including LDL/VLDL, isoleucine, lactate, lipids, choline, and glucose/sugars in serum may be the distinctive metabolic variations of Yang deficiency syndrome patients with HCC. And these metabolites may be potential biomarkers for diagnosis of Yang deficiency syndrome in HCC

Neurodevelopmental disorders as a risk factor for temporomandibular disorder: evidence from Mendelian randomization studies

Objective: This study aims to clarify the incidence rate of temporomandibular joint disease in patients with mental disorders.Methods: Data extracted from the Psychiatric Genomics Consortium and FinnGen databases employed the Mendelian Randomization (MR) method to assess the associations of three neurodevelopmental disorders (NDDs)—Attention-Deficit/Hyperactivity Disorder (ADHD), Autism Spectrum Disorder (ASD), and Tourette’s Disorder (TD)—as exposure factors with Temporomandibular Disorder (TMD). The analysis used a two-sample MR design, employing the Inverse Variance Weighted (IVW) method to evaluate the relationships between these disorders and Temporomandibular Disorder. Sensitivity analysis and heterogeneity assessments were conducted. Potential confounding factors like low birth weight, childhood obesity, and body mass index were controlled for.Results: The study found that ADHD significantly increased the risks for TMD (OR = 1.2342, 95%CI (1.1448–1.3307), p < 0.00001), TMD (including avohilmo) (OR = 1.1244, 95%CI (1.0643–1.1880), p = 0.00003), TMD-related pain (OR = 1.1590, 95%CI (1.0964–1.2252), p < 0.00001), and TMD-related muscular pain associated with fibromyalgia (OR = 1.1815, 95%CI (1.1133–1.2538), p < 0.00001), while other disorders did not show significant causal relationships.Conclusion: This study reveals the elevated risk of various TMD aspects due to ADHD. Furthermore, we discuss the link between low vitamin D levels ADHD and TMD. Future research should address these limitations and delve further into the complex interactions between ADHD, ASD, TD, and TMD