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Optimization Model for Safeguarding Vulnerable Components in Integrated Energy Systems Based on Weighted Betweenness
Published 2025-07-01“…Utilizing the complex network theory to mitigate vulnerabilities mitigation in integrated energy systems is significant for enhancing the resilience of sustained energy supply, especially against deliberate physical attacks and natural disasters. …”
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Modeling and Assessing Vulnerabilities of Aircraft Cyber–Physical Power Systems Based on Complex Network Theory
Published 2025-01-01“…Therefore, it is essential to assess the vulnerability of MEA–EPS for stable operation and optimal system design. …”
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VRU-YOLO: A Small Object Detection Algorithm for Vulnerable Road Users in Complex Scenes
Published 2025-01-01“…Additionally, a lightweight Optimized Shared Detection Head (OSDH-Head) is introduced, reducing computational complexity while improving detection efficiency. …”
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Deep learning vulnerability detection method based on optimized inter-procedural semantics of programs
Published 2023-12-01“…In recent years, software vulnerabilities have been causing a multitude of security incidents, and the early discovery and patching of vulnerabilities can effectively reduce losses.Traditional rule-based vulnerability detection methods, relying upon rules defined by experts, suffer from a high false negative rate.Deep learning-based methods have the capability to automatically learn potential features of vulnerable programs.However, as software complexity increases, the precision of these methods decreases.On one hand, current methods mostly operate at the function level, thus unable to handle inter-procedural vulnerability samples.On the other hand, models such as BGRU and BLSTM exhibit performance degradation when confronted with long input sequences, and are not adept at capturing long-term dependencies in program statements.To address the aforementioned issues, the existing program slicing method has been optimized, enabling a comprehensive contextual analysis of vulnerabilities triggered across functions through the combination of intra-procedural and inter-procedural slicing.This facilitated the capture of the complete causal relationship of vulnerability triggers.Furthermore, a vulnerability detection task was conducted using a Transformer neural network architecture equipped with a multi-head attention mechanism.This architecture collectively focused on information from different representation subspaces, allowing for the extraction of deep features from nodes.Unlike recurrent neural networks, this approach resolved the issue of information decay and effectively learned the syntax and semantic information of the source program.Experimental results demonstrate that this method achieves an F1 score of 73.4% on a real software dataset.Compared to the comparative methods, it shows an improvement of 13.6% to 40.8%.Furthermore, it successfully detects several vulnerabilities in open-source software, confirming its effectiveness and applicability.…”
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Navigating Coastal Vulnerability: Introducing the Coastal Fuzzy Vulnerability Index (CFVI)
Published 2025-05-01“…This study optimized the coastal vulnerability assessment method through fuzzy logic, improved the ability to handle nonlinear relationships between parameters, and provided a new tool for complex and dynamic coastal risk management. …”
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Evolution and vulnerability of the trade network structure of China’s petrochemical industry chain: Based on a comparative perspective between China and the US
Published 2025-07-01“…The trade partner structure expanded from an Asia-centered pattern to a global network, with continuous optimization of the industry chain trade network. However, structural vulnerabilities persisted in the basic chemical raw material network. (2) China and the United States shared highly overlapping trade partners in synthetic materials. …”
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Resilience assessment of interprovincial crop virtual water flow network in China
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Ecological networks construction and optimization in the Longdong Loess Plateau: The advantages of self-organizing map and complex networks
Published 2025-01-01“…Furthermore, we compared the ENs before and after optimization to identify the benefits of the SOM. Finally, we apply the complex network topology edge-adding strategy to enhance the robustness of network. …”
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Graph-Based Warm Solutions for Optimal Resilience Enhancement of Water Distribution Networks
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A hybrid modeling framework for groundwater contamination risk: Optimized AHP-DRASTIC and BME
Published 2025-01-01“…The assessment of groundwater contamination risk is crucial, especially in regions like northern Anhui, where complex geological conditions and limited groundwater sources coexist. …”
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Structure-Enhanced Prompt Learning for Graph-Based Code Vulnerability Detection
Published 2025-05-01“…Recent advances in prompt learning have opened new avenues for enhancing natural language understanding in domain-specific tasks, including code vulnerability detection. Motivated by the limitations of conventional binary classification methods in capturing complex code semantics, we propose a novel framework that integrates a two-stage prompt optimization mechanism with hierarchical representation learning. …”
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Impacts of climate change on a high elevation specialist bird are ameliorated by terrain complexity
Published 2024-12-01“…We then projected the optimized model for two future time periods under different climate scenarios. …”
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Application of MASW, Microtremor, and Seismic Refraction Tests for Buildings in Vulnerable Communities
Published 2025-03-01“…This study emphasizes the importance of merging geophysical and dynamic knowledge to optimize structural performance and promote resilience in complex geophysical environments. …”
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TET2 orchestrates YAP signaling to potentiate targetable vulnerability in hepatocellular carcinoma
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Research on Safety Vulnerability Assessment of Subway Station Construction Based on Evolutionary Resilience Perspective
Published 2025-08-01“…Therefore, it is of great practical significance to scientifically and systematically evaluate the safety vulnerability of subway station construction. This paper takes the Chengdu subway project as an example, and establishes a metro station construction safety vulnerability evaluation index system based on the driving forces–pressures–state–impacts–responses (DPSIR) theory with 5 first-level indexes and 23 second-level indexes, and adopts the fuzzy hierarchical analysis method (FAHP) to calculate the subjective weights, and the improved Harris Hawks optimization–projection pursuit method (HHO-PPM) to determine the objective weights, combined with game theory to calculate the comprehensive weights of the indicators, and finally uses the improved cloud model of Bayesian feedback to determine the vulnerability level of subway station construction safety. …”
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Identification and Diagnosis of Wind Health-Vulnerable Spaces in High-Rise Residential Areas of Xi’an
Published 2025-05-01“…As urbanization accelerates, high-rise residential areas (HRRAs) have become a dominant urban housing typology. However, their complex building layouts significantly alter local wind environments, potentially impacting residents’ health. …”
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Impact of short-term soil disturbance on cadmium remobilization and associated risk in vulnerable regions
Published 2025-01-01“…A comprehensive understanding of cadmium (Cd) migration in soils near contaminated hotspots is crucial for optimizing remediation efforts and ensuring crop health. …”
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Enhanced SVM-based model for predicting cyberspace vulnerabilities: Analyzing the role of user group dynamics and capital influx.
Published 2025-01-01“…Amid substantial capital influx and the rapid evolution of online user groups, the increasing complexity of user behavior poses significant challenges to cybersecurity, particularly in the domain of vulnerability prediction. …”
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Investigating a Novel Activation-Repolarisation Time Metric to Predict Localised Vulnerability to Reentry Using Computational Modelling.
Published 2016-01-01“…The method is robust against highly complex and intramural scar anatomies and low resolution clinical data acquisition. …”
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