Coral reefs worldwide are losing their species diversity and ecosystem function under threats from global warming and anthropogenic activities.In this study,we investigated the diversity and current state of scleracti...Coral reefs worldwide are losing their species diversity and ecosystem function under threats from global warming and anthropogenic activities.In this study,we investigated the diversity and current state of scleractinian corals surrounding the Qizhou Archipelago.A total of 87 species of scleractinian corals,belonging to 29 genera and 12 families,were found across ten survey sites.The family Merulinidae exhibited the highest species richness(39 species and 12 genera),followed by Acroporidae(15 species and 3genera).The living coral coverage was 16.9%±10.3%(mean±SD)and ranged from 4.6%to 35.1%,which varied significantly.Massive corals such as Porites lutea,Porites lobata,Montipora nodosa,and Favites abdita were dominant species.The recruitment rate of coral larvae was(1.20±0.97)ind/m^(2)(mean±SD).In addition,we constructed an ecological vulnerability assessment system and evaluated the ecological vulnerability of scleractinian corals surrounding the Qizhou Archipelago.The results showed that scleractinian corals at Gouluanpaoshi(GLPS)and Duifan(DF)were highly vulnerable,whereas those on other islands had low to medium vulnerability.In general,the scleractinian corals surrounding the Qizhou Archipelago show low to medium levels of ecological vulnerability.Identifying severely afflicted areas and developing effective methods to manage coral reefs in these regions are crucial.展开更多
The Gabes aquifer system,located in southeastern Tunisia,is a crucial resource for supporting local socio-economic activities.Due to its dual porosity structure,is particularly vulnerable to pollution.This study aims ...The Gabes aquifer system,located in southeastern Tunisia,is a crucial resource for supporting local socio-economic activities.Due to its dual porosity structure,is particularly vulnerable to pollution.This study aims to develop a hybrid model that combines the Fracture Aquifer Index(FAI)with the conventional GOD(Groundwater occurrence,Overall lithology,Depth to water table)method,to assess groundwater vulnerability in fractured aquifer.To develop the hybrid model,the classical GOD method was integrated with FAI to produce a single composite index.Each parameter within both GOD and FAI was scored,and a final index was calculated to delineate vulnerable areas.The results show that the study area can be classified into four vulnerability levels:Very low,low,moderate,and high,indicating that approximately 8%of the area exhibits very low vulnerability,29%has low vulnerability,25%falls into the moderate category,and 38%is considered highly vulnerable.The FAI-GOD model further incorporates fracture network characteristics.This refinement reduces the classification to three vulnerability classes:Low,medium,and high.The outcomes demonstrate that 46%of the area is highly vulnerable due to a dense concentration of fractures,while 17%represents an intermediate zone characterized by either shallow or deeper fractures.In contrast,37%corresponds to areas with lightly fractured rock,where the impact on vulnerability is minimal.Multivariate statistical analysis was employed using Principal Components Analysis(PCA)and Hierarchical Cluster Analysis(HCA)on 24 samples across six variables.The first three components account for over 76%of the total variance,reinforcing the significance of fracture dynamics in classifying vulnerability levels.The FAI-GOD model removes the very-low-vulnerability class and expands the spatial extent of low-and high-vulnerability zones,reflecting the dominant influence of fracture networks on aquifer sensitivity.While both indices use a five-class system,FAI-GOD redistributes vulnerability by eliminating very-low-vulnerability areas and amplifying low/high categories,highlighting the critical role of fractures.A strong correlation(R2=0.94)between the GOD and FAI-GOD indices,demonstrated through second-order polynomial regression,confirms the robustness of the FAI-GOD model in accurately predicting vulnerability to pollution.This model provides a useful framework for assessing the vulnerability of complex aquifers and serves as a decision-making tool for groundwater managers in similar areas.展开更多
As artificial Intelligence(AI)continues to expand exponentially,particularly with the emergence of generative pre-trained transformers(GPT)based on a transformer’s architecture,which has revolutionized data processin...As artificial Intelligence(AI)continues to expand exponentially,particularly with the emergence of generative pre-trained transformers(GPT)based on a transformer’s architecture,which has revolutionized data processing and enabled significant improvements in various applications.This document seeks to investigate the security vulnerabilities detection in the source code using a range of large language models(LLM).Our primary objective is to evaluate the effectiveness of Static Application Security Testing(SAST)by applying various techniques such as prompt persona,structure outputs and zero-shot.To the selection of the LLMs(CodeLlama 7B,DeepSeek coder 7B,Gemini 1.5 Flash,Gemini 2.0 Flash,Mistral 7b Instruct,Phi 38b Mini 128K instruct,Qwen 2.5 coder,StartCoder 27B)with comparison and combination with Find Security Bugs.The evaluation method will involve using a selected dataset containing vulnerabilities,and the results to provide insights for different scenarios according to the software criticality(Business critical,non-critical,minimum effort,best effort)In detail,the main objectives of this study are to investigate if large language models outperform or exceed the capabilities of traditional static analysis tools,if the combining LLMs with Static Application Security Testing(SAST)tools lead to an improvement and the possibility that local machine learning models on a normal computer produce reliable results.Summarizing the most important conclusions of the research,it can be said that while it is true that the results have improved depending on the size of the LLM for business-critical software,the best results have been obtained by SAST analysis.This differs in“NonCritical,”“Best Effort,”and“Minimum Effort”scenarios,where the combination of LLM(Gemini)+SAST has obtained better results.展开更多
Background:Exposure to environmental vulnerability poses significant threats to adolescent suicidal ideation,while individual resilience can mitigate these adverse effects with notable gender commonalities and differe...Background:Exposure to environmental vulnerability poses significant threats to adolescent suicidal ideation,while individual resilience can mitigate these adverse effects with notable gender commonalities and differences.However,research examining how these factors co-configure at the individual level remains limited,particularly from a gender-specific perspective.Thus,the present study aims to adopt a person-centered analytic approach to identify gender-specific configurations of environmental vulnerability and individual resilience associated with suicidal ideation among Chinese adolescents.Methods:Data were collected from 2616 Chinese primary and secondary school students(aged 10–17;1223 girls).Participants completed validated scales measuring environmental vulnerability,individual resilience,and suicidal ideation.Latent profile analysis(LPA)was conducted separately by gender.Results:Gender differences were prominent:males exhibited higher resilience and lower suicidal ideation,while females reported higher environmental vulnerability and elevated levels of suicidal ideation.LPA identified three distinct profiles for males:Low Vulnerable–High Protective–Low Risk(LHL),Medium Vulnerable–Low Protective–Low Risk(MLL),and High Vulnerable–Low Protective–High Risk(HLH).Four profiles emerged for females:LHL,MLL,Medium Vulnerable–Low Protective–Medium Risk(MLM),and HLH.Crucially,within the HLH profile,males exhibited particularly deficient humor(η^(2)=0.19)and confidence(η^(2)=0.16),while females formed a distinct subgroup characterized by severe academic and family stressors(η^(2)=0.30–0.36).Conclusion:The study underscores developing gender-specific mental health interventions using a nuanced,person-centered approach that considers both environmental risk and individual resilience factors,which allows for targeted suicide prevention strategies addressing the unique needs of male and female adolescents.展开更多
Rheumatoid arthritis(RA)patients face significant psychological challenges alongside physical symptoms,necessitating a comprehensive understanding of how psychological vulnerability and adaptation patterns evolve thro...Rheumatoid arthritis(RA)patients face significant psychological challenges alongside physical symptoms,necessitating a comprehensive understanding of how psychological vulnerability and adaptation patterns evolve throughout the disease course.This review examined 95 studies(2000-2025)from PubMed,Web of Science,and CNKI databases including longitudinal cohorts,randomized controlled trials,and mixed-methods research,to characterize the complex interplay between biological,psychological,and social factors affecting RA patients’mental health.Findings revealed three distinct vulnerability trajectories(45%persistently low,30%fluctuating improvement,25%persistently high)and four adaptation stages,with critical intervention periods occurring 3-6 months postdiagnosis and during disease flares.Multiple factors significantly influence psychological outcomes,including gender(females showing 1.8-fold increased risk),age(younger patients experiencing 42%higher vulnerability),pain intensity,inflammatory markers,and neuroendocrine dysregulation(48%showing cortisol rhythm disruption).Early psychological intervention(within 3 months of diagnosis)demonstrated robust benefits,reducing depression incidence by 42%with effects persisting 24-36 months,while different modalities showed complementary advantages:Cognitive behavioral therapy for depression(Cohen’s d=0.68),mindfulness for pain acceptance(38%improvement),and peer support for meaning reconstruction(25.6%increase).These findings underscore the importance of integrating routine psychological assessment into standard RA care,developing stage-appropriate interventions,and advancing research toward personalized biopsychosocial approaches that address the dynamic psychological dimensions of the disease.展开更多
Global climate change is intensifying the impact of slope hazards,particularly rainfall-induced landslide hazards(RILH),on mountain road networks(MRNs).However,effective quantitative models for dynamically assessing M...Global climate change is intensifying the impact of slope hazards,particularly rainfall-induced landslide hazards(RILH),on mountain road networks(MRNs).However,effective quantitative models for dynamically assessing MRNs vulnerability under RILH disturbances are still lacking.To bridge this gap,this study develops a Cascading Failure Model for Rainfall-Induced Landslide Hazard(CFM-RILH).Validation via a case study of the GarzêTibetan Autonomous Prefecture Road Network(GTPRNs)reveals key characteristics of MRNs system vulnerability under RILH disturbances:(1)Under the disturbance effects of RILH,the vulnerability of the MRNs system follows a nonlinear phase transition law that intensifies with increasing disturbance intensity,exhibiting a distinct critical threshold.When the disturbance intensity exceeds this threshold,the system undergoes a global cascading failure phenomenon analogous to an“avalanche.”(2)Under RILH disturbances,the robustness of the MRNs system possesses a distinct safety boundary.Exceeding this boundary not only fails to improve hazard resistance but instead substantially elevates the risk of large-scale cascading failure.(3)Increasing network redundancy may be considered one of the primary engineering measures for enhancing MRNs resilience against such disturbances.Based on these findings,we propose a“Two-Stage Emergency Response and Hierarchical Fortification”strategy specifically to improve the resilience of GTPRNs impacted by RILH.The CFM-RILH model provides an effective tool for assessing road network vulnerability under such hazards.Furthermore,its modeling framework can also inform vulnerability assessment and resilience strategy development for road networks affected by other types of slope hazards.展开更多
In the context of modern software development characterized by increasing complexity and compressed development cycles,traditional static vulnerability detection methods face prominent challenges including high false ...In the context of modern software development characterized by increasing complexity and compressed development cycles,traditional static vulnerability detection methods face prominent challenges including high false positive rates and missed detections of complex logic due to their over-reliance on rule templates.This paper proposes a Syntax-Aware Hierarchical Attention Network(SAHAN)model,which achieves high-precision vulnerability detection through grammar-rule-driven multi-granularity code slicing and hierarchical semantic fusion mechanisms.The SAHAN model first generates Syntax Independent Units(SIUs),which slices the code based on Abstract Syntax Tree(AST)and predefined grammar rules,retaining vulnerability-sensitive contexts.Following this,through a hierarchical attention mechanism,the local syntax-aware layer encodes fine-grained patterns within SIUs,while the global semantic correlation layer captures vulnerability chains across SIUs,achieving synergistic modeling of syntax and semantics.Experiments show that on benchmark datasets like QEMU,SAHAN significantly improves detection performance by 4.8%to 13.1%on average compared to baseline models such as Devign and VulDeePecker.展开更多
Located in northern China,the Hetao Plain is an important agro-economic zone and population centre.The deterioration of local groundwater quality has had a serious impact on human health and economic development.Nowad...Located in northern China,the Hetao Plain is an important agro-economic zone and population centre.The deterioration of local groundwater quality has had a serious impact on human health and economic development.Nowadays,the groundwater vulnerability assessment(GVA)has become an essential task to identify the current status and development trend of groundwater quality.In this study,the Convolutional Neural Network(CNN)and Long Short-Term Memory(LSTM)models are integrated to realize the spatio-temporal prediction of regional groundwater vulnerability by introducing the Self-attention mechanism.The study firstly builds the CNN-LSTM modelwith self-attention(SA)mechanism and evaluates the prediction accuracy of the model for groundwater vulnerability compared to other common machine learning models such as Support Vector Machine(SVM),Random Forest(RF),and Extreme Gradient Boosting(XGBoost).The results indicate that the CNNLSTM model outperforms thesemodels,demonstrating its significance in groundwater vulnerability assessment.It can be posited that the predictions indicate an increased risk of groundwater vulnerability in the study area over the coming years.This increase can be attributed to the synergistic impact of global climate anomalies and intensified local human activities.Moreover,the overall groundwater vulnerability risk in the entire region has increased,evident fromboth the notably high value and standard deviation.This suggests that the spatial variability of groundwater vulnerability in the area is expected to expand in the future due to the sustained progression of climate change and human activities.The model can be optimized for diverse applications across regional environmental assessment,pollution prediction,and risk statistics.This study holds particular significance for ecological protection and groundwater resource management.展开更多
Climate change and rising temperatures are accelerating the rate of deglaciation in the Hindu Kush Karakoram Himalaya(HKH)ranges,leading to the formation of new glacial lakes and the expansion of existing ones.These l...Climate change and rising temperatures are accelerating the rate of deglaciation in the Hindu Kush Karakoram Himalaya(HKH)ranges,leading to the formation of new glacial lakes and the expansion of existing ones.These lakes are often vulnerable to failure,posing a significant threat to downstream communities and infrastructure.Therefore,a comprehensive assessment of Glacier-Lake Outburst Flood(GLOF)hazards and risk assessment is crucial to evaluate flood runout characteristics and identify settlements and infrastructure that are exposed and vulnerable to floods,aiding in the development and implementation of risk reduction strategies.This study aims to simulate a GLOF event induced by the Shisper glacier lake in northern Pakistan,using the HEC-RAS,and to assess its impact on settlements,infrastructure,and agricultural land.For the hydrometeorological analysis of the GLOF event,topographic data from unmanned aerial vehicles(UAVs),stream profiles,discharge data,Manning's roughness coefficient(n),and land use/land cover(LULC)were analyzed using HEC-RAS and geographic information system(GIS).During the GLOF event on May 7,2022,a maximum water depth of 6.3 m and a maximum velocity of 9.5 m/s were recorded.Based on the runout characteristics of this event,vulnerability and risk assessments have been calculated.The physical,social,and environmental vulnerabilities of the at-risk elements were evaluated using the analytical hierarchy process(AHP)and integrated with the hazard data to develop a risk map.The study identified the areas,infrastructure and settlements susceptible to GLOF hazard to support the development and implementation of targeted and evidence-based mitigation and adaptation strategies.展开更多
In Niger, farms have been facing negative effects of climate change for several decades. The objective of this work is to assess the vulnerability of farms in Tillabery department by proposing an adaptation approach. ...In Niger, farms have been facing negative effects of climate change for several decades. The objective of this work is to assess the vulnerability of farms in Tillabery department by proposing an adaptation approach. A five-step method and descriptive analysis were used on a sample of 250 farmers. The degree of damage caused by pests and crop diseases is significant, with respective proportions of 52.50% and 40.40%. It appears that the main climate risk factors for vulnerability are droughts, floods, soil degradation, and pest invasions. Additionally, the average level of exposure to agricultural operations is very high, with an index of 0.6. The sensitivity index remained constant in the range of 0.3 to 0.6 and is significant (reaching an index of 0.8). However, 61.2% of farms have a medium level of vulnerability and 33.3% have a high vulnerability to the effects of climate change. Nonetheless, a concerning trend regarding the vulnerability of farms has been observed. To assist policymakers and development actors in improving the vulnerability level of these production units, four phases of action are proposed: a diagnostic phase, evaluation, estimation of adaptation needs, implementation, and proper monitoring of actions.展开更多
Understanding the response of ecological vulnerability to global changes is essential for sustainable ecosystem management.However,incorporating ecological vulnerability assessment into accurate decision-making to ill...Understanding the response of ecological vulnerability to global changes is essential for sustainable ecosystem management.However,incorporating ecological vulnerability assessment into accurate decision-making to illustrate ecosystem dynamics and drivers remains unclear.Here,we used the Vulnerability Scoping Diagram model(VSD)and ecological vulnerability index(EVI)to evaluate the ecosystem vulnerability in the upper reach of Yellow River(URYR).Our results show that EVI increased from southwest to northeast,and EVI first went down from 2005 to 2015 and then slightly up from 2015 to 2020.The lower EVI can be attributed to improving environmental protection and restoration projects in the URYR.The subareas of the High-High cluster were distributed in the northeast,and those of the Low-Low cluster were distributed in the southwest,respectively.Furthermore,we divided the URYR into four zones(e.g.,Protection Zone,Ecological Degradation Zone,Ecological Improvement Zone,and Comprehensive Zone)according to the EVI.Consequently,a long-term detection system and public education should be enhanced to improve environmental awareness,which plays a beneficial role in the sustainable development of four zones.All in all,our findings not only shed light on the dynamic of ecological vulnerability but also provide the diversification management of the upper reach of the Yellow River.展开更多
Vietnam,with its twenty-eight coastal provinces,is one of the nations most profoundly affected by the adverse impacts of climate change(CC).These provinces face severe challenges as they contend with the escalating ef...Vietnam,with its twenty-eight coastal provinces,is one of the nations most profoundly affected by the adverse impacts of climate change(CC).These provinces face severe challenges as they contend with the escalating effects of CC,including rising sea levels,typhoons,flooding,and droughts.In this context,this article aims to assess the vulnerability of households'livelihoods in Quang Nam Province by applying the Livelihood Vulnerability Index(LVI)developed by Hahn et al.,along with the Intergovernmental Panel on Climate Change framework(LVI-IPCC).The study utilises five sources of household capital—human,social,physical,natural,and financial—to construct its indices.The data for this article is based on a survey of 200 households.The research methodology combines both quantitative and qualitative methods,including questionnaire interviews,in-depth interviews,and focus group discussions.The research period spans from 2021 to 2023.The study results revealed that the household LVI was 0.371,while the LVI-IPCC was 0.086,highlighting the critical need for access to food and clean water,which scored 0.458 and 0.351,respectively.The research underscores how CC significantly affects the livelihoods of coastal communities,particularly in sectors such as fishing,aquaculture,and agriculture.The study concludes that CC poses significant challenges to the livelihoods of coastal communities in Quang Nam Province and that adaptation measures are necessary to support these communities.The research highlights the importance of livelihood diversification,job transformation,and improving knowledge and skills to enhance the resilience of coastal communities to CC.展开更多
The negative impacts of natural hazards on communities at all scales have been increasing.Floods comprise one such natural hazard that has emerged as one of the most destructive in the US and worldwide.While a lot of ...The negative impacts of natural hazards on communities at all scales have been increasing.Floods comprise one such natural hazard that has emerged as one of the most destructive in the US and worldwide.While a lot of damage is estimated in terms of the cost of rebuilding infrastructure and direct loss of economy,the negative impacts of such disruptions go beyond the physical infrastructure.The impact on(and of)the social and institutional framework is rarely examined in conjunction with the physical and technical aspects.This paper examines flood vulnerability and risk of a community at an intersection of social,ecological,technical,and intuitional perspectives,and presents a framework for a holistic flood vulnerability and risk assessment that has a strong foundation in all four aspects of a resilient community.The study builds on the existing risk,vulnerability,and hazard assessment approaches,and refines them with a holistic perspective.The study uses a mixed method approach with qualitative and quantitative methodologies to assess flood occurrence probabilities,vulnerability,and risk from the social,ecological,technical,and institutional perspectives.A case study of the City of Atlanta is conducted using the framework to assess the overall vulnerability and risk of the city.The results of this analysis show that the regions that have the highest probability of flood hazard occurrence also appear to have the highest social,ecological,and technical vulnerabilities in the Atlanta area.While the results are intuitive,the applications support a focus on holistic resilience building across these four criteria.This study is potentially useful to practitioners,researchers,government agencies,and community organizations working to mitigate flood risk particularly as this risk continues to evolve with the changing climate.展开更多
Bioethics is the argumentative discipline of decisions and actions that reduce conflicts of interest,dilemmas,or asymmetries between the parties involved in biomedical research.The objective of this work was to review...Bioethics is the argumentative discipline of decisions and actions that reduce conflicts of interest,dilemmas,or asymmetries between the parties involved in biomedical research.The objective of this work was to review and compare the dimensions used by bioethics in the communicative management of the pandemic,namely:risk,vulnerability,resilience,and stigma.A documentary,exploratory,transversal,and retrospective work was carried out with a sample of sources indexed in international repositories,considering the search by keywords and the publication period from 2020 to 2024.The results demonstrate the prevalence of supply,contagion,symptoms,and help against COVID-19.展开更多
Software systems are vulnerable to security breaches as they expand in complexity and functionality.The confidentiality,integrity,and availability of data are gravely threatened by flaws in a system’s design,implemen...Software systems are vulnerable to security breaches as they expand in complexity and functionality.The confidentiality,integrity,and availability of data are gravely threatened by flaws in a system’s design,implementation,or configuration.To guarantee the durability&robustness of the software,vulnerability identification and fixation have become crucial areas of focus for developers,cybersecurity experts and industries.This paper presents a thorough multi-phase mathematical model for efficient patch management and vulnerability detection.To uniquely model these processes,the model incorporated the notion of the learning phenomenon in describing vulnerability fixation using a logistic learning function.Furthermore,the authors have used numerical methods to approximate the solution of the proposed framework where an analytical solution is difficult to attain.The suggested systematic architecture has been demonstrated through statistical analysis using patch datasets,which offers a solid basis for the research conclusions.According to computational research,learning dynamics improves security response and results in more effective vulnerability management.The suggested model offers a systematic approach to proactive vulnerability mitigation and has important uses in risk assessment,software maintenance,and cybersecurity.This study helps create more robust software systems by increasing patch management effectiveness,which benefits developers,cybersecurity experts,and sectors looking to reduce security threats in a growing digital world.展开更多
Spatial seismic vulnerability assessments are primally conducted at the community and grid level,using heuristic and empirical approaches.Building-based spatial statistical vulnerability models are rare because of dat...Spatial seismic vulnerability assessments are primally conducted at the community and grid level,using heuristic and empirical approaches.Building-based spatial statistical vulnerability models are rare because of data limitations.Generating open-access spatial inventories that document seismic damage and building attributes and test their effectiveness in assessing damage would promote the advancement of spatial vulnerability assessment.The 2022 Mw 6.7 Luding earthquake in the western Sichuan Province of China provides an opportunity to validate this approach.The local government urgently dispatched experts to survey building damage,marking all buildings with damage class stickers.In this work,we sampled 2889 buildings as GPS points and documented the damage classes and building attributes,including structure type,number of floors,and age.A polygon-based digital inventory was generated by digitizing the rooftops of the sampled buildings and importing the attributes.Statistical regressions were created by plotting damage against shaking intensity and PGA,and Random Forest modeling was carried out considering not only buildings and seismic parameters but also environmental factors.The result indicates that statistical regressions have notable uncertainties,and the Random Forest model shows a≥79%accuracy.Topographical factors showed notable importance in the Random Forest modeling.This work provides an open-access seismic building damage inventory and demonstrates its potential for damage prediction and vulnerability assessment.展开更多
Smart contracts are widely used on the blockchain to implement complex transactions,such as decentralized applications on Ethereum.Effective vulnerability detection of large-scale smart contracts is critical,as attack...Smart contracts are widely used on the blockchain to implement complex transactions,such as decentralized applications on Ethereum.Effective vulnerability detection of large-scale smart contracts is critical,as attacks on smart contracts often cause huge economic losses.Since it is difficult to repair and update smart contracts,it is necessary to find the vulnerabilities before they are deployed.However,code analysis,which requires traversal paths,and learning methods,which require many features to be trained,are too time-consuming to detect large-scale on-chain contracts.Learning-based methods will obtain detection models from a feature space compared to code analysis methods such as symbol execution.But the existing features lack the interpretability of the detection results and training model,even worse,the large-scale feature space also affects the efficiency of detection.This paper focuses on improving the detection efficiency by reducing the dimension of the features,combined with expert knowledge.In this paper,a feature extraction model Block-gram is proposed to form low-dimensional knowledge-based features from bytecode.First,the metadata is separated and the runtime code is converted into a sequence of opcodes,which are divided into segments based on some instructions(jumps,etc.).Then,scalable Block-gram features,including 4-dimensional block features and 8-dimensional attribute features,are mined for the learning-based model training.Finally,feature contributions are calculated from SHAP values to measure the relationship between our features and the results of the detection model.In addition,six types of vulnerability labels are made on a dataset containing 33,885 contracts,and these knowledge-based features are evaluated using seven state-of-the-art learning algorithms,which show that the average detection latency speeds up 25×to 650×,compared with the features extracted by N-gram,and also can enhance the interpretability of the detection model.展开更多
Coasts are subject to multiple natural hazards,which are increasing nowadays.Coastal flooding and erosion are some of the most common hazards affecting coastlines.Being aware of the vulnerability of coasts is importan...Coasts are subject to multiple natural hazards,which are increasing nowadays.Coastal flooding and erosion are some of the most common hazards affecting coastlines.Being aware of the vulnerability of coasts is important to achieve integrated coastal management.The coastal vulnerability index(CVI)is a common index used to assess coastal vulnerability because it is easily calculated.However,given that its calculation includes numerous manual steps,it requires considerable time,which is often unavailable,to produce accurate and utilizable results.In this work,we developed a ModelBuilder model by using the tools provided by ArcGIS Pro(ESRI).Through this model,we automatized most of the steps involved in CVI calculation.We applied the ModelBuilder model in the northern Peloponnese,for which the CVI has already been calculated in three other works.We were thus able to assess the effectiveness of our ModelBuilder model.Our results demonstrated that through the ModelBuilder,most of the processes could effectively be automatized without problems,and our results are consistent with the findings of previous works in our study area.展开更多
The escalating complexity and heterogeneity of modern energy systems—particularly in smart grid and distributed energy infrastructures—has intensified the need for intelligent and scalable security vulnerability cla...The escalating complexity and heterogeneity of modern energy systems—particularly in smart grid and distributed energy infrastructures—has intensified the need for intelligent and scalable security vulnerability classification.To address this challenge,we propose Vulnerability2Vec,a graph-embedding-based framework designed to enhance the automated classification of security vulnerabilities that threaten energy system resilience.Vulnerability2Vec converts Common Vulnerabilities and Exposures(CVE)text explanations to semantic graphs,where nodes represent CVE IDs and key terms(nouns,verbs,and adjectives),and edges capture co-occurrence relationships.Then,it embeds the semantic graphs to a low-dimensional vector space with random-walk sampling and skip-gram with negative sampling.It is possible to identify the latent relationships and structural patterns that traditional sparse vector methods fail to capture.Experimental results demonstrate a classification accuracy of up to 80%,significantly outperforming baseline methods.This approach offers a theoretical basis for classifying vulnerability types as structured semantic patterns in complex software systems.The proposed method models the semantic structure of vulnerabilities,providing a theoretical foundation for their classification.展开更多
This paper proposes a longitudinal vulnerability-based analysis method to evaluate the impact of foundation pit excavation on shield tunnels,accounting for geological uncertainties.First,the shield tunnel is modeled a...This paper proposes a longitudinal vulnerability-based analysis method to evaluate the impact of foundation pit excavation on shield tunnels,accounting for geological uncertainties.First,the shield tunnel is modeled as an Euler Bernoulli beam resting on the Pasternak foundation incorporating variability in subgrade parameters along the tunnel’s length.A random analysis method using random field theory is introduced to evaluate the tunnel’s longitudinal responses to excavation.Next,a risk assessment index system is established.The normalized relative depth between the excavation and the shield tunnel is used as a risk index,while the maximum longitudinal deformation,the maximum circumferential opening,and the maximum longitudinal bending moment serve as performance indicators.Based on these,a method for analyzing the longitudinal fragility of shield tunnels under excavation-induced disturbances is proposed.Finally,the technique is applied to a case study involving a foundation pit excavation above a shield tunnel,which is the primary application scenario of this method.Vulnerability curves for different performance indicators are derived,and the effects of tunnel stiffness and subgrade stiffness on the tunnel vulnerability are explored.The results reveal significant differences in vulnerability curves depending on the performance index used.Compared to the maximum circumferential opening and the maximum longitudinal bending moment,selecting the maximum longitudinal deformation as the control index better ensures the tunnel’s usability and safety under excavation disturbances.The longitudinal vulnerability of the shield tunnel nonlinearly decreases with the increase of the tunnel stiffness and subgrade stiffness,and the subgrade stiffness has a more pronounced effect.Parametric analyses suggest that actively reinforcing the substratum is more effective on reducing the risk of tunnel failure due to adjacent excavations than passive reinforcement of the tunnel structure.展开更多
基金funded by the Scientific Research Foundation of Third Institute of Oceanography,Ministry of Natural Resources(Nos.2022024 and 2020006)the National Natural Science Foundation of China(No.42106143)。
文摘Coral reefs worldwide are losing their species diversity and ecosystem function under threats from global warming and anthropogenic activities.In this study,we investigated the diversity and current state of scleractinian corals surrounding the Qizhou Archipelago.A total of 87 species of scleractinian corals,belonging to 29 genera and 12 families,were found across ten survey sites.The family Merulinidae exhibited the highest species richness(39 species and 12 genera),followed by Acroporidae(15 species and 3genera).The living coral coverage was 16.9%±10.3%(mean±SD)and ranged from 4.6%to 35.1%,which varied significantly.Massive corals such as Porites lutea,Porites lobata,Montipora nodosa,and Favites abdita were dominant species.The recruitment rate of coral larvae was(1.20±0.97)ind/m^(2)(mean±SD).In addition,we constructed an ecological vulnerability assessment system and evaluated the ecological vulnerability of scleractinian corals surrounding the Qizhou Archipelago.The results showed that scleractinian corals at Gouluanpaoshi(GLPS)and Duifan(DF)were highly vulnerable,whereas those on other islands had low to medium vulnerability.In general,the scleractinian corals surrounding the Qizhou Archipelago show low to medium levels of ecological vulnerability.Identifying severely afflicted areas and developing effective methods to manage coral reefs in these regions are crucial.
文摘The Gabes aquifer system,located in southeastern Tunisia,is a crucial resource for supporting local socio-economic activities.Due to its dual porosity structure,is particularly vulnerable to pollution.This study aims to develop a hybrid model that combines the Fracture Aquifer Index(FAI)with the conventional GOD(Groundwater occurrence,Overall lithology,Depth to water table)method,to assess groundwater vulnerability in fractured aquifer.To develop the hybrid model,the classical GOD method was integrated with FAI to produce a single composite index.Each parameter within both GOD and FAI was scored,and a final index was calculated to delineate vulnerable areas.The results show that the study area can be classified into four vulnerability levels:Very low,low,moderate,and high,indicating that approximately 8%of the area exhibits very low vulnerability,29%has low vulnerability,25%falls into the moderate category,and 38%is considered highly vulnerable.The FAI-GOD model further incorporates fracture network characteristics.This refinement reduces the classification to three vulnerability classes:Low,medium,and high.The outcomes demonstrate that 46%of the area is highly vulnerable due to a dense concentration of fractures,while 17%represents an intermediate zone characterized by either shallow or deeper fractures.In contrast,37%corresponds to areas with lightly fractured rock,where the impact on vulnerability is minimal.Multivariate statistical analysis was employed using Principal Components Analysis(PCA)and Hierarchical Cluster Analysis(HCA)on 24 samples across six variables.The first three components account for over 76%of the total variance,reinforcing the significance of fracture dynamics in classifying vulnerability levels.The FAI-GOD model removes the very-low-vulnerability class and expands the spatial extent of low-and high-vulnerability zones,reflecting the dominant influence of fracture networks on aquifer sensitivity.While both indices use a five-class system,FAI-GOD redistributes vulnerability by eliminating very-low-vulnerability areas and amplifying low/high categories,highlighting the critical role of fractures.A strong correlation(R2=0.94)between the GOD and FAI-GOD indices,demonstrated through second-order polynomial regression,confirms the robustness of the FAI-GOD model in accurately predicting vulnerability to pollution.This model provides a useful framework for assessing the vulnerability of complex aquifers and serves as a decision-making tool for groundwater managers in similar areas.
文摘As artificial Intelligence(AI)continues to expand exponentially,particularly with the emergence of generative pre-trained transformers(GPT)based on a transformer’s architecture,which has revolutionized data processing and enabled significant improvements in various applications.This document seeks to investigate the security vulnerabilities detection in the source code using a range of large language models(LLM).Our primary objective is to evaluate the effectiveness of Static Application Security Testing(SAST)by applying various techniques such as prompt persona,structure outputs and zero-shot.To the selection of the LLMs(CodeLlama 7B,DeepSeek coder 7B,Gemini 1.5 Flash,Gemini 2.0 Flash,Mistral 7b Instruct,Phi 38b Mini 128K instruct,Qwen 2.5 coder,StartCoder 27B)with comparison and combination with Find Security Bugs.The evaluation method will involve using a selected dataset containing vulnerabilities,and the results to provide insights for different scenarios according to the software criticality(Business critical,non-critical,minimum effort,best effort)In detail,the main objectives of this study are to investigate if large language models outperform or exceed the capabilities of traditional static analysis tools,if the combining LLMs with Static Application Security Testing(SAST)tools lead to an improvement and the possibility that local machine learning models on a normal computer produce reliable results.Summarizing the most important conclusions of the research,it can be said that while it is true that the results have improved depending on the size of the LLM for business-critical software,the best results have been obtained by SAST analysis.This differs in“NonCritical,”“Best Effort,”and“Minimum Effort”scenarios,where the combination of LLM(Gemini)+SAST has obtained better results.
基金supported by the Major Planning Project of Philosophy and Social Science of Guangdong Province(GD23ZD17)the Humanities and Social Sciences Program of the Ministry of Education(23YJA190006)+3 种基金the Ministry of Education(MOE)Major Project of Philosophy and Social Sciences Research(2025JZDZ024)the MOE Project of the Key Research Institute of Humanities and Social Sciences in Universities(22JJD190008)a grant from the Research Center for Brain Cognition and Human Development of Guangdong(2024B0303390003)the Psychological Services and Counseling Base for the Happy Guangzhou Project.
文摘Background:Exposure to environmental vulnerability poses significant threats to adolescent suicidal ideation,while individual resilience can mitigate these adverse effects with notable gender commonalities and differences.However,research examining how these factors co-configure at the individual level remains limited,particularly from a gender-specific perspective.Thus,the present study aims to adopt a person-centered analytic approach to identify gender-specific configurations of environmental vulnerability and individual resilience associated with suicidal ideation among Chinese adolescents.Methods:Data were collected from 2616 Chinese primary and secondary school students(aged 10–17;1223 girls).Participants completed validated scales measuring environmental vulnerability,individual resilience,and suicidal ideation.Latent profile analysis(LPA)was conducted separately by gender.Results:Gender differences were prominent:males exhibited higher resilience and lower suicidal ideation,while females reported higher environmental vulnerability and elevated levels of suicidal ideation.LPA identified three distinct profiles for males:Low Vulnerable–High Protective–Low Risk(LHL),Medium Vulnerable–Low Protective–Low Risk(MLL),and High Vulnerable–Low Protective–High Risk(HLH).Four profiles emerged for females:LHL,MLL,Medium Vulnerable–Low Protective–Medium Risk(MLM),and HLH.Crucially,within the HLH profile,males exhibited particularly deficient humor(η^(2)=0.19)and confidence(η^(2)=0.16),while females formed a distinct subgroup characterized by severe academic and family stressors(η^(2)=0.30–0.36).Conclusion:The study underscores developing gender-specific mental health interventions using a nuanced,person-centered approach that considers both environmental risk and individual resilience factors,which allows for targeted suicide prevention strategies addressing the unique needs of male and female adolescents.
基金Supported by Chongqing Health Commission and Chongqing Science and Technology Bureau,No.2023MSXM182。
文摘Rheumatoid arthritis(RA)patients face significant psychological challenges alongside physical symptoms,necessitating a comprehensive understanding of how psychological vulnerability and adaptation patterns evolve throughout the disease course.This review examined 95 studies(2000-2025)from PubMed,Web of Science,and CNKI databases including longitudinal cohorts,randomized controlled trials,and mixed-methods research,to characterize the complex interplay between biological,psychological,and social factors affecting RA patients’mental health.Findings revealed three distinct vulnerability trajectories(45%persistently low,30%fluctuating improvement,25%persistently high)and four adaptation stages,with critical intervention periods occurring 3-6 months postdiagnosis and during disease flares.Multiple factors significantly influence psychological outcomes,including gender(females showing 1.8-fold increased risk),age(younger patients experiencing 42%higher vulnerability),pain intensity,inflammatory markers,and neuroendocrine dysregulation(48%showing cortisol rhythm disruption).Early psychological intervention(within 3 months of diagnosis)demonstrated robust benefits,reducing depression incidence by 42%with effects persisting 24-36 months,while different modalities showed complementary advantages:Cognitive behavioral therapy for depression(Cohen’s d=0.68),mindfulness for pain acceptance(38%improvement),and peer support for meaning reconstruction(25.6%increase).These findings underscore the importance of integrating routine psychological assessment into standard RA care,developing stage-appropriate interventions,and advancing research toward personalized biopsychosocial approaches that address the dynamic psychological dimensions of the disease.
基金financially supported by the National Key R&D Program of China(2024YFE0111900)The National Natural Science Foundation of China(U2468214,52378370,52278372)+1 种基金The National Ten Thousand Talent Program for Young Top-notch Talents(2022QB04978)The Science and Technology Program of Hebei Province(2023HBQZYCSB004)。
文摘Global climate change is intensifying the impact of slope hazards,particularly rainfall-induced landslide hazards(RILH),on mountain road networks(MRNs).However,effective quantitative models for dynamically assessing MRNs vulnerability under RILH disturbances are still lacking.To bridge this gap,this study develops a Cascading Failure Model for Rainfall-Induced Landslide Hazard(CFM-RILH).Validation via a case study of the GarzêTibetan Autonomous Prefecture Road Network(GTPRNs)reveals key characteristics of MRNs system vulnerability under RILH disturbances:(1)Under the disturbance effects of RILH,the vulnerability of the MRNs system follows a nonlinear phase transition law that intensifies with increasing disturbance intensity,exhibiting a distinct critical threshold.When the disturbance intensity exceeds this threshold,the system undergoes a global cascading failure phenomenon analogous to an“avalanche.”(2)Under RILH disturbances,the robustness of the MRNs system possesses a distinct safety boundary.Exceeding this boundary not only fails to improve hazard resistance but instead substantially elevates the risk of large-scale cascading failure.(3)Increasing network redundancy may be considered one of the primary engineering measures for enhancing MRNs resilience against such disturbances.Based on these findings,we propose a“Two-Stage Emergency Response and Hierarchical Fortification”strategy specifically to improve the resilience of GTPRNs impacted by RILH.The CFM-RILH model provides an effective tool for assessing road network vulnerability under such hazards.Furthermore,its modeling framework can also inform vulnerability assessment and resilience strategy development for road networks affected by other types of slope hazards.
基金supported by the research start-up funds for invited doctor of Lanzhou University of Technology under Grant 14/062402。
文摘In the context of modern software development characterized by increasing complexity and compressed development cycles,traditional static vulnerability detection methods face prominent challenges including high false positive rates and missed detections of complex logic due to their over-reliance on rule templates.This paper proposes a Syntax-Aware Hierarchical Attention Network(SAHAN)model,which achieves high-precision vulnerability detection through grammar-rule-driven multi-granularity code slicing and hierarchical semantic fusion mechanisms.The SAHAN model first generates Syntax Independent Units(SIUs),which slices the code based on Abstract Syntax Tree(AST)and predefined grammar rules,retaining vulnerability-sensitive contexts.Following this,through a hierarchical attention mechanism,the local syntax-aware layer encodes fine-grained patterns within SIUs,while the global semantic correlation layer captures vulnerability chains across SIUs,achieving synergistic modeling of syntax and semantics.Experiments show that on benchmark datasets like QEMU,SAHAN significantly improves detection performance by 4.8%to 13.1%on average compared to baseline models such as Devign and VulDeePecker.
基金supported by the National Key Research and Development Program of China(No.2021YFA0715900).
文摘Located in northern China,the Hetao Plain is an important agro-economic zone and population centre.The deterioration of local groundwater quality has had a serious impact on human health and economic development.Nowadays,the groundwater vulnerability assessment(GVA)has become an essential task to identify the current status and development trend of groundwater quality.In this study,the Convolutional Neural Network(CNN)and Long Short-Term Memory(LSTM)models are integrated to realize the spatio-temporal prediction of regional groundwater vulnerability by introducing the Self-attention mechanism.The study firstly builds the CNN-LSTM modelwith self-attention(SA)mechanism and evaluates the prediction accuracy of the model for groundwater vulnerability compared to other common machine learning models such as Support Vector Machine(SVM),Random Forest(RF),and Extreme Gradient Boosting(XGBoost).The results indicate that the CNNLSTM model outperforms thesemodels,demonstrating its significance in groundwater vulnerability assessment.It can be posited that the predictions indicate an increased risk of groundwater vulnerability in the study area over the coming years.This increase can be attributed to the synergistic impact of global climate anomalies and intensified local human activities.Moreover,the overall groundwater vulnerability risk in the entire region has increased,evident fromboth the notably high value and standard deviation.This suggests that the spatial variability of groundwater vulnerability in the area is expected to expand in the future due to the sustained progression of climate change and human activities.The model can be optimized for diverse applications across regional environmental assessment,pollution prediction,and risk statistics.This study holds particular significance for ecological protection and groundwater resource management.
基金the Higher Education Commission of Pakistan for supporting the study through the CRG-CPEC-130 project。
文摘Climate change and rising temperatures are accelerating the rate of deglaciation in the Hindu Kush Karakoram Himalaya(HKH)ranges,leading to the formation of new glacial lakes and the expansion of existing ones.These lakes are often vulnerable to failure,posing a significant threat to downstream communities and infrastructure.Therefore,a comprehensive assessment of Glacier-Lake Outburst Flood(GLOF)hazards and risk assessment is crucial to evaluate flood runout characteristics and identify settlements and infrastructure that are exposed and vulnerable to floods,aiding in the development and implementation of risk reduction strategies.This study aims to simulate a GLOF event induced by the Shisper glacier lake in northern Pakistan,using the HEC-RAS,and to assess its impact on settlements,infrastructure,and agricultural land.For the hydrometeorological analysis of the GLOF event,topographic data from unmanned aerial vehicles(UAVs),stream profiles,discharge data,Manning's roughness coefficient(n),and land use/land cover(LULC)were analyzed using HEC-RAS and geographic information system(GIS).During the GLOF event on May 7,2022,a maximum water depth of 6.3 m and a maximum velocity of 9.5 m/s were recorded.Based on the runout characteristics of this event,vulnerability and risk assessments have been calculated.The physical,social,and environmental vulnerabilities of the at-risk elements were evaluated using the analytical hierarchy process(AHP)and integrated with the hazard data to develop a risk map.The study identified the areas,infrastructure and settlements susceptible to GLOF hazard to support the development and implementation of targeted and evidence-based mitigation and adaptation strategies.
文摘In Niger, farms have been facing negative effects of climate change for several decades. The objective of this work is to assess the vulnerability of farms in Tillabery department by proposing an adaptation approach. A five-step method and descriptive analysis were used on a sample of 250 farmers. The degree of damage caused by pests and crop diseases is significant, with respective proportions of 52.50% and 40.40%. It appears that the main climate risk factors for vulnerability are droughts, floods, soil degradation, and pest invasions. Additionally, the average level of exposure to agricultural operations is very high, with an index of 0.6. The sensitivity index remained constant in the range of 0.3 to 0.6 and is significant (reaching an index of 0.8). However, 61.2% of farms have a medium level of vulnerability and 33.3% have a high vulnerability to the effects of climate change. Nonetheless, a concerning trend regarding the vulnerability of farms has been observed. To assist policymakers and development actors in improving the vulnerability level of these production units, four phases of action are proposed: a diagnostic phase, evaluation, estimation of adaptation needs, implementation, and proper monitoring of actions.
基金supported by Ecological Conservation and High-Quality Development of the Yellow River Basin program(2022-YRUC-01-0102)the Second Tibetan Plateau Scientific Expedition and Research Program(Grant No.2019QZKK0405).
文摘Understanding the response of ecological vulnerability to global changes is essential for sustainable ecosystem management.However,incorporating ecological vulnerability assessment into accurate decision-making to illustrate ecosystem dynamics and drivers remains unclear.Here,we used the Vulnerability Scoping Diagram model(VSD)and ecological vulnerability index(EVI)to evaluate the ecosystem vulnerability in the upper reach of Yellow River(URYR).Our results show that EVI increased from southwest to northeast,and EVI first went down from 2005 to 2015 and then slightly up from 2015 to 2020.The lower EVI can be attributed to improving environmental protection and restoration projects in the URYR.The subareas of the High-High cluster were distributed in the northeast,and those of the Low-Low cluster were distributed in the southwest,respectively.Furthermore,we divided the URYR into four zones(e.g.,Protection Zone,Ecological Degradation Zone,Ecological Improvement Zone,and Comprehensive Zone)according to the EVI.Consequently,a long-term detection system and public education should be enhanced to improve environmental awareness,which plays a beneficial role in the sustainable development of four zones.All in all,our findings not only shed light on the dynamic of ecological vulnerability but also provide the diversification management of the upper reach of the Yellow River.
基金supported by the"Vietnam Sea for the Goals of National Defence and National Development"project managed by the Office of the Vietnam Academy of Social Sciences。
文摘Vietnam,with its twenty-eight coastal provinces,is one of the nations most profoundly affected by the adverse impacts of climate change(CC).These provinces face severe challenges as they contend with the escalating effects of CC,including rising sea levels,typhoons,flooding,and droughts.In this context,this article aims to assess the vulnerability of households'livelihoods in Quang Nam Province by applying the Livelihood Vulnerability Index(LVI)developed by Hahn et al.,along with the Intergovernmental Panel on Climate Change framework(LVI-IPCC).The study utilises five sources of household capital—human,social,physical,natural,and financial—to construct its indices.The data for this article is based on a survey of 200 households.The research methodology combines both quantitative and qualitative methods,including questionnaire interviews,in-depth interviews,and focus group discussions.The research period spans from 2021 to 2023.The study results revealed that the household LVI was 0.371,while the LVI-IPCC was 0.086,highlighting the critical need for access to food and clean water,which scored 0.458 and 0.351,respectively.The research underscores how CC significantly affects the livelihoods of coastal communities,particularly in sectors such as fishing,aquaculture,and agriculture.The study concludes that CC poses significant challenges to the livelihoods of coastal communities in Quang Nam Province and that adaptation measures are necessary to support these communities.The research highlights the importance of livelihood diversification,job transformation,and improving knowledge and skills to enhance the resilience of coastal communities to CC.
文摘The negative impacts of natural hazards on communities at all scales have been increasing.Floods comprise one such natural hazard that has emerged as one of the most destructive in the US and worldwide.While a lot of damage is estimated in terms of the cost of rebuilding infrastructure and direct loss of economy,the negative impacts of such disruptions go beyond the physical infrastructure.The impact on(and of)the social and institutional framework is rarely examined in conjunction with the physical and technical aspects.This paper examines flood vulnerability and risk of a community at an intersection of social,ecological,technical,and intuitional perspectives,and presents a framework for a holistic flood vulnerability and risk assessment that has a strong foundation in all four aspects of a resilient community.The study builds on the existing risk,vulnerability,and hazard assessment approaches,and refines them with a holistic perspective.The study uses a mixed method approach with qualitative and quantitative methodologies to assess flood occurrence probabilities,vulnerability,and risk from the social,ecological,technical,and institutional perspectives.A case study of the City of Atlanta is conducted using the framework to assess the overall vulnerability and risk of the city.The results of this analysis show that the regions that have the highest probability of flood hazard occurrence also appear to have the highest social,ecological,and technical vulnerabilities in the Atlanta area.While the results are intuitive,the applications support a focus on holistic resilience building across these four criteria.This study is potentially useful to practitioners,researchers,government agencies,and community organizations working to mitigate flood risk particularly as this risk continues to evolve with the changing climate.
文摘Bioethics is the argumentative discipline of decisions and actions that reduce conflicts of interest,dilemmas,or asymmetries between the parties involved in biomedical research.The objective of this work was to review and compare the dimensions used by bioethics in the communicative management of the pandemic,namely:risk,vulnerability,resilience,and stigma.A documentary,exploratory,transversal,and retrospective work was carried out with a sample of sources indexed in international repositories,considering the search by keywords and the publication period from 2020 to 2024.The results demonstrate the prevalence of supply,contagion,symptoms,and help against COVID-19.
基金supported by grants received by the first author and third author from the Institute of Eminence,Delhi University,Delhi,India,as part of the Faculty Research Program via Ref.No./IoE/2024-25/12/FRP.
文摘Software systems are vulnerable to security breaches as they expand in complexity and functionality.The confidentiality,integrity,and availability of data are gravely threatened by flaws in a system’s design,implementation,or configuration.To guarantee the durability&robustness of the software,vulnerability identification and fixation have become crucial areas of focus for developers,cybersecurity experts and industries.This paper presents a thorough multi-phase mathematical model for efficient patch management and vulnerability detection.To uniquely model these processes,the model incorporated the notion of the learning phenomenon in describing vulnerability fixation using a logistic learning function.Furthermore,the authors have used numerical methods to approximate the solution of the proposed framework where an analytical solution is difficult to attain.The suggested systematic architecture has been demonstrated through statistical analysis using patch datasets,which offers a solid basis for the research conclusions.According to computational research,learning dynamics improves security response and results in more effective vulnerability management.The suggested model offers a systematic approach to proactive vulnerability mitigation and has important uses in risk assessment,software maintenance,and cybersecurity.This study helps create more robust software systems by increasing patch management effectiveness,which benefits developers,cybersecurity experts,and sectors looking to reduce security threats in a growing digital world.
基金supported by Mission No. 9 "Geological Environment and Hazards" (2019QZKK0900) of "The Second Tibetan Plateau Scientific Expedition and Research" projectNational Natural Science Foundation of China (No.42101087)
文摘Spatial seismic vulnerability assessments are primally conducted at the community and grid level,using heuristic and empirical approaches.Building-based spatial statistical vulnerability models are rare because of data limitations.Generating open-access spatial inventories that document seismic damage and building attributes and test their effectiveness in assessing damage would promote the advancement of spatial vulnerability assessment.The 2022 Mw 6.7 Luding earthquake in the western Sichuan Province of China provides an opportunity to validate this approach.The local government urgently dispatched experts to survey building damage,marking all buildings with damage class stickers.In this work,we sampled 2889 buildings as GPS points and documented the damage classes and building attributes,including structure type,number of floors,and age.A polygon-based digital inventory was generated by digitizing the rooftops of the sampled buildings and importing the attributes.Statistical regressions were created by plotting damage against shaking intensity and PGA,and Random Forest modeling was carried out considering not only buildings and seismic parameters but also environmental factors.The result indicates that statistical regressions have notable uncertainties,and the Random Forest model shows a≥79%accuracy.Topographical factors showed notable importance in the Random Forest modeling.This work provides an open-access seismic building damage inventory and demonstrates its potential for damage prediction and vulnerability assessment.
基金partially supported by the National Natural Science Foundation (62272248)the Open Project Fund of State Key Laboratory of Computer Architecture,Institute of Computing Technology,Chinese Academy of Sciences (CARCHA202108,CARCH201905)+1 种基金the Natural Science Foundation of Tianjin (20JCZDJC00610)Sponsored by Zhejiang Lab (2021KF0AB04)。
文摘Smart contracts are widely used on the blockchain to implement complex transactions,such as decentralized applications on Ethereum.Effective vulnerability detection of large-scale smart contracts is critical,as attacks on smart contracts often cause huge economic losses.Since it is difficult to repair and update smart contracts,it is necessary to find the vulnerabilities before they are deployed.However,code analysis,which requires traversal paths,and learning methods,which require many features to be trained,are too time-consuming to detect large-scale on-chain contracts.Learning-based methods will obtain detection models from a feature space compared to code analysis methods such as symbol execution.But the existing features lack the interpretability of the detection results and training model,even worse,the large-scale feature space also affects the efficiency of detection.This paper focuses on improving the detection efficiency by reducing the dimension of the features,combined with expert knowledge.In this paper,a feature extraction model Block-gram is proposed to form low-dimensional knowledge-based features from bytecode.First,the metadata is separated and the runtime code is converted into a sequence of opcodes,which are divided into segments based on some instructions(jumps,etc.).Then,scalable Block-gram features,including 4-dimensional block features and 8-dimensional attribute features,are mined for the learning-based model training.Finally,feature contributions are calculated from SHAP values to measure the relationship between our features and the results of the detection model.In addition,six types of vulnerability labels are made on a dataset containing 33,885 contracts,and these knowledge-based features are evaluated using seven state-of-the-art learning algorithms,which show that the average detection latency speeds up 25×to 650×,compared with the features extracted by N-gram,and also can enhance the interpretability of the detection model.
文摘Coasts are subject to multiple natural hazards,which are increasing nowadays.Coastal flooding and erosion are some of the most common hazards affecting coastlines.Being aware of the vulnerability of coasts is important to achieve integrated coastal management.The coastal vulnerability index(CVI)is a common index used to assess coastal vulnerability because it is easily calculated.However,given that its calculation includes numerous manual steps,it requires considerable time,which is often unavailable,to produce accurate and utilizable results.In this work,we developed a ModelBuilder model by using the tools provided by ArcGIS Pro(ESRI).Through this model,we automatized most of the steps involved in CVI calculation.We applied the ModelBuilder model in the northern Peloponnese,for which the CVI has already been calculated in three other works.We were thus able to assess the effectiveness of our ModelBuilder model.Our results demonstrated that through the ModelBuilder,most of the processes could effectively be automatized without problems,and our results are consistent with the findings of previous works in our study area.
基金supported by the MSIT(Ministry of Science and ICT),Republic of Korea,under the Convergence Security Core Talent Training Business Support Program(IITP-2025-RS-2023-00266605,50%)in part by the Institute of Information&Communications Technology Planning&Evaluation(lITP)grant funded by the Korea government(MSIT)(RS-2025-02305436,Development of Digital Innovative Element Technologies for Rapid Prediction of Potential Complex Disasters and Continuous Disaster Prevention,30%)supported by the Chung-Ang University Graduate Research Scholar-ship in 2023(20%).
文摘The escalating complexity and heterogeneity of modern energy systems—particularly in smart grid and distributed energy infrastructures—has intensified the need for intelligent and scalable security vulnerability classification.To address this challenge,we propose Vulnerability2Vec,a graph-embedding-based framework designed to enhance the automated classification of security vulnerabilities that threaten energy system resilience.Vulnerability2Vec converts Common Vulnerabilities and Exposures(CVE)text explanations to semantic graphs,where nodes represent CVE IDs and key terms(nouns,verbs,and adjectives),and edges capture co-occurrence relationships.Then,it embeds the semantic graphs to a low-dimensional vector space with random-walk sampling and skip-gram with negative sampling.It is possible to identify the latent relationships and structural patterns that traditional sparse vector methods fail to capture.Experimental results demonstrate a classification accuracy of up to 80%,significantly outperforming baseline methods.This approach offers a theoretical basis for classifying vulnerability types as structured semantic patterns in complex software systems.The proposed method models the semantic structure of vulnerabilities,providing a theoretical foundation for their classification.
基金Project(52178402) supported by the National Natural Science Foundation of China。
文摘This paper proposes a longitudinal vulnerability-based analysis method to evaluate the impact of foundation pit excavation on shield tunnels,accounting for geological uncertainties.First,the shield tunnel is modeled as an Euler Bernoulli beam resting on the Pasternak foundation incorporating variability in subgrade parameters along the tunnel’s length.A random analysis method using random field theory is introduced to evaluate the tunnel’s longitudinal responses to excavation.Next,a risk assessment index system is established.The normalized relative depth between the excavation and the shield tunnel is used as a risk index,while the maximum longitudinal deformation,the maximum circumferential opening,and the maximum longitudinal bending moment serve as performance indicators.Based on these,a method for analyzing the longitudinal fragility of shield tunnels under excavation-induced disturbances is proposed.Finally,the technique is applied to a case study involving a foundation pit excavation above a shield tunnel,which is the primary application scenario of this method.Vulnerability curves for different performance indicators are derived,and the effects of tunnel stiffness and subgrade stiffness on the tunnel vulnerability are explored.The results reveal significant differences in vulnerability curves depending on the performance index used.Compared to the maximum circumferential opening and the maximum longitudinal bending moment,selecting the maximum longitudinal deformation as the control index better ensures the tunnel’s usability and safety under excavation disturbances.The longitudinal vulnerability of the shield tunnel nonlinearly decreases with the increase of the tunnel stiffness and subgrade stiffness,and the subgrade stiffness has a more pronounced effect.Parametric analyses suggest that actively reinforcing the substratum is more effective on reducing the risk of tunnel failure due to adjacent excavations than passive reinforcement of the tunnel structure.
