Vulnerabilities are a known problem in modern Open Source Software(OSS).Most developers often rely on third-party libraries to accelerate feature implementation.However,these libraries may contain vulnerabilities that...Vulnerabilities are a known problem in modern Open Source Software(OSS).Most developers often rely on third-party libraries to accelerate feature implementation.However,these libraries may contain vulnerabilities that attackers can exploit to propagate malicious code,posing security risks to dependent projects.Existing research addresses these challenges through Software Composition Analysis(SCA)for vulnerability detection and remediation.Nevertheless,current solutions may introduce additional issues,such as incompatibilities,dependency conflicts,and additional vulnerabilities.To address this,we propose Vulnerability Scan and Protection(VulnScanPro),a robust solution for detection and remediation vulnerabilities in Java projects.Specifically,VulnScanPro builds a finegrained method graph to identify unreachable methods.The method graph is mapped to the project’s dependency tree,constructing a comprehensive vulnerability propagation graph that identifies unreachable vulnerable APIs and dependencies.Based on this analysis,we propose three solutions for vulnerability remediation:(1)Removing unreachable vulnerable dependencies,thereby resolving security risks and reducing maintenance overhead.(2)Upgrading vulnerable dependencies to the closest non-vulnerable versions,while pinning the versions of transitive dependencies introduced by the vulnerable dependency,in order to mitigate compatibility issues and prevent the introduction of new vulnerabilities.(3)Eliminating unreachable vulnerable APIs,particularly when security patches are either incompatible or absent.Experimental results show that these solutions effectively mitigate vulnerabilities and enhance the overall security of the project.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
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
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
Since the advent of smart contracts,security vulnerabilities have remained a persistent challenge,compromsing both the reliability of contract execution and the overall stability of the virtual currency market.Consequ...Since the advent of smart contracts,security vulnerabilities have remained a persistent challenge,compromsing both the reliability of contract execution and the overall stability of the virtual currency market.Consequently,the academic community has devoted increasing attention to these security risks.However,conventional approaches to vulnerability detection frequently exhibit limited accuracy.To address this limitation,the present study introduces a novel vulnerability detection framework called GNNSE that integrates symbolic execution with graph neural networks(GNNs).The proposedmethod first constructs semantic graphs to comprehensively capture the control flow and data flow dependencies within smart contracts.These graphs are subsequently processed using GNNs to efficiently identify contracts with a high likelihood of vulnerabilities.For these high-risk contracts,symbolic execution is employed to perform fine-grained,path-level analysis,thereby improving overall detection precision.Experimental results on a dataset comprising 10,079 contracts demonstrate that the proposed method achieves detection precisions of 93.58% for reentrancy vulnerabilities and 92.73% for timestamp-dependent vulnerabilities.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
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
This study presents a hybrid methodology for predicting building collapses within the Intelligent Circular Resilience(ICR)framework.This uses a supervised Machine Learning(ML)approach,earthquake damage re-ports,and th...This study presents a hybrid methodology for predicting building collapses within the Intelligent Circular Resilience(ICR)framework.This uses a supervised Machine Learning(ML)approach,earthquake damage re-ports,and the Simplified Resilience Index(SRI),derived from existing earthquake damage models(EDM)-based on fragility and vulnerability functions-used in the probabilistic seismic risk assessment(PSRA).A curated building damage database comprising 89 structures(71 collapsed and 18 non-collapsed)from ten countries affected by major earthquakes(Mw 6.1-8.1,epicentral distances of 3-125 km,and PGA values ranging from 0.14 g to 0.82 g)was developed,including attributes related to exposure:occupancy,main structural material,number of stories,construction year,and hazard:magnitude,epicentral distance,intensity measures(Peak-ground acceleration,PGA,and elastic spectral acceleration).The dataset includes events such as the 2017 Puebla-Morelos earthquake(Mw 7.1,Mexico),the 1999 Kocaeli earthquake(Mw 7.6,Turkey),and the 2011 Christchurch earthquake(Mw 6.1,New Zealand),among others.Likewise,dependent attributes such as time elapsed and SRI(under 120-,180-,and 365-day recovery scenarios)were calculated using 2-EDMs.Eight Random Forest models were trained and tested for collapse and non-collapse classification using combinations of independent and dependent attributes.The results indicate that models incorporating exposure-related varia-bles-such as structural material,number of stories,construction year,and occupancy-alongside the SRI significantly improve collapse classification performance,achieving recall and F1 scores above 95%.Notably,many collapsed buildings exhibited low intensities(PGA≤0.25 g),emphasizing the influence of local site effects-particularly in Mexico City.The findings demonstrate that incorporating SRI enhances the reliability of collapse prediction and supports its use as an interpretable resilience proxy during early ICR stages.This hybrid methodology bridges empirical data,traditional PSRA models,and ML techniques,contributing to more accurate and scalable post-earthquake resilience assessments.展开更多
Background Frailty is common and significantly impacts prognosis in heart failure(HF). The Vulnerable Elders Survey-13(VES-13), widely used in oncogeriatrics and public health, remains unexplored as a frailty screenin...Background Frailty is common and significantly impacts prognosis in heart failure(HF). The Vulnerable Elders Survey-13(VES-13), widely used in oncogeriatrics and public health, remains unexplored as a frailty screening tool in HF outpatients. In this study, we prospectively evaluated VES-13 against a multimodal screening assessment in detecting frailty and predicting individual risk of adverse prognosis.Methods Frailty was assessed at the initial visit using both a multimodal approach, incorporating Barthel Index, Older American Resources and Services scale, Pfeiffer Test, abbreviated Geriatric Depression Scale, age > 85 years, lacking support systems,and VES-13. Patients scoring ≥ 3 on VES-13 or meeting at least one multimodal criterion were classified as frail. Endpoints included all-cause mortality, a composite of death or HF hospitalization, and recurrent HF hospitalizations.Results A total of 301 patients were evaluated. VES-13 identified 40.2% as frail and the multimodal assessment 33.2%. In Cox regression analyses, frailty identified by VES-13 showed greater prognostic significance than the multimodal assessment for allcause mortality(HR = 3.70 [2.15–6.33], P < 0.001 vs. 2.40 [1.46–4.0], P = 0.001) and the composite endpoint(HR = 3.13 [2.02–4.84], P< 0.001 vs. 1.96 [1.28–2.99], P = 0.002). Recurrent HF hospitalizations were four times more frequent in VES-13 frail patients while two times in those identified as frail by the multimodal assessment. Additionally, stratifying patients by VES-13 tertiles provided robust risk differentiation.Conclusions VES-13, a simple frailty tool, outperformed a comprehensive multimodal assessment and could be easily integrated into routine HF care, highlighting its clinical utility in identifying patients at risk for poor outcomes.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
Ecosystems along the eastern margin of the Qinghai-Tibet Plateau(EQTP)are highly fragile and extremely sensitive to climate change and human disturbances.To quantitatively assess climate-induced ecosystem responses,th...Ecosystems along the eastern margin of the Qinghai-Tibet Plateau(EQTP)are highly fragile and extremely sensitive to climate change and human disturbances.To quantitatively assess climate-induced ecosystem responses,this study proposes a Climate-Induced Productivity Index(CIPI)based on the Super Slack-Based Measure(Super-SBM)model using remote sensing data from 2001 to 2020.The results reveal persistently low CIPI values(0.47-0.53)across major ecosystem types,indicating widespread vulnerability to climatic variability.Among these ecosystems,forests exhibit the highest CIPI(0.55),followed by shrublands(0.54),croplands(0.53),grasslands(0.51),and barelands(0.43).The Theil index analysis further demonstrates significant intra-group disparities,suggesting that extreme climatic events amplify CIPI heterogeneity.Moreover,the dominant environmental drivers differ among ecosystem types:the Palmer Drought Severity Index(PDSI)primarily constrains grassland productivity,solar radiation(SRAD)strongly influences shrub and cropland systems,whereas subsurface factors exert greater control in forested regions.This study provides a quantitative framework for evaluating climate-ecosystem interactions and offers a scientific basis for long-term ecological monitoring and security planning across the EQTP.展开更多
We develop and implement a Stochastic Discrete Event Simulation(SDES)algorithm to model the housing re-covery trajectory after an extreme event.The algorithm models discrete events and their underlying uncertainties i...We develop and implement a Stochastic Discrete Event Simulation(SDES)algorithm to model the housing re-covery trajectory after an extreme event.The algorithm models discrete events and their underlying uncertainties in each construction phase.Specifically,the algorithm is developed for the Government Assisted Owner Driven(GAOD)reconstruction system to simulate long-term recovery trajectory.SDES,as a flexible modeling approach,can simulate any housing recovery scenario that follows phased reconstruction.The 2015 M 7.8 Gorkha earthquake sequence in Nepal is considered the extreme event,with 796,245 buildings requiring reconstruction.We present some recovery trajectories from severely hit,crisis hit,and earthquake hit parishes,comparing them with the actual reconstruction progress.We also assess quality and improvement of reconstructed buildings using seismic fragility functions,compared to pre-earthquake constructions.Housing recovery uncertainties are dissected in relation to reconstruction pace.We conclude that the vast majority of the reconstructed buildings followed the Build Back Better(BBB)approach and missed the opportunity to pursue the Build Back Resilient(BBR)approach due to multifaceted challenges ranging from unclear policies to economic constraints.We critically assess the GAOD vs Owner Driven(OD)recovery framework and conclude that insurance-supported and technically assisted OD approach could be the most suitable model for post extreme event housing recovery.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
Hurricanes are one of the most destructive natural disasters that can cause catastrophic losses to both communities and infrastructure.Assessment of hurricane risk furnishes a spatial depiction of the interplay among ...Hurricanes are one of the most destructive natural disasters that can cause catastrophic losses to both communities and infrastructure.Assessment of hurricane risk furnishes a spatial depiction of the interplay among hazard,vulnerability,exposure,and mitigation capacity,crucial for understanding and managing the risks hurricanes pose to communities.These assessments aid in gauging the efficacy of existing hurricane mitigation strategies and gauging their resilience across diverse climate change scenarios.A systematic review was conducted,encompassing 94 articles,to scrutinize the structure,data inputs,assumptions,methodologies,perils modelled,and key predictors of hurricane risk.This review identified key research gaps essential for enhancing future risk assessments.The complex interaction between hurricane perils may be disastrous and underestimated in the majority of risk assessments which focus on a single peril,commonly storm surge and flood,resulting in inadequacies in disaster resilience planning.Most risk assessments were based on hurricane frequency rather than hurricane damage,which is more insightful for policymakers.Furthermore,considering secondary indirect impacts stemming from hurricanes,including real estate market and business interruption,could enrich economic impact assessments.Hurricane mitigation measures were the most under-utilised category of predictors leveraged in only 5%of studies.The top six predictive factors for hurricane risk were land use,slope,precipitation,elevation,population density,and soil texture/drainage.Another notable research gap identified was the potential of machine learning techniques in risk assessments,offering advantages over traditional MCDM and numerical models due to their ability to capture complex nonlinear relationships and adaptability to different study regions.Existing machine learning based risk assessments leverage random forest models(42%of studies)followed by neural network models(19%of studies),with further research required to investigate diverse machine learning algorithms such as ensemble models.A further research gap is model validation,in particular assessing transferability to a new study region.Additionally,harnessing simulated data and refining projections related to demographic and built environment dynamics can bolster the sophistication of climate change scenario assessments.By addressing these research gaps,hurricane risk assessments can furnish invaluable insights for national policymakers,facilitating the development of robust hurricane mitigation strategies and the construction of hurricane-resilient communities.To the authors’knowledge,this represents the first literature review specifically dedicated to quantitative hurricane risk assessments,encompassing a comparison of Multi-criteria Decision Making(MCDM),numerical models,and machine learning models.Ultimately,advancements in hurricane risk assessments and modelling stand poised to mitigate potential losses to communities and infrastructure both in the immediate and long-term future.展开更多
Agricultural pests cause enormous losses in annual agricultural production.Understanding the evolutionary responses and adaptive capacity of agricultural pests under climate change is crucial for establishing sustaina...Agricultural pests cause enormous losses in annual agricultural production.Understanding the evolutionary responses and adaptive capacity of agricultural pests under climate change is crucial for establishing sustainable and environmentally friendly agricultural pest management.In this study,we integrate climate modeling and landscape genomics to investigate the distributional dynamics of the cotton bollworm(Helicoverpa armigera)in the adaptation to local environments and resilience to future climate change.Notably,the predicted inhabitable areas with higher suitability for the cotton bollworm could be eight times larger in the coming decades.Climate change is one of the factors driving the dynamics of distribution and population differentiation of the cotton bollworm.Approximately 19,000 years ago,the cotton bollworm expanded from its ancestral African population,followed by gradual occupations of the European,Asian,Oceanian,and American continents.Furthermore,we identify seven subpopulations with high dispersal and adaptability which may have an increased risk of invasion potential.Additionally,a large number of candidate genes and SNPs linked to climatic adaptation were mapped.These findings could inform sustainable pest management strategies in the face of climate change,aiding future pest forecasting and management planning.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
Objectives:This study aimed to assess the impact of exercise rehabilitation during the vulnerable period on cardiac recovery(CR)outcomes in patients with acute decompensated heart failure(ADHF).Methods:Multiple databa...Objectives:This study aimed to assess the impact of exercise rehabilitation during the vulnerable period on cardiac recovery(CR)outcomes in patients with acute decompensated heart failure(ADHF).Methods:Multiple databases including PubMed,Web of Science,Embase,the Cochrane Library,CINAHL,China National Knowledge Infrastructure(CNKI),Chinese Science and Technology Periodical Database(VIP),Wanfang database,SinoMed,ClinicalTrials.gov,and American Heart Association(AHA)and European Society of Cardiology(ESC)were searched for RCTs on exercise rehabilitation in ADHF patients’vulnerable period from inception to April 2,2025.The risk of bias was assessed with Cochrane Risk of Bias 2.0,and data were analyzed in RevMan 5.3.Results:A total of seven RCTs involving 946 patients were included.The results demonstrated that exercise rehabilitation training during the vulnerable period in patients with ADHF significantly increased the 6-min walk test distance(6-MWTD)(SMD=0.37;95%CI:0.09,0.65;P=0.01),short physical performance battery(SPPB)score(MD=1.26;95%CI:0.82,1.70;P<0.001)and peak oxygen consumption(VO2peak)(SMD=1.43;95%CI:0.53,2.34;P=0.002),improved quality of life(QoL)(SMD=0.85;95%CI:0.07,1.64,P=0.03),reduced depression score(MD=-0.73;95%CI:1.27,-0.18;P=0.009),frailty(MD=-0.22;95%CI:-0.48,0.05;P=0.11),and decreased 6-month all-cause readmission(OR=0.67;95%CI:0.49,0.91;P=0.01).However,no statistically significantdifferences were observed between the two groups in left ventricular ejection fraction(LVEF)(MD=0.96;95%CI:-1.84,3.77;P=0.50),6-month heart failure(HF)-related readmission(OR=1.01;95%CI:0.66,1.53;P=0.98),and all-cause mortality(OR=0.63;95%CI:0.18,2.24;P=0.47).There were no adverse events reported.Conclusions:Exercise rehabilitation during the vulnerable phase improves exercise tolerance,QoL,and depressive symptoms while reducing 6-month all-cause readmissions in ADHF patients,with no reported adverse events.Although trends toward improved LVEF,HF-related readmissions,and all-cause mortality were observed.Large-scale,high-quality studies are warranted to explore individualized responses and long-term outcomes.展开更多
Allogeneic hematopoietic cell transplantation(allo-HCT) remains a cornerstone therapy for severe hematologic malignancies, offering a potential cure when conventional therapies are ineffective. However, not all patien...Allogeneic hematopoietic cell transplantation(allo-HCT) remains a cornerstone therapy for severe hematologic malignancies, offering a potential cure when conventional therapies are ineffective. However, not all patients are suitable recipients of allo-HCT, particularly the elderly patients and those with high comorbidity burdens.Furthermore, patients who develop relapse or graft failure after initial transplantation encounter additional challenges when evaluated for a second transplant.展开更多
This study presents an innovative approach to enhancing the security of visual medical data in the generative AI environment through the integration of blockchain technology.By combining the strengths of blockchain an...This study presents an innovative approach to enhancing the security of visual medical data in the generative AI environment through the integration of blockchain technology.By combining the strengths of blockchain and generative AI,the research team aimed to address the timely challenge of safeguarding visual medical content.The participating researchers conducted a comprehensive analysis,examining the vulnerabilities of medical AI services,personal information protection issues,and overall security weaknesses.This multi faceted exploration led to an indepth evaluation of the model’s performance and security.Notably,the correlation between accuracy,detection rate,and error rate was scrutinized.This analysis revealed insights into the model’s strengths and limitations,while the consideration of standard deviation shed light on the model’s stability and performance variability.The study proposed practical improvements,emphasizing the reduction of false negatives to enhance detection rate and leveraging blockchain technology to ensure visual data integrity in medical applications.Applying blockchain to generative AI-created medical content addresses key personal information protection issues.By utilizing the distributed ledger system of blockchain,the research team aimed to protect the privacy and integrity of medical data especially medical images.This approach not only enhances security but also enables transparent and tamperproof record-keeping.Additionally,the use of generative AI models ensures the creation of novel medical content without compromising personal information,further safeguarding patient privacy.In conclusion,this study showcases the potential of blockchain-based solutions in the medical field,particularly in securing sensitive medical data and protecting patient privacy.The proposed approach,combining blockchain and generative AI,offers a promising direction toward more robust and secure medical content management.Further research and advancements in this area will undoubtedly contribute to the development of robust and privacy-preserving healthcare systems,and visual diagnostic systems.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.62141210)the Fundamental Research Funds for the Central Universities(Grant No.N2217005)+1 种基金Open Fund of State Key Lab.for Novel Software Technology,Nanjing University(KFKT2021B01)111 Project(B16009).
文摘Vulnerabilities are a known problem in modern Open Source Software(OSS).Most developers often rely on third-party libraries to accelerate feature implementation.However,these libraries may contain vulnerabilities that attackers can exploit to propagate malicious code,posing security risks to dependent projects.Existing research addresses these challenges through Software Composition Analysis(SCA)for vulnerability detection and remediation.Nevertheless,current solutions may introduce additional issues,such as incompatibilities,dependency conflicts,and additional vulnerabilities.To address this,we propose Vulnerability Scan and Protection(VulnScanPro),a robust solution for detection and remediation vulnerabilities in Java projects.Specifically,VulnScanPro builds a finegrained method graph to identify unreachable methods.The method graph is mapped to the project’s dependency tree,constructing a comprehensive vulnerability propagation graph that identifies unreachable vulnerable APIs and dependencies.Based on this analysis,we propose three solutions for vulnerability remediation:(1)Removing unreachable vulnerable dependencies,thereby resolving security risks and reducing maintenance overhead.(2)Upgrading vulnerable dependencies to the closest non-vulnerable versions,while pinning the versions of transitive dependencies introduced by the vulnerable dependency,in order to mitigate compatibility issues and prevent the introduction of new vulnerabilities.(3)Eliminating unreachable vulnerable APIs,particularly when security patches are either incompatible or absent.Experimental results show that these solutions effectively mitigate vulnerabilities and enhance the overall security of the project.
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.
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supported by the National Key Research and Development Program of China(2020YFB1005704).
文摘Since the advent of smart contracts,security vulnerabilities have remained a persistent challenge,compromsing both the reliability of contract execution and the overall stability of the virtual currency market.Consequently,the academic community has devoted increasing attention to these security risks.However,conventional approaches to vulnerability detection frequently exhibit limited accuracy.To address this limitation,the present study introduces a novel vulnerability detection framework called GNNSE that integrates symbolic execution with graph neural networks(GNNs).The proposedmethod first constructs semantic graphs to comprehensively capture the control flow and data flow dependencies within smart contracts.These graphs are subsequently processed using GNNs to efficiently identify contracts with a high likelihood of vulnerabilities.For these high-risk contracts,symbolic execution is employed to perform fine-grained,path-level analysis,thereby improving overall detection precision.Experimental results on a dataset comprising 10,079 contracts demonstrate that the proposed method achieves detection precisions of 93.58% for reentrancy vulnerabilities and 92.73% for timestamp-dependent vulnerabilities.
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Vicerrectoría de Inves-tigaciones of the UMNG for the financial support of the IMP-ING-3743 Project.
文摘This study presents a hybrid methodology for predicting building collapses within the Intelligent Circular Resilience(ICR)framework.This uses a supervised Machine Learning(ML)approach,earthquake damage re-ports,and the Simplified Resilience Index(SRI),derived from existing earthquake damage models(EDM)-based on fragility and vulnerability functions-used in the probabilistic seismic risk assessment(PSRA).A curated building damage database comprising 89 structures(71 collapsed and 18 non-collapsed)from ten countries affected by major earthquakes(Mw 6.1-8.1,epicentral distances of 3-125 km,and PGA values ranging from 0.14 g to 0.82 g)was developed,including attributes related to exposure:occupancy,main structural material,number of stories,construction year,and hazard:magnitude,epicentral distance,intensity measures(Peak-ground acceleration,PGA,and elastic spectral acceleration).The dataset includes events such as the 2017 Puebla-Morelos earthquake(Mw 7.1,Mexico),the 1999 Kocaeli earthquake(Mw 7.6,Turkey),and the 2011 Christchurch earthquake(Mw 6.1,New Zealand),among others.Likewise,dependent attributes such as time elapsed and SRI(under 120-,180-,and 365-day recovery scenarios)were calculated using 2-EDMs.Eight Random Forest models were trained and tested for collapse and non-collapse classification using combinations of independent and dependent attributes.The results indicate that models incorporating exposure-related varia-bles-such as structural material,number of stories,construction year,and occupancy-alongside the SRI significantly improve collapse classification performance,achieving recall and F1 scores above 95%.Notably,many collapsed buildings exhibited low intensities(PGA≤0.25 g),emphasizing the influence of local site effects-particularly in Mexico City.The findings demonstrate that incorporating SRI enhances the reliability of collapse prediction and supports its use as an interpretable resilience proxy during early ICR stages.This hybrid methodology bridges empirical data,traditional PSRA models,and ML techniques,contributing to more accurate and scalable post-earthquake resilience assessments.
文摘Background Frailty is common and significantly impacts prognosis in heart failure(HF). The Vulnerable Elders Survey-13(VES-13), widely used in oncogeriatrics and public health, remains unexplored as a frailty screening tool in HF outpatients. In this study, we prospectively evaluated VES-13 against a multimodal screening assessment in detecting frailty and predicting individual risk of adverse prognosis.Methods Frailty was assessed at the initial visit using both a multimodal approach, incorporating Barthel Index, Older American Resources and Services scale, Pfeiffer Test, abbreviated Geriatric Depression Scale, age > 85 years, lacking support systems,and VES-13. Patients scoring ≥ 3 on VES-13 or meeting at least one multimodal criterion were classified as frail. Endpoints included all-cause mortality, a composite of death or HF hospitalization, and recurrent HF hospitalizations.Results A total of 301 patients were evaluated. VES-13 identified 40.2% as frail and the multimodal assessment 33.2%. In Cox regression analyses, frailty identified by VES-13 showed greater prognostic significance than the multimodal assessment for allcause mortality(HR = 3.70 [2.15–6.33], P < 0.001 vs. 2.40 [1.46–4.0], P = 0.001) and the composite endpoint(HR = 3.13 [2.02–4.84], P< 0.001 vs. 1.96 [1.28–2.99], P = 0.002). Recurrent HF hospitalizations were four times more frequent in VES-13 frail patients while two times in those identified as frail by the multimodal assessment. Additionally, stratifying patients by VES-13 tertiles provided robust risk differentiation.Conclusions VES-13, a simple frailty tool, outperformed a comprehensive multimodal assessment and could be easily integrated into routine HF care, highlighting its clinical utility in identifying patients at risk for poor outcomes.
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National Key R&D Program of China,No.2022YFF1302401National Natural Science Foundation of China,No.42271007。
文摘Ecosystems along the eastern margin of the Qinghai-Tibet Plateau(EQTP)are highly fragile and extremely sensitive to climate change and human disturbances.To quantitatively assess climate-induced ecosystem responses,this study proposes a Climate-Induced Productivity Index(CIPI)based on the Super Slack-Based Measure(Super-SBM)model using remote sensing data from 2001 to 2020.The results reveal persistently low CIPI values(0.47-0.53)across major ecosystem types,indicating widespread vulnerability to climatic variability.Among these ecosystems,forests exhibit the highest CIPI(0.55),followed by shrublands(0.54),croplands(0.53),grasslands(0.51),and barelands(0.43).The Theil index analysis further demonstrates significant intra-group disparities,suggesting that extreme climatic events amplify CIPI heterogeneity.Moreover,the dominant environmental drivers differ among ecosystem types:the Palmer Drought Severity Index(PDSI)primarily constrains grassland productivity,solar radiation(SRAD)strongly influences shrub and cropland systems,whereas subsurface factors exert greater control in forested regions.This study provides a quantitative framework for evaluating climate-ecosystem interactions and offers a scientific basis for long-term ecological monitoring and security planning across the EQTP.
文摘We develop and implement a Stochastic Discrete Event Simulation(SDES)algorithm to model the housing re-covery trajectory after an extreme event.The algorithm models discrete events and their underlying uncertainties in each construction phase.Specifically,the algorithm is developed for the Government Assisted Owner Driven(GAOD)reconstruction system to simulate long-term recovery trajectory.SDES,as a flexible modeling approach,can simulate any housing recovery scenario that follows phased reconstruction.The 2015 M 7.8 Gorkha earthquake sequence in Nepal is considered the extreme event,with 796,245 buildings requiring reconstruction.We present some recovery trajectories from severely hit,crisis hit,and earthquake hit parishes,comparing them with the actual reconstruction progress.We also assess quality and improvement of reconstructed buildings using seismic fragility functions,compared to pre-earthquake constructions.Housing recovery uncertainties are dissected in relation to reconstruction pace.We conclude that the vast majority of the reconstructed buildings followed the Build Back Better(BBB)approach and missed the opportunity to pursue the Build Back Resilient(BBR)approach due to multifaceted challenges ranging from unclear policies to economic constraints.We critically assess the GAOD vs Owner Driven(OD)recovery framework and conclude that insurance-supported and technically assisted OD approach could be the most suitable model for post extreme event housing recovery.
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supported by the Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS),University of Technology Sydney(UTS),Australia and was supported by the Research Training Program(RTP)of the Australian Government.
文摘Hurricanes are one of the most destructive natural disasters that can cause catastrophic losses to both communities and infrastructure.Assessment of hurricane risk furnishes a spatial depiction of the interplay among hazard,vulnerability,exposure,and mitigation capacity,crucial for understanding and managing the risks hurricanes pose to communities.These assessments aid in gauging the efficacy of existing hurricane mitigation strategies and gauging their resilience across diverse climate change scenarios.A systematic review was conducted,encompassing 94 articles,to scrutinize the structure,data inputs,assumptions,methodologies,perils modelled,and key predictors of hurricane risk.This review identified key research gaps essential for enhancing future risk assessments.The complex interaction between hurricane perils may be disastrous and underestimated in the majority of risk assessments which focus on a single peril,commonly storm surge and flood,resulting in inadequacies in disaster resilience planning.Most risk assessments were based on hurricane frequency rather than hurricane damage,which is more insightful for policymakers.Furthermore,considering secondary indirect impacts stemming from hurricanes,including real estate market and business interruption,could enrich economic impact assessments.Hurricane mitigation measures were the most under-utilised category of predictors leveraged in only 5%of studies.The top six predictive factors for hurricane risk were land use,slope,precipitation,elevation,population density,and soil texture/drainage.Another notable research gap identified was the potential of machine learning techniques in risk assessments,offering advantages over traditional MCDM and numerical models due to their ability to capture complex nonlinear relationships and adaptability to different study regions.Existing machine learning based risk assessments leverage random forest models(42%of studies)followed by neural network models(19%of studies),with further research required to investigate diverse machine learning algorithms such as ensemble models.A further research gap is model validation,in particular assessing transferability to a new study region.Additionally,harnessing simulated data and refining projections related to demographic and built environment dynamics can bolster the sophistication of climate change scenario assessments.By addressing these research gaps,hurricane risk assessments can furnish invaluable insights for national policymakers,facilitating the development of robust hurricane mitigation strategies and the construction of hurricane-resilient communities.To the authors’knowledge,this represents the first literature review specifically dedicated to quantitative hurricane risk assessments,encompassing a comparison of Multi-criteria Decision Making(MCDM),numerical models,and machine learning models.Ultimately,advancements in hurricane risk assessments and modelling stand poised to mitigate potential losses to communities and infrastructure both in the immediate and long-term future.
基金funded by the National Natural Science Foundation of China(32372546)Shenzhen Science and Technology Program(KQTD20180411143628272)+1 种基金the Agricultural Science and Technology Innovation Program of Chinese Academy of Agricultural Sciences and STI 2030-Major Projects(2022ZD04021)the National Key Research and Development Program of China(2023YFD2200700)。
文摘Agricultural pests cause enormous losses in annual agricultural production.Understanding the evolutionary responses and adaptive capacity of agricultural pests under climate change is crucial for establishing sustainable and environmentally friendly agricultural pest management.In this study,we integrate climate modeling and landscape genomics to investigate the distributional dynamics of the cotton bollworm(Helicoverpa armigera)in the adaptation to local environments and resilience to future climate change.Notably,the predicted inhabitable areas with higher suitability for the cotton bollworm could be eight times larger in the coming decades.Climate change is one of the factors driving the dynamics of distribution and population differentiation of the cotton bollworm.Approximately 19,000 years ago,the cotton bollworm expanded from its ancestral African population,followed by gradual occupations of the European,Asian,Oceanian,and American continents.Furthermore,we identify seven subpopulations with high dispersal and adaptability which may have an increased risk of invasion potential.Additionally,a large number of candidate genes and SNPs linked to climatic adaptation were mapped.These findings could inform sustainable pest management strategies in the face of climate change,aiding future pest forecasting and management planning.
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.
基金funded by the Young Clinical Research Special Fund Project of Peking University First Hospital(No.2024YC05)。
文摘Objectives:This study aimed to assess the impact of exercise rehabilitation during the vulnerable period on cardiac recovery(CR)outcomes in patients with acute decompensated heart failure(ADHF).Methods:Multiple databases including PubMed,Web of Science,Embase,the Cochrane Library,CINAHL,China National Knowledge Infrastructure(CNKI),Chinese Science and Technology Periodical Database(VIP),Wanfang database,SinoMed,ClinicalTrials.gov,and American Heart Association(AHA)and European Society of Cardiology(ESC)were searched for RCTs on exercise rehabilitation in ADHF patients’vulnerable period from inception to April 2,2025.The risk of bias was assessed with Cochrane Risk of Bias 2.0,and data were analyzed in RevMan 5.3.Results:A total of seven RCTs involving 946 patients were included.The results demonstrated that exercise rehabilitation training during the vulnerable period in patients with ADHF significantly increased the 6-min walk test distance(6-MWTD)(SMD=0.37;95%CI:0.09,0.65;P=0.01),short physical performance battery(SPPB)score(MD=1.26;95%CI:0.82,1.70;P<0.001)and peak oxygen consumption(VO2peak)(SMD=1.43;95%CI:0.53,2.34;P=0.002),improved quality of life(QoL)(SMD=0.85;95%CI:0.07,1.64,P=0.03),reduced depression score(MD=-0.73;95%CI:1.27,-0.18;P=0.009),frailty(MD=-0.22;95%CI:-0.48,0.05;P=0.11),and decreased 6-month all-cause readmission(OR=0.67;95%CI:0.49,0.91;P=0.01).However,no statistically significantdifferences were observed between the two groups in left ventricular ejection fraction(LVEF)(MD=0.96;95%CI:-1.84,3.77;P=0.50),6-month heart failure(HF)-related readmission(OR=1.01;95%CI:0.66,1.53;P=0.98),and all-cause mortality(OR=0.63;95%CI:0.18,2.24;P=0.47).There were no adverse events reported.Conclusions:Exercise rehabilitation during the vulnerable phase improves exercise tolerance,QoL,and depressive symptoms while reducing 6-month all-cause readmissions in ADHF patients,with no reported adverse events.Although trends toward improved LVEF,HF-related readmissions,and all-cause mortality were observed.Large-scale,high-quality studies are warranted to explore individualized responses and long-term outcomes.
基金supported by National Natural Science Foundation of China (No. 82370215).
文摘Allogeneic hematopoietic cell transplantation(allo-HCT) remains a cornerstone therapy for severe hematologic malignancies, offering a potential cure when conventional therapies are ineffective. However, not all patients are suitable recipients of allo-HCT, particularly the elderly patients and those with high comorbidity burdens.Furthermore, patients who develop relapse or graft failure after initial transplantation encounter additional challenges when evaluated for a second transplant.
文摘This study presents an innovative approach to enhancing the security of visual medical data in the generative AI environment through the integration of blockchain technology.By combining the strengths of blockchain and generative AI,the research team aimed to address the timely challenge of safeguarding visual medical content.The participating researchers conducted a comprehensive analysis,examining the vulnerabilities of medical AI services,personal information protection issues,and overall security weaknesses.This multi faceted exploration led to an indepth evaluation of the model’s performance and security.Notably,the correlation between accuracy,detection rate,and error rate was scrutinized.This analysis revealed insights into the model’s strengths and limitations,while the consideration of standard deviation shed light on the model’s stability and performance variability.The study proposed practical improvements,emphasizing the reduction of false negatives to enhance detection rate and leveraging blockchain technology to ensure visual data integrity in medical applications.Applying blockchain to generative AI-created medical content addresses key personal information protection issues.By utilizing the distributed ledger system of blockchain,the research team aimed to protect the privacy and integrity of medical data especially medical images.This approach not only enhances security but also enables transparent and tamperproof record-keeping.Additionally,the use of generative AI models ensures the creation of novel medical content without compromising personal information,further safeguarding patient privacy.In conclusion,this study showcases the potential of blockchain-based solutions in the medical field,particularly in securing sensitive medical data and protecting patient privacy.The proposed approach,combining blockchain and generative AI,offers a promising direction toward more robust and secure medical content management.Further research and advancements in this area will undoubtedly contribute to the development of robust and privacy-preserving healthcare systems,and visual diagnostic systems.
