An integrated approach for GLOF hazard,vulnerability and risk assessment in the Karakoram Mountain Range of northern Pakistan

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.

Assessment of ecological geological vulnerability in Mu Us Sandy Land based on GIS and suggestions of ecological protection and restoration

Eco-geological vulnerability assessment is a significant research topic within the field of eco-geology,but it remains poorly studied.The Mu Us Sandy Land,located in the central part of the farming-pastoral ecotone in northern China,plays a critical role in maintaining the ecological security pattern in this region.However,this sandy land also faces severe sandy desertification and ecological degradation.This study conducted a regional eco-geological vulnerability assessment of the Mu Us Sandy Land using a comprehensive index evaluation method based on eco-geological theories and survey results.To construct an appropriate index system for the eco-geological vulnerability assessment of the Mu Us Sandy Land,the study considered the sandy land’s unique characteristics and identified 15 factors of five categories,namely geology,meteorology,soil,topography,and vegetation.The paper calculated the comprehensive weights of all the indices using the analytic hierarchy process(AHP)and the entropy weight method(EWM).Furthermore,it established the eco-geological vulnerability index(EGVI)and obtained the assessment results.The results showed that the eco-geological vulnerability of the Mu Us Sandy Land gradually intensifies from east to west,manifested as vulnerable eco-geological conditions overall.Specifically,extremely vulnerable zones are found in the northwestern and southeastern parts of the study area,highly vulnerable zones in the western and southern parts,moderately vulnerable zones in the central part,and slightly and potentially vulnerable zones in the eastern and southern parts.Areas with high spatial autocorrelations include the northern Uxin Banner-Otog Banner-Angsu Town area,the surrounding areas of Hongdunjie Town in the southeastern part of the study area,the Hongshiqiao Township-Xiaohaotu Township area,Otog Front Banner,and Bainijing Town,which should be prioritized in the ecological conservation and restoration.Additionally,the paper proposed suggestions for the ecological conservation and restoration of county-level administrative areas in the study area.Overall,the findings provide a valuable reference for the ecological conservation and restoration of the Mu Us Sandy Land and other desert areas in arid and semi-arid regions.

Security Vulnerability Analyses of Large Language Models (LLMs) through Extension of the Common Vulnerability Scoring System (CVSS) Framework

Large Language Models (LLMs) have revolutionized Generative Artificial Intelligence (GenAI) tasks, becoming an integral part of various applications in society, including text generation, translation, summarization, and more. However, their widespread usage emphasizes the critical need to enhance their security posture to ensure the integrity and reliability of their outputs and minimize harmful effects. Prompt injections and training data poisoning attacks are two of the most prominent vulnerabilities in LLMs, which could potentially lead to unpredictable and undesirable behaviors, such as biased outputs, misinformation propagation, and even malicious content generation. The Common Vulnerability Scoring System (CVSS) framework provides a standardized approach to capturing the principal characteristics of vulnerabilities, facilitating a deeper understanding of their severity within the security and AI communities. By extending the current CVSS framework, we generate scores for these vulnerabilities such that organizations can prioritize mitigation efforts, allocate resources effectively, and implement targeted security measures to defend against potential risks.

Research Progress of Vulnerability Assessment on Grassland Ecosystem under Climate Change

[Objective] The aim was to review on vulnerability evaluation of grassland ecosystem under climate change.[Method] In the research,vulnerability evaluation methods of ecosystem under climate changes,at home and abroad,and the related cases and research progress in China were reviewed.In addition,the future of evaluation research was predicted.[Result] Ecosystem vulnerability to climate change is an important part of climate change research.It is necessary and urgent to improve evaluation methods and reduce uncertainty of future vulnerability evaluation to make evaluation more comprehensive and objective.Furthermore,evaluation on effects of human activity on natural ecosystem vulnerability should be highlighted.[Conclusion] The research provided reference for establishment of a new management model for ecosystem adaptability to climate and sustainable development of grasslands in northern China.

The Systematic Diagnoses and Analysis of the Eco-vulnerability in Daqinggou National Natural Reserve

[Objective] The aim is to conduct systematic diagnoses on the eco-vulnerability in Daqinggou National Natural Reserve. [Method] A kind of ＂problem pile＂ is proposed about 58 interrelated problems of ecological vulnerability by field survey and questionnaires and a ladder diagram of interrelations among the problems is established. The root problems, basic problems, core problems, interrelation and solving approaches are proposed, as well. [Result] It can be concluded from system diagnosis that fragile ecosystem of the Reserve can be observed from poor disasterresistibility, lower species diffusivity, lower system metabolism and poor system restorability. According to system diagnoses, management planning and control subsystem is key for protection of system vulnerability and solving different problems in process-oriented system. Conservation of the Reserve and ecological education on tourists are indispensible. It can be concluded from diagnoses that plant updating, evolution and ill growth in the Reserve are the focus of the vulnerable ecosystem, which influence species diffusivity, system metabolism and restorability and constitute central nodes of process-oriented and effect-oriented subsystems. [Conclusion] The research provides references for solving vulnerability issues of conservation and tourism management in Daqinggou National Nature Reserve.

Automated Vulnerability Detection of Blockchain Smart Contacts Based on BERT Artificial Intelligent Model

The widespread adoption of blockchain technology has led to the exploration of its numerous applications in various fields.Cryptographic algorithms and smart contracts are critical components of blockchain security.Despite the benefits of virtual currency,vulnerabilities in smart contracts have resulted in substantial losses to users.While researchers have identified these vulnerabilities and developed tools for detecting them,the accuracy of these tools is still far from satisfactory,with high false positive and false negative rates.In this paper,we propose a new method for detecting vulnerabilities in smart contracts using the BERT pre-training model,which can quickly and effectively process and detect smart contracts.More specifically,we preprocess and make symbol substitution in the contract,which can make the pre-training model better obtain contract features.We evaluate our method on four datasets and compare its performance with other deep learning models and vulnerability detection tools,demonstrating its superior accuracy.

Spatial patterns of ecosystem vulnerability changes during 2001–2011 in the three-river source region of the Qinghai-Tibetan Plateau, China

The three-river source region (TRSR, including Yangtze, Yellow and Lancang rivers), located in the Qinghai-Tibetan Plateau, China, is a typical alpine zone with apparent ecosystem vulnerability and sensitivity. In this paper, we introduced many interdisciplinary factors, such as landscape pattern indices (Shannon diversity index and Shannon evenness index) and extreme climate factors (number of extreme high temperature days, number of extreme low temperature days, and number of extreme precipitation days), to establish a new model for evaluating the spatial patterns of ecosystem vulnerability changes in the TRSR. The change intensity (CI) of ecosystem vulnerability was also analyzed. The results showed that the established evaluation model was effective and the ecosystem vulnerability in the whole study area was intensive. During the study period of 2001–2011, there was a slight degradation in the eco-environmental quality. The Yellow River source region had the best eco-environmental quality, while the Yangtze River source region had the worst one. In addition, the zones dominated by deserts were the most severely deteriorated areas and the eco-environmental quality of the zones occupied by evergreen coniferous forests showed a better change. Furthermore, the larger the change rates of the climate factors (accumulative temperature of ≥10°C and annual average precipitation) are, the more intensive the CI of ecosystem vulnerability is. This study would provide a scientific basis for the eco-environmental protection and restoration in the TRSR.

Spatio-temporal Evolution of Marine Fishery Industry Ecosystem Vulnerability in the Bohai Rim Region

The building of the ocean power strategy and the implementation of the blue agriculture plan urgently need to strengthen the sustainable development of marine fishery.Taking vulnerability as the starting point, this paper constructs the vulnerability index system of marine fishery industry ecosystem from the aspects of sensitivity and response capacity, and combines the entropy method with the Topsis to comprehensively analyze the spatio-temporal evolution characteristics of vulnerability of marine fishery industry ecosystem in the Bohai Rim Region from 2001 to 2015.The results show that: 1) In the time dimension, from 2001 to 2015, the vulnerability of the marine fishery industry ecosystem in the Bohai Rim Region shows a fluctuant and degressive trend;2) In the spatial dimension, the spatial distribution of the marine fishery industry ecosystem vulnerability in the Bohai Rim Region presents the gradient characteristics which shows high vulnerability in the east and low vulnerability in the west.According to the evolution track of the system’s vulnerability level, the vulnerability of the marine fishery industry ecosystem is divided into ‘declining’ and ‘stable’ types of evolutionary structures;3) The development of marine fishery in the Bohai Rim Region needs to be derived from the marine fishery’s ecological environment and the industrial development mode and structure, which can improve the marine environment remediation efforts, optimize the marine fishery industry structure, vigorously focus on pelagic fishery, and enhance the introduction of marine fishery’s science and technology talents, etc.Then, the marine fishery’s development in the Bohai Rim Region will be moving in the green, circular and sustainable direction.

Vulnerability assessment of UAV engine to laser based on improved shotline method

Laser anti-drone technology is entering the sequence of actual combat,and it is necessary to consider the vulnerability of typical functional parts of UAVs.Since the concept of"vulnerability"was proposed,a variety of analysis programs for battlefield targets to traditional weapons have been developed,but a comprehensive assessment methodology for targets'vulnerability to laser is still missing.Based on the shotline method,this paper proposes a method that equates laser beam to shotline array,an efficient vulnerability analysis program of target to laser is established by this method,and the program includes the circuit board and the wire into the vulnerability analysis category,which improves the precision of the vulnerability analysis.Taking the UAV engine part as the target of vulnerability analysis,combine with the"life-death unit method"to calculate the laser penetration rate of various materials of the UAV,and the influence of laser weapon system parameters and striking orientation on the killing probability is quantified after introducing the penetration rate into the vulnerability analysis program.The quantitative analysis method proposed in this paper has certain general expansibility,which can provide a fresh idea for the vulnerability analysis of other targets to laser.

A New Framework for Software Vulnerability Detection Based on an Advanced Computing

The detection of software vulnerabilities written in C and C++languages takes a lot of attention and interest today.This paper proposes a new framework called DrCSE to improve software vulnerability detection.It uses an intelligent computation technique based on the combination of two methods:Rebalancing data and representation learning to analyze and evaluate the code property graph(CPG)of the source code for detecting abnormal behavior of software vulnerabilities.To do that,DrCSE performs a combination of 3 main processing techniques:(i)building the source code feature profiles,(ii)rebalancing data,and(iii)contrastive learning.In which,the method(i)extracts the source code’s features based on the vertices and edges of the CPG.The method of rebalancing data has the function of supporting the training process by balancing the experimental dataset.Finally,contrastive learning techniques learn the important features of the source code by finding and pulling similar ones together while pushing the outliers away.The experiment part of this paper demonstrates the superiority of the DrCSE Framework for detecting source code security vulnerabilities using the Verum dataset.As a result,the method proposed in the article has brought a pretty good performance in all metrics,especially the Precision and Recall scores of 39.35%and 69.07%,respectively,proving the efficiency of the DrCSE Framework.It performs better than other approaches,with a 5%boost in Precision and a 5%boost in Recall.Overall,this is considered the best research result for the software vulnerability detection problem using the Verum dataset according to our survey to date.

Assessing the vulnerability of ecosystems to climate change based on climate exposure, vegetation stability and productivity

Background: Global warming has brought many negative impacts on terrestrial ecosystems, which makes the vulnerability of ecosystems one of the hot issues in current ecological research. Here, we proposed an assessment method based on the IPCC definition of vulnerability. The exposure to future climate was characterized using a moisture index(MI) that integrates the effects of temperature and precipitation. Vegetation stability, defined as the proportion of intact natural vegetation that remains unchanged under changing climate, was used together with vegetation productivity trend to represent the sensitivity and adaptability of ecosystems. Using this method, we evaluated the vulnerability of ecosystems in Southwestern China under two future representative concentration pathways(RCP 4.5 and RCP 8.5) with MC2 dynamic global vegetation model.Results:(1) Future(2017–2100) climate change will leave 7.4%(under RCP 4.5) and 57.4% of(under RCP 8.5) of areas under high or very high vulnerable climate exposure;(2) in terms of vegetation stability, nearly 45% of the study area will show high or very high vulnerability under both RCPs. Beside the impacts of human disturbance on natural vegetation coverage(vegetation intactness), climate change will cause obvious latitudinal movements in vegetation distribution, but the direction of movements under two RCPs were opposite due to the difference in water availability;(3) vegetation productivity in most areas will generally increase and remain a low vulnerability in the future;(4) an assessment based on the above three aspects together indicated that future climate change will generally have an adverse impact on all ecosystems in Southwestern China, with non-vulnerable areas account for only about 3% of the study area under both RCPs. However, compared with RCP 4.5, the areas with mid-and highvulnerability under RCP 8.5 scenario increased by 13% and 16%, respectively.Conclusion: Analyses of future climate exposure and projected vegetation distribution indicate widespread vulnerability of ecosystems in Southwestern China, while vegetation productivity in most areas will show an increasing trend to the end of twenty-first century. Based on new climate indicators and improved vulnerability assessment rules, our method provides an extra option for a more comprehensive evaluation of ecosystem vulnerability, and should be further tested at larger spatial scales in order to provide references for regional, or even global, ecosystem conservation works.

Topological optimization of ballistic protective structures through genetic algorithms in a vulnerability-driven environment

Reducing the vulnerability of a platform,i.e.,the risk of being affected by hostile objects,is of paramount importance in the design process of vehicles,especially aircraft.A simple and effective way to decrease vulnerability is to introduce protective structures to intercept and possibly stop threats.However,this type of solution can lead to a significant increase in weight,affecting the performance of the aircraft.For this reason,it is crucial to study possible solutions that allow reducing the vulnerability of the aircraft while containing the increase in structural weight.One possible strategy is to optimize the topology of protective solutions to find the optimal balance between vulnerability and the weight of the added structures.Among the many optimization techniques available in the literature for this purpose,multiobjective genetic algorithms stand out as promising tools.In this context,this work proposes the use of a in-house software for vulnerability calculation to guide the process of topology optimization through multi-objective genetic algorithms,aiming to simultaneously minimize the weight of protective structures and vulnerability.In addition to the use of the in-house software,which itself represents a novelty in the field of topology optimization of structures,the method incorporates a custom mutation function within the genetic algorithm,specifically developed using a graph-based approach to ensure the continuity of the generated structures.The tool developed for this work is capable of generating protections with optimized layouts considering two different types of impacting objects,namely bullets and fragments from detonating objects.The software outputs a set of non-dominated solutions describing different topologies that the user can choose from.

Livelihood Vulnerability and Adaptation for Households Engaged in Forestry in Ecological Restoration Areas of the Chinese Loess Plateau

Chinese Loess Plateau has achieved a win-win situation concerning ecological restoration and socio-economic development.However,synergistic development may not be realized at the local scale.In areas undergoing ecological restoration,livelihood vulner-ability may be more pronounced due to the inflexibility,policy protection,and susceptibility to climate and market changes in forestry production.Although this issue has attracted academic interest,empirical studies are relatively scarce.This study,centered on Jiaxian County,Shaanxi Province of China explored the households’livelihood vulnerability and coping strategies and group heterogeneity con-cerned with livelihood structures or forestry resources through field investigation,comprehensive index assessment,and nonparametric tests.Findings showed that:1)the percentage of households with high livelihood vulnerability indicator(LVI)(>0.491)reached 46.34%.2)Eight groups in livelihood structures formed by forestry,traditional agriculture,and non-farm activities were significantly different in LVI,land resources(LR),social networks(SN),livelihood strategies(LS),housing characteristics(HC),and socio-demo-graphic profile(SDP).3)The livelihood vulnerability of the groups with highly engaged/reliance on jujube(Ziziphus jujuba)forest demonstrated more prominent livelihood vulnerability due to the increased precipitation and cold market,where the low-engaged with reliance type were significantly more vulnerable in LVI,SDP,LR,and HC.4)The threshold of behavioral triggers widely varied,and farmers dependent on forestry livelihoods showed negative coping behavior.Specifically,the cutting behavior was strongly associated with lagged years and government subsidies,guidance,and high returns of crops.Finally,the findings can provide guidance on the dir-ection of livelihood vulnerability mitigation and adaptive government management in ecologically restored areas.The issue of farmers’livelihood sustainability in the context of ecological conservation calls for immediate attention,and eco-compensations or other forms of assistance in ecologically functional areas are expected to be enhanced and diversified.

Systematic Security Guideline Framework through Intelligently Automated Vulnerability Analysis

This research aims to propose a practical framework designed for the automatic analysis of a product’s comprehensive functionality and security vulnerabilities,generating applicable guidelines based on real-world software.The existing analysis of software security vulnerabilities often focuses on specific features or modules.This partial and arbitrary analysis of the security vulnerabilities makes it challenging to comprehend the overall security vulnerabilities of the software.The key novelty lies in overcoming the constraints of partial approaches.The proposed framework utilizes data from various sources to create a comprehensive functionality profile,facilitating the derivation of real-world security guidelines.Security guidelines are dynamically generated by associating functional security vulnerabilities with the latest Common Vulnerabilities and Exposure(CVE)and Common Vulnerability Scoring System(CVSS)scores,resulting in automated guidelines tailored to each product.These guidelines are not only practical but also applicable in real-world software,allowing for prioritized security responses.The proposed framework is applied to virtual private network(VPN)software,wherein a validated Level 2 data flow diagram is generated using the Spoofing,Tampering,Repudiation,Information Disclosure,Denial of Service,and Elevation of privilege(STRIDE)technique with references to various papers and examples from related software.The analysis resulted in the identification of a total of 121 vulnerabilities.The successful implementation and validation demonstrate the framework’s efficacy in generating customized guidelines for entire systems,subsystems,and selected modules.

Evaluation of soil erosion vulnerability in Hubei Province of China using RUSLE model and combination weighting method

Soil erosion has been recognized as a critical environmental issue worldwide.While previous studies have primarily focused on watershed-scale soil erosion vulnerability from a natural factor perspective,there is a notable gap in understanding the intricate interplay between natural and socio-economic factors,especially in the context of spatial heterogeneity and nonlinear impacts of human-land interactions.To address this,our study evaluates the soil erosion vulnerability at a provincial scale,taking Hubei Province as a case study to explore the combined effects of natural and socio-economic factors.We developed an evaluation index system based on 15 indicators of soil erosion vulnerability:exposure,sensitivity,and adaptability.In addition,the combination weighting method was applied to determine index weights,and the spatial interaction was analyzed using spatial autocorrelation,geographical temporally weighted regression and geographical detector.The results showed an overall decreasing soil erosion intensity in Hubei Province during 2000 and 2020.The soil erosion vulnerability increased before 2000 and then.The areas with high soil erosion vulnerability were mainly confined in the central and southern regions of Hubei Province(Xiantao,Tianmen,Qianjiang and Ezhou)with obvious spatial aggregation that intensified over time.Natural factors(habitat quality index)had negative impacts on soil erosion vulnerability,whereas socio-economic factors(population density)showed substantial spatial variability in their influences.There was a positive correlation between soil erosion vulnerability and erosion intensity,with the correlation coefficients ranging from-0.41 and 0.93.The increase of slope was found to enhance the positive correlation between soil erosion vulnerability and intensity.

HCRVD: A Vulnerability Detection System Based on CST-PDG Hierarchical Code Representation Learning

Prior studies have demonstrated that deep learning-based approaches can enhance the performance of source code vulnerability detection by training neural networks to learn vulnerability patterns in code representations.However,due to limitations in code representation and neural network design,the validity and practicality of the model still need to be improved.Additionally,due to differences in programming languages,most methods lack cross-language detection generality.To address these issues,in this paper,we analyze the shortcomings of previous code representations and neural networks.We propose a novel hierarchical code representation that combines Concrete Syntax Trees(CST)with Program Dependence Graphs(PDG).Furthermore,we introduce a Tree-Graph-Gated-Attention(TGGA)network based on gated recurrent units and attention mechanisms to build a Hierarchical Code Representation learning-based Vulnerability Detection(HCRVD)system.This system enables cross-language vulnerability detection at the function-level.The experiments show that HCRVD surpasses many competitors in vulnerability detection capabilities.It benefits from the hierarchical code representation learning method,and outperforms baseline in cross-language vulnerability detection by 9.772%and 11.819%in the C/C++and Java datasets,respectively.Moreover,HCRVD has certain ability to detect vulnerabilities in unknown programming languages and is useful in real open-source projects.HCRVD shows good validity,generality and practicality.

Software Vulnerability Mining and Analysis Based on Deep Learning

In recent years,the rapid development of computer software has led to numerous security problems,particularly software vulnerabilities.These flaws can cause significant harm to users’privacy and property.Current security defect detection technology relies on manual or professional reasoning,leading to missed detection and high false detection rates.Artificial intelligence technology has led to the development of neural network models based on machine learning or deep learning to intelligently mine holes,reducing missed alarms and false alarms.So,this project aims to study Java source code defect detection methods for defects like null pointer reference exception,XSS(Transform),and Structured Query Language(SQL)injection.Also,the project uses open-source Javalang to translate the Java source code,conducts a deep search on the AST to obtain the empty syntax feature library,and converts the Java source code into a dependency graph.The feature vector is then used as the learning target for the neural network.Four types of Convolutional Neural Networks(CNN),Long Short-Term Memory(LSTM),Bi-directional Long Short-Term Memory(BiLSTM),and Attention Mechanism+Bidirectional LSTM,are used to investigate various code defects,including blank pointer reference exception,XSS,and SQL injection defects.Experimental results show that the attention mechanism in two-dimensional BLSTM is the most effective for object recognition,verifying the correctness of the method.

Optimal Cyber Attack Strategy Using Reinforcement Learning Based onCommon Vulnerability Scoring System

Currently,cybersecurity threats such as data breaches and phishing have been on the rise due to the many differentattack strategies of cyber attackers,significantly increasing risks to individuals and organizations.Traditionalsecurity technologies such as intrusion detection have been developed to respond to these cyber threats.Recently,advanced integrated cybersecurity that incorporates Artificial Intelligence has been the focus.In this paper,wepropose a response strategy using a reinforcement-learning-based cyber-attack-defense simulation tool to addresscontinuously evolving cyber threats.Additionally,we have implemented an effective reinforcement-learning-basedcyber-attack scenario using Cyber Battle Simulation,which is a cyber-attack-defense simulator.This scenarioinvolves important security components such as node value,cost,firewalls,and services.Furthermore,we applieda new vulnerability assessment method based on the Common Vulnerability Scoring System.This approach candesign an optimal attack strategy by considering the importance of attack goals,which helps in developing moreeffective response strategies.These attack strategies are evaluated by comparing their performance using a variety ofReinforcement Learning methods.The experimental results show that RL models demonstrate improved learningperformance with the proposed attack strategy compared to the original strategies.In particular,the success rateof the Advantage Actor-Critic-based attack strategy improved by 5.04 percentage points,reaching 10.17%,whichrepresents an impressive 98.24%increase over the original scenario.Consequently,the proposed method canenhance security and risk management capabilities in cyber environments,improving the efficiency of securitymanagement and significantly contributing to the development of security systems.

A Review of Deep Learning-Based Vulnerability Detection Tools for Ethernet Smart Contracts

In recent years,the number of smart contracts deployed on blockchain has exploded.However,the issue of vulnerability has caused incalculable losses.Due to the irreversible and immutability of smart contracts,vulnerability detection has become particularly important.With the popular use of neural network model,there has been a growing utilization of deep learning-based methods and tools for the identification of vulnerabilities within smart contracts.This paper commences by providing a succinct overview of prevalent categories of vulnerabilities found in smart contracts.Subsequently,it categorizes and presents an overview of contemporary deep learning-based tools developed for smart contract detection.These tools are categorized based on their open-source status,the data format and the type of feature extraction they employ.Then we conduct a comprehensive comparative analysis of these tools,selecting representative tools for experimental validation and comparing them with traditional tools in terms of detection coverage and accuracy.Finally,Based on the insights gained from the experimental results and the current state of research in the field of smart contract vulnerability detection tools,we suppose to provide a reference standard for developers of contract vulnerability detection tools.Meanwhile,forward-looking research directions are also proposed for deep learning-based smart contract vulnerability detection.

Climatic and non-climatic factors driving the livelihood vulnerability of smallholder farmers in Ahafo Ano North District,Ghana

Smallholder farmers in Ahafo Ano North District,Ghana,face multiple climatic and non-climatic issues.This study assessed the factors contributing to the livelihood vulnerability of smallholder farmers in this district by household surveys with 200 respondents and focus group discussions(FGDs)with 10 respondents.The Mann–Kendall trend test was used to assess mean annual rainfall and temperature trends from 2002 to 2022.The relative importance index(RII)value was used to rank the climatic and non-climatic factors perceived by respondents.The socioeconomic characteristics affecting smallholder farmers’perceptions of climatic and non-climatic factors were evaluated by the binary logistic regression model.Results showed that mean annual rainfall decreased(P>0.05)but mean annual temperature significantly increased(P<0.05)from 2002 to 2022 in the district.The key climatic factors perceived by smallholder farmers were extreme heat or increasing temperature(RII=0.498),erratic rainfall(RII=0.485),and increased windstorms(RII=0.475).The critical non-climatic factors were high cost of farm inputs(RII=0.485),high cost of healthcare(RII=0.435),and poor condition of roads to farms(RII=0.415).Smallholder farmers’perceptions of climatic and non-climatic factors were significantly affected by their socioeconomic characteristics(P<0.05).This study concluded that these factors negatively impact the livelihoods and well-being of smallholder farmers and socioeconomic characteristics influence their perceptions of these factors.Therefore,to enhance the resilience of smallholder farmers to climate change,it is necessary to adopt a comprehensive and context-specific approach that accounts for climatic and non-climatic factors.