Evaluation model of the grey fuzzy on eco-environment vulnerability

The basic theory and evaluation index system of eco-environment vulnerability were reviewed. Based on the grey theory and fuzzy mathematics, a new comprehensive evaluation method from qualitative to quantitative, called grey-fuzzy evaluation, was proposed for evaluating eco-environment vulnerability. It was integrated of Association for Healthcare Philanthropy （AHP）, grey correlation analysis, grey statistics and fuzzy judgment. The constitutional principle and method of the new evaluation method were given and its feasibility and effectiveness were proved by the practical example.

Comprehensive Assessment of Eco-environment Vulnerability in Hebei Province Based on ArcGIS

[Objective] This study aimed to assess the vulnerability of ecological environment in Hebei Province.[Method] Based on ArcGIS,by using the dominant factor and maximum limits factor method,we established the sensitivity-reality indicator system and assessment model of the eco-environment vulnerability in Hebei Province to quantitatively evaluate its eco-environment vulnerability,and analyzed its spatial distribution.[Result] The status quo of environmental degradation was inconsistent with the sensitivity of eco-environment in Hebei Province.The area of severely vulnerable region accounted for only 4.1% of total area in Hebei Province,mainly distributed in nine counties or districts of Baxia region,Zhangjiakou,northwestern Hebei,which was covered with mountains and basins,showing bands,which resulted from the vulnerability of soil erosion and land desertification.In addition,the moderately vulnerable region made up 38.4%,having the largest proportion,and mainly distributed in the mountain and basin region in northwestern Hebei,Yanshan Mountain and the most regions of Taihang Mountain,due to the vulnerability of soil erosion.At the same time,there were also large patches of moderately vulnerable region in Bashang and Baxia of Zhangjiakou,owning to the vulnerability of land desertification.Besides,most plains were mildly vulnerable and slightly vulnerable regions,and their areas accounted for 25.1% and 32.4% respectively of total area.[Conclusion] The research could provide scientific references for safeguarding the ecological security and main functional district planing of Hebei Province.

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.

Socio-economic vulnerability level in the Jeneberang watershed in Gowa Regency,South Sulawesi Province,Indonesia

Jeneberang watershed is vital,particularly for people living in Gowa Regency(South Sulawesi Province,Indonesia),who benefit from its many advantages.Landslides and floods occur every year in the Jeneberang watershed,so it is imperative to understand the socio-economic vulnerability of this region.This research aims to identify the vulnerability level of the Jeneberang watershed so that the government can prioritize areas with high vulnerability level and formulate effective strategies to reduce these the vulnerability.Specifically,this study was conducted in 12 districts located in the Jeneberang watershed.The primary data were collected from questionnaires completed by community members,community leaders,and various stakeholders,and the secondary data were from the Landsat satellite imagery in 2020,the Badan Push Statistic of Gowa Regency,and some governmental agencies.The socio-economic vulnerability variables were determined using the Multiple Criteria Decision Analysis(MCDA)method,and each variable was weighted and analyzed using the Geographical Information System(GIS).The study reveals that the levels of socio-economic vulnerability are affected by variables such as population density,vulnerable groups(disabled people,elderly people,and young people),road network and settlement,percentage of poor people,and productive land area in the Jeneberang watershed.Moreover,all of the 12 districts in the Jeneberang watershed are included in the medium vulnerability level,with the mean percentage of socio-economic vulnerability around 50.92%.The socio-economic vulnerability of Bajeng,Pallangga,and Somba Opu districts is categorized at high level,the socio-economic vulnerability of Bungaya,Parangloe,and Tombolo Pao districts is classified as medium level,and the remaining 6 districts(Barombong,Bontolempangan,Bontomarannu,Manuju,Parigi,and Tinggimoncong)are ranked as low socio-economic vulnerability.This study can help policy-makers to formulate strategy that contributes to the protection of biodiversity and sustainable development of the Jeneberang watershed,while improving disaster resilience and preparedness of the watershed.

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.

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.

Understanding livelihood vulnerability:a perspective from Western Sichuan’s ethnic rural settings

To explore the livelihood status and key influencing factors of rural households in the minority areas,we collected flat data from 284 rural households in 32 villages across 12 counties of Western Sichuan from 2021 to 2022.We conducted participatory household survey on the livelihood status of the rural households and try to identify the key factors to influence their livelihood vulnerability using multiple linear regression.The results showed that:the livelihood situation of the rural households is relatively vulnerable.The vulnerability varies significantly with the income levels,education levels,and income sources.The vulnerability of farm households,categorized from low to high livelihood types,follows the sequence:non-agricultural dominant households,non-agricultural households,agricultural dominant households,and pure agricultural households.The degree of damage to the natural environment,education costs,loan opportunities,the proportion of agricultural income to annual household income,and the presence of sick people in the household have significant positive effects on the livelihood vulnerability index(LVI)of rural households;while help from relatives and friends,net income per capita,household size,household education,agricultural land area,participation in industrial organizations,number of livestock,purchase of commercial houses,drinking water source,and self-supply of food have significant negative effects.Based on the findings,we believe that local rural households operate in a complex livelihood system and recommend continuous interventions targeting key influences to provide empirical research support for areas facing similar situations.

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 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.

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.

Binary Program Vulnerability Mining Based on Neural Network

Software security analysts typically only have access to the executable program and cannot directly access the source code of the program.This poses significant challenges to security analysis.While it is crucial to identify vulnerabilities in such non-source code programs,there exists a limited set of generalized tools due to the low versatility of current vulnerability mining methods.However,these tools suffer from some shortcomings.In terms of targeted fuzzing,the path searching for target points is not streamlined enough,and the completely random testing leads to an excessively large search space.Additionally,when it comes to code similarity analysis,there are issues with incomplete code feature extraction,which may result in information loss.In this paper,we propose a cross-platform and cross-architecture approach to exploit vulnerabilities using neural network obfuscation techniques.By leveraging the Angr framework,a deobfuscation technique is introduced,along with the adoption of a VEX-IR-based intermediate language conversion method.This combination allows for the unified handling of binary programs across various architectures,compilers,and compilation options.Subsequently,binary programs are processed to extract multi-level spatial features using a combination of a skip-gram model with self-attention mechanism and a bidirectional Long Short-Term Memory(LSTM)network.Finally,the graph embedding network is utilized to evaluate the similarity of program functionalities.Based on these similarity scores,a target function is determined,and symbolic execution is applied to solve the target function.The solved content serves as the initial seed for targeted fuzzing.The binary program is processed by using the de-obfuscation technique and intermediate language transformation method,and then the similarity of program functions is evaluated by using a graph embedding network,and symbolic execution is performed based on these similarity scores.This approach facilitates cross-architecture analysis of executable programs without their source codes and concurrently reduces the risk of symbolic execution path explosion.

An Analysis of Liberia’s Vulnerability to Climate Change in the Context of Least Developed Countries (LDCs): A Review

Climate change is an alarming global challenge, particularly affecting the least developed countries (LDCs) including Liberia. These countries, located in regions prone to unpredictable temperature and precipitation changes, are facing significant challenges, particularly in climate-sensitive sectors such as mining and agriculture. LDCs need more resilience to adverse climate shocks but have limited capacity for adaptation compared to other developed and developing nations. This paper examines Liberia’s susceptibility to climate change as a least developed country, focusing on its exposure, sensitivity, and adaptive capacity. It provides an overview of LDCs and outlines the global distribution of carbon dioxide emissions. The paper also evaluates specific challenges that amplify Liberia’s vulnerability and constrain sustainable adaptation, providing insight into climate change’s existing and potential effects. The paper emphasizes the urgency of addressing climate impacts on Liberia and calls for concerted local and international efforts for effective and sustainable mitigation efforts. It provides recommendations for policy decisions and calls for further research on climate change mitigation and adaptation.

Groundwater vulnerability assessment using a GIS-based DRASTIC method in the Erbil Dumpsite area (Kani Qirzhala), Central Erbil Basin, North Iraq

Groundwater vulnerability assessment is a crucial step in the efficient management of groundwater resources,especially in areas with intensive anthropogenic activities and groundwater pollution.In the present study,the DRASTIC method was applied using Geographic Information System(GIS)to delineate groundwater vulnerability zones in the Erbil Dumpsite area,Central Erbil Basin,North Iraq.Results showed that the area was classified into four vulnerability classes:Very low(16.97%),low(27.67%),moderate(36.55%)and high(18.81%).The southern,south-eastern and northern parts of the study area exhibited the highest vulnerability potential,while the central-northern,northern and north-western regions displayed the lowest vulnerability potential.Moreover,results of the single-parameter sensitivity analysis indicated that amongst the seven DRASTIC parameters,the unsaturated zone and the aquifer media were the most influencing parameters.In conclustion,the correlation of 25 nitrate concentration values with the final vulnerability map,assessed using the Pearson correlation coefficient,yielded a satisfactory result of R=0.72.

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.

Application of Hazard Vulnerability Analysis Based on Kaiser Model in Neonatal Breast Milk Management

Objective:To analyze the existing risks in breast milk management at the neonatal department and provide corresponding countermeasures.Methods:22 risk events were identified in 7 risk links in the process of bottle-feeding of breast milk.Hazard Vulnerability Analysis based on the Kaiser model was applied to investigate and evaluate the risk events.Results:High-risk events include breast milk quality inspection,hand hygiene during collection,disinfection of collectors,cold chain management,hand hygiene during the reception,breast milk closed-loop management,and post-collection disposal.Root cause analysis of high-risk events was conducted and breast milk management strategies outside the hospital and within the neonatal department were proposed.Conclusion:Hazard Vulnerability Analysis based on the Kaiser model can identify and assess neonatal breast milk management risks effectively,which helps improve the management of neonatal breast milk.It is conducive to the safe development and promotion of bottle feeding of breast milk for neonates,ensuring the quality of medical services and the safety of children.

Assessment on the Vulnerability of Agricultural Eco-environment of Hilly Basin Area in the Middle of Hunan Province——A Case Study of Hengyang Basin

[Objective] The study aimed to assess the vulnerability of agricultural eco-environment of hilly basin area in the middle of Hunan Prov- ince. [ Method] Taking Hengyang basin as an example, we firstly chose 13 indicators from natural, social and economic subsystem to assess the vulnerability of agricultural eco-environment in hilly basin area in the middle of Hunan Province. Afterwards, by using principal component analysis, we calculated the weight of each indicator and the vulnerability of agricultural eco-environment of seven regions, finally analyzed the spatial distribu- tion of agricultural eco-envimnment vulnerability. [ Result] The weight of farmers＇ net income per capita was up to 0.140, followed by forest coverage （0.137）. Among the seven counties, the vulnerability of agricultural eco-environment was primarily moderate, while Hengnan County had the highest vulnerability, followed by Hengyang County, and their vulnerability was extremely serious. In addition, Leiyang City was the minimum fragile region, namely slightly fragile region. In general, the vulnerability of peripheral counties was lower than that of central counties. [ Conclusion] The research could provide scientific references for the restoration and reconstruction of agro-ecological environment as well as the establishment of agri- cultural Dreduction decisions.

Eco-Environmental Vulnerability Evaluation in the Yellow River Basin,China

Using remote sensing(RS)data and geographical information system(GIS),eco-environmental vulnerability and its changes were analyzed for the Yellow River Basin,China.The objective of this study was to improve our understanding of eco-environmental changes so that a strategy of sustainable land use could be established.An environmental numerical model was developed using spatial principal component analysis(SPCA)model.The model contains twelve factors that include variables of land use,soil erosion,topography,climate,and vegetation.Using this model,synthetic eco- environmental vulnerability index(SEVI)was computed for 1990 and 2000 for the Yellow River Basin.The SEVI was classified into six levels,potential,slight,light,medium,heavy,and very heavy,following the natural breaks classification. The eco-environmental vulnerability distribution and its changes over the ten years from 1990 to 2000 were analyzed and the driving factors of eco-environmental changes were investigated.The results show that the eco-environmental vulnerability in the study area was at medium level,and the eco-environmental quality had been gradually improved on the whole.However,the eco-environmental quality had become worse over the ten years in some regions.In the study area,population growth,vegetation degradation,and governmental policies for eco-environmental protection were found to be the major factors that caused the eco-environmental changes over the ten years.

Critical Relation Path Aggregation-Based Industrial Control Component Exploitable Vulnerability Reasoning

With the growing discovery of exposed vulnerabilities in the Industrial Control Components(ICCs),identification of the exploitable ones is urgent for Industrial Control System(ICS)administrators to proactively forecast potential threats.However,it is not a trivial task due to the complexity of the multi-source heterogeneous data and the lack of automatic analysis methods.To address these challenges,we propose an exploitability reasoning method based on the ICC-Vulnerability Knowledge Graph(KG)in which relation paths contain abundant potential evidence to support the reasoning.The reasoning task in this work refers to determining whether a specific relation is valid between an attacker entity and a possible exploitable vulnerability entity with the help of a collective of the critical paths.The proposed method consists of three primary building blocks:KG construction,relation path representation,and query relation reasoning.A security-oriented ontology combines exploit modeling,which provides a guideline for the integration of the scattered knowledge while constructing the KG.We emphasize the role of the aggregation of the attention mechanism in representation learning and ultimate reasoning.In order to acquire a high-quality representation,the entity and relation embeddings take advantage of their local structure and related semantics.Some critical paths are assigned corresponding attentive weights and then they are aggregated for the determination of the query relation validity.In particular,similarity calculation is introduced into a critical path selection algorithm,which improves search and reasoning performance.Meanwhile,the proposed algorithm avoids redundant paths between the given pairs of entities.Experimental results show that the proposed method outperforms the state-of-the-art ones in the aspects of embedding quality and query relation reasoning accuracy.

Gender Perspectives of Vulnerability to Climate Change: A Descriptive Evidence from Farming Households at Ikpayongo Community in Gwer Lga, Benue State, Nigeria

Climate change impact and risks on agricultural livelihood affect women and men disproportionately and often to the disadvantage of women and girls. Consequently, this study assessed gender perspectives of vulnerability to climate change of farming households at Ikpayongo community in Gwer local government area, Benue State, Nigeria using descriptive approach. The study identified a total of 120 male-headed and female-headed farming households across four neighbourhoods and administered structured questionnaire on them using simple random sampling method, while data analysis was done using descriptive statistics. The results indicate lower education and income status among female-headed households, though male-headed households have high household size. Both sexes have relatively equal access to land for farming, however men have large farm size compared to women. The major crops cultivated by men were rice and yam, while women cultivated largely groundnut and cassava. Women are more exposed and sensitive to climate-related hazards such as floods and heat stress due to the location of their farms. The result further shows that males possess better adaptive capacity given their higher incomes, social networks and more access to training/capacity building programmes and credit facilities. The study concludes that female-headed farming households are more vulnerable to climate change and variability than male-headed farming households due to higher exposure and a lower adaptive capacity. Programme and policies to improve women access to credit facilities and relevant training to boost their adaptive capacity and build resilience are highly recommended. This would also limit exposure with attendant reduction in vulnerability.