The Influence of Vulnerable Narcissism on Social Anxiety among Adolescents:The Mediating Role of Self-Concept Clarity and Self-Esteem

Social anxiety (SA) is a prevalent mental health issue among adolescents, and vulnerable narcissism (VN) can exacerbate thiscondition. This study aims to investigate the impact of vulnerable narcissism on social anxiety in adolescents, specificallyfocusing on the mediating effects of self-concept clarity (SCC) and self-esteem (SE) in the relationship between vulnerablenarcissism and social anxiety. Through cluster sampling, a questionnaire survey was conducted among 982 students from threesecondary schools in two provinces. The data was analyzed using structural equation modeling (SEM). The results revealedthat there was a significant negative correlation between vulnerable narcissism and both self-concept clarity and self-esteem,while there was a significant positive correlation between vulnerable narcissism and social anxiety. Additionally, self-conceptclarity showed a significant positive correlation with self-esteem but had a negative correlation with social anxiety. Both selfconceptclarity and self-esteem played an intermediary role in the chain linking vulnerable narcissism to social anxiety. Thisstudy confirms the mediating role of both self-concept clarity and self-esteem in explaining how vulnerable narcissisminfluences social anxiety, providing valuable insights into its underlying mechanism.

Spatial Optimization Strategies for High Temperature Heat Exposure Based on Thermally Vulnerable Populations and Case Studies

The objective of this study is to investigate the factors that contribute to brittleness and to identify strategies for mitigating these factors in populations with varying degrees of thermal vulnerability,based on the potential impact of extreme heat exposure on human survival and habitability.The physiological condition of lower adaptability to high temperature environments and the assessment of individuals who may have higher tolerance time in high temperature environments based on spatial perspectives suggest the need for targeted spatial optimization strategies for commuters and disadvantaged populations.This is demonstrated through a case study.These optimization measures encompass a variety of aspects,including the integration of transportation systems,the expansion of grey space corridors,the improvement of green space layout,and the implantation of green infrastructure.The study aims to reduce the exposure time of thermally vulnerable individuals to high temperature environments through spatial optimization strategies,to enhance the resilience of urban green spaces to heat stress,and to reduce the probability of heat-wave occurrence.

Are vulnerable farmers more easily influenced?Heterogeneous effects of Internet use on the adoption of integrated pest management

The Internet is believed to bring more technological dividends to vulnerable farmers during the green agriculture transformation.However,this is different from the theory of skill-biased technological change,which emphasizes that individuals with higher levels of human capital and more technological endowments benefit more.This study investigates the effects of Internet use on farmers'adoption of integrated pest management(IPM),theoretically and empirically,based on a dataset containing 1015 farmers in China's Shandong Province.By exploring the perspective of rational inattention,the reasons for the heterogeneity of the effects across farmers with different endowments,i.e.,education and land size,are analyzed.The potential endogeneity issues are addressed using the endogenous switching probit model.The results reveal that:(1)although Internet use significantly positively affects farmers'adoption of IPM,vulnerable farmers do not benefit more from it.Considerable selection bias leads to an overestimation of technological dividends for vulnerable farmers;(2)different sources of technology information lead to the difference in the degree of farmers'rational inattention toward Internet information,which plays a crucial role in the heterogeneous effect of Internet use;and(3)excessive dependence on strong-tie social network information sources entraps vulnerable farmers in information cocoons,hindering their ability to reap the benefits of Internet use fully.Therefore,it is essential to promote services geared towards elderly-oriented Internet agricultural technology information and encourage farmers with strong Internet utilization skills to share technology information with other farmers actively.

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.

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.

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.

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.

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.

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.

Summarizing vehicle driving decision-making methods on vulnerable road user collision avoidance

With the development of intelligent vehicles and autonomous driving technology,the safety of vulnerable road user(VRU)in traffic has been more guaranteed,and many research achievements have been made in the key area of collision avoidance decision-making methods.In this paper,the knowledge mapping method is used to mine the available literature in depth,and it is found that the research focus has shifted from the traditional accident cause analysis to emerging deep learning and virtual reality technology.This paper summarizes research on the three core dimensions of environmental perception,behavior cognition and collision avoidance decision-making in intelligent vehicle systems.In terms of perception,accurate identification of pedestrians and cyclists in complex environments is a major demand for VRU perception;in terms of behavior cognition,the coupling of VRU intention identification and motion trajectory prediction and other multiple factors needs further research;in terms of decision-making,the intention identification and trajectory prediction of collision objects are not included in the risk assessment model,and there is a lack of exploration specifically for cyclists'collision risk.On this basis,this paper provides guidance for the improvement of traffic safety of contemporary VRU under the conditions of intelligent and connected transportation.

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.

Genetic variation of yellow pigment and its components in foxtail millet(Setaria italica(L.) P.Beauv.) from different eco-regions in China

Kernel color is an important trait for assessing the commercial and nutritional quality of foxtail millet. Yellow pigment content （YPC） and carotenoid components （lutein and zeaxanthin） of 270 foxtail millet accessions, including 50 landraces and 220 improved cultivars, from four different eco-regions in China were surveyed using spectrophotometry and high performance liquid chromatography methods. Results indicated that YPC had rich variance, ranging from 1.91 to 28.54 mg kg-1, with an average value of 17.80 mg kg-1. The average YPC of improved cultivars （18.31 mg kg-1） was significantly higher than that of landraces （15.51 mg kg-l）. The YPC in cultivars from the Loess Plateau spring sowing region （LPSSR） was the highest （20.59 mg kg-~）, followed by the North China summer sowing region （NCSSR, 18.25 mg kg-1）, the northeast spring sowing region （NSSR, 17.25 mg kg-1）, and the Inner Mongolia Plateau spring sowing region （IMPSSR, 13.92 mg kg-1）. The variation coefficients of YPC in cultivars from NSSR, LPSSR, and IMPSSR were higher than that from NCSSR. A similar carotenoid profile was also obtained for 270 foxtail millet cultivars. Lutein and zeaxanthin accounted for approximately 55-65% of YPC in accessions. The lutein content was higher than zeaxanthin content in all cultivars. The ratio of lutein to zeaxanthin ranged from 1.51 to 6.06 with an average of 3.34. YPC was positively correlated with lutein （r=0.935, P〈0.01）, zeaxanthin （r=0.808, P〈0.01 ）, and growth duration （t=0.488, P〈0.01 ）, whereas it was negatively correlated with grain protein （t=-0.332, P〈0.01） and 1 000-kernel weight （t=-0.153, P〈0.05）. Our study is useful for screening and selecting cultivars with high levels of yellow pigment and for enhancing phytochemical concentrations in breeding programs.

Noninvasive diagnosis of vulnerable coronary plaque

Myocardial infarction and sudden cardiac death are frequently the first manifestation of coronary artery disease.For this reason,screening of asymptomatic coronary atherosclerosis has become an attractive field of research in cardiovascular medicine.Necropsy studies have described histopathological changes associated with the development of acute coronary events.In this regard,thin-cap fibroatheroma has been identified as the main vulnerable coronary plaque feature.Hence,many imaging techniques,such as coronary computed tomography,cardiac magnetic resonance or positron emission tomography,have tried to detect noninvasively these histomorphological characteristics with different approaches.In this article,we review the role of these diagnostic tools in the detection of vulnerable coronary plaque with particular interest in their advantages and limitations as well as the clinical implications of the derived findings.