Vulnerability Of Systems Research Articles

Vulnerability of firn to hydrofracture: poromechanics modeling

Abstract On the Greenland Ice Sheet, hydrofracture connects the supraglacial and subglacial hydrologic systems, coupling surface runoff dynamics and ice velocity. In recent decades, the growth of low-permeability ice slabs in the wet snow zone has expanded Greenland's runoff zone, but observations suggest that surface-to-bed connections are rare, because meltwater drains through crevasses into the porous firn beneath ice slabs. However, there is little quantitative evidence confirming the absence of surface-to-bed fracture propagation. Here, we use poromechanics to investigate whether water-filled crevasses in ice slabs can propagate vertically through an underlying porous firn layer. Based on numerical simulations, we develop an analytical estimate of the water injection-induced effective stress in the firn given the water level in the crevasse, ice slab thickness, and firn properties. We find that the firn layer substantially reduces the system's vulnerability to hydrofracture because much of the hydrostatic stress is accommodated by a change in pore pressure, rather than being transmitted to the solid skeleton. This result suggests that surface-to-bed hydrofracture will not occur in ice slab regions until all pore space proximal to the initial flaw has been filled with solid ice.

L-Index-Based Technique for Voltage Collapse Prediction and Voltage Stability Enhancement in Electrical Power Systems

Recent years have witnessed a notable increase in the occurrence of blackouts, especially in developing nations, attributed to the continuously growing demand on modern power networks. Given that the demand shows no signs of abating and is projected to increase further in the coming years, additional research on power system stability is imperative. This study, therefore, investigates voltage stability assessment in power systems using the L-index methodology, focusing on the Nigerian 28-bus system and the IEEE system. The L-index offers a practical means of identifying weak buses and evaluating voltage stability margins. Calculating L-index values for load buses under diverse conditions identifies critical points, with higher values indicating vulnerability. The research investigates injecting reactive power at load buses to prevent collapse, comparing outcomes with and without compensation. Analyzing the L-index's performance across varied loading scenarios confirms its precision in predicting breakdown points and identifying critical buses. Load flow analysis of the Nigerian 28-Bus system reveals that only bus 16 exceeds voltage limits, while line analysis shows total power losses. Increasing loadability exposes bus 16 as the weakest, supported by its low voltage magnitude. The research confirms bus 16 as the system's weakest point, guiding corrective measures to enhance stability and prevent collapse. Utilizing Matlab for implementation, this study contributes valuable insights into system vulnerability and provides a framework for improving voltage stability in power systems.

Mapping varieties of farmers’ experience in the digital transformation: a new perspective on transformative dynamics

The COVID-19 pandemic has highlighted the vulnerabilities of the global food system, underscoring the need for a sustainable transformation of the food system. With the advent of new digital technologies emerging as critical tools for achieving the agricultural shift, it is important to understand farmers’ adoption decisions better. This study aims to systematically uncover and delineate the varied forms of experiences farmers have with new digital technologies and investigate how these experiences impact the organizational adoption decisions on the farm. In this study, twenty interviews with apple growers, wine makers, and intermediaries from a German region encompassing Saxony, Thuringia, and Saxony–Anhalt were conducted and analyzed. Through the lens of the modified adaptive capacity wheel and alongside the interview data, five relevant types of experiences were identified. These types of experiences are closely related to farmers’ adaptation motivation (AM) and adaptation belief (AB), potentially influencing their future decisions about the adoption of digital technologies. This study highlights the importance of creating meaningful experiences with technologies to strengthen farmers’ AM and AB.

Data Privacy and Security in the Age of IoT A Comprehensive Study on Information System Vulnerabilities

In the era of the Internet of Things (IoT), data privacy and security issues have become a primary concern in information systems. This research aims to investigate the underlying vulnerabilities of information systems concerning data privacy and security in the context of IoT. The research methods employed include literature surveys, case analyses, and reviews of best practices in data protection. The findings highlight several key vulnerability points, including weaknesses in credential management, network attacks, and deficiencies in data encryption implementation. Moreover, the findings indicate that despite advancements in security technology, significant gaps still exist that can be exploited by unethical parties to access users' IoT personal data. In conclusion, data protection in the context of IoT requires a holistic approach involving preventive measures, detection, and rapid response to evolving security threats. This research underscores the importance of data security education and awareness among IoT users and the necessity of collaboration among government, industry, and society to address these challenges.

Unveiling The Evolving Dynamics Of Mountain Pastoralism At Various Altitudinal Zones Of Upper Swat Valley, Pakistan

The confluence of environmental, climatic, and socioeconomic transformations is intensifying the vulnerability of pastoral social-ecological systems worldwide, with mountainous regions being excessively affected. This research paper explores the adaptive strategies employed by mountain pastoralists in Upper Swat, Pakistan, to navigate the complexities of rangeland management amidst dynamic climatic, social, and environmenta[1]l contexts. Through a comprehensive mixed-methods approach, involving observations, focus groups, and an extensive survey (2022-2023), this study reveals that mountain pastoralists have developed a multifaceted, adaptive strategy that integrates stall feeding with six distinct grazing mechanisms to optimize the use of spatially and seasonally heterogeneous rangeland resources. This strategy involves a coordinated sequence of winter stall-feeding near villages, spring rotational grazing in adjacent pastures, summer migration to high-elevation settlements, and intense autumn browsing of stubble fields prior to winter crop planting. The findings of this research contribute nuanced insights into the resilience and adaptability of mountain pastoralists and their rangeland management systems, informing the development of effective policies and programs to enhance the sustainability and reduce the vulnerability of mountain social-ecological systems.

Deep learning adversarial attacks and defenses on license plate recognition system

The breakthroughs in Machine learning and deep neural networks have revolutionized the handling of critical practical challenges, achieving state-of-the-art performance in various computer vision tasks. Notably, the application of deep neural networks in optical character recognition (OCR) has significantly enhanced the performance of OCR systems, making them a pivotal preprocessing component in text analysis pipelines for crucial applications such as license plate recognition (LPR) systems, where the efficiency of OCR is paramount. However, despite the advancements, the integration of deep neural networks in OCR introduces inherent security vulnerabilities, particularly susceptibility to adversarial examples. Adversarial examples in LPR systems are crafted by introducing perturbations to original license plate images, which can effectively compromise the integrity of the license plate recognition process, leading to erroneous license plate number identification. Given that the primary goal of OCR in this context is to accurately recognize license plate numbers, even a single misinterpreted character can significantly impact the overall performance of the LPR system. The vulnerability of LPR systems to adversarial attacks underscores the urgent need to address the security weaknesses inherited from deep neural networks. In response to these challenges, the exploration of alternative defense mechanisms, such as image denoising and in-painting, presents a compelling approach to bolstering the resilience of LPR systems against adversarial attacks. By prioritizing practical implementation and integration of image denoising and inpainting techniques align with the operational requirements of real-world LPR systems. These methods can be seamlessly integrated into existing pipelines, offering pragmatic and accessible means of enhancing security without imposing significant computational overhead. By embracing a multi-faceted approach that combines the strengths of traditional image processing techniques, the research endeavors to develop comprehensive and versatile defense strategies tailored to the specific vulnerabilities and requirements of LPR systems. This holistic approach aims to fortify LPR systems against adversarial threats, thereby fostering increased trust and reliability in the deployment of OCR and LPR technologies across various domains and applications.

Analysis Effect of Parameters of Genetic Algorithm on a Model for Optimization Design of Sustainable Supply Chain Network Under Disruption Risks

Abstract Over the last decade, our world exposed to many types of unpredictable disasters (recently Coronavirus). These disasters have clearly shown the uncertainty and vulnerability of supply chain systems. Also, it confirmed that adopting Just-in-Time (JIT) strategy to reduce the logistic chain cost may lead to inbuilt complexity and risks. Efficient tools are therefore needed to make complexity optimized supply chain decisions. Evolutionary algorithms, including genetic algorithms (GA), have proven effective in identifying optimal solutions that address the trade-offs between total supply chain cost and carbon emissions regulatory policy represented by carbon tax charges. These solutions pertain to the design challenges of supply networks exposed to potential disruption risks. However, GA have a set of parameters must be chosen for effective and robust performance of the algorithms. This paper aims to set the most suitable values of these parameters that used via GA – ased optimization cost and risk reduction model in firms using a JIT as a delivery system. The model has been conceptualized for addressing the design complexities of the supply chain, referred to as SCRRJITS (Simultaneous Cost and Risk Reduction in a Just-in-Time System). A complete analysis of the different parameters and operators of the algorithm is carried out using design of experiments approach. The algorithm performance measure used in this study is convergence of solutions. The results show the extent to which the quality of solution can be changed depending on selection of these parameters.

Power Network Vulnerability Assessment Based on Electric Power System Cyber Security Simulation Platform

Abstract This paper introduces a modeling approach for the integration of electric power and information networks, along with the creation of an electric power system network simulation platform. The system, achieved through the deep integration of computation, communication, and control technologies, offers new insights into power grid intelligence. The paper discusses the concept of power system vulnerability and presents a modeling scheme that effectively simulates and analyzes power systems. Using the Analytic Hierarchy Process (AHP), the impact of cyber attacks under different load conditions is evaluated, revealing that attacks have more severe consequences under heavy loads. In conclusion, this research contributes to enhancing the security and stability of power systems.

Klasifikasi Malware Menggunakan Metode Convolutional Neural Network (CNN) Berbasis Website

This study aims to develop a web-based malware detection system using Convolutional Neural Network (CNN) utilizing the IoT23 dataset. Malware is malicious software that can exploit security vulnerabilities in computer systems, steal data, and degrade performance. The implementation of this detection system involves CNN, capable of extracting important features from both visual and textual data, applied to malware classification. The IoT23 dataset comprises 23 scenarios of IoT network traffic, including traffic from malware-infected devices. The study results show that the developed web application can detect malware attacks with accuracy, precision, recall, and F1-score of 99% on separate data scenarios. This CNN-based detection system has proven effective in identifying and classifying malware attacks, contributing to the enhancement of network and device security.

Integrating Syrian refugees into Lebanon’s healthcare system 2011–2022: a mixed-method study

IntroductionThe Lebanese government estimates the number of Syrian refugees to be 1.5 million, representing 25% of the population. Refugee healthcare services have been integrated into the existing Lebanese health system. This study aims to describe the integration of Syrian refugee health services into the Lebanese national health system from 2011 to 2022, amid an ongoing economic crisis since 2019 and the COVID-19 pandemic.MethodsThis paper employs a mixed-methods approach drawing upon different data sources including: 1- document review (policies, legislation, laws, etc.); 2- semi-structured interviews with policymakers, stakeholders, and health workers; 3- focus group discussions with patients from both host and refugee populations; and 4- health systems and care seeking indicators.ResultsAlthough the demand for primary health care increased due to the Syrian refugee crisis, the provision of primary health care services was maintained. The infusion of international funding over time allowed primary health care centers to expand their resources to accommodate increased demand. The oversupply of physicians in Lebanon allowed the system to maintain a relatively high density of physicians even after the massive influx of refugees. The highly privatized, fragmented and expensive healthcare system has impeded Syrian refugees’ access to secondary and tertiary healthcare services. The economic crisis further exacerbated limits on access for both the host and refugee populations and caused tension between the two populations. Our findings showed that the funds are not channeled through the government, fragmentation across multiple financing sources and reliance on international funding. Common medications and vaccines were available in the public system for both refugee and host communities and were reported to be affordable. The economic crisis hindered both communities’ access to medications due to shortages and dramatic price increases.ConclusionIntegrating refugees in national health systems is essential to achieve sustainable development goals, in particular universal health coverage. Although it can strengthen the capacity of national health systems, the integration of refugees in low-resource settings can be challenging due to existing health system arrangements (e.g., heavily privatized care, curative-oriented, high out-of-pocket, fragmentation across multiple financing sources, and system vulnerability to economic shocks).

A Review of Modifiable Health Care Factors Contributing to Inpatient Suicide: An Analysis of Coroners’ Reports Using the Human Factors Analysis and Classification System for Healthcare

Inpatient suicides have devastating and long-lasting consequences for patients, families, and health care organizations, posing a major challenge for hospitals. Although many studies have identified patient risk factors for inpatient suicide, the modifiable health care factors are less understood. Failure to understand these modifiable factors weakens organizations' ability to design and implement effective prevention strategies. The Human Factors Analysis and Classification System for Healthcare (HFACS-Healthcare) was used to classify and analyze modifiable health care factors that contributed directly or indirectly to inpatient suicides in Australian hospitals between 2009 and 2018. Comparisons were made between general and psychiatric hospital units to identify context-specific recommendations. Of the 367 cases, 216 (58.9%) had enough information to analyze the contributing factors, and 214 (58.3%) included unit location information. Multiple modifiable health care factors were identified in the cases as contributing to the patients' suicides. Commonly, cases reported decision errors made by individuals (57.4%), problems with the physical environment (56.0%), and unit-level operational decision-making errors (that is, planned inappropriate operations) (48.6%). An association was found between unit type and problems with coordination, mental state, tasks, physical environment, planned inappropriate operations, and organizational culture (p < 0.05). General prevention initiatives may not be effective in addressing inpatient suicides across specialty units. HFACS-Healthcare enabled a deeper understanding of inpatient suicide and the identification of priority areas that, if addressed, could help reduce the number of preventable suicides in hospitals. Hospital suicide prevention initiatives need to be tailored to specific units and target individual and system vulnerabilities to improve safety and reduce inpatient suicide rates.

Palmprint spoofing detection by using deep learning technique on Multispectral database

The protection of Biometric systems against attacks is crucial as biometric devices proliferate in the field of personal authentication. The presentation assault is the most prevalent type of attack on biometric systems; it entails presenting a fake copy (artefact) of the true biometric to the sensor in order to gain unauthorised access. The vulnerability in palmprint-based biometric systems has not received much attention despite the substantial threat posed by these assaults. In this research, we show how to detect a spoof palmprint image. Spoofing attacks involving faked images pose a significant threat to biometric systems. For the suggested method, we use the CASIA palmprint database, from which we constructed our own spoof database using printed photos. After that, we did some pre-processing to obtain the ROI image and a noise-free image for feature extraction using the SIFT approach. We use the convolution neural network for classification and the SVM for comparison. We obtained a result of 96.2% for our proposed palmprint system identification and 89% for SVM. But our main goal is to train the model for spoof detection, so we take some normal images and some spoof images for our train model and use the confusion matrix to calculate the accuracy of our model. We obtain an overall accuracy of 86% for our spoof detection by computing the confusion matrix.

Is It Still Time for Safety Walkaround? Pilot Project Proposing a New Model and a Review of the Methodology.

Background and Objectives: Healthcare facilities are complex systems due to the interaction between different factors (human, environmental, management, and technological). As complexity increases, it is known that the possibility of error increases; therefore, it becomes essential to be able to analyze the processes that occur within these contexts to prevent their occurrence, which is the task of risk management. For this purpose, in this feasibility study, we chose to evaluate the application of a new safety walkaround (SWA) model. Materials and Methods: A multidisciplinary working group made up of experts was established and then the subsequent phases of the activity were divided into three stages, namely the initial meeting, the operational phase, and the final meeting, to investigate knowledge regarding patient safety before and subsequently through visits to the department: the correct compilation of the medical record, adherence to evidence-based medicine (EBM) practices, the overall health and the degree of burnout of the various healthcare professionals, as well as the perception of empathy of staff by patients. Results: This working group chose to start this pilot project in the vascular surgery ward, demonstrating the ability of the tool used to capture the different aspects it set out to collect. In detail, the new version of SWA proposed in this work has made it possible to identify risk situations and system vulnerabilities that have allowed the introduction of corrective tools; detect adherence to existing company procedures, reschedule training on these specific topics after reviewing, and possibly update the same procedures; record the patient experience about the doctor-patient relationship and communication to hypothesize thematic courses on the subject; evaluate workers' perception of their health conditions about work, and above all reassure operators that their well-being is in the interest of the management of the healthcare company, which is maintained. Conclusions: Therefore, the outcome of the present study demonstrates the versatility and ever-present usefulness of the SWA tool.

Enhancing the Evaluation of District Heating System Resilience: A Literature Review

Ensuring an uninterrupted supply of energy, electricity, and heat is becoming an increasingly pressing necessity. Energy provides both social and economic growth therefore, the energy systems should be resilient to different external conditions. Resilience is the ability of a system to recover from adversity [1] and in connection with the critical infrastructure - the ability to prepare for and adapt to changing conditions and withstand and recover rapidly from disruptions. Resilience includes the ability to withstand and recover from deliberate attacks, accidents, or naturally occurring threats or incidents. Several factors influence the resilience of the energy supply system: energy security, reliability, robustness, flexibility, and resilience. If energy resilience has not been well considered or even ignored in the planning, design, and operation stages in district energy communities can lead to system fragility and vulnerability. Therefore, the research assesses the different methods for resilience assessment in energy systems by focusing on district heating. During climate change mitigation and adaptation energy supply system resilience is unclear. Based on current studies, the energy resilience factor for most is considered in the context of the electricity transmission system, and the heat transmission resilience study is limited. During the research of the literature, it has been concluded that there is no consistent quantitative and quality approach to resilience because there is no consistent treatment of the concept of resilience. The available literature sources about energy resilience are limited in their scope and usability and hence are not amenable for use outside the scope where they have been developed. The metric and formula used for calculating resilience, if any, and the input data required for such calculations are also dependent on the research scope. There is a need for a fundamental generic quantitative and quality approach for resilience that would be usable and useful across various scopes or sectors in a consistent manner, which can be used for the development of resilient systems and effective resilience strategies for district heating systems.

A Dual-Layer Complex Network-Based Quantitative Flood Vulnerability Assessment Method of Transportation Systems

Evaluating the vulnerability of urban transportation systems to flood disasters can provide scientific support for urban disaster prevention and mitigation. Current methods for assessing the flood vulnerability of urban roads often overlook the internal relationships within the complex spatial composition of road networks and surface structures. In this study, based on the theory of complex networks, a dual-layer network assessment model is established for evaluating the flood vulnerability of urban transportation systems by coupling basic geographic data with road network vector data. Unlike traditional methods, this model considers the complex relationship between road network structures and ground surfaces, uncovering a correlation between road network structure and road flood vulnerability. By utilizing this model, the flood vulnerability of road networks in Shenzhen, as well as the city’s spatial flood vulnerability, are quantitatively assessed. Based on the quantitative results, we create maps illustrating the distribution of road and spatial flood vulnerability in Shenzhen. The study results reflect that roads highly vulnerable to flooding are mainly located in the central urban area of the southwest, with the flood vulnerability spatially concentrated primarily in the northern and western regions. Using data from government reports, news stories, and other sources over the past five years, we compile recorded instances of urban waterlogging. The quantitative results of the model are consistent with the distribution trend in recorded waterlogging points, indicating that the model’s outcomes are authentic and reliable.

Adversarial Item Promotion on Visually-Aware Recommender Systems by Guided Diffusion

Visually-aware recommender systems have found widespread applications in domains where visual elements significantly contribute to the inference of users’ potential preferences. While the incorporation of visual information holds the promise of enhancing recommendation accuracy and alleviating the cold-start problem, it is essential to point out that the inclusion of item images may introduce substantial security challenges. Some existing works have shown that the item provider can manipulate item exposure rates to its advantage by constructing adversarial images. However, these works cannot reveal the real vulnerability of visually-aware recommender systems because (1) the generated adversarial images are markedly distorted, rendering them easily detected by human observers; (2) the effectiveness of these attacks is inconsistent and even ineffective in some scenarios or datasets. To shed light on the real vulnerabilities of visually-aware recommender systems when confronted with adversarial images, this paper introduces a novel attack method, IPDGI (Item Promotion by Diffusion Generated Image). Specifically, IPDGI employs a guided diffusion model to generate adversarial samples designed to promote the exposure rates of target items (e.g., long-tail items). Taking advantage of accurately modeling benign images’ distribution by diffusion models, the generated adversarial images have high fidelity with original images, ensuring the stealth of our IPDGI. To demonstrate the effectiveness of our proposed methods, we conduct extensive experiments on two commonly used e-commerce recommendation datasets (Amazon Beauty and Amazon Baby) with several typical visually-aware recommender systems. The experimental results show that our attack method significantly improves both the performance of promoting the long-tailed (i.e., unpopular) items and the quality of generated adversarial images.

Using machine learning algorithms to enhance IoT system security.

The term "Internet of Things" (IoT) refers to a system of networked computing devices that may work and communicate with one another without direct human intervention. It is one of the most exciting areas of computing nowadays, with its applications in multiple sectors like cities, homes, wearable equipment, critical infrastructure, hospitals, and transportation. The security issues surrounding IoT devices increase as they expand. To address these issues, this study presents a novel model for enhancing the security of IoT systems using machine learning (ML) classifiers. The proposed approach analyzes recent technologies, security, intelligent solutions, and vulnerabilities in ML IoT-based intelligent systems as an essential technology to improve IoT security. The study illustrates the benefits and limitations of applying ML in an IoT environment and provides a security model based on ML that manages autonomously the rising number of security issues related to the IoT domain. The paper proposes an ML-based security model that autonomously handles the growing number of security issues associated with the IoT domain. This research made a significant contribution by developing a cyberattack detection solution for IoT devices using ML. The study used seven ML algorithms to identify the most accurate classifiers for their AI-based reaction agent's implementation phase, which can identify attack activities and patterns in networks connected to the IoT. The study used seven ML algorithms to identify the most accurate classifiers for their AI-based reaction agent's implementation phase, which can identify attack activities and patterns in networks connected to the IoT. Compared to previous research, the proposed approach achieved a 99.9% accuracy, a 99.8% detection average, a 99.9 F1 score, and a perfect AUC score of 1. The study highlights that the proposed approach outperforms earlier machine learning-based models in terms of both execution speed and accuracy. The study illustrates that the suggested approach outperforms previous machine learning-based models in both execution time and accuracy.

SECURE &amp; VERIFIABLE E VOTING USING BLOCKCHAIN

In response to the persistent challenges surrounding the aspect of security, verifiability of electronic voting (e-voting) systems, this paper proposes an innovative approach. By incorporating blockchain technology into face recognition authentication, our solution aims to fortify the integrity and transparency of the voting process. Blockchain offers a distributed and immutable ledger, ensuring data integrity, while face recognition authentication offers an additional layer of security. This integrated system establishes a secure and transparent platform for casting and verifying votes, offering a promising solution to the vulnerabilities of traditional e-voting systems. Key Words- Electronic voting, Blockchain technology, Face recognition, Security, Verifiability, Authentication, Transparency, Trust, Democracy.

Climatic Variations and Challenges to Socio-economic Development amongst Mbororo Communities in Mbum Plateau, North West Region of Cameroon

This study sets out to investigate the challenges hindering Mbororo sources of livelihood in Mbum Plateau, North West Region of Cameroon. Explanatory research was used where it specify the nature and direction of the relationships between the studied variables. The study used mixed methods of qualitative and quantitative approaches. The study employed the primary and secondary data sources to bring out realistic results. Rainfall Seasonality Index (SI) and Standardized Precipitation Index (SPI) were employed to assess the vulnerability of the agro-hydrological system to climate variation. The major primary data sources used were field survey, interviews and questionnaires. A total of 200 questionnaires were distributed using stratified random and snow ball sampling techniques. The data obtained was complemented by secondary sources from published and unpublished materials. Data collected during the field survey were analyzed through two statistical techniques. The qualitative data obtained was analyzed through the content analysis whereas themes and codes were given to the different opinions and perceptions of the informants and their frequencies and percentages were being determined from there. Data were presented in tables, charts, maps, and graphs with some attempted interpretations. Data were analyzed using the chi-square and multiple regression models. The findings revealed that climate variability, dwindling pasture and low streams discharge are having negative effects on Mbororo communities. It was observed from satellite images that, land use over the years has been changing with reducing aerial extent and quality of pasture, reducing water points in Mbum Plateau. In order to adapt the decreasing cattle numbers on the side of the Mborroro, they have taken farming, diving, hawking arm rubbery, and schooling as new livelihoods sustenance. The study recommends a sustainable pastoral activity by encouraging the introduction of drought resistant cattle species, Bracharia and Guatemala grass to supplement the natural pasture. MINEPAI should regularly control herd size so as to ensure ecosystem balance and avoid overgrazing. Transhumance tracts and areas should be well demarcated to avoid frequent farmer-grazers’ conflicts and frequent cattle theft observed in the region.

Electrical Power Systems Reinforcement through Overall Contingency Index Analysis and Improvement

This paper analyzes the behavior of an electrical power system when N-1 contingencies occur in the transmission stage, which can be produced by incorrect operation of the protection relays, phenomena of natural origin, or increased loadability, which affect the operation and reliability of the electrical system. The operation output of a transmission line results in the variation of the nominal values of the electrical parameters involved because they disturb the stability of the generation, transmission systems, and the supply of electrical energy to the loads, such as voltages and angles of the nodes and the active and reactive power of the system. The proposed methodology was based on analyzing the different electrical parameters of the power system, quantifying the contingency index in a state of regular operation, and comparing it to operation in contingency N-1, with which the most severe contingency was determined and, therefore, achieved; identifying contingencies that can cause system collapses; improving the contingency index from 23.08555 to 22.9276624 for the L16–19 contingency and to 22.9795235 for the L21–22 contingency, which are the most severe contingencies determined with the proposed methodology. To test the proposed methodology, the IEEE 39 bus-bar test system was considered, and the elements that should be implemented to avoid the vulnerability of the power system to N-1 contingencies were determined.