Vulnerability Assessment Research Articles

Abstract B067: A mixed-methods health services analysis of disparities and social vulnerabilities in childhood cancer survivors’ long-term follow-up care

Abstract Pediatric cancer affects >15,000 US children annually. Treatment advances have led to an increase in those surviving the disease, and these patients must be managed to control their long-term comorbidities. Unfortunately, a majority are nonadherent to post-treatment care guidelines and become lost to follow-up. This is especially true for those from underserved backgrounds, including survivors who are Black and Latine. To help understand this disparity, we studied the organization and delivery of pediatric cancer survivorship care at an urban comprehensive cancer center. Research included: 1) a qualitative study exploring healthcare barriers and facilitators of survivors’ adherence to long-term follow-up care, as determined by N=31 key informants (pediatric oncology care providers, administrators), and; 2) a retrospective cohort study (2007- 2020) among N>350 survivors (10% Black, 28% Latine, 12% Asian; 47% from vulnerable neighborhoods) assessing social determinants of health (SDoH) and its cofactors, adjusted for vital status. Interviews were recorded, transcribed, and thematically analyzed using software (K>.70). Three major themes emerged in survivors’ adherence: 1) healthcare system-level influences (69%), SDoH (25%), and intra/interpersonal factors (16%). At the system-level, informants noted opportunities to strengthen adherence, especially for survivors from underserved backgrounds, including the provision of greater social support (14%), easing appointment scheduling (11%), offering high-quality ambulatory and specialty care within the system upon entering young adulthood (12%), and cultural competency training for staff (6%). Among SDoH, informants noted transportation and geographic challenges in accessing the healthcare system (25%), insurance coverage for follow-up care (17%), and strengthening trust with marginalized communities (17%). For intra/interpersonal factors, time- and age-related changes in survivors’ medical needs were acknowledged as barriers to be overcome (28%), along with providing greater patient education (11%) and heightened salience of follow-up care (17%). Conversely, the provider-patient relationship was noted as a strength in promoting adherence (23%). Retrospective cohort results revealed that survivors’ attendance at dedicated clinics was suboptimal (52% >= 2 years off-therapy), as were rates of system-wide ambulatory care encounters 3+ and 5+ years post-therapy. At the bivariate level, non-Hispanic Black survivors (68%), adolescents (58%), and those with CNS tumors (81%) had the lowest likelihood of survivorship clinic attendance (p<.05)–underscoring qualitative findings on SDoH. Area-level SDoH indices were unrelated to the cohort’s adherence, highlighting qualitative findings on intra/interpersonal barriers and facilitators. These data lend insights into transformations possible within the organization and delivery of healthcare to young survivors, especially those from underrepresented backgrounds, including greater attention to SDoH and strengthening cultural competence among health system employees. Citation Format: Marcelo Sleiman Jr, Tara Suntum, Nina Kadan-Lottick, Brianna Miller, Mary R Yockel, Arnold Potosky, Chiranjeev Dash, Adil Alaoui, Burton Appel, Ken Tercyak. A mixed-methods health services analysis of disparities and social vulnerabilities in childhood cancer survivors’ long-term follow-up care [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B067.

Epidemic and Pandemic Preparedness and Response in a Multi-Hazard Context: COVID-19 Pandemic as a Point of Reference

Infectious diseases manifesting in the form of epidemics or pandemics do not only cause devastating impacts on public health systems but also disrupt the functioning of the socio-economic structure. Further, risks associated with pandemics and epidemics become exacerbated with coincident compound hazards. This study aims to develop a framework that captures key elements and components of epidemic and pandemic preparedness and response systems, focusing on a multi-hazard context. A systematic literature review was used to collect data through peer-reviewed journal articles using three electronic databases, and 17 experts were involved in the validation. Epidemiological surveillance and early detection, risk and vulnerability assessments, preparedness, prediction and decision making, alerts and early warning, preventive strategies, control and mitigation, response, and elimination were identified as key elements associated with epidemic and pandemic preparedness and response systems in a multi-hazard context. All elements appear integrated within three interventional phases: upstream, interface, and downstream. A holistic approach focusing on all interventional phases is required for preparedness and response to pandemics and epidemics to counter their cascading and systemic effects. Further, a paradigm shift in the preparedness for multi-hazards during an epidemic or pandemic is essential due to the multiple challenges posed by concurrent hazards.

Evaluating Methods of Software Bill of Materials Generation to Enhance Nuclear Power Plant Cybersecurity

Instrumentation and control (I&C) systems in nuclear power plants (NPPs) are potential targets of cyberattacks and can prove deleterious for the safety of the NPPs. A Software Bill of Materials (SBOM) provides a detailed list of the various components and their dependencies in software, which helps in vulnerability and risk assessment for cyber hygiene and situational awareness. For an NPP, the process of generating an accurate SBOM report can be complex due to the legacy systems and firmware binaries involved. While most current SBOM tools are focused more on modern internet technology software, this research provides insights and guidelines for an NPP to generate an accurate and efficient SBOM. The paper proposes a new methodology to help NPPs categorize software and use appropriate tools to generate SBOMs for their digital I&C systems.

Life-Cycle Cost Assessment of the Base-Isolated-Reinforced Concrete Structure

Seismic isolation structures have excellent seismic mitigation effects, but implementing isolation measures may increase the initial cost of the structure. This paper presents a method to calculate the seismic loss and assess the life-cycle cost of isolated structures, which is convenient for owners to make decisions. The life-cycle cost assessment model is divided into two parts: initial cost assessment and seismic loss assessment over life-cycle period. Among them, the assessment method of the isolation layer is proposed, including the cost assessment of isolation bearings and dampers. The assessment method of seismic loss considers direct loss, indirect loss, casualties and integrating casualties into a unified assessment system. Direct loss includes building loss, indirect loss includes rental loss, relocation loss, income loss, personal property loss and business inventory loss. The seismic loss model is associated with seismic damage probability, which is obtained through seismic vulnerability and seismic probability risk assessment. The 8-story base-isolated-reinforced concrete structure illustrates the feasibility of the life-cycle-cost assessment model. The example shows that the initial cost of the isolated structure exceeds that of the nonisolated structure, however, the rental loss, income loss, personal property loss and casualties are much lower than those of nonisolated structures. The income loss of the isolated structure is the maximum proportion in life-cycle cost, and the casualty loss is the minimum proportion in the seismic loss, due to its excellent seismic mitigation effects.

Hot spots for risk-based planning in Greece – the cases of floods and forest fires

ABSTRACT Recent flood and forest fire disasters in Greece highlight the local spatial development history and territorial vulnerability as principal causes. However, despite the strong spatial aspects of disaster risk, disaster management rarely finds its way into spatial planning. The article attempts to locate high disaster risk areas (hot-spots) in Greece and activate risk-based planning. The approach combines theoretical assumptions on the spatial dimension of disaster risk; macro-scale hazard zoning at the national-regional level through past disaster event mapping; meso-scale territorial vulnerability analysis to delimit disaster risk hot-spots; and discussion on statutory planning tools and scales of intervention for risk mitigation.

Vulnerability Analysis of a Multilayer Logistics Network against Cascading Failure

One of the most challenging issues in contemporary complex network research is to understand the structure and vulnerability of multilayer networks, even though cascading failures in single networks have been widely studied in recent years. The goal of this work is to compare the similarities and differences between four single layers and understand the implications of interdependencies among cities on the overall vulnerability of a multilayer global logistics network. In this paper, a global logistics network model set as a multilayer network considering cascading failures is proposed in different disruption scenarios. Two types of attack strategies—a highest load attack and a lowest load attack—are used to evaluate the vulnerability of the global logistics network and to further analyze the changes in the topology properties. For a multilayer network, the vulnerability of single layers is compared as well. The results suggest that compared with the results of a single global logistics network, a multilayer network has a higher vulnerability. In addition, the heterogeneity of networks plays an important role in the vulnerability of a multilayer network against targeted attacks. Protecting the most important nodes is critical to safeguard the potential “vulnerability” in the development of the global logistics network. The three-step response strategy of “Prewarning–Response–Postrepair” is the main pathway to improving the adjustment ability and adaptability of logistics hub cities in response to external shocks. These findings supplement and extend the previous attack results on nodes and can thus help us better explain the vulnerability of different networks and provide insight into more tolerant, real, complex system designs.

Comprehensive Assessment of Flood Risk and Vulnerability for Essential Facilities: Iowa Case Study

In this study, nine different types of essential facilities in the state of Iowa (such as hospitals, fire stations, schools, etc.) were analyzed on a county level in terms of flood depth, functionality and restoration time after flooding, and damage sustained during flooding. These essential facilities were also analyzed on the state level in terms of their location relative to the 100 y and 500 y flood zones. Results show that the number of essential facilities within the flood extent reached up to 39%, and during the 100 y flood scenario all but one of the six chosen counties lost functionality of 100% of their facilities. Most essential facilities were found to have a flood depth of 1 to 4 ft deep and a restoration time of 480 days. The purpose of this study is to bring awareness to decisionmakers regarding the risk that flooding events pose to essential facilities and to highlight the increasing dangers of flooding on a broader scale. This study will be beneficial to improve mitigation strategies, emergency response plans, and ensuring that emergency services and facilities are available in the event of future floods for the affected areas.

Vulnerability assessment of buildings adjacent to natural gas pipelines under explosion accident

ABSTRACT Natural gas pipeline leak may lead to explosion accidents, which may threaten the safety of the adjacent buildings. This paper conducts a vulnerability assessment of buildings adjacent to natural gas pipeline under explosion, which is implemented by Copula Bayesian network (CBN) model. The incident escalations and evolution nodes were identified based on the accident reports. The correlation between nodes is calculated using the Kendall correlation coefficient to develop a CBN model of natural gas pipeline accidents. The weaknesses in the buildings adjacent to pipelines are identified using spider diagrams. The vulnerability of building adjacent to pipelines is characterized by the damage degree. The model is verified by a case study. The results indicate that the occurrence probability of the explosion accident reaches 85%. The identified weaknesses are ignition sources and confined spaces. The occurrence probability of a delayed ignition within buildings is 0.23, and the explosion overpressure is estimated to be 84.25 kPa. The vulnerability degree of building 1 is higher than building 2. These findings can help to understand the system weaknesses and support the safety improvement of buildings in the case of natural gas pipeline explosion.

Abstract C028: Challenging PDAC adaptability: Uncovering therapeutic vulnerabilities in APE1 DNA repair mechanisms

Abstract Pancreatic ductal adenocarcinoma (PDAC) is a therapeutically challenging malignancy notorious for its resilience to therapeutic strategies and ability to thrive under extreme conditions. The cancer's inherent ability to adaptively resist treatments underscores the need for novel and combinatorial therapeutic approaches. Our research focuses on elucidating the role of Apurinic-apyrimidinic endonuclease/Redox effector factor 1 (APE1/Ref-1), a critical multifunctional protein in PDAC pathobiology. APE1 serves two essential functions: as the principal endonuclease in the base excision repair (BER) pathway for DNA repair and as a redox regulator of transcription factors essential for cancer cell proliferation. While the therapeutic targeting of APE1’s redox function has been effective and continues to yield significant advances, the development of treatments aimed at its DNA repair function has proven more challenging with comparatively limited progress. Since standard PDAC treatment regimens involve DNA damaging agents potentially repaired via BER and APE1, we engineered three mutant cell lines with knock-in mutations resulting in a reduction of APE1’s BER activity while maintaining full redox activity. Biochemical evaluations confirmed the effects of the mutation, revealing a 30-fold reduction in DNA repair capacity. Surprisingly, with respect to growth and proliferation, the mutant and wild-type cell lines behaved similarly. Analyses of cytotoxic vulnerabilities revealed similar findings with the mutant lines exhibiting minimal phenotypic differences in sensitivity when challenged with cytotoxic and DNA damaging agents in proliferation-based assays. These findings led us to posit that the mutant cell lines were relying on compensatory DNA repair pathways which were providing sufficient compensation for the decrease in APE1 activity. We further hypothesized that the cell lines may still have hidden, long-term deficiencies in DNA damage processing which are not apparent during the initial stress response. Subsequent colony formation assays revealed that the mutant cell lines do exhibit poor long-term survival even after only short-term exposure to DNA damage. In vivo orthotopic mouse model experiments further corroborated these findings, showing a significant reduction in tumor size and decreased metastasis to the lungs and liver in mutant cell lines compared to wild-type controls. While APE1 is the primary endonuclease in the BER pathway, studies have demonstrated APE1-independent BER in rare, specific contexts. We are probing the hypothesis that these APE1-independent BER pathways function as the compensatory mechanism safeguarding these cells in short-term stress situations. Identifying and characterizing these backup pathways will not only offer a better understanding of APE1’s DNA repair capabilities within PDAC but will also provide a better understanding of how DNA repair mechanisms can be therapeutically targeted for novel therapeutic options or combinations. This opens the path to enhance standard of care treatments such as FOLFIRINOX. Citation Format: Eyram K Kpenu, Randall Wireman, Mahmut Mijiti, Silpa Gampala, Melissa L Fishel, Mark R Kelley. Challenging PDAC adaptability: Uncovering therapeutic vulnerabilities in APE1 DNA repair mechanisms [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr C028.

Risk and vulnerability analysis of road network in landslide prone areas in Munnar region, India

Catastrophes like landslides have the potential to impair critical transportation infrastructure, particularly road networks. The hilly regions in the state of Kerala in India are particularly prone to hazards and changing climate conditions. During the monsoon season, landslides are common in the Western Ghats, and the intensity of adverse impacts is more severe due to its densely populated regions. The study area of this research is in Idukki district of Kerala, where over 60 % of the land is prone to landslides. The monsoon rains bring with them a slew of disastrous landslides in the region. Most of the roadways in the study area are often disrupted due to landslides. Landslide risk assessment (LRA) is a crucial component of research on adverse impacts of landslide. The use of Geographical information System (GIS) and Multi Criteria Decision Analysis (MCDA) using Analytical Hierarchical Process (AHP) for susceptibility mapping and hazard risk assessment assists in the identification of disaster-prone areas. The data collected by field surveys were used to confirm the study results and the high-risk zones of the region were identified and the risk maps are prepared for the region as well as for the road network in the region. The risk of landslides may be described as the possibility of negative repercussions on road network thereby adversely impacting the inhabitants of the region. The landslide risk assessment of the road network in a hilly region is carried out which gives important insights for risk management and for future planning of resilient development in the region. This study enhances the knowledge for management of road network risk and vulnerabilities in intricate hilly region settings by developing a thorough vulnerability analysis framework. The research advances by giving transportation engineers a useful quantitative tool for identifying the risk and vulnerabilities of road network and directing design strategies and mitigation measures to lessen the possible negative effects of disruptive events like landslides on road infrastructure. The research findings could be an input for policy makers to plan for alternative resilient strategies for landslide risk management in road networks. The rational methodology adopted here could be replicated for carrying out risk and vulnerability assessment in other landslide prone areas.

Hybrid nonlinear probabilistic model using Monte Carlo simulation and hybrid support vector regression for evaporation predictions

This study presents a hybrid model combining Support Vector Regression (SVR) with the Gray Wolf Optimizer (GWO) for evaluating evaporation dynamics. The model was tested using 50 years of meteorological data from Zabol and Chabahar stations in Iran, and compared against standard SVR. Results show that the SVR-GWO significantly outperforms SVR, reducing predictive errors by over 41% on average. For Zabol, the SVR-GWO markedly improved performance, particularly in July where R2 increased from 0.867 to 0.973. The model more closely matched observed statistics, especially for mean and standard deviation, though challenges remain in reproducing higher-order statistics. Interestingly, 5,000 functions evaluation proved nearly as effective as 500,000 suggesting computational efficiency. This integrated approach, incorporating Monte Carlo simulations and kernel density estimation, provides valuable probabilistic insights into evaporation dynamics, offering a promising tool for drought vulnerability assessment and water resources planning in regions with complex climatic patterns.

Efficient finite element reliability analysis employing sequentially-updated surrogate model for fragility curve derivation

As the threat of natural disasters to structures intensifies, risk assessment of infrastructure has gained much importance. Fragility curves are essential tools in predicting disaster-related losses and making disaster mitigation decisions. In this paper, we propose a new method to efficiently derive accurate fragility curves for structures with high levels of nonlinearity or complexity, addressing the computational challenges of conventional finite element reliability analysis (FERA). To reduce the computational cost for calculating probability of failure in FERA, the proposed method utilizes the first-order reliability method (FORM). However, even with this approach, the computational cost of deriving the fragility curve may remain high; therefore, a surrogate model is used to further reduce costs. By training the surrogate model using the initial structural damage probabilities for a few hazard intensities, an optimal starting point can be calculated for the subsequent FORM analysis. During the fragility analysis, the surrogate model can be updated sequentially to increase the efficiency of FORM analysis continuously. In particular, the training process of the surrogate model requires no separate or additional finite element analysis because it is constructed using previous FERA results. The accuracy and efficiency of the proposed method are tested using conventional FERA and Monte Carlo simulations through a hypothetical short-column example. In addition, fragility curves are derived through a bridge flood fragility assessment considering the scour and seismic vulnerability assessment of a buried gas pipeline considering soil-structure interactions. The derived fragility curves closely match those derived using the conventional FERA, and the computational costs are reduced by 36.54 % and 52.38 %, respectively, compared with the conventional FERA, confirming its cost-effectiveness.

A Bayesian network-based probabilistic framework for seismic vulnerability assessment of road networks

After an earthquake, roadside debris from collapsed buildings, structural damage to bridges, and large-scale emergency evacuations are major obstructions to the connectivity of road networks (RNs). However, few studies have comprehensively examined the impact of all these obstructions on the seismic vulnerability of RNs while considering uncertainties. In this paper, a novel probabilistic framework is proposed for seismic vulnerability assessment of RNs based on Bayesian networks (BNs). Herein, the vulnerability of RNs is defined as a combination of the failure probability of a road link and its conditional consequences in terms of network dis-connectivity. The framework can be used to assess the effects of debris distributions, the functionality losses of bridges, and evacuation flows on the failure probabilities of road links and the seismic vulnerability of RNs. Uncertainties in the post-earthquake damage states of buildings and bridges, building collapse types, building-to-road distances, and travel costs are explicitly considered and propagated in the framework using Monte Carlo (MC) simulations. The MC results are used to establish prior probabilities, and based on bidirectional inference with BNs, it is possible to assess the seismic vulnerability of RNs and identify critical roads. The proposed methodology is verified through a real-world RN in China.

VOLCANBOX: a software platform for Volcanic Hazard Assessment

Volcanic eruptions have an intrinsic multi-hazard nature, in which a variety of volcanic products (lava flows, fallout, lahars, and pyroclastic flows) and associated hazards (seismic shocks, landslides, tsunamis, or floods) may interact and impact sequentially. Volcanic hazard assessment relies on probabilistic approaches based on the past eruptive history, from which scientists identify eruptive scenarios and their expected recurrence. However, not enough attempts have yet been made to address volcanic hazard assessment in a systematic way, based on principles that may be equally applicable to different volcanoes and volcanic zones regardless of their particular nature. Hence, we present VOLCANBOX, a new multiplatform (GNU/Linux, MacOS, Windows) system designed to conduct long-and short-term volcanic hazard assessment and risk analysis in a systematic and rational manner, aimed at reducing volcanic risk by anticipating future eruptions. VOLCANBOX includes several sets of software tools designed to evaluate long and short-term volcanic hazards, to conduct vulnerability analysis, and to assist decision-makers during the management of a volcanic crisis. VOLCANBOX can be implemented before an emergency, in order to identify optimum mitigating actions and how these may have to be adapted as new information is obtained. VOLCANBOX is aimed at experts in volcanic hazard and risk assessment and management and is designed to facilitate the interaction and cooperation between scientists, policy makers and civil protection agencies to anticipate volcanic disasters in a timely manner, by quantifying volcanic hazards, understanding volcanic unrest, forecasting volcanic eruptions, and identifying the most probable scenarios in a simple and clear manner.

Unveiling economic resilience: exploring the impact of financial vulnerabilities on economic volatility through the economic vulnerability index

While earlier studies have explored the relationship between economic vulnerability and economic resilience, they have repeatedly overlooked the significance of financial vulnerabilities within the economic vulnerability index, and the effects of financial and economic vulnerabilities on economic volatility. An attempt is made in this study to close this research gap by conducting a detailed analysis of the relationship between financial vulnerabilities, economic vulnerability and economic volatility, stressing the significance of tweaking prevailing vulnerability indices to fully encapsulate its multidimensional nature in developing countries. Employing panel data for 142 countries over the 2002 to 2022 period and a robust econometric approach like the Driscoll and Kraay fixed effect method, the study reveals that financial vulnerabilities yield significant coefficients to influence economic volatility, thereby accentuating their significance in the Economic Vulnerability Index. Sub-group analyses reinforce the need for incorporating financial variables in vulnerability investigations. Moreover, the causality tests reveal that all the variables and indices meant to capture the economic and financial vulnerabilities Granger causes economic volatility across the sample. In essence, this study fills a critical gap in existing research by demonstrating, that financial vulnerabilities significantly influence economic volatility, underscoring the imperative of integrating financial variables into vulnerability assessments for policymakers and scholars focusing on sustainable development. This study contributes to a broader understanding of economic vulnerability by highlighting the crucial role of financial vulnerabilities in driving economic volatility, suggesting a fundamental reconsideration of existing vulnerability assessment frameworks for policymakers and researchers focused on sustainable development frameworks. By uncovering the causal relationship between financial vulnerabilities and economic volatility across a diverse set of countries, the findings underscore the imperative of integrating financial factors into vulnerability investigations to enhance resilience and stability in developing economies.

Comparison and theoretical conceptualization analysis of statistical methods used to develop heat vulnerability indices in urban areas

In view of the impact of extreme temperatures on physical and psychological health, particularly in urban areas, several studies have focused on assessing social vulnerability using quantitative indexing approaches with the aim of creating a heat vulnerability index (HVI). In this context, this study employs three statistical methodologies frequently used to construct HVIs on the territory of the Toulouse Métropole, France, at the census block (IRIS) scale to assess the efficiency of this type of approach for evaluating social vulnerability in urban environments considering the current theoretical conceptualization. The three HVIs show the same general trends, with a spatial configuration in which high levels of vulnerability are concentrated in the downtown and suburbs of the Toulouse municipality. Vulnerability gradually decreases away from the urban core, becoming moderate in the inner suburbs and low on the outskirts. However, a spatial analysis of the clusters reveals variability in the boundaries of the vulnerability hotspots. Value class matching indicates that a significant number of census blocks are classified differently according to the method considered. These results raise questions concerning the ability of HVIs to provide reliable vulnerability assessments, given their geostatistical and conceptual limitations. Indexing approaches therefore appear to contradict current theoretical conceptualizations promoting the concept of vulnerability as being complex and multifactorial.

Comparative Vulnerability Analysis of Thai and Non-Thai Mobile Banking Applications

The rapid adoption of mobile banking applications has raised significant concerns about their security vulnerabilities. This study presents a comparative vulnerability analysis of mobile banking applications from Thai and non-Thai banks, utilising the OWASP Mobile Top 10 framework. Nine mobile banking applications (five Thai and four non-Thai) were assessed using three vulnerability detection tools: AndroBugs, MobSF, and QARK. The results showed that both Thai and non-Thai mobile banking applications had vulnerabilities across multiple OWASP Mobile Top 10 categories, with reverse engineering, code tampering, and insufficient cryptography being the most common. Statistical analysis revealed that Thai banking applications exhibited significantly more vulnerabilities compared to non-Thai banking applications. In the context of vulnerability detection tools, AndroBugs and QARK proved more effective in detecting vulnerabilities compared to MobSF. Additionally, the study highlights critical security challenges in mobile banking applications, particularly for Thai banks, and emphasises the need for enhanced security measures. The findings also show the importance of using multiple assessment tools for comprehensive security evaluation and suggest potential areas for improvement in mobile banking applications.

Integrated Environmental Vulnerability Assessment and Adaptation Strategies for Coastal Areas under Sustainable Development

Integrated Environmental Vulnerability Assessment and Adaptation Strategies for Coastal Areas under Sustainable Development

Identification and Evaluation of Vulnerable Links in a Distribution Network with Renewable Energy Source Based on Minimum Discriminant Information

With the increase in the proportion of photovoltaic and wind power access, the scale and form of distribution networks are becoming more and more complex. The traditional single distribution network vulnerability assessment method is difficult to use to identify the vulnerable links in the distribution network. Therefore, this paper proposes a method for identifying and evaluating vulnerable links in distribution networks based on minimum discriminant information. First, considering the influence of distributed grid connection, an improved probabilistic power flow calculation method is proposed, which improves the calculation efficiency and accuracy. Second, considering the correlation degree, transmission capacity, and voltage stability of branches in the distribution network, the identification index of vulnerable lines is defined. Based on power quality and operating state, the identification index of vulnerable nodes in a distribution network is defined. Finally, based on the indicators of vulnerable nodes and vulnerable lines, the vulnerable links in the distribution network are comprehensively evaluated based on the principle of minimum discriminant information, and the vulnerable links of the entire distribution network are evaluated according to different degrees of vulnerability. The rationality and effectiveness of the proposed method are verified via an example analysis of actual power grid data.

Vulnerability assessment and evolution analysis of Beijing's Urban Rail Transit Network

With the development of urban rail transit system, the complexity of the network intensifies, and its vulnerability shifts correspondingly. Understanding the characteristics and evolution of network vulnerability, as well as identifying developmental patterns, enables more scientific network planning. Current researches on network vulnerability predominantly focus on the static vulnerability assessment of existing networks, with limited studies on vulnerability evolution. This paper divides the topological evolution of the Beijing Urban Rail Transit Network (BURTN) from 2000 to 2020 into Formation and Improvement stages using the K-means++ method. By constructing a multidimensional vulnerability assessment model that considers node degree uniformity, network efficiency, and connectivity, the vulnerability evolution characteristics and patterns of BURTN are explored in cases of both Single-station failures and Multi-station consecutive failures (including random and intentional failures). Furthermore, the evolutionary relationship between network vulnerability and network structure is explored using the Ridge regression model. Calculations reveal that in the case of Single-station failures, during the Formation stage, the proportion of highly vulnerable stations (HVS) and the impact of each station failure on network performance decrease significantly, by 69.36 % and 67.67 %, respectively. During the Improvement stage, the proportion of HVS decreases significantly, while the impact of each station failure on network performance decreases slightly, by 79.10 % and 37.04 %, respectively. In the case of Multi-station consecutive failures, during the Formation stage, the network’s ability to cope with both random and intentional failures decreases, with the percentage of network nodes removed at the collapse state decreasing by 24.95 % and 11.12 %, respectively. During the Improvement stage, the network’s ability to cope with random failures remains stable, while its ability to cope with intentional failures decreases. This study helps to understand vulnerability from an evolutionary perspective and provides practical strategies for reducing vulnerability.