Vulnerability Model Research Articles

Vulnerability Detection using BERT based LLM Model with Transparency Obligation Practice Towards Trustworthy AI

Vulnerabilities in the source code are one of the main causes of potential threats in software-intensive systems. There are a large number of vulnerabilities published each day, and effective vulnerability detection is critical to identifying and mitigating these vulnerabilities. AI has emerged as a promising solution to enhance vulnerability detection, offering the ability to analyse vast amounts of data and identify patterns indicative of potential threats. However, AI-based methods often face several challenges, specifically when dealing with large datasets and understanding the specific context of the problem. Large Language Model (LLM) is now widely considered to tackle more complex tasks and handle large datasets, which also exhibits limitations in terms of explaining the model outcome and existing works focus on providing overview of explainability and transparency. This research introduces a novel transparency obligation practice for vulnerability detection using BERT based LLMs. We address the black-box nature of LLMs by employing XAI techniques, unique combination of SHAP, LIME, heat map. We propose an architecture that combines the BERT model with transparency obligation practices, which ensures the assurance of transparency throughout the entire LLM life cycle. An experiment is performed with a large source code dataset to demonstrate the applicability of the proposed approach. The result shows higher accuracy of 91.8% for the vulnerability detection and model explainability outcome is highly influenced by “vulnerable”, “function”, "mysql_tmpdir_list", “strmov” tokens using both SHAP and LIME framework. Heatmap of attention weights, highlights the local token interactions that aid in understanding the model's decision points.

A theoretical integration of the social skill deficit vulnerability model and social determination theory to examine young adult loneliness

A theoretical integration of the social skill deficit vulnerability model and social determination theory to examine young adult loneliness

APPLICATION OF GEO-INFORMATICS IN THE MAPPING OF AREAS VULNERABLE TO SURFACE WATER STRESS IN KADUNA STATE, NIGERIA

The main purpose of this paper is to identify water-stressed areas as a response to land use/land cover (LULC), climate variability, and increasing population in Kaduna State using Remote Sensing and Geographic Information systems. To achieve this aim, the paper set out to identify/map out areas vulnerable to water stress in the study area. The Meteorological data for the study were sourced from twelve remotely sensed stations around Kaduna State for thirty-seven years (1984 to 2021), land use data from remotely sensed satellite imageries that covered thirty-seven years, and projected population for thirty-seven years respectively. There are several indices used to compute water stress and the paper opted for indices that could monitor climate variability and surface water resources. Thereafter, the six indicators were standardized, weighted, and aggregated in Microsoft Excel, and imported into the ArcGIS 10.2.1 environment, and a weightier vulnerability model analysis was used to determine the weights of each variable’s contribution to water stress. The result achieved indicated that: 54.41% of the entire study area is vulnerable to water stress, 15.80% is moderately vulnerable, 26.23% is less vulnerable and 3.56% is not vulnerable to water stress within the period under study. Furthermore, Birnin Gwari, Chikun, Igabi, Ikara, and Kajuru Local Government Areas (LGAs) were the most vulnerable areas in the State while not vulnerable areas are; Kauru, Kagarko, and Jaba LGAs. The paper recommends the implementation of water conservation measures which include the creation of wetlands and shelter belts in areas vulnerable to water stress.

Rapid remote volcanic ashfall impact assessment for the 2022 eruption of Hunga volcano, Tonga: a bespoke approach and lessons identified

When disasters occur, rapid impact assessments are required to prioritise response actions, support in-country efforts and inform the mobilisation of aid. The 15 January 2022 eruption of Hunga volcano, Tonga, and the resulting atmospheric shockwave, ashfall, underwater mass disturbance and tsunami, caused substantial impacts across the Kingdom of Tonga. Volcanic impacts on the scale observed after the eruption are rare, necessitating a reliance on international advice and assistance. The situation was complicated by the loss of Tonga’s international submarine fibreoptic cable (causing a complete loss of communications for approximately 20 days) along with border closures due to the COVID-19 pandemic. A need emerged for a rapid remote volcanic impact assessment and provision of specialist advice to help inform the response of international partners. Here we present a novel methodology for conducting rapid remote volcanic ashfall impact assessments, conducted over a 10-day period following the eruption. We used three different hazard models for ashfall thickness across the main island of Tongatapu and available asset information and vulnerability functions for buildings, agriculture, electricity networks, water supply and roads, to provide initial estimates of losses due to ashfall from the 15 January eruption. For buildings, we estimated losses both as total losses and as percentages of the total replacement cost of buildings on Tongatapu. For agriculture, we made probabilistic estimates of production losses for three different crop classes. For ashfall clean-up, we estimated ranges of ashfall volumes requiring clean-up from road surfaces and roofs. For water supply, electricity networks and roads, our analysis was limited to assessing the exposure of important assets to ashfall, as we had insufficient information on system configurations to take the analysis further. Key constraints on our analysis were the limited nature of critical infrastructure asset inventories and the lack of volcanic vulnerability models for tropical regions including Pacific Island nations. Key steps towards iteratively improving rapid remote impact assessments will include developing vulnerability functions for tropical environments as well as ground-truthing estimated losses from remote approaches against in-person impact assessment campaigns.

Effects of positive mGlu5 modulation on D2 signaling and nicotine-conditioned place preference: Mechanisms of epigenetic inheritance in a transgenerational model of drug abuse vulnerability in psychosis.

The metabotropic glutamate type 5 (mGlu5) receptor has emerged as a potential target for the treatment of psychosis that is suggested to have greater efficacy than antipsychotic medications that are currently utilized. This study sought to elucidate mechanisms of therapeutic action associated with the modulation of the mGlu5 receptor in a disordered system marked by dopamine dysfunction. We further explored epigenetic mechanisms contributing to heritable transmission of a psychosis-like phenotype in a novel heritable model of drug abuse vulnerability in psychosis. F1 generation male and female Sprague-Dawley rats that were the offspring of two neonatal quinpirole-treated (QQ) or two saline-treated (SS) animals were tested on nicotine-conditioned place preference (CPP). Regulators of G protein signaling 9 (RGS9) and β-arrestin 2 (βA2), which mediate dopamine (DA) D2 signaling, were measured in the nucleus accumbens shell, prelimbic and infralimbic cortices. Reduced Representation Bisulfite Sequencing (RRBS) was used to analyze the cytosine methylation in these brain regions. Pretreatment with the mGlu5-positive allosteric modulator 3-Cyano-N-(1,3-diphenyl-1H-pyrazol-5-yl)benzamide (CDPPB) 20 min prior to conditioning trials blocked enhanced nicotine CPP and mitigated aberrant G protein-dependent and -independent signaling in QQ animals. RRBS analysis revealed region-specific changes in several pathways, including nicotine addiction, dopamine synapses, and neural connectivity. These results reveal an important region-specific mechanism of action for CDPPB in a system marked by enhanced DAD2 receptor signaling. Results additionally reveal DNA methylation as an epigenetic mechanism of heritability, further validating the current model as a useful tool for the study of psychosis and comorbid nicotine use.

Proteomic Signatures of Right Ventricular Outcomes in Pulmonary Arterial Hypertension.

Pulmonary arterial hypertension (PAH) is a disease of progressive right ventricular (RV) failure with high morbidity and mortality. Our goal is to investigate proteomic features and pathways associated with RV-focused outcomes including mortality, RV dilation, and NT-proBNP (N-terminal pro-B-type natriuretic peptide) in PAH. Participants in a single-institution cohort with 3 years of follow-up underwent proteomic profiling of their plasma using 7288 aptamers (targeting 6467 unique human proteins). Partial least squares discriminant analysis was performed to assess global protein variation associated with mortality, RV dilation, and NT-proBNP levels. Differentially abundant proteins and enriched pathways associated with outcomes were identified following baseline adjustments. RV vulnerability models estimated associations for individuals with similar afterload following adjustment for pulmonary vascular resistance. A total of 117 participants with PAH were included. Partial least squares discriminant analysis of the proteome showed clear separation between survivors and nonsurvivors, participants with dilated versus nondilated RVs, and across NT-proBNP levels. Proteins and pathways involving the ECM (extracellular matrix) were upregulated in participants who died during follow-up, those with severe RV dilation, and those with higher levels of NT-proBNP. Pulmonary vascular resistance adjustment reinforced the importance of ECM proteins in the association with RV vulnerability, independent of afterload. These findings were confirmed in independent PAH cohorts with available plasma proteomics and RV tissue gene and protein expression. Distinct plasma proteomic profiles are associated with mortality, RV dilation, and NT-proBNP in PAH. Proteins and pathways governing tissue remodeling are strongly associated with poor outcomes, may mediate RV vulnerability to right heart failure, and represent promising candidates as biomarkers and potential therapeutic targets.

Construction of vulnerable plaque prediction model based on multimodal vascular ultrasound parameters and clinical risk factors

The rupture of vulnerable plaque (VP) are significant pathogenic factors leading to cardiovascular and cerebrovascular diseases. This study aims to construct a vulnerable plaque prediction model (VPPM) by combining multimodal vascular ultrasound parameters and clinical risk factors, and to validate it. A total of 196 atherosclerotic patients who underwent carotid endarterectomy (CEA) from January 2017 to December 2023 were collected and divided into a modeling group (n = 137) and a validation group (n = 59). Clinical information including: hypertension, diabetes, smoking history, and body mass index (BMI) was included in the analysis. All patients underwent carotid ultrasound and contrast-enhanced ultrasound (CEUS) examination after admission, with main ultrasound parameters including thickness, echogenicity types, stenosis degree, and CEUS neovascularization grading of plaques. Independent risk factors for VP in CEA patients were screened through binary Logistic regression analysis, and a prediction model was established along with a nomogram. The calibration curve, receiver-operating characteristic curve (ROC), and decision curve analysis (DCA) were employed to assess the calibration, diagnostic efficacy, and clinical utility of the VPPM model. There were no significant statistical differences in multimodal vascular ultrasound parameters and clinical risk factors between the modeling and validation groups (P > 0.05). Binary Logistic regression analysis identified plaque thickness, echo type, CEUS neovascularization grading, BMI, and smoking history as 5 variables entering the prediction model. The VPPM model showed good diagnostic efficacy, with an area under the ROC curve of 0.959 (95% CI 0.915–0.999). Using the nomogram with a VPPM risk assessment score of 135.42 as the diagnostic cutoff value in the modeling group, the sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and Youden index were 88.1%, 94.1%, 14.98, 0.126, and 82.2%, respectively. In the DCA curve, the VPPM model curve was significantly better than two extreme lines, indicating good clinical utility. The VPPM model constructed by integrating multimodal ultrasound parameters and clinical key risk factors has high diagnostic efficacy and is expected to be an auxiliary tool for clinical diagnosis of vulnerable plaques.

Deep learning algorithms based landslide vulnerability modeling in highly landslide prone areas of Tamil Nadu, India

Deep learning algorithms based landslide vulnerability modeling in highly landslide prone areas of Tamil Nadu, India

Critical Vulnerability in Leadership as a Key Factor in Successful Change Management Communication

This research explores the relationship between the discovered phenomenon of critical vulnerability in leadership during organizational change and its successful outcome. The phenomenon under study is fundamentally distinguished from vulnerable leadership, which is covered in literature as a deliberate tactic leaders use to improve interpersonal relationships and performance. This study concentrates on challenging and uncontrollable leader vulnerability manifested as a decline in the authority and influence of the leader among stakeholders. The main reason for the observed critical leader vulnerability is hypothesized to be the gap in leadership mindset levels between the leader and stakeholders at the onset of the organizational change. It is also hypothesized that the change communication of an authentic leader, predetermined by a high level of leadership mindset, triggers the leader's vulnerability and, at the same time, paradoxically contributes to the evolution of stakeholders' leadership mindset. This, in turn, is a prerequisite for achieving remarkable prosocial results. Relying on the described assumptions, we executed preliminary research, aiming to identify the change communication patterns of leaders in the state of vulnerability (hereinafter referred to as "vulnerable change communication") and to explore how they catalyze the stakeholder mindset evolution. The final sample group involved 9 top managers who faced vulnerability while leading the change process and made significant contributions to the development of their organizations. The research methodology relied on qualitative methods—in-depth interviews on which narrative and thematic analysis was based. Leaders were asked questions to discover goals of change, the presence of periods of vulnerability, the causes and consequences of its occurrence, their communication patterns, and results achieved. The obtained data were analyzed by applying recognized frameworks such as the Michigan Model of Leadership with its Competing Values Framework (CVF), the Approach of Management of Organizational Change Paradoxes, Programmed and Adaptive Approaches to Change, and Stakeholder Communication Model of Change. The present study has taken the first steps towards confirming the existence of an unambiguous connection between certain patterns of leadership change communication, embracing vulnerability by leaders, and remarkable prosocial outcomes. We found that change communication strategy based on embracing personal vulnerability and a high level of leadership mindset characterized by a value-based position, commitment to outstanding prosocial results, and synthetic decision-making ensures the leader the following: 1) Construction of Shared Leadership. The leader creates an organizational culture that transforms stakeholder resistance based on self and group interests into the evolution of a stakeholder leadership mindset and consolidation of the company. The formula for combining the best of both programmed and adaptive approaches to change includes preserving the integrity of the vision and managing meaning and outcome on the one hand, and supporting the strategy by the deployment of communication and bottom-up initiative on the other. 2) Managing Organizational Change Paradoxes. The leader addresses institutional contradictions and stakeholder divergent interests that naturally arise during the change process and creates the basis for subsequent institutionalization on a much stronger and healthier foundation. The paradoxical effect of a leader's personal vulnerability on organizational vulnerability is that the leader helps to reduce the organization's risks by taking them on themselves. Further exploration of the phenomenon of vulnerability will allow us to consider the inclusion of this component in the framework of change management models, to identify vulnerable leaders, specify the vulnerable communication model of change, and develop a list of recommendations for leaders to more consciously navigate the difficult stage of vulnerability as a systemic and inevitable attribute of achieving remarkable prosocial results.

Age-Dependent Relationship between Self-Esteem and Depressive Symptoms in Korean Adolescents: a Meta-Analysis of Longitudinal Studies.

Self-esteem is a powerful protective factor against depressive symptoms, with cognitive vulnerability theory suggesting that early self-esteem more strongly predicts later depressive symptoms than vice versa. While some meta-analyses have examined these relationships, limited knowledge exists on the prospective relationship between self-esteem and depressive symptoms, particularly how it changes with age. This study conducted a longitudinal meta-analysis of nine studies (20,733 participants) on the prospective relationship between self-esteem and depressive symptoms in Korean children and adolescents. Relationships were analyzed by integrating the effect sizes of 22 zero-order correlations and 17 partial correlations and examining age-related changes. The results indicated significant negative correlations in both zero-order and partial correlations. Age-related changes showed a reverse U-shape trend in partial correlations, with the strongest negative association during childhood, weakening until age 14, and then strengthening again. Moderator analysis revealed no significant effect size variation based on publication type or gender proportion, but more recent birth cohorts exhibited a stronger negative relationship in zero-order correlations. Significant differences were also found based on the measurement instrument of depressive symptoms. These findings highlight the importance of tailored interventions to enhance self-esteem and mitigate depressive symptoms, extending the cognitive vulnerability model within Korea's cultural context and providing insights into Korean youth mental health.

Systemic vulnerability as a lens to explore young people’s experiences of transition from alternative provision settings to post-16 mainstream education

ABSTRACT This paper considers the experiences of transition for young people from alternative provision (AP) to post-16 mainstream settings, drawing on an online survey distributed via Headteachers of AP/ post-16 institutions to staff and young people in a large local authority in the Northwest of England. It reflects on previous transition research and argues that transition can be viewed as a site of vulnerability for young people, with consideration of transition from a lens of vulnerability apparently absent in previous research. The pervasive narratives describing young people in alternative provision as “vulnerable” are critiqued, as well as some of the issues in conceptualising vulnerability. “Toblerone model” of relational spaces of vulnerability has been reconceptualised in this paper to model the possible sites of vulnerability that impact on the transitions young people make from alternative provision to post-16 mainstream settings. The paper concludes by presenting an adapted model of vulnerability which could be used to reconsider some of the issues which may impact on young people’s transition to post-16 settings, before and after the point of administrative transfer, and provide the staff who support them, both in alternative provision and in the transfer setting, with new possibilities for action.

New forest fire assessment model based on artificial neural network and analytic hierarchy process or fuzzy-analytic hierarchy process methodology for fire vulnerability map

Forest fires significantly disrupt global ecosystems. Many forecasting techniques predict fire activity and allocate prevention resources, but various factors are missing from the assessments, and multi-criteria decision approaches alone are insufficient. This work introduced a novel methodology combining artificial neural networks (ANN) with the analytical hierarchy process (AHP), fuzzy AHP multi-criteria methods, and spatial data to detect potential forest fire vulnerability areas using twenty variables. The results from AHP or fuzzy AHP, were processed using a multilayer perceptron with a backpropagation algorithm. The final ANN model has two objectives: first, to create a forest fire vulnerability with four classes (low, moderate, high, and very high), and second, to classify burned area sizes in regions highly or very highly vulnerable to fire. Evaluation metrics were also applied for validation. The fire model was tested using both literature review data and in situ observations. Comparative analysis showed that the burned area size model performed better than other machine learning methods, achieving an accuracy score of 89%. Meanwhile, the fire vulnerability model scored 82%. The study addresses the problem of prediction and provides an algorithm for classifying fire risk based on historical data in the Czech Republic, offering a master model for future forest management. Therefore, the ANN model, once trained, validated, and tested, does not require resetting and is effective for estimating forest fire vulnerability. Moreover, it produces results quickly, providing rapid insights into complex forest ecosystems, saving time and enhancing understanding for decision-makers.

Predictive models for cholesterol crystals and plaque vulnerability in acute myocardial infarction: Insights from an optical coherence tomography study

BackgroundCholesterol crystals (CCs) are recognized as a risk factor for vulnerable atherosclerotic plaque rupture (PR) and major adverse cardiovascular events. However, their predictive factors and association with plaque vulnerability in patients with acute myocardial infarction (AMI) remain insufficiently explored. Therefore, This study aims to investigate the association between CCs and plaque vulnerability in culprit lesions of AMI patients, identify the factors influencing CCs formation, and develop a predictive model for CCs. MethodsA total of 431 culprit lesions from AMI patients who underwent pre-intervention optical coherence tomography (OCT) imaging were analyzed. Patients were divided into groups based on the presence or absence of CCs and PR. The relationship between CCs and plaque vulnerability was evaluated. A risk nomogram for predicting CCs was developed using the least absolute shrinkage and selection operator and logistic regression analysis. ResultsCCs were identified in 64.5 % of patients with AMI. The presence of CCs was associated with a higher prevalence of vulnerable plaque features, such as thin-cap fibroatheroma (TCFA), PR, macrophage infiltration, neovascularization, calcification, and thrombus, compared to patients without CCs. The CCs model demonstrated an area under the curve (AUC) of 0.676 for predicting PR. Incorporating CCs into the TCFA model (AUC = 0.656) significantly enhanced predictive accuracy, with a net reclassification improvement index of 0.462 (95 % confidence interval [CI]: 0.263–0.661, p < 0.001) and an integrated discrimination improvement index of 0.031 (95 % CI: 0.013–0.048, p = 0.001). Multivariate regression analysis identified the atherogenic index of plasma (odds ratio [OR] = 2.417), TCFA (OR = 1.759), macrophage infiltration (OR = 3.863), neovascularization (OR = 2.697), calcification (OR = 1.860), and thrombus (OR = 2.430) as independent risk factors for CCs formation. The comprehensive model incorporating these factors exhibited reasonable discriminatory ability, with an AUC of 0.766 (95 % CI: 0.717–0.815) in the training set and 0.753 (95 % CI: 0.704–0.802) in the internal validation set, reflecting good calibration. Decision curve analysis suggested that the model has potential clinical utility within a threshold probability range of approximately 18 % to 85 %. ConclusionsCCs were associated with plaque vulnerability in the culprit lesions of AMI patients. Additionally, this study identified key factors influencing CCs formation and developed a predictive model with potential clinical applicability.

When Chilling Out Casts Doubt: Exploring the Temporal Associations Among Anxiety, Depression, and Cannabis Use in Emerging Adulthood

Research shows that both cannabis use and emotional problems (anxiety and depression) tend to peak in emerging adulthood. There is a relative paucity of research examining the temporal associations between cannabis use and emotional problems among emerging adults. Accordingly, this multi-wave longitudinal study examined three competing models of temporal precedence: the vulnerability model (negative emotions precede cannabis use); the scar model (cannabis use precedes negative emotions), and the reciprocal model (bidirectional associations between cannabis use and negative emotions). A sample of 299 North American emerging adults ( Mage = 23.69 years, 58% female) completed three waves of survey measures and cross-lagged panel models were run to evaluate how anxiety and depression were related to both cannabis use and related problems across the 1-month study period. Regarding anxiety symptoms, some support was found for the vulnerability model, in that anxiety preceded cannabis problems across some waves. No directional or reciprocal associations between anxiety and cannabis use were found. As for depression symptoms, there was support for reciprocal links between cannabis problems and depression across waves. However, consistent with the anxiety-related findings, no directional or reciprocal associations between depression and cannabis use were found. The scientific and practical implications of these findings are discussed.

Changes in plantation area and spatial modelling of forest and land fire vulnerability based on hotspot analysis 2019-2023 Ogan Ilir regency, South Sumatra

Changes in plantation area and spatial modelling of forest and land fire vulnerability based on hotspot analysis 2019-2023 Ogan Ilir regency, South Sumatra

Variation in community heat vulnerability for Shenyang City under local climate zone perspective

Assessing heat vulnerability is essential for analyzing and improving the urban thermal environment. We developed a heat vulnerability model to examine the spatial distribution characteristics of thermal vulnerability and its relationship with local climate zones (LCZs) in Shenyang City at the community level. Additionally, we thoroughly explored the change patterns of heat vulnerability in communities with similar LCZ components. Our findings revealed that: (1) Building-type LCZs exhibited a non-uniform distribution, with LCZ8 representing the largest proportion, and LCZ1 and LCZ2 accounting for the smallest proportions. Nature-type LCZ communities were distributed along the Hun River and in some areas of the northwest and south, with LCZD having the largest proportion and LCZB the smallest. (2) Building height was positively correlated with the heat vulnerability index (HVI), while building density had minimal impact on HVI. LCZ8 had the highest HVI, and LCZG had the lowest. (3) Clustering the communities revealed that buildings had a greater impact on HVI than impervious surfaces. Creating strong ventilation and increasing the number of nature-type LCZs were identified as the most important factors for community development. These results highlight the differences in heat vulnerability among communities with various landscape configurations, providing a theoretical basis for targeted community structure adjustments and the reduction of urban thermal risks.

Integrated coastal vulnerability index for coastal flooding: A case study of the Croatian coast

The combination of accelerated urbanization and tourism-related activities, along with frequent coastal flooding, has generated pressure on the coastline of the Republic of Croatia (RH). In this paper, an integrated coastal vulnerability model (ICVI) for coastal flooding was developed. The ICVI was generated using a GIS multicriteria approach and derived from two sub-indices: the physical vulnerability index (CVIN) and the socio-economic vulnerability index (CVIS). In total, 30 criteria were used in the derivation of the ICVI, with twelve contributing to CVIS and eighteen to CVIN. The ICVI model is represented as a line divided into smaller segments, each segment indicating ICVI vulnerability levels ranging from 1 (very low) to 5 (very high). The accuracy of CVIN was evaluated using 159 geocoded coastal flood locations obtained from various websites and the official register of Hrvatske Vode flood events from 2008 to 2023. More than 80% of geocoded flood locations are situated in areas with very high (5) or high (4) CVIN. Furthermore, out of 32 settlements with officially registered flood events, 90.6% of them are located in areas with very high or high ICVI. Since all data used in the ICVI derivation were acquired from open-source databases and a user-friendly GIS-MCDA toolbox was utilized, this paper presents a cost-effective approach to modeling integrated coastal vulnerability. This model can guide decision-makers and provide them with new insights for implementing an effective integrated coastal zone management strategy.

Projected risk and vulnerability to heat waves for Montreal, Quebec, using Gaussian processes

Urban areas face increasing climate risks and are at the forefront of adaptation challenges. Despite the growing number of cities that are developing adaptation plans, they often fail to implement, monitor, and evaluate them. This article addresses this issue by modelling a comprehensive risk assessment that includes vulnerability using Gaussian processes. Mortality during heat waves for the City of Montreal, Quebec, is used as a case study. The vulnerability model includes sensitivity components (age and socioeconomic variables) and an adaptive capacity component (a suitable level of vegetation to decrease the urban heat island effect). Various aging and climate scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) are used for projections up to year 2100. SHAP values are used to show features contributions to the model. As the climate warms, Montreal will face increasing summer mortality. The city should therefore increase its vegetation cover in vulnerable neighbourhoods. Despite inherent limitations to the complexity of risk modelling, this approach facilitates the implementation of adaptation solutions and their monitoring. Greater effort should be made in the future to improve comprehensive risk modelling and more research is required to validate which framework is best in closing the gap between science and political decisions.

Probabilistic assessment of earthquake casualties in residential areas

Accurately and reasonably assessing earthquake casualties is crucial for seismic disaster planning and governmental decision-making. However, current research on earthquake casualty assessments often lacks sufficient consideration of the uncertainties related to seismic scenarios and influencing factors. To enhance the authenticity and reliability of casualty assessments, this study proposes a seismic casualty assessment model capable of simultaneously considering multiple influencing factors. It quantitatively evaluates each influencing factor and comprehensively integrates the uncertainty characteristics associated with seismic occurrence time, building damage states, and the number of occupants. Using a residential area as an example, the research compares casualty assessment results with and without considering apartment layouts and the age structure of residents. It also explains the probabilistic method for quantifying casualties and constructing vulnerability models for seismic events in the residential area. Additionally, a simplified analytical method applicable to probabilistic assessment of earthquake casualties in masonry residential areas in Beijing is proposed. The research findings indicate that the apartment layouts within buildings have a relatively minor impact on the assessment results of casualties in the residential area, while the age structure of residents significantly affects earthquake casualty estimates. Casualty rates in the residential area, considering uncertainties in the three factors, approximately follow a log-normal distribution. For seismic intensities ranging from VI(0.05g) to IX(0.40g), the standard deviation of casualty rates across different seismic intensities exceeds 0.4. The probabilistic analysis method proposed in this study enhances existing models for earthquake casualty assessments and addresses the shortcomings in probabilistic evaluation research.

National risk assessment of Italian school buildings: The MARS project experience

Strategic buildings, such as schools, play a crucial role in civil society. They are essential not only for the well-being of communities but also for their significance during the emergency and recovery phases following an earthquake. Reliable risk assessment is a powerful tool for implementing effective mitigation strategies. While considerable work has been already done in the literature to develop fragility curves and risk studies for residential buildings, there remains a noticeable gap in addressing risk assessment for school portfolios. As part of the 2019–2021 MARS (Seismic Risk MAps) project, funded by the Italian Civil Protection Department and involving the ReLUIS consortium along with the EUCENTRE foundation, a specific task was dedicated to evaluating seismic risk for the Italian school buildings. In this context, eight research units combined their expertise to develop a consensus-based vulnerability model, known as the “School-MARS model”. This model integrates fragility curves based on various approaches (i.e. empirical, mechanical-analytical, mechanical-numerical and hybrid) and is tailored to archetypes representative of the Italian school buildings. The main goal of the paper is outlining the methodology used to define the “School-MARS vulnerability model”. Moreover, the paper demonstrates the application of the latter for performing intensity-based and time-based risk analyses. Specifically, the results are presented in terms of damage distribution and usability ratings expected for the entire stock of Italian school building. Notably, these results indicate that approximately 26 % of Reinforced Concrete (RC) buildings and 31 % of Unreinforced Masonry (URM) structures may become unusable due to potential earthquakes expected in the next 50 years.