Vulnerability Characteristics Research Articles

Ethereum Smart Contract Vulnerability Detection Model Based on Triplet Loss and BiLSTM

The wide application of Ethereum smart contracts in the Internet of Things, finance, medical, and other fields is associated with security challenges. Traditional detection methods detect vulnerabilities by stacking hard rules, which are associated with the bottleneck of a high false-positive rate and low detection efficiency. To make up for the shortcomings of traditional methods, existing deep learning methods improve model performance by combining multiple models, resulting in complex structures. From the perspective of optimizing the model feature space, this study proposes a vulnerability detection scheme for Ethereum smart contracts based on metric learning and a bidirectional long short-term memory (BiLSTM) network. First, the source code of the Ethereum contract is preprocessed, and the word vector representation is used to extract features. Secondly, the representation is combined with metric learning and the BiLSTM model to optimize the feature space and realize the cohesion of similar contracts and the discreteness of heterogeneous contracts, improving the detection accuracy. In addition, an attention mechanism is introduced to screen key vulnerability features to enhance detection observability. The proposed method was evaluated on a large-scale dataset containing four types of vulnerabilities: arithmetic vulnerabilities, re-entrancy vulnerabilities, unchecked calls, and inconsistent access controls. The results show that the proposed scheme exhibits excellent detection performance. The accuracy rates reached 88.31%, 93.25%, 91.85%, and 90.59%, respectively.

Comparison of RC girder bridge and building vulnerability considering empirical seismic damage

To explore the vulnerability of a reinforced concrete girder bridge and reinforced concrete building during an earthquake, and to compare the difference in the seismic capacity of the two types of engineering structures, the nonlinear vulnerability numerical and probabilistic model analysis methods were combined. Overall, 1069 reinforced concrete girder bridges and 949 reinforced concrete buildings damaged in the Wenchuan earthquake of May 12, 2008 were selected for vulnerability analysis. The vulnerability grades of damaged samples were evaluated according to the Chinese seismic intensity scale (CSIS-08), and the vulnerability matrix of reinforced concrete girder bridges and reinforced concrete buildings in multiple intensity regions was established. Moreover, the novel vulnerability comparison curve model was developed by considering the failure ratio and exceeding probability. The anomaly characteristics of seismic damage vulnerability between reinforced concrete girder bridges and reinforced concrete buildings under the influence of age, seismic design, the coupling of age and seismic design, and foundation type factors were compared and analysed. By employing the matrix calculation model of the mean damage index, the matrix and curve comparison model of the mean damage index vulnerability parameters of reinforced concrete girder bridges and reinforced concrete buildings in multiple intensity zones were developed.

Assessment of marine radiological impact after an hypothetical nuclear accident in Eastern Mediterranean Sea

The consequences after an hypothetical nuclear accident at the Akkuyu Nuclear Power Plant in the region of Eastern Mediterranean Sea are presented. The dispersion of the radioactive plume in the sea is simulated applying a regional hydrodynamic/Lagrangian drift model and the radioecological impact is estimated after the release of 137Cs, 238Pu and 131I. The doses to marine biota and the human habitants of the affected regions are presented, while the sensitivity analysis of the results revile the most vulnerable features of this marine environment.

Perinatal Health Outcomes Across Rural and Nonrural Counties Within a Single Health System Catchment.

Perinatal health outcomes are influenced by a variety of socioeconomic, behavioral, and economic factors that reduce access to health services. Despite these observations, rural communities continue to face barriers, including a lack of resources and the fragmentation of health services. To evaluate patterns in health outcomes, health behaviors, socioeconomic vulnerability, and sociodemographic characteristics across rural and nonrural counties within a single health system catchment area. Socioeconomic vulnerability metrics, health care access as determined by licensed provider metrics, and behavioral data were obtained from FlHealthCHARTS.gov and the County Health Rankings. County-level birth and health data were obtained from the Florida Department of Health. The University of Florida Health Perinatal Catchment Area (UFHPCA) was defined as all Florida counties where ≥5% of all infants were delivered at Shands Hospital between June 2011 and April 2017. The UFHPCA included 3 nonrural and 10 rural counties that represented more than 64,000 deliveries. Nearly 1 in 3 infants resided in a rural county, and 7 out of 13 counties did not have a licensed obstetrician gynecologist. Maternal smoking rates (range 6.8%-24.8%) were above the statewide rate (6.2%). Except for Alachua County, breastfeeding initiation rates (range 54.9%-81.4%) and access to household computing devices (range 72.8%-86.4%) were below the statewide rate (82.9% and 87.9%, respectively). Finally, we found that childhood poverty rates (range 16.3%-36.9%) were above the statewide rate (18.5%). Furthermore, risk ratios suggested negative health outcomes for residents of counties within the UFHPCA for each measure, except for infant mortality and maternal deaths, which lacked sample sizes to adequately test. The health burden of the UFHPCA is characterized by rural counties with increased maternal death, neonatal death, and preterm birth, as well as adverse health behaviors that included increased smoking during pregnancy and lower levels of breastfeeding relative to nonrural counties. Understanding perinatal health outcomes across a single health system has potential to not only estimate community needs but also facilitate planning of health care initiatives and interventions in rural and low-resource communities.

Spatial distribution of socio-demographic and housing-based factors in relation to flash and slow-rise flooding hazards in the U.S

Previous studies have drawn attention to racial and socioeconomic disparities in exposures associated with flood events at varying spatial scales, but most of these studies have not differentiated flood risk. Assessing flood risk without differentiating floods by their characteristics (e.g. duration and intensity of precipitation leading to flooding) may lead to less accurate estimates of the most vulnerable locations and populations. In this study, we compare the spatial patterning of social vulnerability, types of housing, and housing tenure (i.e. rented vs. owned) between two specific flood types used operationally by the National Weather Service—flash floods and slow-rise floods—in the floodplains across the Contiguous United States (CONUS). We synthesized several datasets, including established distributions of flood hazards and flooding characteristics, indicators of socioeconomic status, social vulnerability, and housing characteristics, and used generalized estimating equations to examine the proportion of socially vulnerable populations and housing types and tenure residing in the flash and slow-rise flood extents. Our statistical findings show that the proportion of the slow-rise flooded area in the floodplains is significantly greater in tracts characterized by higher percentages of socially vulnerable. However, the results could not confirm the hypothesis that they are exposed considerably more than less vulnerable in the flash flooded floodplains. Considering housing-occupancy vulnerability, the percentage of renter-occupancies are greater in the flash flood floodplains compared to slow-rise, especially in areas with high rainfall accumulation producing storms (e.g. in the Southeast). This assessment contributes insights into how specific flood types could impact different populations and housing tenure across the CONUS and informs strategies to support urban and rural community resilience and planning at local and state levels.

Lipoprotein(a) Is Associated With the Progression and Vulnerability of New-Onset Carotid Atherosclerotic Plaque.

Although important progress has been made in understanding Lp(a) (lipoprotein[a])-mediated stroke risk, the contribution of Lp(a) to the progression of vulnerable plaque features associated with stroke risk remains unclear. This study aims to evaluate whether Lp(a) is associated with carotid plaque progression, new-onset plaque features, and plaque vulnerability in a prospective community-based cohort study. Baseline Lp(a) levels were measured using latex-enhanced turbidimetric immunoassay among 804 participants aged 45 to 74 years and free of cardiovascular disease in the Chinese Multi-provincial Cohort Study-Beijing project. Carotid atherosclerosis was measured twice by B-mode ultrasonography over a 10-year interval during the 2002 and 2012 surveys to assess the progression of total, vulnerable and stable plaques, and plaque vulnerability. The total plaque area and plaque vulnerability score were calculated. The median baseline Lp(a) level was 10.20 mg/dL (interquartile range, 6.20 to 17.18 mg/dL). Modified Poisson regression analysis showed that Lp(a) ≥50 mg/dL was significantly associated with 10-year progression of total carotid plaque (relative risk [RR], 1.41 [95% CI, 1.21-1.64]; E-value=2.17), vulnerable plaque (RR, 1.93 [95% CI, 1.54-2.41]), and stable plaque (RR, 1.51 [95% CI, 1.11-2.07]) compared with Lp(a) <50 mg/dL. Moreover, among participants without plaque at baseline, Lp(a) ≥50 mg/dL was related to an increased total plaque area (β=0.36 [95% CI, 0.06-0.65]; P=0.018) and increased plaque vulnerability score (β=0.30 [95% CI, 0.01-0.60]; P=0.045) in multivariable linear regression. Elevated Lp(a) levels were associated with 10-year carotid plaque progression and plaque vulnerability, providing a basis for Lp(a) as a treatment target for stroke prevention.

Heat Stress Vulnerability of Populations and Role of Urban Heat Island

A city's ecology and energy use can be affected by an urban heat island (UHI). However, its role in exacerbating populations' heat stress has been extensively studied. This study reviewed and summarized the literature on the UHI, its causes, and its effects. After that, the characteristics of population heat stress vulnerability are examined. The results of studies on UHI, its health effects, and potential mitigation tactics in existing settlements show the necessity of examining various relationships between UHI and the locations of susceptible populations and possible responses. By examining literature from related domains, the present study intends to identify the research potential and convince researchers about the necessity of such a study in the urban area, especially in developing nations, since such case studies were only a few. Further, the study highlighted the relevance of urban planning to mitigate UHI. A fundamental examination of a city's urban climate can be the initial step in creating urban design standards, followed by research and models considering socio-economic and environmental elements.

Estimated sdLDL-C for predicting high-risk coronary plaque features in psoriasis: a prospective observational study

BackgroundPsoriasis (PSO) is a skin disorder with systemic inflammation and high coronary artery disease risk. A distinct lipid phenotype occurs in psoriasis, which is characterized by high plasma triglycerides (TGs) with typically normal or even low LDL-C. The extent to which cholesterol on LDL subfractions, such as small dense LDL-C (sdLDL-C), are associated with vulnerable coronary plaque characteristics in PSO remains elusive.MethodsA recently developed equation for estimating sdLDL-C from the standard lipid panel was utilized in a PSO cohort (n = 200) with 4-year follow-up of 75 subjects. Coronary plaque burden was assessed by quantitative coronary computed tomography angiography (CCTA). Multivariate regression analyses were used for establishing associations and prognostic value of estimated sdLDL-C.ResultsEstimated sdLDL-C was positively associated with non-calcified burden (NCB) and fibro-fatty burden (FFB), which remained significant after multivariate adjustment for NCB (β = 0.37; P = 0.050) and LDL-C adjustment for FFB (β = 0.29; P < 0.0001). Of note, total LDL-C calculated by the Friedewald equation was not able to capture these associations in the study cohort. Moreover, in the regression modelling estimated sdLDL-C was significantly predicting necrotic burden progression over 4 years follow-up (P = 0.015), whereas LDL-C did not. Finally, small LDL particles (S-LDLP) and small HDL particles (S-HDLP), along with large and medium TG-rich lipoproteins (TRLPs) had the most significant positive correlation with estimated sdLDL-C.ConclusionsEstimated sdLDL-C has a stronger association than LDL-C with high-risk features of coronary atherosclerotic plaques in psoriasis patients.Clinical trial registrationURL: https://www.clinicaltrials.gov. Unique identifiers: NCT01778569

Characteristics of coronary artery ectasia and accompanying plaques: an optical coherence tomography study

Coronary artery ectasia (CAE) in adults is often caused by atherosclerotic plaques. CAE can affect atherosclerotic plaques through hemodynamic changes. However, no study has evaluated the characteristics of CAE with atherosclerotic plaques. Therefore, we aimed to disclose the characteristics of atherosclerotic plaques in patients with CAE using optical coherence tomography (OCT). We evaluated patients with CAE, confirmed by coronary angiography, who underwent pre-intervention OCT between April 2015 and April 2021. Each millimeter of the OCT images was analyzed to assess the characteristics of CAEs, plaque phenotypes, and plaque vulnerability. A total of 286 patients (344 coronary vessels) met our criteria, 82.87% of whom were men. Right coronary artery lesions were the most common, comprising 44.48% (n = 153) of the total. We found 329 CAE vessels with plaques, accounting for 95.64% of the coronary vessels. After grouping CAEs and plaques by their relative positions, we found that the length of plaques within CAE lesions was longer than that of plaques in other sites (P < 0.001). Plaques within CAE lesions had greater maximum lipid angles and lipid indexes (P = 0.007, P = 0.004, respectively) than those on other sites. This study revealed the most common vascular and morphological characteristics of CAE. While the accompanying plaques were not affected by the location or morphology of the CAE vessels, they were affected by their position relative to the CAE lesion.

LCVD: Loop-oriented code vulnerability detection via graph neural network

Due to the unique mechanism and complex structure, loops in programs can easily lead to various vulnerabilities such as dead loops, memory leaks, resource depletion, etc. Traditional approaches to loop-oriented program analysis (e.g. loop summarization) are costly with a high rate of false positives in complex software systems. To address the issues above, recent works have applied deep learning (DL) techniques to vulnerability detection. However, existing DL-based approaches mainly focused on the general characteristics of most vulnerabilities without considering the semantic information of specific vulnerabilities. As a typical structure in programs, loops are highly iterative with multi-paths. Currently, there is a lack of available approaches to represent loops, as well as useful methods to extract the implicit vulnerability patterns. Therefore, this paper introduces LCVD, an automated loop-oriented code vulnerability detection approach. LCVD represents the source code as the Loop-flow Abstract Syntax Tree (LFAST), which focuses on interleaving multi-paths around loop structures. Then a novel Loop-flow Graph Neural Network (LFGNN) is proposed to learn both the local and overall structure of loop-oriented vulnerabilities. The experimental results demonstrate that LCVD outperforms the three static analysis-based and four state-of-the-art DL-based vulnerability detection approaches across evaluation settings.

Are at-risk sociodemographic attributes stable across COVID-19 transmission waves?

COVID-19 health impacts and risks have been disproportionate across social, economic, and racial gradients (Chen et al., 2021; Thompson et al., 2021; Mamuji et al., 2021; COVID-19 and Ethnicity, 2020). By examining the first five waves of the pandemic in Ontario, we identify if Forward Sortation Area (FSAs)based measures of sociodemographic status and their relationship to COVID-19 cases are stable or vary by time. COVID-19 waves were defined using a time-series graph of COVID-19 case counts by epi-week. Percent Black visible minority, percent Southeast Asian visible minority and percent Chinese visible minority at the FSA level were then integrated into spatial error models with other established vulnerability characteristics. The models indicate that area-based sociodemographic patterns associated with COVID-19 infection change over time. If sociodemographic characteristics are identified as high risk (increased COVID-19 case rates) increased testing, public health messaging, and other preventative care may be implemented to protect populations from the inequitable burden of disease.

Peroxisome proliferator-activated receptor δ improves the features of atherosclerotic plaque vulnerability by regulating smooth muscle cell phenotypic switching.

Vascular smooth muscle cells (SMCs) undergo phenotypic switching during sustained inflammation, contributing to an unfavourable atherosclerotic plaque phenotype. PPARδ plays an important role in regulating SMC functions; however, its role in atherosclerotic plaque vulnerability remains unclear. Here, we explored the pathological roles of PPARδ in atherosclerotic plaque vulnerability in severe atherosclerosis and elucidated the underlying mechanisms. Plasma levels of PPARδ were measured in patients with acute coronary syndrome (ACS) and stable angina (SA). SMC contractile and synthetic phenotypic markers, endoplasmic reticulum (ER) stress, and features of atherosclerotic plaque vulnerability were analysed for the brachiocephalic artery of apolipoprotein E-knockout (ApoE-/- ) mice, fed a high-cholesterol diet (HCD) and treated with or without the PPARδ agonist GW501516. In vitro, the role of PPARδ was elucidated using human aortic SMCs (HASMCs). Patients with ACS had significantly lower plasma PPARδ levels than those with SA. GW501516 reduced atherosclerotic plaque vulnerability, a synthetic SMC phenotype, ER stress markers, and NLRP3 inflammasome expression in HCD-fed ApoE-/- mice. ER stress suppressed PPARδ expression in HASMCs. PPARδ activation inhibited ER stress-induced synthetic phenotype development, ER stress-NLRP3 inflammasome axis activation and matrix metalloproteinase 2 (MMP2) expression in HASMCs. PPARδ inhibited NFκB signalling and alleviated ER stress-induced SMC phenotypic switching. Low plasma PPARδ levels may be associated with atherosclerotic plaque vulnerability. Our findings provide new insights into the mechanisms underlying the protective effect of PPARδ on SMC phenotypic switching and improvement the features of atherosclerotic plaque vulnerability.

Research on the Resilience Assessment of Rural Landscapes in the Context of Karst Rocky Desertification Control: A Case Study of Fanhua Village in Guizhou Province

The ecological rehabilitation project has greatly curbed the serious problem of karst rocky desertification (KRD) in southern China and significantly changed the ecological environment and landscape pattern of the karst rocky desertification control areas (KRDCA). As one of the most important social–ecological fragile areas in the world, rural landscapes in KRDCA still show a strong sensitivity to disturbance. To reduce risks and improve the resilience of landscapes, this paper constructs a framework for assessing rural landscape resilience in KRDCA from the three dimensions of ecology, engineering, and social culture, based on the concept of resilience defined by the United Nations International Agency for Disaster Reduction. Considering the characteristics of rural landscapes in KRDCA, we select typical villages for empirical study. The results show the following: (1) The KRDCA is highly sensitive to natural disasters due to its special dual geomorphic structure characteristics. The disaster preparedness capacity of villages is the key factor determining the resilience of rural landscapes. The analysis of the disaster preparedness capacities of rural landscape structures with different vulnerability characteristics can be used as an effective means of evaluating the resilience level of rural landscapes in KRDCA. (2) Based on the empirical analysis of Fanhua village, which is a typical KRDCA in southern China, we found that the ecological system and engineering system of the village landscape have high resilience, while the resilience of the social and cultural systems are weak. This is due to the fact that the large number of rural population emigration in recent years has resulted in villages being at the key node of the reorganization of the social and cultural value system. The unstable sociocultural value system reduces the ability of rural landscapes to adapt to disturbance or environmental change. The study results could guide improvement strategies for subsequent landscape planning and inspire new ideas and methods for the implementation of rural revitalization strategies and the improvement of landscape resilience in KRDCA.

VDoTR: Vulnerability detection based on tensor representation of comprehensive code graphs

Code vulnerability detection has long been a critical issue due to its potential threat to computer systems. It is imperative to detect source code vulnerabilities in software and remediate them to avoid cyber attacks. To automate detection and reduce labor costs, many deep learning-based methods have been proposed. However, these approaches have been found to be either ineffective in detecting multiple classes of vulnerabilities or limited by treating original source code as a natural language sequence without exploiting the structural information of code. In this paper, we propose VDoTR, a model that leverages a new tensor representation of comprehensive code graphs, including AST, CFG, DFG, and NCS, to detect multiple types of vulnerabilities. Firstly, a tensor structure is introduced to represent the structured information of code, which deeply captures code features. Secondly, a new Circle Gated Graph Neural Network (CircleGGNN) is designed based on tensor for hidden state embedding of nodes. CircleGGNN can perform heterogeneous graph information fusion more directly and effectively. Lastly, a 1-D convolution-based output layer is applied to hidden embedding features for classification. The experimental results demonstrate that the detection performance of VDoTR is superior to other approaches with higher accuracy, precision, recall, and F1-measure on multiple datasets for vulnerability detection. Moreover, we illustrate which code graph contributes the most to the performance of VDoTR and which code graph is more sensitive to represent vulnerability features for different types of vulnerabilities through ablation experiments.

Assessment of Agricultural Drought Vulnerability Based on Crop Growth Stages: A Case Study of Huaibei Plain, China

Climate change can lead to and intensify drought disasters. Quantifying the vulnerability of disaster-affected elements is significant for understanding the mechanisms that transform drought intensity into eventual loss. This study proposed a growth-stage-based drought vulnerability index (GDVI) of soybean using meteorological, groundwater, land use, and field experiment data and crop growth model simulation. The CROPGRO-Soybean model was used to simulate crop growth and water deficit. Four growth stages were considered since the sensitivity of soybean to drought is strictly related to the growth stage. The GDVI was applied to the Huaibei Plain, Anhui Province, China, with the goal of quantifying the spatiotemporal characteristics of soybean drought vulnerability in typical years and growth stages. The results show that: (1) The sensitivity of leaf-related parameters exceeded that of other parameters during the vegetative growth stage, whereas the top weight and grain yield showed a higher sensitivity in the reproductive growth stage; (2) A semi-logarithmic law can describe the relationship between the drought sensitivity indicators and the GDVI during the four growth stages. The pod-filling phase is the most vulnerable stage for water deficit and with the highest loss upper limit (over 70%); (3) The 2001 and 2002 seasons were the driest time during 1997−2006. Fuyang and Huainan Cities were more vulnerable to drought than other regions on the Huaibei Plain in 2001, while Huaibei and Suzhou Cities were the most susceptible areas in 2002. The results could provide effective decision support for the categorization of areas vulnerable to droughts.

Analysis of carotid vulnerable plaque MRI high-risk features and clinical risk factors associated with concomitant acute cerebral infarction

BackgroundThis study aimed to investigate the correlation between the high-risk characteristics of high-resolution MRI carotid vulnerable plaques and the clinical risk factors and concomitant acute cerebral infarction (ACI).MethodsForty-five patients diagnosed with a single vulnerable carotid plaque by MRI were divided into two groups based on whether they had ipsilateral ACI. The clinical risk factors and the observation values or frequency of occurrence of high-risk MRI phenotypes of plaque volume, LRNC, IPH and ulcer were statistically compared between the two groups.ResultsA total of 45 vulnerable carotid artery plaques were found in 45 patients, 23 patients with ACI and 22 patients without ACI. There were no significant differences in age, sex, smoking, serum TC, TG and LDL between the two groups (all P > 0.05), but the ACI group had significantly more patients with hypertension (P < 0.05) and the without ACI group coronary heart disease (P < 0.05). The volume of vulnerable carotid plaque in the group with ACI (1004.19 ± 663.57 mm3) was significantly larger than that in the group without ACI (487.21 ± 238.64 mm3) (P < 0.05). The phenotype of vulnerable carotid artery plaque was 13 cases of LRNC, 8 cases of LRNC + IPH, 5 cases of LRNC + Ulcer, and 19 cases of LRNC + IPH + Ulcer. There was no significant difference in this distribution between the two groups (all P > 0.05) with the exception of LRNC + IPH + Ulcer. The 14 cases of LRNC + IPH + LRNC + IPH + Ulcer (60.87%) in the group with ACI and was significantly greater than the 5 (22.73%) in patients without ACI (P < 0.05).ConclusionIt is preliminarily thought that hypertension is the main clinical risk factor for vulnerable carotid plaques with ACI and the combination of plaque volume with vulnerable carotid plaque and LRNC + IPH + Ulcer is a high-risk factor for complicated ACI. It has high clinical therapeutic value due to the accurate diagnosis of responsible vessels and plaques with high-resolution MRI.

Change of the spatial and temporal pattern of ecological vulnerability: A case study on Cheng-Yu urban agglomeration, Southwest China

China's urban economy has developed rapidly over the decades, and the Cheng-Yu urban agglomeration has become one of China's four typical urban agglomerations, with a large population and a high level of economic development. However, the conflict between humans and the environment is becoming increasingly prominent together with economic development. In order to protect the ecological environment in urban areas, thus scientific understanding and assessment of ecological vulnerability are beneficial to establishing regional conservation measures, and serve as a key means to maintain environmental health. Based on the “Sensitivity-Resilience-Pressure” (SRP) model, this study considered remote sensing, geographic and statistical data to construct an evaluation system for regional ecological vulnerability. In addition, the coupled AHP (Analytic hierarchy process)-Entropy weighting model was proposed to obtain the weight of each evaluation indicator and analyze the spatio-temporal distribution characteristics of the ecological vulnerability of the study area during 2000–2020. The changes and the divergence pattern were depicted by the transfer matrix, dynamic degree and spatial auto-correlation. The results indicated that the ecological vulnerability of Cheng-Yu urban agglomeration is mainly mild and moderate, with an overall high distribution in Chongqing and Chengdu, while low in the central and north zone (e.g., Ziyang, Mianyang). It is consistent with the distribution of HH (High-High) and L-L (Low-Low) clusters, respectively, having a significant positive spatial correlation. In particular, the severely vulnerable area increased from 7059 km2 in 2000 to 23553 km2 in 2020, with an increased rate of 233.66 %. Combining the transfer matrix and dynamic degree, it was found that the ecological environment underwent a rapid deterioration followed by a slow recovery. This study provides a scientific reference for the ecological policy making which serves sustainable urban development.

A framework to estimate a long-term power shortage risk following large-scale earthquake and tsunami disasters.

While power shortages during and after a natural disaster cause severe impacts on response and recovery activities, related modeling and data collection efforts have been limited. In particular, no methodology exists to analyze long-term power shortages such as those that occurred during the Great East Japan Earthquake. To visualize a risk of supply shortage during a disaster and assist the coherent recovery of supply and demand systems, this study proposes an integrated damage and recovery estimation framework including the power generator, trunk distribution systems (over 154 kV), and power demand system. This framework is unique because it thoroughly investigates the vulnerability and resilience characteristics of power systems as well as businesses as primary power consumers observed in past disasters in Japan. These characteristics are essentially modeled by statistical functions, and a simple power supply-demand matching algorism is implemented using these functions. As a result, the proposed framework reproduces the original power supply and demand status from the 2011 Great East Japan Earthquake in a relatively consistent manner. Using stochastic components of the statistical functions, the average supply margin is estimated to be 4.1%, but the worst-case scenario is a 5.6% shortfall relative to peak demand. Thus, by applying the framework, the study improves knowledge on potential risk by examining a particular past disaster; the findings are expected to enhance risk perception and supply and demand preparedness after a future large-scale earthquake and tsunami disaster.

Vulnerability Assessment of Ecological–Economic–Social Systems in Urban Agglomerations in Arid Regions—A Case Study of Urumqi–Changji–Shihezi Urban Agglomeration

This study aims to clarify the vulnerability characteristics of the ecological–economic–social system of oasis city clusters in arid zones, promote the deepening of research on the sustainable development of urban clusters, and provide crucial practical reference significance for solving the series of problems brought about by urbanization. This article takes the arid zone oasis city cluster, the Urumqi–Changji–Shihezi urban agglomeration, as the research object and constructs an indicator system from three dimensions of ecological environment, regional economic, and social development, and adopts the comprehensive index method, GeoDetector, the GM(1, 1) gray prediction model, and other methods to study the vulnerability pattern and spatial and temporal changes of the urban cluster from 2009 to 2018. The results show that (1) from 2009 to 2018, the change in the integrated ecological–economic–social system vulnerability index of the Urumqi–Changji–Shihezi urban agglomeration shows a general downward trend, followed by significant differences in the vulnerability of each dimension, with an average vulnerability index of 1.8846, 1.6377, and 0.9831 for the social vulnerability, regional economic, and ecological environment dimensions, respectively; (2) the evolution of the spatial pattern of changes in the vulnerability index of different systems in each region of the Urumqi–Changji–Shihezi urban agglomeration tends to change from large to slight spatial differences, in which the social and ecological environmental vulnerability changes are more prominent in addition to the vulnerability changes of the regional economy; (3) parkland area per capita, arable land area per capita, GDP per capita, social fixed asset investment, population density, and urban road area per capita are the main drivers of decreasing vulnerability of ecological–economic–social systems in urban agglomerations; (4) by predicting and calculating the vulnerability index of each region of the ecological–economic–social system of urban agglomerations, it is found that the vulnerability index of urban agglomerations will show a decreasing trend from 2009 to 2018, and the difference of the vulnerability index between systems will narrow; (5) finally, targeted countermeasures and suggestions to reduce the vulnerability of ecological–economic–social systems are proposed to provide scientific references for the sustainable development of arid oasis cities.

Study on Spatiotemporal Changes of Rural Vulnerability in China’s Southwest Mountainous Provinces from 2000 to 2020 Based on Remote Sensing Image Interpretation: A Case in Yunnan Province

The three dimensions of ecological, economic, and social vulnerability in the mountainous countryside are caused by the fragility of the natural ecosystems of the mountains, which overlap with the geographical location of the countryside and are associated with farm production and low productivity. This study conducts a vulnerability evaluation of mountainous rural areas based on the fundamentals of ecological vulnerability in mountainous regions by combining “RS and GIS + multistage land use/land cover change (LUCC) + rural vulnerability evaluation theory and quantitative and practical techniques”. The goal of this project is to establish a comprehensive evaluation system that takes into account the various vulnerability levels in rural areas. It is based on the results of the three phases (2000, 2010, and 2020) of land use/land cover remote sensing images of Yunnan Province. The Delphi approach was used to determine the indicator weights. In order to quantitatively assess the rural vulnerability of 129 counties in Yunnan Province in 2000, 2010, and 2020, as well as to reveal the spatiotemporal characteristics of the rural vulnerability of the entire province in each county from 2000 to 2020, the qualitative comprehensive analysis method and the quantitative multi-index comprehensive evaluation method were organically combined. This paper’s objective is to offer a basis for the strategic planning and management of sustainable rural development and revitalization in mountainous provinces. The findings demonstrate that the ecological, economic, and social vulnerability levels all dramatically decreased over the previous two decades in rural Yunnan Province and its counties. The overall vulnerability of rural areas has been somewhat diminished, with an average annual decline of 1.17% over the past 20 years in the whole province. This demonstrates how the construction and development of rural areas over the past two decades have significantly decreased the overall vulnerability of rural areas. There are, nevertheless, substantial geographical variations. At present, the levels of ecological, economic, social, and overall vulnerability in rural areas of Yunnan Province are high.