Quantification Of Vulnerability Research Articles

Hybrid seismic vulnerability models for regional structures considering bivariate intensity measures

The multiple-probability decision-making method for earthquake hazards considers multiple performance indicators and can be used to effectively estimate the seismic risk of structures. The seismic vulnerability model directly affects the development of the earthquake risk distribution in regional structures. This paper innovatively developed a bivariate intensity measurement method based on macroscopic and instrumental seismic intensities. An optimized instrument intensity calculation model was established considering the actual monitoring data of historical earthquakes in China. A hybrid seismic vulnerability quantification method based on empirical, expert judgement and analysis has been proposed and applied to evaluate the vulnerability levels of three types of building clusters in two typical earthquakes in Qinghai Province, China (the 2008 Dachaidan (DCD) earthquake and the 2009 Haixi (HX) earthquake). A structural hybrid vulnerability matrix and surface for nine zones based on bivariate intensity measures were developed. A synthetic vulnerability membership index considering fuzzy failure criteria and decision theory was proposed, and vulnerability membership curves for different vulnerability levels were established. An improved earthquake economic loss prediction function is proposed, and synthetic economic vulnerability index prediction models for three typical buildings in different regions are established. The estimated and calculated results of the developed multiscale variable parameter synthetic vulnerability model are highly consistent with the actual earthquake loss observations.

SandSnap: Measuring and mapping beach grain size using crowd-sourced smartphone images

Sediment grain size is a critical parameter for sediment mobilization and transport, but often has the highest uncertainty of any coastal sediment transport model input parameter. SandSnap is an initiative to engage the public to amass a beach grain size database by taking photos of the beach sand with a coin in the image for scale and uploading the image to a web application. Images are analyzed with two deep learning convolutional neural networks one to detect the coin and the second to measure the grain size, which is trained on sediment samples within the sand regime. The results for nine gradation metrics are returned to the user within 2 min of image upload. Results from 263 test images have a mean percent error of −6.5% and median absolute error of 22.4% for the median grain size (d50) with a small fine bias of −0.042 mm. The use of the database is highlighted by applying SandSnap output as an input to the AeoLiS aeolian sediment transport model to predict coastal dune growth at a nearly national scale using the full eight grain size classes (d10 – d90) from the SandSnap database. These outputs are used to inform the potential value of having spatially comprehensive grain size distribution information as part of coastal engineering design and planning. Education and outreach techniques for the SandSnap initiative are described in the manuscript. Though some challenges remain, the spatially and temporally robust beach grain size database being developed by SandSnap will help to improve numerous coastal engineering analyses including coastal resilience and vulnerability quantification, beach nourishment life cycle and uncertainty analysis, beach compatibility for the beneficial use of dredged sediment, and large-scale coastal morphology modeling.

Resilience of coastal bridges under extreme wave-induced loads

Records of wave-induced damage on coastal bridges during natural hazards have been well documented over the past two decades. It is of utmost importance to decipher the loading mechanism and enhance the resilience of coastal bridges during extreme wave-inducing events. Quantification of vulnerability of these structures is an essential step in designing a resilient bridge system. Recently, considerable efforts have been made to study the force applied and the response of coastal bridge systems during extreme wave loading conditions. Although remarkable progress can be found in the quantification of load and response of coastal superstructures, very few studies assessed coastal bridge resiliency against extreme wave-induced loads. This paper adopts a simplified and practical technique to analyze and assess the resilience of coastal bridges exposed to extreme waves. Component-level and system-level fragility analyses form the basis of the resiliency analysis where the recovery functions are adopted based on the damage levels. It is shown that wave period has the highest contribution to the variation of bridge resiliency. Moreover, this study presents the uncertainty quantification in resiliency variation due to changes in wave load intensity. Results show that the bridge resiliency becomes more uncertain as the intensity of wave parameters increases. Finally, possible restoration strategies based on the desired resilience level and the attitude of decision-makers are also discussed.

Quantification of cogeneration vulnerability towards the climate change: framework development

Cogeneration is a vigorous measure to both decrease greenhouse gases emissions and improve an overall efficiency of the primary energy use. The climate change poses serious risks on operation of combined heat and power (CHP) plants due to the shifts in heating load patterns. This work investigates linking of the heating demand to the climate conditions and introduces a framework which can be used to develop an adaptation strategy for district heating systems. We demonstrate viability of the proposed approach by a case study assessing sensitivity of the CHP efficiency to the climate warming across China considering a modern combined-cycle gas-fired power plant with steam extraction. Outputs of the case study have demonstrated that CHP plants are generally more vulnerable towards the climate change in world regions with harsher climate conditions. An effective measure to increase resilience of the cogeneration sector is to account for anticipated climate change effects by applying adjustments to the reference climate inputs used when designing CHP plants.

Detecting and Quantifying Xylem Embolism by Synchrotron-Based X-Ray Micro-CT.

The vulnerability to xylem embolism is a key trait underlying species-specific drought tolerance of plants, and hence is critical for screening climate-resilient crops and understanding vegetation responses to drought and heat waves. Yet, accurate determination of embolism in plant's xylem is challenging, because most traditional hydraulic techniques are destructive and prone to artefacts. Hence, direct and in vivo synchrotron-based X-ray micro-CT observation of xylem conduits has emerged as a key reference technique for accurate quantification of vulnerability to xylem embolism. Micro-CT is nowadays a fundamental tool for studies of plant hydraulic architecture, and this chapter describes the fundamentals of acquisition and processing of micro-CT images of plant xylem.

The association between frailty and perceived physical and mental fatigability: The Long Life Family Study.

Higher levels of frailty, quantified by a frailty index (FI), may be linked to fatigue severity as tasks become more physically and mentally demanding. Fatigue, a component of frailty research, has been ambiguous and inconsistent in its operationalization. Fatigability-the quantification of vulnerability to fatigue in relation to specific intensity and duration of activities-offers a more sensitive and standardized approach, though the associationbetween frailty and fatigability has not been assessed. Using cross-sectional data from the Long Life Family Study at Visit 2 (2014-2017; N = 2524; mean age ± standard deviation (SD) 71.4 ± 11.2 years; 55% women; 99% White), we examined associations between an 83-item FI after excluding fatigue items (ratio of number of health problems reported (numerator) out of the total assessed (denominator); higher ratio = greater frailty) and perceived physical and mental fatigability using the Pittsburgh Fatigability Scale (PFS) (range 0-50; higher scores = greater fatigability). Participants had mean ± standard deviation FI (0.08 ± 0.06; observed range: 0.0-0.43), PFS Physical (13.7 ± 9.6; 39.5% more severe, ≥15), and PFS Mental (7.9 ± 8.9; 22.8% more severe, ≥13). The prevalence of more severe physical and mental fatigability was higher across FI quartiles. In mixed effects models accounting for family structure, a clinically meaningful 3%-higher FI was associated with 1.9 points higher PFS Physical score (95% confidence interval (CI) 1.7-2.1) and 1.7 points higher PFS Mental score (95% CI 1.5-1.9) after adjusting for covariates. Frailty was associated with perceived physical and mental fatigability severity. Understanding this association may support the development of interventions to mitigate the risks associated with greater frailty and perceived fatigability. Including measurements of perceived fatigability, in lieu of fatigue, in frailty indices has the potential to alleviate the inconsistencies and ambiguity surrounding the operationalization of fatigue and provide a more precise and sensitive measurement of frailty.

Atherosclerotic plaque vulnerability quantification system for clinical and biological interpretability

Acute myocardial infarction dominates coronary artery disease mortality. Identifying bio-signatures for plaque destabilization and rupture is important for preventing the transition from coronary stability to instability and the occurrence of thrombosis events. This computational systems biology study enrolled 2,235 samples from 22 independent bulks cohorts and 14 samples from two single-cell cohorts. A machine-learning integrative program containing nine learners was developed to generate a warning classifier linked to atherosclerotic plaque vulnerability signature (APVS). The classifier displays the reliable performance and robustness for distinguishing ST-elevation myocardial infarction from chronic coronary syndrome at presentation, and revealed higher accuracy to 33 pathogenic biomarkers. We also developed an APVS-based quantification system (APVSLevel) for comprehensively quantifying atherosclerotic plaque vulnerability, empowering early-warning capabilities, and accurate assessment of atherosclerosis severity. It unraveled the multidimensional dysregulated mechanisms at high resolution. This study provides a potential tool for macro-level differential diagnosis and evaluation of subtle genetic pathological changes in atherosclerosis.

Quantification of heat vulnerability using system dynamics

One of the major climate threats is extreme heat events, as they pose significant risks to public health that are well documented in the epidemiologic literature. The effects of extreme heat events have been evident over the past years by several extreme heat events worldwide. With the growing concerns of future heat exposure, numerous studies in the literature have developed heat vulnerability indices based on determinants that have heat-related impacts. However, there has been limited guidance on heat vulnerability assessment that accounts for the impacts of the characteristics of the built environment and changes in population dynamics over time. This paper focuses on developing the methodology for heat vulnerability assessment in urban areas using System Dynamics (SD) based on integrating three levels of the physical urban environment: the urban level, the building level, and the human adaptive capacity to heat exposure. We examine the viability of using SD modeling as an approach to examine the key drivers in heat vulnerability assessment in urban areas. Thus, the paper assesses the dynamic relationship between heat vulnerability components, namely, Susceptibility, Exposure, Coping Capacity, and Adaptive Capacity, and their effect on increased or decreased vulnerability under extreme heat events. The paper concludes with an applied case study in Cairo, Egypt, to test the use of the SD approach in assessing heat vulnerability in urban settings. Results from the proposed SD model confirm the underlying hypothesis that vulnerability from heat exposure is dynamically linked to the coping and adaptive capacity of the surrounding built environment with the urban population’s socioeconomic characteristics. The main contribution of this approach is that it allows for parallel examination of the effect of the human system that simulation models cannot include and the performance of the built environment system that epidemic heat vulnerability studies cannot capture.

Empirical vulnerability estimation models considering updating the structural earthquake damage database

The seismic vulnerability of regional buildings is a fundamental index to effectively predict and estimate the seismic resilience of urban and rural areas. The structural empirical vulnerability database can be used to develop and generate relatively accurate multidimensional vulnerability models. With the development of instrument intensity and the updating of macroseismic intensity scales, the structural vulnerability assessment model established using the traditional regional structural empirical vulnerability database shows features of low accuracy. Therefore, using innovative methods to update the conventional vulnerability estimation model has become a fundamental scientific problem to be urgently solved. In this study, the mixed effects of instrument intensity and macrointensity and their evaluation scale were sufficiently considered, and the traditional vulnerability level quantification scale was improved. The structural empirical vulnerability database of three typical earthquakes in China (Wenchuan in 2008, Yushu in 2010, and Jiuzhaigou in 2017) has been comprehensively updated, and the multiscale statistical and computational model has been developed. A nonlinear regression model based on multidimensional vulnerability quantification parameters was proposed. A comparison model for predicting the actual seismic vulnerability of four types of buildings considering typical earthquakes was developed. The traditional vulnerability index algorithm was updated, and the point cloud and strip domain quantitative model based on the updated empirical vulnerability database (27,394 buildings) was established.

GLOSI taxonomy: A tool for ‘seismic risk assessment’ oriented classification of school buildings

For the seismic vulnerability and risk assessment of school infrastructure in a region, it becomes necessary first to identify and classify these constructions into a distinct number of structural typologies characterised by their vulnerability features. This enables us to rank the expected vulnerability of different typologies at the initial screening stage as well as to characterise the representative index buildings of different typologies for detailed vulnerability quantification. Currently, a systematic and comprehensive taxonomy tailored for the school buildings is not available. The present paper thus develops a globally applicable structural taxonomy to be used in the seismic risk assessment of school infrastructure within the framework of the Global Program for Safer Schools (GPSS) of the World Bank. Application as well as verification of the proposed taxonomy is tested to a range of school construction types from different countries across the world.

An Integrated PCA–AHP Method to Assess Urban Social Vulnerability to Sea Level Rise Risks in Tampa, Florida

Coastal flooding risks are increasing with the rise of sea level due to global climate change. Such risk presents different groups of residents with diverse vulnerability. Using a coastal city as the study area, this study quantitatively and qualitatively examines social vulnerability across different communities at risk of sea level rising. This study develops a novel social vulnerability assessment method that integrates principal component analysis (PCA) and analytical hierarchical process (AHP), inputting the advantages of each into factor analysis for social vulnerability quantification. Twenty-four socioeconomic factors are first grouped into four different themes. PCA is then performed to identify major components within each theme. We apply AHP to generate the weighting scheme for each theme. Therefore, the AHP-derived weights to those PCA components according to each theme are used to obtain an overall social vulnerability index. The thematic map of PCA–AHP SVI showed that minority communities with low income, mobile home, and unemployed populations aggregated in the East Tampa shore (over 69% of the total minority), compared with the West Tampa (31% of the total minority). Our findings provide insightful spatial information of the social vulnerability index (SVI) that allows decision makers to make optimal efforts to mitigate damages and unexpected impacts on different communities, especially those marginalized minorities due to sea level rising risks.

A machine learning species distribution model for the critically endangered East Pacific leatherback turtle Dermochelys coriacea

The Eastern Pacific population of leatherback turtles Dermochelys coriacea is Critically Endangered, with incidental capture in coastal and pelagic fisheries as one of the major causes. Given the population’s broad geographic range, status, and extensive overlap with fisheries throughout the region, identifying areas of high importance is essential for effective conservation and management. In this study, we created a machine-learning species distribution model trained with remotely sensed environmental data and fishery-dependent leatherback presence (n = 1088) and absence data (>500000 fishing sets with no turtle observations) from industrial and small-scale fisheries that operated in the eastern Pacific Ocean between 1995 and 2020. The data were obtained through a participatory collaboration between the Inter-American Convention for the Protection and Conservation of Sea Turtles and the Inter-American Tropical Tuna Commission as well as non-governmental organizations to support the quantification of leatherback vulnerability to fisheries bycatch. A daily process was applied to predict the probability of leatherback occurrence as a function of dynamic and static environmental covariates. Coastal areas throughout the region were highlighted as important habitats, particularly highly productive feeding areas over the continental shelf of Ecuador, Peru, and offshore from Chile, and breeding areas off Mexico and Central America. Our model served as the basis to quantify leatherback vulnerability to fisheries bycatch and the potential efficacy of conservation and management measures (Griffiths & Wallace et al. 2024; Endang Species Res 53:295-326). In addition, this approach can provide a modeling framework for other data-limited vulnerable populations and species.

THE ASSOCIATION BETWEEN FRAILTY AND PERCEIVED FATIGABILITY IN THE LONG LIFE FAMILY STUDY

Abstract Higher levels of frailty, quantified by a frailty index (FI), may be linked to fatigue severity as tasks become more physically and mentally demanding. However, the association between frailty and fatigability—quantification of vulnerability to fatigue in relation to specific intensity and duration of activities—has not been assessed. Using cross-sectional data from the Long Life Family Study Visit 2 (2014–2017; n=2,524; mean age +/- standard deviation 71.4+/-11.2 years; 55% women; 99% White), we examined the association between a 79-item FI (ratio of number of health problems reported (numerator) out of the 79 (denominator); higher percentage=greater frailty) and perceived physical and mental fatigability using the Pittsburgh Fatigability Scale (PFS) (range 0–50; higher scores=greater fatigability). Mean+/-SD FI scores were 0.08+/-0.06 and mean+/-SD PFS Physical and Mental scores were 13.7+/-9.6 (39.5% more severe, >=15) and 7.9+/-8.9 (22.8% more severe, >=13), respectively. Both PFS subscale scores were higher for each 0.10 increment in FI. Mean PFS scores were 10.7 and 34.2 (Physical) and 5.7 and 28.8 (Mental) for FI scores of < 0.10 (non-frail) and ≥0.30 (moderate-severely frail), respectively. In mixed effects models, a 0.03 higher FI score (accepted clinically meaningful increase in FI) was associated with 1.9-point higher PFS Physical (95% confidence interval (CI) 1.7–2.1) and 1.7-point higher PFS Mental (95% CI 1.5–1.9) scores after accounting for family structure and adjusting for age, sex, field center, body mass index, smoking status, education, and marital status. Individuals with higher FI scores may benefit from targeted interventions to mitigate further poor health outcomes.

Cascading Impacts of Change in Carbon Dioxide Concentration, Temperature and Precipitation on Horticulture Crops: A Review

The impacts of an increase in carbon dioxide concentration, temperature and precipitation on physiology, yield and biochemical contents of horticulture crops including its mitigation measures are evident globally. This study mainly focuses on South-East Asia and Africa. We have reviewed crop-wise published literature mainly from 2000-2021 regarding the impacts of rising carbon dioxide concentration, temperature and precipitation on horticulture. Visible phenophase changes in horticultural crops such as budburst, leaf initiation, flowering, fruiting, seedling and the overall development stages of the crops are evident from the available literature. The prevalence of many diseases like blight, mildew, abaca, black sigatoka and Pierce’s disease are the results of climate change. Changes in the biochemical content of the horticulture crops such as tannins, flavonol, alcohol level, acidic content, starch, amino acids, lipids, chloroplasts, sugar, organic acids and antioxidants were observed. The recommended mitigation measures throughout the literature are delayed sowing dates, efficient seed varieties, controlled fertilizer use, soil moisture conservation and other biotechnological measures. Salinity, moisture and environmental stress-tolerant crops need to develop for better management of climate change. Breeding of short-duration and heat-tolerant varieties, vulnerability quantification and early warning disease forecasting are some of the important research areas.

Urban road network resilience metrics and their relationship: Some experimental findings

Resilience is a complex term, bearing multiple definitions and resilience-related metrics. The most commonly utilized metrics are network efficiency and criticality that examine the impact that the removal of a structural element of a graph/ network has on its operation. Efficiency examines this term from a pure topological aspect and takes into account the shortest paths between the nodes whereas, on the other hand, criticality takes into account transportation-related variables like travel demand. In the present research, the interrelation between efficiency and criticality is further examined through a series of simulation experiments on the Athens city-center urban road network. For the quantification of efficiency, vulnerability, and criticality an iterative approach is used where, per iteration, one link of the network was removed. The outputs of the experiments are, then, statistically analyzed, using unsupervised and supervised techniques. Findings reveal that a polynomial relationship between the ratio of criticality and efficiency, and traffic flow exists. Finally, the implications of these findings to traffic and network management are discussed.

Global assessment of subnational drought impact based on the Geocoded Disasters dataset and land reanalysis

Abstract. Despite the importance of a link between hydrometeorological drought hazards and their socioeconomic impact, the link at a subnational level has yet to be evaluated due to the lack of precise subnational information on disaster locations. Using the newly developed Geocoded Disasters (GDIS) dataset, we examined whether the subnational socioeconomic drought impact information in GDIS could be represented by hydrometeorological hazards quantified from soil moisture in ERA5-Land during 1964–2018. We found that the socioeconomic drought impacts shown in GDIS were generally represented by drought hazards quantified from ERA5-Land soil moisture. Our comparison between GDIS and ERA5-Land could benefit the quantification of vulnerability to drought, and we found that sub-Saharan Africa and South Asia were vulnerable to drought, while North America and Europe were less vulnerable to drought. Both GDIS and ERA5-Land indicated that the Horn of Africa, northern China, and western India were drought-prone areas. Since it is difficult for national-level analyses to accurately identify the locations of drought-prone areas, especially in large countries such as China and India, our analysis clarifies the importance of the use of subnational disaster information.

Environmental vulnerability assessment of Brazilian Amazon Indigenous Lands

Amazonian Indigenous Lands (ILs) are human-environment systems facing a multitude of environmental threats. Yet, the resulting vulnerability of these systems are to date unknown. We adopt the theoretical vulnerability framework of the IPCC to assess the environmental vulnerability of Brazilian Amazon ILs for two periods (2001–2010 and 2011–2019) and overall (2001–2019). Vulnerability is deemed a function of exposure (EX), sensitivity (SE) and adaptive capacity (AC) of a system to threats. Sensitivity (threats within IL) and exposure (threats in IL’s buffer zones) indicators are changes in forest cover, economic activities, and road access, quantified using data of deforestation, forest degradation, land-use, fire, roads and mining. Adaptive capacity indicators represent Indigenous self-organization, education and access to knowledge, land ownership, external incomes, and institutional arrangement. We find a concentration of ILs with high vulnerability in the Arc of Deforestation and South, and advancing in Pará and Roraima states. A strong relationship (Spearman r = 0.79) between EX and SE indicates the strong pressure exerted by external processes. An increase in EX (73.9% of the ILs) and in SE (64.8% of the ILs) in 2011–2019 compared to 2001–2010 signals a worrying rise in vulnerability recently. We advise the adoption of policies by the State, such as combating illegal activities, and strengthening National Policy for Environmental and Territorial Management of ILs. Herein, our vulnerability quantification can prioritize help to certain ILs, and the understanding of the contribution of the underlying dimensions can direct these policies, possibly according to the vulnerability profile of each IL.

Assessment of direct economic losses of flood disasters based on spatial valuation of land use and quantification of vulnerabilities: a case study on the 2014 flood in Lishui city of China

Abstract. Detailed and reliable assessment of direct economic losses of flood disasters is important for emergency dispatch and risk management in small- and medium-sized cities. In this study, a single flood disaster in Lishui city in 2014 was taken as an example to study and verify a method for rapid and detailed assessment of direct economic loss. First, based on a field investigation, the inundation data simulated by the one-dimensional hydrodynamic model and geographic information system (GIS) analysis method were verified. Next, the urban land use map and high-resolution land use classifications based on remote sensing data were fused and combined with expert questionnaire surveys, thereby providing the 47 types and values of land use. Then, based on the previous depth–damage function in the past study, the vulnerability curves of 47 types of land use in Liandu district were fitted by the lognormal cumulative distribution function and then calibrated using disaster loss report data. Finally, the distributions of the loss ratio and loss value were estimated by spatial analysis. It is found that the land use data have detailed types and value attributes as well as high resolution. Secondly, the vulnerability curves after function fitting and calibration effectively reflect the change characteristics of land use loss ratio in this area. Finally, except for the three types of land for agriculture, recreational and sports facilities, and green parking spaces, the optimized simulated total loss is CNY 322.6 million, which is 0.16 % higher than the statistics report data. The estimated loss ratio and loss value can reliably reflect the distribution pattern of disaster losses in detail, which can be applied by government and private sectors to implement effective disaster reduction and relief measures.

IVQFIoT: An intelligent vulnerability quantification framework for scoring internet of things vulnerabilities

AbstractWith time smart services have become more domineering than ever before however, the pertinent security considerations fade to correspond with growing heterogeneity in the internet of things (IoT) devices and new technologies coupled with resource constraints, crafting IoT‐based systems more susceptible to cyber‐attacks. To ensure a secure IoT environment, pro‐active security mechanisms, like scanning vulnerabilities and prioritizing to remediate them timely, should be embedded in the system. Motivated by the facts, we in this paper, highlight the state of the art of several works trading with a common vulnerability scoring system (CVSS), its limitations, and the emendations recommended to conclude its maturity. CVSS is an industry standard that has been adopted worldwide to quantify the vulnerabilities in organizations for IT and IoT‐based systems. The vulnerabilities mathematical score coalesces with environmental knowledge for finding attack paths and apt score for prioritization. The specific functionality and exclusive dynamics of IoT and cyber‐physical systems in comparison to traditional computer networks, make the legacy cyber‐security exemplars unfit for these advanced networks. This paper studies the relevance of CVSS for smart systems and present an intelligent vulnerability quantification framework for IoT systems grounded on the CVSS v3.1 framework with threat intelligence and machine learning models. Further by applying blockchain technology in the proposed framework, the issues concerning security, lack of trust, and privacy possibly will resolve by hiring a smart contract.

Order Matters: The Benefits of Ordinal Fragility Curves for Damage and Loss Estimation.

Probabilistic loss assessments from natural hazards require the quantification of structural vulnerability. Building damage data can be used to estimate fragility curves to obtain realistic descriptions of the relationship between a hazard intensity measure and the probability of exceeding certain damage grades. Fragility curves based on the lognormal cumulative distribution function are popular because of their empirical performance as well as theoretical properties. When we are interested in estimating exceedance probabilities for multiple damage grades, these are usually derived per damage grade via separate probit regressions. However, they can also be obtained simultaneously through an ordinal model which treats the damage grades as ordered and related instead of nominal and distinct. When we use nominal models, a collapse fragility curve is constructed by treating data of “near‐collapse” and “no damage” the same: as data of noncollapse. This leads to a loss of information. Using synthetic data as well as real‐life data from the 2015 Nepal earthquake, we provide one of the first formal demonstrations of multiple advantages of the ordinal model over the nominal approach. We show that modeling the ordering of damage grades explicitly through an ordinal model leads to higher sensitivity to the data, parsimony and a lower risk of overfitting, noncrossing fragility curves, and lower associated uncertainty.