Estimation Of Vulnerability Research Articles

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.

Vulnerability Estimation of DNN Model Parameters with Few Fault Injections

The reliability of deep neural networks (DNN) against hardware errors is essential as DNNs are increasingly employed in safety-critical applications such as automatic driving. Transient errors in memory, such as radiation-induced soft error, may propagate through the inference computation, resulting in unexpected output, which can adversely trigger catastrophic system failures. As a first step to tackle this problem, this paper proposes constructing a vulnerability model (VM) with a small number of fault injections to identify vulnerable model parameters in DNN. We reduce the number of bit locations for fault injection significantly and develop a flow to incrementally collect the training data, i.e., the fault injection results, for VM accuracy improvement. We enumerate key features (KF) that characterize the vulnerability of the parameters and use KF and the collected training data to construct VM. Experimental results show that VM can estimate vulnerabilities of all DNN model parameters only with 1/3490 computations compared with traditional fault injection-based vulnerability estimation.

On the emergence of a predicted climate change signal: When and where it could appear over Pakistan

Emergence of climate change signal attributed to change in mean temperature can bring serious implications to economic stability of developing countries like Pakistan. Likewise, unawareness of vulnerability in regions of a country can direct mitigation efforts towards unwanted areas instead of towards ones that are genuinely deprived of. To address these two issues for Pakistan, we adopted a compendium of five metrics by using climate model data of near surface mean monthly temperature from output of a general circulation model MRI-ESM2-0 of Meteorological Research Institute (MRI), simulated under historical (1850-2014) and projected (2015-2100) periods for five shared socioeconomic pathways (SSPs) described in the sixth assessment report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) originally published in the year 2021. To identify potential hotspot regions, we used four out of the five metrics i.e., change in mean (DM–vulnerability metric), standard Euclidean distance (SED–vulnerability metric), change in standard deviation (DSD–stability metric), and standard score (Z-Score–stability metric) statistics for regional bounds of Pakistan. To investigate emergence of climate change signal, we computed the fifth metric viz. signal to noise ratio (SNR–agility metric) from time series of the near surface mean monthly temperature and checked how rapidly the subject signal emerged out of variability in the studied data under different scenarios. On the estimation of vulnerability and stability, our results revealed that the Himalayan region of Pakistan (the northeast corner) repeatedly appeared to be the most qualified region to be acclaimed as a hotspot due to its reach to optimal echelons in the associated metrics of the DM (more than four degrees), the SED (up to one), the DSD (close to null) and the Z-Score (close to null) under all the studied SSP scenarios. On the estimation of agility, our results revealed that owing to allegedly sustainable scenarios (with low to medium challenges to mitigation), the SSP1, the SSP2, and the SSP4 delayed the evolution of climate change signal (between 2070 to 2100) by at least two decades as compared to allegedly perplexing (high challenges to mitigation) SSP3 and SSP5 scenarios that accelerated the appearance of the signal by crossing the SNR threshold fairly earlier (between 2040 to 2060) in the 21<sup>st</sup> century. With such knowledge at hand, this scientific contribution can advise policymakers and stakeholder agencies to exercise conversant decisions and to equip themselves with evidence to prioritize and target their resources in an informed way over Pakistan region.

A Methodology to Estimate Functional Vulnerability Using Floating Car Data

In this work, a new methodology to estimate the functional vulnerability of the road network of the city of Catania (Italy) is developed with the purpose to improve the resilience of urban transport during critical events. While the traditional approach for the estimation of vulnerability is based on topological data, the proposed methodology is based on spatial-temporal mobility profiles obtained with floating car data (FCD). The algorithm developed for the estimation of vulnerability combines topological properties of the road network with mobility patterns obtained from FCD to evaluate the consequences of failure events on trajectories and their associated travel times. The core operation of the algorithm is based on the computation of all possible travel paths within their assigned geographical zone every time a road link is disrupted. The procedure may prove useful to evaluate wide failure events and to facilitate emergency plans.

Coastal vulnerability map of Jagatsinghpur District, Odisha, India: A satellite based approach to develop two-dimensional vulnerability maps

Assessment of coastal vulnerability has achieved a recent pace due to the increasing frequency and intensity of tropical cyclones and anticipated sea-level rise. The coastal vulnerability is often assessed along the shoreline and it fails to capture the inland physical variabilities. During storm surge, rivers and creeks act as carriers that induce inundation to the low-lying inland areas causing damage to the invaluable coastal livelihood. The study addresses this gap by developing an approach to assess the vulnerability of a coastal inland as two-dimensional vulnerability maps that captures the physical and demographic variabilities of the inland region. We have estimated the coastal vulnerability for Jagatsinghpur District, Odisha, along the eastern Indian coast using coastal landforms, elevation, creek order, proximity to the creek, proximity to the open coast as the physical variables. The analysis resulted in the classification of 31 villages as highly vulnerable that are mostly located over swales and low-lying areas adjacent to creeks. More than half of the villages are located away from the shoreline, which are not considered in the conventional method of vulnerability assessment. Further, we assessed the social vulnerability of the 31 vulnerable villages and the composite map identified 18 villages as highly vulnerable, out of which 9 villages are located away from the shoreline. As the drainage network carry the surge water landwards, the vulnerability estimation have to consider the physical and social characteristics of the coastal inland region. The study finds its applicability in identifying the vulnerable areas along the coast for disaster reduction under a climate change scenario.

The influence of ASCE 7–16 wind load provisions on a vulnerability model of Florida residential construction

The Florida Public Hurricane Loss Model is a probabilistic risk model designed to estimate wind induced insurance losses to residential infrastructure in Florida. In the physical damage estimation module, Monte Carlo simulation is employed to generate damage matrices as a function of wind speed by comparing probabilistic building component capacities to wind loads. These damage matrices are input to a repair cost algorithm, ultimately producing damage ratios as a function of wind speed for different building classes. It is required that the inventory of residential building models reflect the history of construction practice within Florida. Differing eras of construction are represented by developing component-wise options for probabilistic capacities and load paths, resulting in a suite of hundreds of model variants. Regarding loads, the probabilistic wind load model is based on a directionalized adaptation of ASCE 7, and its representation in the model is kept current with the version adopted by the Florida Building Code (FBC). In 2020 the Florida Building Code adopted by reference ASCE 7–16, which contains important changes to the wind-load provisions. These changes have implications for both the loading and resistance aspects of the physical damage model, thus influencing insured loss projections. This study discusses the development and implementation of these changes within the model and investigates the impacts on the modeled vulnerability of residential structures in Florida. Comparing vulnerability estimates of structures designed before and after the FBC adoption of ASCE 7–16 provide insight into the potential effectiveness of such changes to improve community resilience.

Constructing Mobile Crowdsourced COVID-19 Vulnerability Map With Geo-Indistinguishability

Preventing COVID-19 disease from spreading in communities will require proactive and effective healthcare resource allocations, such as vaccinations. A fine-grained COVID-19 vulnerability map will be essential to detect the high-risk communities and guild the effective vaccine policy. A mobile-crowdsourcing-based self-reporting approach is a promising solution. However, an accurate mobile-crowdsourcing-based map construction requests participants to report their actual locations, raising serious privacy concerns. To address this issue, we propose a novel approach to effectively construct a reliable community-level COVID-19 vulnerability map based on mobile crowdsourced COVID-19 self-reports without compromising participants’ location privacy. We design a geo-perturbation scheme where participants can locally obfuscate their locations with the geo-indistinguishability guarantee to protect their location privacy against any adversaries’ prior knowledge. To minimize the data utility loss caused by location perturbation, we first design an unbiased vulnerability estimator and formulate the location perturbation probability generation into a convex optimization. Its objective is to minimize the estimation error of the direct vulnerability estimator under the constraints of geo-indistinguishability. Given the perturbed locations, we integrate the perturbation probabilities with the spatial smoothing method to obtain reliable community-level vulnerability estimations that are robust to a small-sampling-size problem incurred by location perturbation. Considering the fast-spreading nature of coronavirus, we integrate the vulnerability estimates into the modified susceptible-infected-removed (SIR) model with vaccination for building a future trend map. It helps to provide a guideline for vaccine allocation when supply is limited. Extensive simulations based on real-world data demonstrate the proposed scheme superiority over the peer designs satisfying geo-indistinguishability in terms of estimation accuracy and reliability.

Public health threats of diminished treatment of onsite sewage

Public health threats of diminished treatment of onsite sewage

Predicting parasitoid accumulation by potential Brazilian peppertree biological control agents from assessments in the native and invaded ranges

During their initial releases, classical biological control agents may benefit from enemy-free-space as they are introduced to new habitats with relatively low attack rates by natural enemies. The benefits of classical biological control may be reduced by natural enemies that exploit new agents in the introduced range. Estimates of agent vulnerability to natural enemies prior to release could assist in predicting attack rates and prioritization of proposed agents. Additionally, release of vulnerable biological control agents could have unintended consequences that disrupt native food webs. Our goal was to predict the vulnerability of potential agents to attack by natural enemies by determining parasitism rates in the native range of the weed Brazilian peppertree, Schinus terebinthifolia. Furthermore, we conducted assessments of the rate of attack of ecological analogues in the invaded range in North America, where the agents are to be released, to assist in predicting post release vulnerability. Potential agents and analogues were assigned to four feeding niches, leaf folders, cryptic feeders, thrashers, and sap feeders. Cryptic members were generally most common in the invaded range. However, compared with the other niches, leaf folders were most common in at least one region of the native range. Surveys of potential agents of Brazilian peppertree in their native range of South America indicated parasitism rates were generally <20% for leaf folders (10.5 ± 6.3%; n = 485), cryptic feeders (17.6 ± 3.7%; n = 495), thrashers (12.1 ± 1.2%; n = 381), and sap feeders (2.5% ± 1.1%; n = 200). With exceptions, parasitism rates of the ecological analogues of North America were similar to those from South America for leaf folders (19.2 ± 7.9%; n = 19), cryptic feeders (16.5 ± 5.2%; n = 285), and sap feeders (0.2 ± 0.2%; n = 1247). Thrashers were not included in the analysis as a single parasitoid was found from few (n = 9) hosts recovered in North America. These results suggest that if species of these defoliating leaf folder or cryptic feeder niches are released for biological control in North America they will be parasitized at these same levels. However, if sap feeders are released, these results predict very low parasitism would result. These pre-release assessments of parasitism may assist in the prioritization of potential agent species for biological control of weeds and reduce the likelihood of indirect effects impacting native communities.

The Influence of Seasonal Recharge and Groundwater Pumping on the Seawater Intrusion in a Coastal Karst Aquifer

Wu, X.; Xu, Z.; Xu, Z.; Hu, B.X.; Chang, Q., and Hu, Y., 2022. The influence of seasonal recharge and groundwater pumping on the seawater intrusion in a coastal karst aquifer. Journal of Coastal Research, 38(4), 785–794. Coconut Creek (Florida), ISSN 0749-0208. Groundwater resources in the coastal karst aquifers are threatened by long-distance seawater intrusion. The severity of groundwater contamination can be affected by hydrologic and anthropogenic factors, including rainfall conditions and pumping situations. A better understanding of these factors' control on the seawater intrusion is critical for their vulnerability estimation and aquifer management. The aquifer salinization, pumping safety, and the vulnerability of inland springs were examined by a synthetic-modeling work in this study. Different pumping locations, pumping rates, the comparison of dynamic seasonal recharge (DSR), and steady-state recharge (SSR) were considered in this study. Several time-based metrics were developed to estimate the aquifer vulnerability and water quality. The results show that the distance of pumping location to the shoreline significantly impacts seawater intrusion. The near-shore pumping leads to lower aquifer contamination while higher salinity in pumped water, and the inland pumping causes more aquifer salinization with cleaner pumped water. In addition, aquifer, pumping water quality, and inland spring are more vulnerable to seawater intrusion under the DSR condition than SSR condition. The difference of aquifer salinization between DSR and SSR condition increases with pumping rate from 50 to 150 m3/day and then decreases from 150 to 200 m3/day, which shows a threshold pumping rate in comparing DSR and SSR conditions. The insights from this work provide essential suggestions for groundwater simulations and related management decisions.

Coronary plaque characterization assessed by delayed enhancement dual-layer spectral CT angiography and optical coherence tomography.

This study aimed to prospectively evaluate delayed enhancement imaging by spectral computed tomography using soluble iodine containing contrast media to improve the in vivo characterization of coronary plaque types based on the quantification of delayed iodine enhancement. Patients with known or suspected coronary artery disease (CAD) underwent spectral coronary CT-angiography (SCCTA). Absolute delayed iodine enhancement in all visible coronary plaques was assessed. Patients with significant CAD (> 50% stenosis) further underwent invasive coronary angiography (ICA) including optical coherence tomography (OCT). We identified 50 non-calcified coronary plaques in 72 patients undergoing SCCTA. 17 patients with significant CAD underwent further ICA including OCT imaging. In those, we were able to match 35 plaques by both SCCTA and OCT. Based on OCT imaging, 22/35 matched plaques (63%) were characterized as high-risk coronary plaques (thin-cap fibroatheroma n = 2, fibroatheroma n = 20), whereas 13/35 (37%) were characterized as low-risk plaques (fibrocalcific lesion n = 3, fibrous plaques n = 9, and early-onset fibroatheroma n = 1). All plaques showed similar HU's and could not be classified into high-risk or low-risk plaques by conventional CT measures. Minimal delayed iodine enhancement within plaques as quantified by SCCTA demonstrated significantly lower values in high-risk as compared to low-risk coronary plaques (1.0 ± 1.5mg/ml vs. 2.2 ± 1.1mg/ml, p = 0.021) which allowed estimation of high-risk plaques with high sensitivity and moderate specificity (77% and 56%). Measurement of delayed enhancement iodine uptake within stable coronary artery plaques using dual-layer SCCTA might contribute to a more precise estimation of plaque vulnerability surpassing conventional CT techniques.

Analysis of the vulnerability estimation and neighbor value prediction in autonomous systems.

The security within autonomous systems (AS)s is one of the important measures to keep network users safe and stable from the various type of Distributed Denial of Service (DDoS) attacks. Similar to the other existing attack types Internet control message protocol (ICMP) based attacks are remained open challenge on the Internet environment. In this study, we have proposed a method to estimate the vulnerability of 600 AS provider edge (PE) routers by sending ICMP packets and predicted AS neighbor values using least square regression (LSR) approach. The results of our study show that 265 AS PE routers are vulnerable due to ICMP flood attack from the 600 ASs which were analyzed. Additionally, we have predicted that about 60% of total AS neighbors will be reduced in the next 3 years. Our results indicate that some ASs still did not deploy the firewall system in the boundary of their networks. Similarly, we also observed that the majority of ASs which expected to have less neighbor values in the next 3 years is due to change their routing paths to find adjacent paths.

Integrating physiology into correlative models can alter projections of habitat suitability under climate change for a threatened amphibian

Rapid global change has increased interest in developing ways to identify suitable refugia for species of conservation concern. Correlative and mechanistic species distribution models (SDMs) represent two approaches to generate spatially‐explicit estimates of climate vulnerability. Correlative SDMs generate distributions using statistical associations between environmental variables and species presence data. In contrast, mechanistic SDMs use physiological traits and tolerances to identify areas that meet the conditions required for growth, survival and reproduction. Correlative approaches assume modeled environmental variables influence species distributions directly or indirectly; however, the mechanisms underlying these associations are rarely verified empirically. We compared habitat suitability predictions between a correlative‐only SDM, a mechanistic SDM and a correlative framework that incorporated mechanistic layers (‘hybrid models'). Our comparison focused on green salamanders Aneides aeneus, a priority amphibian threatened by climate change throughout their disjunct range. We developed mechanistic SDMs using experiments to measure the thermal sensitivity of resistance to water loss (ri) and metabolism. Under current climate conditions, correlative‐only, hybrid and mechanistic SDMs predicted similar overlap in habitat suitability; however, mechanistic SDMs predicted habitat suitability to extend into regions without green salamanders but known to harbor many lungless salamanders. Under future warming scenarios, habitat suitability depended on climate scenario and SDM type. Correlative and hybrid models predicted a 42% reduction or 260% increase in area considered to be suitable depending on the climate scenario. In mechanistic SDMs, energetically suitable habitat declined with both climate scenarios and was driven by the thermal sensitivity of ri. Our study indicates that correlative‐only and hybrid approaches produce similar predictions of habitat suitability; however, discrepancies can arise for species that do not occupy their entire fundamental niche, which may hold consequences of conservation planning of threatened species.

Vegetation Characteristics Based Climate Change Vulnerability Assessment of Temperate Forests of Western Himalaya

Forests are under stress due to variety of climatic and non-climatic factors. Therefore for suitably managing the forests, vulnerability of the forests needs to be understood. The present paper attempts to estimate the vulnerability of various temperate forests of Western Himalaya due to climate change by analyzing the patterns of different taxonomical indices, based on primary data i.e., vegetation data. The paper presents a novel approach for climate change vulnerability assessment based on field data through a bottom-up approach. The vulnerability of the forests was assessed through the IPCC framework by suitably selecting indicators (taxonomy indices and climatic parameters) for the three dimensions of vulnerability i.e., exposure, sensitivity and adaptive capacity. The field data were collected from 17 different temperate forests distributed at the elevation “1600 to 3500 m” in Uttarakhand and Himachal Pradesh, India. Abundance and richness for each forest were collected by randomly laying ten quadrats of size 0.1 ha each. The analysis resulted into identifying the most and the least vulnerable temperate forests of the western Himalaya to climate change. The analysis showed that the Neoza Pine; Moist Deodar; Ban Oak and Dry Broadleaved and Coniferous forest were the most vulnerable forests in the Himalayan temperate forests due to climate change. Moreover, the variation in the levels of the vulnerability status of the selected forests was insignificant with elevational range as well as exposure to climate. The proposed method will serve for vulnerability estimation of forests due to climate change based on the actual realization of the species in the field.

Extreme precipitation-based vulnerability assessment of road-crossing drainage structures in forested watersheds using an integrated environmental modeling approach

The goal of this study is to develop geospatial-hydrology models incorporating design rainfall intensities and land morphologic features to identify erosion hazards and vulnerability risks to road culverts/stream crossings in three watersheds at USDA Forest Service long-term experimental forests: i) Coweeta Hydrologic Laboratory, NC, ii) Santee Experimental Forest, SC, and iii) Alum Creek Experimental Forest, AR. These models developed in an ArcGIS ModelBuilder platform were: i) Streambank Erosion Vulnerability Assessment (SBEVA) and ii) Modified Revised Universal Soil Loss Equation (MODIFIED-RUSLE) for potential erosion and streambank vulnerability estimation. The SBEVA model, developed using a Delphi-based weighted-probability scale, and MODIFIED-RUSLE were integrated to identify locations of culverts/stream-crossings morphologically vulnerable to erosion and scouring, which were ground-truthed for the SC site only. As the MODIFIED-RUSLE model does not assess streambank erosion, its integration with the SBEVA helps to develop a better decision-support tool for relevant agencies for safeguarding these road culverts/stream crossings.

First application of the Integrated Karst Aquifer Vulnerability (IKAV) method – potential and actual vulnerability in Yucatán, Mexico

Abstract. Groundwater vulnerability maps are important decision support tools for water resource protection against pollution and helpful in minimizing environmental damage. However, these tools carry a high subjectivity along the multiple steps taken for the development of such maps. Additionally, the theoretical model on which they are based does not consider other important parameters, such as pollutant concentration or pollutant residence time in a given section of the aquifer, solely focusing on the theoretical travel time of a pollutant particle from a release point towards a target. In this work, an integrated methodology for the evaluation of potential (intrinsic) and actual vulnerability is presented. This integrated method, named Integrated Karst Aquifer Vulnerability (IKAV), was developed after the analysis of several study cases around the world and the application of multiple intrinsic groundwater vulnerability methods in a selected study area. Also, a solute transport model served as the basis to define additional parameters for vulnerability analysis for areas severely affected by anthropogenic practices. However, the focus of the transport model must not be mistaken to be hazards and risk mapping. A defined workflow and several criteria for parameters and attributes selection, rating and weighting, and vulnerability classification are presented here. The first application of the IKAV method was carried out in the Yucatán karst, demonstrating to be a reliable method for vulnerability estimation. Results demonstrated the scope of the IKAV method in highlighting important regional conditions, minimizing the subjectivity, and expanding the analysis of vulnerability.

Rockfall Vulnerability of a Rural Road Network—A Methodological Approach in the Harz Mountains, Germany

Mass movements are linked to increasing amounts of damage and disruptions to transportation infrastructures. A valid risk assessment in order to reduce future costs is not always appropriate, as adequate information on landslide data is missing. The presented study estimates the rockfall susceptibility on a rural road network in the Harz mountains using a bivariate statistical method (information value method). The model is validated using a receiver operating characteristic (ROC) analysis. In addition, the vulnerability of the road network is estimated using vulnerability indicators. The susceptibility model assigns a high or very high susceptibility to 23% of the area in the road network corridor. The relevant road sections are linked to high slope values, NE orientations of road sections, and low-to-moderate vulnerability values. The highest vulnerability values can be found on marginal road sections with high average daily traffic volumes. The combination of the presented methods proposes an easily applicable estimate of vulnerability where conventional methods (i.e., vulnerability curves, matrices) cannot be implemented.

EVALUATION AND COMPARISON OF GROUNDWATER VULNERABILITY TO POLLUTION BY THE DRASTIC AND GOD METHODS: A CASE OF WADI NIL ALLUVIAL PLAIN (JIJEL, NE ALGERIA)

The current research aims to compare the results of two vulnerability estimation methods (DRASTIC and GOD) in superficial and deep aquifer of Wadi Nil alluvial plain (NE Algerian). Analysis of the obtained results showed that the vulnerability of the superficial aquifer is divided into three classes of vulnerability according to the DRASTIC method, and only into two classes according to the GOD method. In deep aquifer, the study of vulnerability using DRASTIC method has shown us a dominance of the middle and high classes. On the other hand, GOD method shows an equitable distribution of high, low and middle classes. The statistical study of the agreement between the two methods shows a weak agreement in superficial aquifer and a strong agreement in deep aquifer. The surface analysis per class of the two methods shows an identical index of superficial and deep aquifers. In this study, we find that the DRASTIC method provides better results in both aquifers (superficial and deep). It provides better information and gives the necessary amount of information required in the vulnerability assessment compared to the GOD method.

Unrealistic pessimism and obsessive-compulsive symptoms during the COVID-19 pandemic: Two longitudinal studies.

ObjectiveUnrealistic pessimism (UP) is an aspect of overestimation of threat (OET) that has been associated with obsessive‐compulsive disorder/symptoms (OCD/OCS). During the COVID‐19 pandemic, UP may have played an important role in the course of OCD. To investigate the relationship, we conducted two longitudinal studies assuming that higher UP predicts an increase in OCS.MethodIn Study 1, we investigated UP in the general population (N = 1,184) at the start of the pandemic asking about overall vulnerability to infection with SARS‐CoV‐2 and UP regarding infection and outcome of severe illness. Further, OCS status (OCS+/−) was assessed at the start of the pandemic and 3 months later. In Study 2, we investigated UP in individuals with OCD (N = 268) regarding the likelihood of getting infected, recovering, or dying from an infection with SARS‐CoV‐2 at the start of the pandemic and re‐assessed OCS 3 months later.ResultsIn Study 1, UP was higher in the OCS+ compared to the OCS− group, and estimates of a higher overall vulnerability for an infection predicted a decrease in OCS over time. UP regarding severe illness predicted an increase in symptoms over time. In Study 2, UP was found for a recovery and death after an infection with SARS‐CoV‐2, but not for infection itself.ConclusionsExaggeration of one’s personal vulnerability rather than OET per se seems pivotal in OCD, with UP being associated with OCD/OCS+ as well as a more negative course of symptomatology over the pandemic in a nonclinical sample.Practitioner points Unrealistic optimism, a bias common in healthy individuals, is thought to be a coping mechanism promoting well‐being in the face of danger or uncertainty.The current study extends findings that its inversion, unrealistic pessimism, may play an important role in obsessive‐compulsive disorder and may also be involved in the development of the disorder.This study highlights the importance that prevention programs during a pandemic should include targeting unrealistic pessimism.

Integrated variable weight model and improved DRASTIC model for groundwater vulnerability assessment in a shallow porous aquifer

Accurate and effective assessment of groundwater vulnerability is very important for ensuring a healthy groundwater ecosystem. We evaluate the vulnerability of shallow porous aquifers by modifying the DRASTIC model and verify the effectiveness of the variable weight model (VWM). Firstly, topography, aquifer media, and the impact of the vadose zone are replaced by land-use type, aquifer thickness, and the hydraulic resistance of the vadose zone. Second, the weighting is optimized using the analytical hierarchy process (AHP) and variable weight theory (VWT). Thirdly, the original and the improved DRASTIC methods were used to evaluate groundwater vulnerability. Finally, three sets of samples hydrochemical parameters (NO3-, Mg2+,COD) were used to verify the improved frameworks using the Receiver Operating Curve (ROC) method. The improved model increases the dispersion degree of groundwater vulnerability. Compared with the original DRASTIC method, the correlation of the VWM is significantly improved in terms of the area under curve (AUC) for NO3- (0.786), COD (0.753), and Mg2+ (0.831). In short, it is necessary to optimize the parameters and weights of the model in order to realize reliable estimations of groundwater vulnerability. In particular, the use of VWM brings the results in line with the actual situation by changing the weights.