Low Vulnerability Research Articles

A rapid approach to assessing the vulnerability of Mozambican fisheries’ species to climate change

Mozambique is amongst the most vulnerable of Western Indian Ocean (WIO] countries to the impacts of climate change on its marine fisheries. We used rapid assessment methods to evaluate sensitivity, exposure and vulnerability of fisheries species to climate change, appropriate for data-deficient, developing countries in the region. Species were selected based on their importance in industrial and artisanal fisheries’ landings, further prioritized by local experts. Species’ attributes likely to be sensitive to climate change were identified and scored, utilizing life history or biological characteristics. Sea Surface Temperature (SST] was the most prominent climate exposure factor and for which we could confidently predict likely future change. Most species had low or medium overall sensitivity to climate change, with only eight considered highly sensitive. Climate exposure (SST] scores were high off northern Mozambique, while the central and southern regions were generally of medium exposure. Ten species received a High vulnerability score, 14 were Medium, and 16 had a Low vulnerability score. The highly vulnerable species were all fishes, apart from one crustacean; 4 of the 9 were strongly estuarine-associated; the most vulnerable species was the parrotfish Scarus ghobban. This is the first attempt to use a rapid, semi-quantitative, specialist- and trait-based vulnerability assessment of the anticipated effect of climate change on marine fisheries species in the WIO. Challenges experienced were data paucity, limited resources, the large study area, and the complex oceanography of the region. However, a simple methodology was developed, derived from efforts elsewhere, and which can be used to undertake similar assessments for other WIO countries. Raised awareness of climate change among small-scale fishing communities is a necessity, combined with adaptation by promoting fisheries co-management. Also required is support from government to ensure that people can be flexible to change.

The Association Between Food Insecurity and Census Tract-Based Indexes in an Urban Academic Reproductive Clinic: Can Residence Predict Food Insecurity in Reproductive Patients? [ID 2683613

INTRODUCTION: Identifying food-insecure patients remains a priority in reproductive clinics given nutrition’s effect on maternal health. We sought to determine the association between self-reported food insecurity with census tract-based indexes including the Childhood Opportunity Index (COI), Social Vulnerability Index (SVI), and the U.S. Department of Agriculture (USDA) low-income and low-food access areas (LILAs), to identify which indexes can help identify at-risk patients. METHODS: This is a cross-sectional survey study (UMBHP-00099693) of patients presenting for reproductive care at one urban academic center. Food insecurity was scored as greater than 0 on the 6-Item USDA Food Security Module, and living within a LILA was collected from the 2019 USDA database. We categorized social vulnerability as moderate/low/very low vulnerability or high/very high vulnerability using the SVI. We categorized childhood opportunity using the COI as very low/low and moderate/high/very high. RESULTS: Of the 179 participants in the study, 84 (47%) reported food insecurity. The following proportions lived in a: LILA 96 (54%); high/very high vulnerability tract 138 (77%); and low/very low childhood opportunity (CO) tract 144 (86%). Living within a LILA was associated with an increased odds of experiencing food insecurity (odds ratio [OR] 1.8 [1.0, 3.4]). Living within a high/very high vulnerability tract was not significantly associated with food insecurity (OR 1.2 [0.6, 2.4]), nor was living within a low/very low CO tract (OR 1.6 [0.8, 3.5]). CONCLUSION: Residence within a LILA could be associated with food insecurity in patients seeking reproductive care, which can inform the development of future clinical and geographic interventions. As one in two patients were food insecure, we recommend universal screening of all patients at reproductive appointments.

Assessment of Flood Vulnerability Levels in the Lower Orashi River, Niger Delta Region, Nigeria

Flooding is one of the most frequent and widespread of all environmental hazards and of various types and sizes. This study investigated the flood vulnerability levels in the lower Orashi River, Niger Delta Region, Nigeria. The study considered the landuse, soil texture, elevation, slope, stream power index (SPI), rainfall, drainage density and proximity to river channels as factors determining flood. The components of these factors were reclassified as lowly, moderately and highly vulnerable areas with assigned numbers 1, 2 and 3 respectively. All the maps were overlaid using the UNION module for the final flood vulnerability levels. Descriptive statistics and inferential statistics were used in the data analysis. Results showed that the very low flood vulnerability levels covered spatial extent of 244.4 sq km (7.31%), low flood vulnerability covered 901.31 sq km (26.95%), moderate flood vulnerability level 1240.18 sq km (37.08%), high flood vulnerability level 846.47 sq km (25.31%) and very high had 112.11 sq km (3.35%). It can be concluded that more than 65% of the entire lower Orashi River are highly and moderately vulnerable to flood. It is therefore recommended among others that periodic studies should be carried out in lower Orashi River to monitor the factors that can cause flood in the area.

Pemetaan Kerawanan Bencana Longsor Menggunakan Teknik UAV (Studi Kasus: Jalur Sitinjau Lauik Padang – Solok)

One of the most common natural disasters in Indonesia is landslides Heavy rains have caused several landslides in various regions, causing a large impact every year. Landslides caused by rainfall have also become a global problem in many regions, leading to large-scale landslides. Landslides that occur in Sitinjau Lauik, Padang City are influenced by high rainfall with very steep slopes (70˚-80˚), making this area very prone to landslides. One of the techniques used in mapping landslide vulnerability is the Unmanned Aerial Vehicle (UAV) technique, which is a flying machine controlled by a remote controller. The data obtained will be processed using ArcGIS with Weighted Overlay technique, and factors affecting landslides have been conditioned based on Multi Influencing Factor (MIF). This research aims to achieve: 1) the landslide vulnerability level of Sitinjau Lauik Padang-Solok, 2) determine the landslide prone zones in Sitinjau Lauik. The type of research used in this research is quantitative research with spatial approach. The data required are primary data and secondary data. Primary data consists of aerial photographs taken using drones and processed to produce slope, elevation, aspect, and TWI. The secondary data used is the Landsat 8 image in 2023 which is used to determine the vegetation index. This research resulted in 1) The landslide vulnerability level of Sitinjau Lauik is divided into four classes, namely, low landslide vulnerability level with an area of 4342m or 10.81%. Medium landslide vulnerability level with an area of 24575m or 61.16%. High landslide vulnerability level with an area of 11,294m or 28%. And very high landslide vulnerability level with an area of 14m or 0.03%. 2) Landslide vulnerability zonation based on slope condition is divided into 3 zones: Zone A with an area of 20,901m, Zone B with an area of 4642m. And Zone C with an area of 14,614m.

Fuzzy comparison of drought vulnerability based on the analytical hierarchy process

The vulnerability represents the level to which any ecosystem experiences impairment due to natural distress. One such distress is drought, a prolonged phase of deficient rainfall that causes the depletion of water resources. The possibility of the influences of drought principally depends on the vulnerability of the affected area. The predominantly arid climate of Pakistan and the dependence of its economy on agriculture make it vulnerable to climate change. Rahim Yar Khan and Tharparkar have similar environmental conditions, but Tharparkar experiences more droughts. Vulnerability factors observed by the two districts among the regions are different even with similar climate conditions. The system vulnerability to drought was observed for two districts by using the analytical hierarchy process (AHP) and drought vulnerability index (DVI). The drought vulnerability index (DVI) was used to assess the meteorological, agricultural, hydrological, and socio-economic indicators to build the hierarchy for these inconsistence factors and evaluate the most vulnerable and affecting cause among the meteorological, agricultural, hydrological, and socio-economic indicators. The selected measures specify the hypotheses and the relativity of the indicators to the overall vulnerability of the system. The evaluated weights for each variable are derived from the analytical hierarchy process by comparing the variables of the selected indicators. Due to different meteorological, agricultural, and socio-economic conditions, the weights for the DVI were not the same in both study districts, i.e., Rahim Yar Khan and Tharparkar. The classification of vulnerability depending upon the drought vulnerability index with AHP suggests that the DV indexation is favorable in assessing the regional drought vulnerability. The drought vulnerability assessment of Tharparkar indicates that Tharparkar is highly vulnerable to drought, i.e., DVI = 0.066, as it is highly dependent on irrigation resources for its agriculture and livestock. The unavailability of hydrological resources has increased the drought vulnerability of the Tharparkar, which corresponds to a high level of agricultural damage due to droughts. However, the district of Rahim Yar Khan with developed irrigation resources has a lower drought vulnerability index (DVI = 0.011). It is necessary for Tharparkar to establish countermeasures to mitigate the drought vulnerability through the development of water resources. Future studies are needed to be focused on more indicators for assessing particular drought vulnerability issues in order to reduce the drought vulnerability of Tharparkar.

Groundwater salinization risk assessment using combined artificial intelligence models.

Assessing the risk of groundwater contamination is of crucial importance for the management of water resources, particularly in arid regions such as Menzel Habib (south-eastern Tunisia). The aim of this research is to create and validate artificial intelligence models based on the original DRASTIC vulnerability methodology to explain groundwater salinization risk (GSR). To this end, several algorithms, such as artificial neural networks (ANN), support vector regression (SVR), and multiple linear regression (MLR), were applied to the Menzel Habib aquifer system. The results obtained indicate that the DRASTIC Vulnerability Index (VI) ranges from 91 to 141 and is classified into two categories: low and moderate vulnerability. However, the correlation between groundwater total dissolved solids (TDS) and the Vulnerability Index is relatively weak (r < 0.5). Indeed, the original DRASTIC index needs some improvements. To improve it, some adjustments are required, notably by incorporating the TDS-groundwater salinization risk (GSR) indicator. The seven parameters of the original DRASTIC model were used as inputs for the artificial intelligence models, while the GSR values were used as outputs. Performance indicators, such as the correlation coefficient (r) and the Willmott Agreement Index (d), showed that the ANN model outperformed the SVR and MLR models. Indeed, during the training phase, the ANN model obtained r values equal to 0.89 and d values of 0.4, demonstrating the superiority, robustness, and accuracy of ANN-based methodologies over the original DRASTIC model. The findings could provide valuable information to guide management of groundwater contamination risks, especially in arid regions.

Assessment and spatialization of vulnerability of Benin coast to sea level rise using composite/blended approach

Climate change, particularly sea level rise, poses significant challenges to global coastal regions, necessitating a profound understanding of their vulnerability for effective mitigation and adaptation strategies. This study focuses on enhancing scientific insight into West African coastal vulnerability, aiming to assist policymakers and coastal managers in developing sustainable coastal zone management strategies. The methodology employed combines Geographic Information System (GIS) and remote sensing techniques with the Coastal Vulnerability Index (CVI) formula. Parameters such as slope, topography, distance to shoreline, population density, accessibility, land cover, and soil and vegetation types are assessed for their contribution to physical and socio-economic changes induced by sea level rise. These parameters undergo weighting using the Analytic Hierarchy Process (AHP). The socio-economic vulnerability is quantified using the Community-Based Risk Screening Tool-Adaptation and Livelihoods (CRiSTAL). The assessment of the Benin coast reveals significant vulnerability, with very high vulnerability (36.69–70.4, indicated by red cells) and high vulnerability (30.84–36.69, indicated by orange cells) observed in all coastal municipalities (Grand-Popo, Ouidah, Abomey-Calavi, Cotonou, Sèmè-Kpodji), encompassing 40 % and 21 % of the study area, respectively. Moderate (25.69–30.84) and low (10.87–25.69) vulnerability areas are situated at higher elevations inland, covering 35 % and 4 % of the total study area, respectively. No areas of very low vulnerability have been identified. This research contributes significantly to coastal management decisions, adaptation strategies, and climate change policies in Benin by advancing scientific understanding of coastal vulnerability and facilitating the development of sustainable strategies.

The role of environmental gradients and microclimates in structuring communities and functional groups of lizards in a rainforest-savanna transition area.

Environmental heterogeneity poses a significant influence on the functional characteristics of species and communities at local scales. Environmental transition zones, such as at the savanna-forest borders, can act as regions of ecological tension when subjected to sharp variations in the microclimate. For ectothermic organisms, such as lizards, environmental temperatures directly influence physiological capabilities, and some species use different thermoregulation strategies that produce varied responses to local climatic conditions, which in turn affect species occurrence and community dynamics. In the context of global warming, these various strategies confer different types of vulnerability as well as risks of extinction. To assess the vulnerability of a species and understand the relationships between environmental variations, thermal tolerance of a species and community structure, lizard communities in forest-savanna transition areas of two national parks in the southwestern Amazon were sampled and their thermal functional traits were characterized. Then, we investigated how community structure and functional thermal variation were shaped by two environmental predictors (i.e., microclimates estimated locally and vegetation structure estimated from remote sensing). It was found that the community structure was more strongly predicted by the canopy surface reflectance values obtained via remote sensing than by microclimate variables. Environmental temperatures were not the most important factor affecting the occurrence of species, and the variations in ecothermal traits demonstrated a pattern within the taxonomic hierarchy at the family level. This pattern may indicate a tendency for evolutionary history to indirectly influence these functional features. Considering the estimates of the thermal tolerance range and warming tolerance, thermoconformer lizards are likely to be more vulnerable and at greater risk of extinction due to global warming than thermoregulators. The latter, more associated with open environments, seem to take advantage of their lower vulnerability and occur in both habitat types across the transition, potentially out-competing and further increasing the risk of extinction and vulnerability of forest-adapted thermoconformer lizards in these transitional areas.

Assessing the Vulnerability and Adaptation Needs of Mozambique's Health Sector to Climate: A Comprehensive Study.

Climate change poses severe consequences, particularly in sub-Saharan Africa, where poverty rates may escalate by 2050 without significant climate and development action. The health impacts are diverse, encompassing communicable and non-communicable diseases. Mozambique, a climate-vulnerable nation, has experienced significant natural disasters in the past 42 years, impacting its health system. This study aims to assess Mozambique's health sector's vulnerability and adaptation needs to climate change. Following a methodology proposed by the World Health Organization and the Intergovernmental Panel for Climate Change, a six-step vulnerability and adaptation assessment was conducted to conduct the Health Vulnerability Index (HVI) for Mozambique's regions (n=161). The HVI integrates historical climate, epidemiological, and socio-economic data at the district level, and was computed using exposure, sensitivity, and adaptive capacity dimensions. The results revealed spatial patterns in exposure to climate variables, extreme weather events, and variations in sensitivity and adaptive capacity across the country. The HVI mirrored the exposure findings. Notably, high vulnerability was observed in several districts, while major urban centers displayed lower vulnerability. These findings highlight the country's vulnerability to climate change and underscore the potential for adverse impacts on livelihoods, the economy, and human health. The study provides a foundation for developing strategies and adaptation actions.

GIS-Based Analytical Hierarchy Process Modelling and Mapping of Erosion Vulnerability in the Coastal Areas of Rivers State, Nigeria

The problem of coastal erosion in rivers State Nigeria is a significant issue that has far-reaching consequences for the environment and local communities. Despite the efforts of previous research there remains a lack of comprehensive understanding of the factors contributing to erosion vulnerability and their relative importance, hindering effective decision-making and management practices aimed at mitigating the effects of coastal erosion in Rivers State. Therefore, this study aimed at a GIS-based analytical hierarchy process modeling and mapping of coastal erosion vulnerability in Rivers State, Nigeria. The objectives are to establish and classify the geophysical factors according to the levels of coastal erosion risk, calculate the reliability index of the classified geophysical factors, determine the coastal vulnerable areas across Rivers State using analytical hierarchical process and to produce a coastal vulnerability index map defining the extent of erosion vulnerability in Rivers State. The methodology comprises of the acquisition of primary and secondary data, image pre-processing, image classification, DEM processing, classification and standardization of factors, development of pairwise comparism, and weighted linear combination analysis. The study revealed three distinct coastal erosion vulnerability zones: high, moderate, and low vulnerability. The high vulnerability zone encompassed a total expanse of 545.29 square kilometers, constituting 6.38% of the study area. In contrast, the moderate and low vulnerability zones covered 1941.33 square kilometers and 6052.51 square kilometers, respectively, making up 22.73% and 70.89% of the total area. Bonny (139.28 sq km) was ranked as the most vulnerable due to its role as an oil and gas hub. Degema (111.28 sq km) ranked second and requires urgent erosion control. Okrika and Andoni (71.73 sq km and 62.20 sq km) were third and fourth respectively. It is recommended that an advocate for the systematic approach to coastal vulnerability zoning be introduced in the study. The categorization of areas into high, moderate, and low vulnerability zones provides a standardized framework for assessing coastal regions' susceptibility to erosion. This approach can be applied to other regions to facilitate consistent vulnerability assessments.

Vulnerability of ground water to polution in the highlands by a combined approach of AHP method and remote sensing

The aim of this work is to develop a simple and convenient decision support tool for the highlands using a combined approach of the Analytic Hierarchy Process (AHP) method and remote sensing. The vulnerability assessment method developed is called O3P (Highlands method). It is a parametric method, adapted in the highlands and implement in the town of Bafoussam. This method takes into account 4 factors, including land use (O), permeability (P1), slope (P2) and water depth in the piezometers (P3). The AHP multi-criteria analysis coupled with geographic information systems (GIS) and the analysis of nitrate pollution, allowed the development and validation of the O3P method. The results obtained show 3 classes of vulnerabilities which evolve from the low vulnerability class (20.22% of the study area) to the classes of medium vulnerability (50.16 %) and high vulnerability (29.62 %). Classes of high vulnerability to pollution are more present in the central-northern part of the study area, with some occurrences towards the south. The superposition of the highest nitrate concentrations with the areas of high vulnerability confirms the validity of the method. It emerges from it following the study of the sensitivity that the land occupation factor has the most important effect in this model and whose reduction would influence to 35.54% the method. The statistical study shows that this method is acceptable because the value of the standard deviations of the IVO3P lower than that its mean.

Using the five-factor model of personality to identify an optimal SWAT team member

This study, based on the five-factor model (FFM) of personality, aimed to identify personality traits associated with the most optimal special weapons and tactics (SWAT) team member. SWAT teams are tasked with pre-empting and responding to terrorist threats, conducting hostage rescue operations, and managing situations that exceed the capabilities of conventional law enforcement agencies. The study sought to provide a deeper understanding of how personality traits function within the high-stakes context of these teams. It involved 159 male SWAT officers in active service within the Swedish Police Authority. A work analysis instrument, the Personality Job Profiler (PJP), was administered to these officers, who served as subject matter experts (SMEs) in a comprehensive personality work analysis. Specifically, the study addressed two research questions: How can an optimal SWAT police officer personality profile be operationalized when translated to the FFM of personality? What is the association between this optimal SWAT profile and the ratings of ten Diagnostic and Statistical Manual of Mental Disorders personality disorder profiles, based on the FFM of personality? The findings suggest that successful SWAT officers should exhibit low neuroticism and high conscientiousness, specifically featuring low vulnerability and high levels of competence, dutifulness, and self-discipline. Notably, the SWAT profile differs significantly from personality disorders, such as borderline, schizotypal, dependent, and avoidant, when operationalized with the FFM of personality. These results have significant implications for developing predictors for the selection process and can enhance the selection procedures for future SWAT members. This may also prompt further studies on criteria-related validity using various personality assessment methods.

Evaluating Sea Level Rise Impacts on the Southeastern Türkiye Coastline: a Coastal Vulnerability Perspective

Coastal areas are inherently sensitive and dynamic, susceptible to natural forces like waves, winds, currents, and tides. Human activities further accelerate coastal changes, while climate change and global sea level rise add to the challenges. Recognizing and safeguarding these coasts, vital for both socioeconomic and environmental reasons, becomes imperative. The objective of this study is to categorize the coasts of the Mersin and İskenderun bays along the southeastern coast of Türkiye based on their vulnerability to natural forces and human-induced factors using the coastal vulnerability index (CVI) method. The study area encompasses approximately 520 km of coastline. The coastal vulnerability analysis reveals that the coastal zone comprises various levels of vulnerability along the total coastline: 24.7% (128 km) is categorized as very high vulnerability, 27.4% (142 km) as high vulnerability, 23.7% (123 km) as moderate vulnerability, and 24.3% (126 km) as low vulnerability. Key parameters influencing vulnerability include coastal slope, land use, and population density. High and very high vulnerability are particularly prominent in coastal plains characterized by gentle slopes, weak geological and geomorphological features, and significant socioeconomic value.

Empirically derived profiles of neurocognitive functioning in youth and young adults with sickle cell disease.

Sickle cell disease (SCD) is an inherited blood disorder associated with neurocognitive deficits. In contrast to variable-centered approaches, no known research has utilized person-centered strategies to identify multidimensional patterns of neurocognitive functioning of an individual with SCD. The purpose of the present study was to create empirically derived profiles and identify predictors of neurocognitive functioning subgroups among youth and young adults with SCD. Individuals with SCD (N = 393, mean age 14.05 years, age range 8-24, 50.4% female/49.6% male) completed neurocognitive assessments. Latent profile analysis derived subgroups/classes of neurocognitive functioning and determined relations with demographic and medical variables. Three latent classes emerged: average functioning (n = 102, 27%), low average functioning (n = 225, 60%), and exceptionally low functioning (n = 46, 12%). Older age was associated with membership in the low average and exceptionally low functioning groups (relative to the average group). Being prescribed hydroxyurea was associated with membership in the average functioning group (relative to the low average group) and absence of hydroxyurea use was associated with membership in the exceptionally low group (relative to the low average group). Lower social vulnerability was associated with membership in the average functioning group compared to the low average and exceptionally low groups. Clinicians can help reduce disparities in cognitive development for individuals with SCD by promoting early treatment with hydroxyurea and implementing methods to reduce social vulnerabilities that can interfere with access to evidence-based care.

Risk-Taking, Social Support, and Belongingness Contribute to the Risk for Cannabis Use Frequency in University Students

Background: Cannabis use and misuse is known to be associated with a variety of negative health, academic, and work-related outcomes; therefore, it is important to study the factors that contribute to or moderate its use. Objectives: The aim of this study was to determine whether risky behavior, belongingness and social support as clustering variables play a role in cannabis use frequency. Method: In a university student sample, participant data on risky behavior, belongingness and social support were used to generate vulnerability profiles through cluster analysis (low vulnerability with low risk, low vulnerability with high belonging, moderate vulnerability, and high vulnerability). Using an analysis of variance, the vulnerability profiles were compared with respect to cannabis use frequency and quantity. Through chi-square tests we assessed whether these profiles are overrepresented in certain demographics. Results: The cluster analysis yielded four groups, which differed in their vulnerability for cannabis use. The most vulnerable cluster group had higher cannabis use frequency relative to the two least vulnerable groups. Low income vs. high income was also associated with high vulnerability group membership. International students were overrepresented in the low vulnerability with high belonging group relative to the low vulnerability with low-risk group. The opposite was observed for domestic students. Conclusions: This research adds to the expanding body of literature on cannabis use and misuse in Canada, which may contribute to public health policy and the prevention and treatment of cannabis addiction by providing new insight on who may be at risk.

Exploring the nexus of globalization and natural resource scarcity in driving green technology innovation: Insights from advanced panel data techniques

AbstractGreen technology innovation (GTI) plays a pivotal role in improving environmental sustainability. Our study fills a significant research gap by investigating how globalization and natural resource depletion (NRD) impact eco‐innovation. Rooted in knowledge spillover and ecological modernization theories, we utilize advanced panel data techniques, an aspect underexplored in empirical studies. Our panel data span G20 countries from 1986 to 2019, further dissected into sub‐panels—BRICS (emerging economies) and G7 (advanced economies). Initial tests reveal cross‐sectional dependencies and slope heterogeneities across all panels, corroborated by Westerlund cointegration test indicating a long‐term equilibrium. Driscoll–Kraay estimator highlights that economic and social globalization significantly drive eco‐innovation, while political globalization deteriorates GTI. The effect of NRD is also negative for G20 and BRICS countries, while G7 economies exhibit lower vulnerability to NRD shocks. Long‐run estimates from CS‐ARDL underscore the positive role of economic globalization in both the short and long run, while the negative role of NRD remains persistent in the long run. Panel quantile regression results prove that the effect of globalization is asymmetric across GTI distribution and varying across underlying groups. NRD, while hindering progress at higher quantiles, supports eco‐innovation at lower quantiles of advanced economies. Panel threshold estimations confirm the positive influence of both globalization and NRD, particularly for highly eco‐innovative nations. These findings bear significant policy implications, charting a path toward sustainable economic growth through the widespread adoption of green technology.

PEMETAAN KERAWANAN BANJIR DI KOTA SURAKARTA

&lt;p class="Keywords"&gt;&lt;em&gt;&lt;span lang="EN-GB"&gt;Floods have become one of the problems occurring in the city of Surakarta. The floods are caused by the overflow of the tributaries of the Bengawan Solo River, located within the Surakarta City area. One of the strategies for flood disaster management is through flood zone mapping. In flood vulnerability mapping, hydrological analysis is conducted to determine the magnitude of the flood discharge for different return periods (Q&lt;sub&gt;2&lt;/sub&gt;, Q&lt;sub&gt;5&lt;/sub&gt;, Q&lt;sub&gt;10&lt;/sub&gt;, Q&lt;sub&gt;25&lt;/sub&gt;, and Q&lt;sub&gt;50&lt;/sub&gt;) using the HSS SCS method, which will serve as input for the HEC-RAS program to simulate floods, used as determining variables in flood vulnerability mapping. The results of the flood simulation analysis indicate that the level of vulnerability in non-vulnerable areas has a flood area limit ranging from 1.05 ha to 8.93 ha, a maximum flood depth limit ranging from 0.24 m to 0.57 m, and a maximum flood velocity limit ranging from 0.06 m/sec to 0.21 m/sec. For areas with low vulnerability, the flood area limit ranges from 8.93 ha to 16.80 ha, the maximum flood depth limit ranges from 0.57 m to 0.90 m, and the maximum flood velocity limit ranges from 0.21 m/sec to 0.35 m/sec. For areas with moderate vulnerability, the flood area limit ranges from 16.80 ha to 24.68 ha, the maximum flood depth limit ranges from 0.90 m to 1.23 m, and the maximum flood velocity limit ranges from 0.35 m/sec to 0.50 m/sec. For areas with high vulnerability, the flood area limit ranges from 24.68 ha to 32.55 ha, the maximum flood depth limit ranges from 1.23 m to 1.55 m, and the maximum flood velocity limit ranges from 0.50 m/sec to 0.64 m/sec.&lt;/span&gt;&lt;/em&gt;&lt;/p&gt;

How studentification reshapes social vulnerability in urban Korea: Impressions based on regeneration and shrinkage

This work examines to what degree studentification may be associated with social vulnerability and be influenced by urban regeneration and shrinkage across four anchor institutions and their surrounding areas. Focusing on four South Korean metropolitan cities and utilizing longitudinal secondary data concerning studentification, social vulnerability, and shrinkage, we conducted correlation analysis and comparative case studies. Our findings demonstrate that each campus and surrounding areas reflected distinct urban phenomenon, particularly in terms of the relationships among studentification, social vulnerability, urban shrinkage, and urban regeneration. Of notable concern, student influx (as a representative studentification outcome) was determined to be a driver in influencing lower social vulnerability and less shrinkage. In this vein, our study provides initial insights for policymakers and urban residents in potentially addressing urban challenges and studentification in combination with urban regeneration and shrinkage.

Groundwater pollution vulnerability assessment using a modified DRASTIC model in Ho Chi Minh City, Vietnam

AbstractThe Pleistocene aquifer serves as a vital water source for various activities in Ho Chi Minh City, Vietnam, including concentrated and individual exploitation. In the present study, the DRASTIC (D, depth of water; R, net recharge; A, aquifer media; S, soil media; T, topography; I, impact of vadose zone; C, hydraulic conductivity) model was used to evaluate the groundwater sensitivity of the study area. To analyse Ho Chi Minh City's Upper‐Middle Pleistocene aquifer vulnerability, the analytic hierarchy process (AHP) method was used to optimize the DRASTIC score and include land use (LU) characteristics. Four distinct weights were used: DRASTIC, modified DRASTIC‐LU, AHP‐DRASTIC and modified AHP‐DRASTIC‐LU. This study identified low, moderate and high vulnerability for 12%, 55% and 33% of the DRASTIC‐LU index values, respectively. The AHP‐DRASTIC index classifies 61%, 26% and 13% of sites as low, moderate and highly vulnerable, respectively. The study reveals that 52%, 30% and 18% of the area are vulnerable to the modified AHP‐DRASTIC‐LU index classes. The most sensitive factors are shallow aquifer roofs, recharge and LU. The real‐world accuracy of the DRASTIC models was tested using 106 groundwater nitrate concentrations. The modified AHP‐DRASTIC‐LU is the most accurate and appropriate model for the current research region.

Shore Power System Load Forecasting Model based on IDBO algorithm and PCA-BiLSTM Network

Shore power offers convenient and sustainable power services to ships docking in ports, playing a pivotal role in advancing environmental preservation and the effective utilization of resources. Shore power system loads are susceptible to numerous influencing factors. Therefore, it is crucial to identify a highly accurate load forecasting model to facilitate the judicious allocation of power resources. A short-term shore power system load forecasting model based on the improved dung beetle optimization algorithm (IDBO) and principal component analysis (PCA) in conjunction with the enhanced bidirectional long and short-term memory (BiLSTM) network is proposed in this paper. The IDBO algorithm improves the problem of low convergence accuracy and vulnerability to local optima in DBO. This is achieved by population initialization based on cubic chaotic mapping, amalgamating walrus optimization algorithm, and adaptive t-distribution perturbation strategy. The performance of the BiLSTM, IDBO-BiLSTM, and PCA-IDBO-BiLSTM shore power system load forecasting models are compared through case studies, revealing that the proposed PCA-IDBO-BiLSTM model demonstrates superior predictive capabilities.