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Introduction Urban flooding is caused by the lack of capacity of the harvesting channel network and occurs when the amount of precipitation exceeds the network's capacity. One of the two main factors contributing to the aggravation of damage caused by urban floods is population growth and the expansion of urbanization, and the second factor is heavy rainfall caused by climate change, which plays an essential role in intensifying and accelerating the hydrological cycle and may change the amount and frequency of precipitation. This factor affects the probability of flooding, runoff volume, and peak flow. It is more visible in arid and semi-arid areas where rainfall usually occurs briefly but with high intensity. Urban flooding is a growing threat to urban infrastructure and public health, posing significant challenges to urban resilience and sustainability. One of the urban infrastructures that is of great importance is the runoff collection network. The increase of impervious surfaces wear and tear on the network. changes in the rainfall pattern due to climate change have increased the occurrence of urban floods and raised the importance of network redesign to minimize the system vulnerability. Materials and Methods In this research, the runoff harvesting network of ten districts of Tehran Municipality was redesigned and optimized. This area, with a population of 327,000 people, is located in the relatively dense fabric of the Tehran metropolis, and its area is 807 ha. Simulating the runoff collection network and checking the performance of the network by two indicators of vulnerability and reliability requires an accurate model with great details. For this purpose, in this research, SWMM version 5.1 software was used to simulate the runoff collection network. The study area was divided into 285 sub-basins to simulate the sub-channels in better detail. Then, information such as slope, area, and percentage of impervious space was introduced through ArcMap software version 10.3.1 as information under the watersheds. The sub-watershed width parameter was calculated by dividing the sub-watershed area by its most significant length using Q-GIS software and applied to the sub-basins. The LARS-WG model has also been used for the exponential micro-scale output of climate models. To simulate the network in the current or present situation, the historical precipitation information of the Mehrabad synoptic station was used, and to affect the network in future conditions, the precipitation information of the climate models of the sixth climate change report was used. Among the predictions of climate models, the most incremental changes in threshold precipitation were selected as a pessimistic scenario, and a system redesign was done to reduce vulnerability under this scenario. Results and Discussion This study was conducted to assess the performance of Tehran municipality's runoff collection network under current and future conditions. The SWMM hydraulic model was employed to simulate the network under various rainfall scenarios. Current conditions revealed a total runoff volume of 45.9, 51.14, and 59.7 thousand m3 for return periods of 2, 5, and 10 years, respectively. This runoff volume resulted in a vulnerability increase from 10.4 to 12.2% and a reliability reduction from 97.5 to 95.8%. To evaluate the network's performance under future climate change scenarios, the SWMM model was used with data from the IPCC sixth assessment report. Among the top five climate models, the one with the highest precipitation was selected as the pessimistic scenario. Simulation results under future conditions indicated a significant runoff volume increase, reaching 64.04 and 72.18 thousand m3 in 5- and 10-year return periods, respectively. This increase corresponded to vulnerability indices of 12.7 and 13.9% and reliability indices of 95.3 and 94.3% for the same return periods. To enhance the network's resilience, a genetic algorithm-based optimization approach was employed. Cost, reliability, and vulnerability index were considered optimization objectives with specific weighting functions. The algorithm converged to an optimal design solution in the 168th iteration, resulting in a 7.6% vulnerability reduction and a 98.1% reliability enhancement. Conclusion The vulnerability index in the return periods of 5 and 10 years is equal to 12.7 % and 13.9 %, respectively, and the reliability index is equal to 95.3 % and 94.3 %. After checking the network in its current state and future conditions, an optimal network redesign was done to improve system performance in both current and future conditions. For this purpose, the genetic algorithm was used for optimization, and the objective function consisting of cost, vulnerability index, and reliability index and the importance weights of each, were defined. Then, MATLAB software did the optimal network redesign by connecting the simulator and optimizer model. The results showed that in the 168th iteration, the algorithm reached its final answer of 0.3, which remained constant until the 300th iteration. Also, the optimal redesign has reduced network vulnerability by 7.6% and increased reliability by 98.1%. This research showed that with an optimal redesign and solving the current network problems, the system's ability to face future climate change threats could also be increased. Of course, spending the least money to achieve the best result was one of the main goals of this research. In future studies, it is recommended to use low-impact development tools along with optimal redesign to fix defects and improve the performance of the runoff collection network.

Introduction Urban flooding is caused by the lack of capacity of the harvesting channel network and occurs when the amount of precipitation exceeds the network's capacity. One of the two main factors contributing to the aggravation of damage caused by urban floods is population growth and the expansion of urbanization, and the second factor is heavy rainfall caused by climate change, which plays an essential role in intensifying and accelerating the hydrological cycle and may change the amount and frequency of precipitation. This factor affects the probability of flooding, runoff volume, and peak flow. It is more visible in arid and semi-arid areas where rainfall usually occurs briefly but with high intensity. Urban flooding is a growing threat to urban infrastructure and public health, posing significant challenges to urban resilience and sustainability. One of the urban infrastructures that is of great importance is the runoff collection network. The increase of impervious surfaces wear and tear on the network. changes in the rainfall pattern due to climate change have increased the occurrence of urban floods and raised the importance of network redesign to minimize the system vulnerability. Materials and Methods In this research, the runoff harvesting network of ten districts of Tehran Municipality was redesigned and optimized. This area, with a population of 327,000 people, is located in the relatively dense fabric of the Tehran metropolis, and its area is 807 ha. Simulating the runoff collection network and checking the performance of the network by two indicators of vulnerability and reliability requires an accurate model with great details. For this purpose, in this research, SWMM version 5.1 software was used to simulate the runoff collection network. The study area was divided into 285 sub-basins to simulate the sub-channels in better detail. Then, information such as slope, area, and percentage of impervious space was introduced through ArcMap software version 10.3.1 as information under the watersheds. The sub-watershed width parameter was calculated by dividing the sub-watershed area by its most significant length using Q-GIS software and applied to the sub-basins. The LARS-WG model has also been used for the exponential micro-scale output of climate models. To simulate the network in the current or present situation, the historical precipitation information of the Mehrabad synoptic station was used, and to affect the network in future conditions, the precipitation information of the climate models of the sixth climate change report was used. Among the predictions of climate models, the most incremental changes in threshold precipitation were selected as a pessimistic scenario, and a system redesign was done to reduce vulnerability under this scenario. Results and Discussion This study was conducted to assess the performance of Tehran municipality's runoff collection network under current and future conditions. The SWMM hydraulic model was employed to simulate the network under various rainfall scenarios. Current conditions revealed a total runoff volume of 45.9, 51.14, and 59.7 thousand m3 for return periods of 2, 5, and 10 years, respectively. This runoff volume resulted in a vulnerability increase from 10.4 to 12.2% and a reliability reduction from 97.5 to 95.8%. To evaluate the network's performance under future climate change scenarios, the SWMM model was used with data from the IPCC sixth assessment report. Among the top five climate models, the one with the highest precipitation was selected as the pessimistic scenario. Simulation results under future conditions indicated a significant runoff volume increase, reaching 64.04 and 72.18 thousand m3 in 5- and 10-year return periods, respectively. This increase corresponded to vulnerability indices of 12.7 and 13.9% and reliability indices of 95.3 and 94.3% for the same return periods. To enhance the network's resilience, a genetic algorithm-based optimization approach was employed. Cost, reliability, and vulnerability index were considered optimization objectives with specific weighting functions. The algorithm converged to an optimal design solution in the 168th iteration, resulting in a 7.6% vulnerability reduction and a 98.1% reliability enhancement. Conclusion The vulnerability index in the return periods of 5 and 10 years is equal to 12.7 % and 13.9 %, respectively, and the reliability index is equal to 95.3 % and 94.3 %. After checking the network in its current state and future conditions, an optimal network redesign was done to improve system performance in both current and future conditions. For this purpose, the genetic algorithm was used for optimization, and the objective function consisting of cost, vulnerability index, and reliability index and the importance weights of each, were defined. Then, MATLAB software did the optimal network redesign by connecting the simulator and optimizer model. The results showed that in the 168th iteration, the algorithm reached its final answer of 0.3, which remained constant until the 300th iteration. Also, the optimal redesign has reduced network vulnerability by 7.6% and increased reliability by 98.1%. This research showed that with an optimal redesign and solving the current network problems, the system's ability to face future climate change threats could also be increased. Of course, spending the least money to achieve the best result was one of the main goals of this research. In future studies, it is recommended to use low-impact development tools along with optimal redesign to fix defects and improve the performance of the runoff collection network. [ABSTRACT FROM AUTHOR]

The impacts of natural hazards on education systems are an understudied subject. This paper conducts a systematic review of the literature on multiple hazards and school education to explore hazard-induced disruptions in school education and the determinants of its vulnerability and resilience. The PRISMA framework is adopted to examine how indicators of vulnerability are clustered into various components for school vulnerability assessment. As the involvement of school children is vitally important in disaster risk reduction (DRR) and management at the local, regional, and global levels for resilience development in school education, the study also reviews school children's participation in initiatives from various international organizations. The review results show that participation by children in DRR is generally weak due to the authoritarian tendency of society and lack of trust in the ability of children. It is observed that damaged infrastructure causes school closures for prolonged periods, leading to discontinuity of education. The review also highlights the lack of policies in implementing the Comprehensive School Safety Framework and the role of social networks in building resilience at schools. Future research should provide clear guidelines on children's participation in DRR and the linkage between disruptions of school education and critical structural damage due to disasters. [ABSTRACT FROM AUTHOR]

ABSTRACTThe impacts of natural hazards on education systems are an understudied subject. This paper conducts a systematic review of the literature on multiple hazards and school education to explore hazard-induced disruptions in school education and the determinants of its vulnerability and resilience. The PRISMA framework is adopted to examine how indicators of vulnerability are clustered into various components for school vulnerability assessment. As the involvement of school children is vitally important in disaster risk reduction (DRR) and management at the local, regional, and global levels for resilience development in school education, the study also reviews school children’s participation in initiatives from various international organizations. The review results show that participation by children in DRR is generally weak due to the authoritarian tendency of society and lack of trust in the ability of children. It is observed that damaged infrastructure causes school closures for prolonged periods, leading to discontinuity of education. The review also highlights the lack of policies in implementing the Comprehensive School Safety Framework and the role of social networks in building resilience at schools. Future research should provide clear guidelines on children’s participation in DRR and the linkage between disruptions of school education and critical structural damage due to disasters.

Background: Findings of reward disturbances in unaffected relatives of patients with schizophrenia suggest reward disturbances as an endophenotype for schizophrenia. Twin studies, where 1 twin has been diagnosed with a schizophrenia spectrum disorder, can further explore this. Methods: We used Danish registries to identify twin pairs with at least 1 twin having a schizophrenia spectrum disorder diagnosis and control twin pairs matched on age, sex, and zygosity. The analyses included data from 34 unaffected co-twins (16 females), 42 probands with schizophrenia spectrum disorder (17 females), and 83 control twins (42 females). Participants performed a modified incentive delay task during functional magnetic resonance imaging. Whole-brain group differences were analyzed by performing comparisons between co-twins and control twins. Correlations with cognitive flexibility were tested. Results: Compared with control twins, co-twins showed no differences in striatal regions, but increased signal in the dorsolateral prefrontal cortex (DLPFC) during missed target contrast was observed. In co-twins, increased DLPFC signal was associated with lower intra-extra dimensional set-shifting scores indicative of higher cognitive flexibility. Conclusions: Unaffected co-twins did not have decreased striatal activity during anticipation as previously reported for patients with schizophrenia. Instead, they showed increased activity in the DLPFC during evaluation of missed target contrast, which correlated with their level of cognitive flexibility. Unaffected co-twins had no diagnosis at a mean age of 40 years. This could indicate that greater cognitive flexibility and increased activity in the right DLPFC during processing of unexpected negative outcome represents a compensatory resilience mechanism in predisposed twins.

In this study, we explore climate justice with specific reference to vulnerability at the level of different sectors in 90 developing countries in Asia, Africa and Europe, over a period from 2010 to 2019. The paper seeks to advance the discussion on the idea that adaptation financing is allocated according to the level of need in the recipient country and sector. By considering five crucial sectors (food, water, health, infrastructure and habitat), we explore the linear and quadratic effect of the vulnerability of each sector on the allocated endowment. The study is based on a dynamic panel regression method based on the Generalized Method of Moments (GMM) in the system model. Our findings reveal that vulnerability is an important consideration in funding allocation. The results suggest that the relationship between adaptation funding and vulnerability is sector-dependent. We also observe that this relationship is non-linear, providing further evidence of distributive justice in terms of allocating more funding to the most vulnerable sectors. Climate justice begins to emerge when vulnerability reaches a certain threshold. However, it appears that the infrastructure sector is dysfunctional in terms of adaptation financing needs and investments undertaken. Overall, the regulations put in place should further integrate climate risk parameters into technical and procedural standards to make projects more effective and climate justice more widespread. [ABSTRACT FROM AUTHOR]

The term vulnerability has been used in a variety of contexts, including climate change impact assessment. This study aimed to set up and evaluate climate change vulnerability indicators (CCVI) of agricultural zones based on exposure, sensitivity, and adaptation capacity to climate change in Ho Chi Minh city. Data from consultations with 10 experts was collected and analysed by the analysis hierarchy process (AHP). The CCVI, which includes 3 primary indicators and 22 secondary indicators, was applied to the agricultural districts in Ho Chi Minh that have been demonstrated to be the most vulnerable to climate change under both current conditions and over a longer timescales under various climate change exposure scenarios. The CCVI was weighted to support the climate change vulnerability assessment and indicate comparatively low or high climate change vulnerability areas. Finally, the areas most needy of further adaptation activities for agriculture in Ho Chi Minh city were identified.

With increasing population growth and urban sprawl, many coastal lowlands are unprecedentedly vulnerable to climate change and its impacts, such as rising sea levels, increasing extreme storm events, and coastal flooding. Quantifying coastal flood vulnerability serves as a tool to identify a system’s weakness, monitor its change, and support making targeted climate adaptation policies. The assessment framework proposed in this research uses principal component analysis (PCA) and a weighting method to build a composite indicator of flood vulnerability index and evaluate the vulnerability for 256 coastal census tracts and 24 municipalities along the coast of Connecticut, USA. The research uses Keiser-Meyer-Olkin (KMO) test and Bartlett’s test of sphericity to test sample adequacy and performs data standardization for all indicators. Through PCA, 30 coastal vulnerability-related indicators were grouped into four major dimensions: hazard exposure, socio-economic, physical/land use and land cover, and natural. The findings highlight the variations of flood vulnerability across highly urbanized areas, suburban areas, and rural areas; and the gradient from coastal low-elevation region to high-elevation inland area. This variance is unevenly caused by different dimensions although they may trade-off with each other when aggregated, the dominant dimensions play a significant or decisive role in the vulnerability assessment. This research built an automatic and objective assessment framework that is flexible enough to be applied at a smaller scale so as to obtain detailed analysis and it can be used as a decision-making support system.

Integrated community energy system (ICES) enables multi-energy synergy and impulses interaction of integrated demand response (IDR) from the demand side. Nevertheless, the randomness lies in IDR resources and renewable generation in the ICES configuration issue still lacks thorough analysis. Furthermore, the widely focused concern of resiliency urges more proactive consideration of potential emergencies at the planning stage of ICES. To this end, this paper proposed a resilience-oriented stochastic ICES configuration framework considering IDR influence. First, generalized IDR models are set up in detail with elaborate fuzzy feature analysis of price-responsive multi-energy loads. Then, the vulnerability indicators of tie lines and converting devices of the ICES are first introduced to demonstrate the occasional outage in the normal operation and blackout in the emergent case. In addition, the worst-case conditional value-at-risk (WCVaR) theory is innovatively integrated to the traditional risk-neutral model, which is formulated as a two-stage stochastic chance-constrained programming problem, aiming to combine portfolio with minimizing the worst-case cost caused by a disaster. The models are then transformed into mixed-integer linear programming problems via several linearization techniques. Finally, the results of the case studies showed the established model's superiority in improving the overall economy (reduce 6.94% of the worst-case cost) and system resiliency (decrease the value-at-risk by 3.20%) against an unforeseen hurricane. Meanwhile, the ICES reliability was also improved by declining 25.65% of unintended load curtailment faced with an emergency. The proposed approach provided an efficient preventive portfolio scheme of the IDR-integrated ICES for decision-makers' different risk preferences of natural hazards.

The risk of climate change impacts occurring is a function of a socioecological system’s exposure and vulnerability to climate-related hazards. Vulnerability itself is the result of a system's sensitivity and adaptive capacity. The potential climate change driven biophysical impacts on the municipality Totonicapán in the western highlands of Guatemala are well documented in outcome vulnerability studies and projected to be severe. They include droughts, frosts, forest fires and life zone changes which also represent important hazards to the municipality’s population. Yet, recent detailed socioeconomic information on the municipality’s contextual vulnerability is scarce. Moreover, social capital which is central to the yet successful management of the unique communal coniferous forests is poorly understood. The present study evaluates the contextual vulnerability of the municipality's population and communal forests using 5 interviews and 167 household surveys from 3 communities for 15 socioeconomic indicators. Qualitative analysis of the interviews urges for further investigation into the link between emigration to the USA, the loss of social capital and communal forest management. Quantitative analysis of the indicators and their aggregation into a vulnerability index by Principal Component Analysis demonstrates that education is the most important vulnerability factor, followed by income which was negatively related to natural resource dependency. An overarching theme was gender inequality. The study is a plea for location and population specific research and adaptation strategies as it identifies significant differences even between communities of the same municipality.

Highlights • A new hierarchical model to assess climate vulnerability of urban systems. • A new approach to assess climate impact on urban systems. • Implementation of the climatic vulnerability and impact models to heat wave and pluvial flooding. • Testing on an urban area in the municipality of Naples (Italy). Abstract We present a hierarchical model developed for assessing the climate vulnerability and impact on the urban system to heat wave and pluvial flooding phenomena, which allows measuring the "urban performances" under extreme climate conditions by applying physical, environmental and socioeconomic indicators. The model is structured in five levels, from the knowledge of heterogeneous urban characteristics extracted from different sources to the vulnerability indicators; each level provides a synthesis of the information of the previous level. Moreover, given the complexity of the urban system, the model is based on the identification of three subsystems, in order to gradually synthesize information from one level to another. Each indicator, and the associated data and characteristics, are related to a specific subsystem, which represents technological/environmental (building, open spaces) or socioeconomic dimension (population). Finally, we assess the impacts related to a climate hazard scenario by evaluating the combination of the hazard with the exposure and the vulnerability of the subsystems with which the exposure interacts. We test our method on the study area of East Naples (Italy) by considering heat wave and pluvial flooding climate hazard scenarios; specific functions are performed in a GIS environment to implement the spatial analysis processes need to build the vulnerability indicators and impact scenarios. The results show that the distribution of vulnerability and impacts are related to the local urban context and to the hazard scenario considered. The proposed model can be applied to assess the benefits of adaptive solution design. [ABSTRACT FROM AUTHOR]

The risk of climate change impacts occurring is a function of a socioecological system's exposure and vulnerability to climate-related hazards. Vulnerability itself is the result of a system's sensitivity and adaptive capacity. The potential climate change driven biophysical impacts on the municipality Totonicapán in the western highlands of Guatemala are well documented in outcome vulnerability studies and projected to be severe. They include droughts, frosts, forest fires and life zone changes which also represent important hazards to the municipality's population. Yet, recent detailed socioeconomic information on the municipality's contextual vulnerability is scarce. Moreover, social capital which is central to the yet successful management of the unique communal coniferous forests is poorly understood. The present study evaluates the contextual vulnerability of the municipality's population and communal forests using 5 interviews and 167 household surveys from 3 communities for 15 socioeconomic indicators. Qualitative analysis of the interviews urges for further investigation into the link between emigration to the USA, the loss of social capital and communal forest management. Quantitative analysis of the indicators and their aggregation into a vulnerability index by Principal Component Analysis demonstrates that education is the most important vulnerability factor, followed by income which was negatively related to natural resource dependency. An overarching theme was gender inequality. The study is a plea for location and population specific research and adaptation strategies as it identifies significant differences even between communities of the same municipality. [ABSTRACT FROM AUTHOR]

In recent years, Germany has seen an increase in forest fires, and many fires have occurred in military training areas that are difficult to access for firefighting. While casualties are still low and mostly restricted to firefighting personnel, settlements are also increasingly threatened. Increasing impacts from extreme events due to climate change will likely increase the ignition and spread of fires. More people are being affected by fires, and in need of external help to evacuate and cope with the resulting damages and losses. Forest fires also threaten military training site, with additional risks created by the presence of ammunition depots. Despite this hazard scenario, Germany so far lacks an overview of the spatial occurrence of forest fires and the related risk. This study develops a spatial exposure and social vulnerability assessment of settlements to forest fires in Germany. The results reveal that social vulnerability is an important variable in determining which settlements are potentially exposed to fire. Areas exposed to fire risk are characterised by having a higher proportion of women, a higher-than-average age, a higher number of young people under 18 years, a higher number of persons over 65 years and foreigners than the national average. Furthermore, exposed communities are characterised by higher rates of unoccupied housing units and lower living space per dwelling, as well as living in areas with high population densities within forested areas. The results can help to improve emergency management and spatial planning to prevent the development of fire risk areas. [ABSTRACT FROM AUTHOR]

Irrigated agriculture is an important economic factor in the rural parts of the metropolitan area of Hamburg. It is commonly expected that climate change will reduce the groundwater quantities available for field irrigation. Against this background, the ratio of irrigation need and groundwater recharge (IGR-ratio) is suggested as an indicator to assess climate change impacts on the vulnerability of groundwater resources towards overexploitation by agricultural irrigation. The IGR-ratio has been assessed based on the distributed water balance model mGROWA, i.e. under consideration of the simulated groundwater recharge levels and the field crop-specific irrigation need of the commonly cultivated field crops. The spatial IGR-ratio distribution determined for the observed reference period 1971–2000 has shown that the delineated vulnerable areas coincide with the regions for which high irrigation quantities have been documented at present. Additionally, the IGR-ratio depicts the areas in which irrigation is currently still negligible, but in which the introduction of irrigation into agricultural practice would lead to an immediate overexploitation of the sustainably available groundwater budget. The possible impact of future climate on IGR-ratios was determined by using a model chain of mGROWA and the regional climate models REMO and WETTREG2010. The related ensemble simulations did not provide a uniform tendency of possible future IGR-ratio changes. Whereas the mGROWA–WETTREG2010 realisations projected a very high increase in the IGR-ratios, the mGROWA–REMO realisations did not show a pronounced trend of increasing IGR-ratios. Therefore, considerable uncertainties remain regarding the future bandwidth of IGR-ratio changes. [ABSTRACT FROM AUTHOR]

• Comprehensive vulnerability assessment serves as a decision-making support system. • Coastal vulnerability varies across urbanized areas, suburban and rural areas. • Socio-economic factors and hazard exposure significantly influence vulnerability. • Applying the assessment at a smaller scale helps identify targeted decision-making. With increasing population growth and urban sprawl, many coastal lowlands are unprecedentedly vulnerable to climate change and its impacts, such as rising sea levels, increasing extreme storm events, and coastal flooding. Quantifying coastal flood vulnerability serves as a tool to identify a system's weakness, monitor its change, and support making targeted climate adaptation policies. The assessment framework proposed in this research uses principal component analysis (PCA) and a weighting method to build a composite indicator of flood vulnerability index and evaluate the vulnerability for 256 coastal census tracts and 24 municipalities along the coast of Connecticut, USA. The research uses Keiser-Meyer-Olkin (KMO) test and Bartlett's test of sphericity to test sample adequacy and performs data standardization for all indicators. Through PCA, 30 coastal vulnerability-related indicators were grouped into four major dimensions: hazard exposure, socio-economic, physical/land use and land cover, and natural. The findings highlight the variations of flood vulnerability across highly urbanized areas, suburban areas, and rural areas; and the gradient from coastal low-elevation region to high-elevation inland area. This variance is unevenly caused by different dimensions although they may trade-off with each other when aggregated, the dominant dimensions play a significant or decisive role in the vulnerability assessment. This research built an automatic and objective assessment framework that is flexible enough to be applied at a smaller scale so as to obtain detailed analysis and it can be used as a decision-making support system. [ABSTRACT FROM AUTHOR]

Background: Findings of reward disturbances in unaffected relatives of patients with schizophrenia suggest reward disturbances as an endophenotype for schizophrenia. Twin studies, where 1 twin has been diagnosed with a schizophrenia spectrum disorder, can further explore this., Methods: We used Danish registries to identify twin pairs with at least 1 twin having a schizophrenia spectrum disorder diagnosis and control twin pairs matched on age, sex, and zygosity. The analyses included data from 34 unaffected co-twins (16 females), 42 probands with schizophrenia spectrum disorder (17 females), and 83 control twins (42 females). Participants performed a modified incentive delay task during functional magnetic resonance imaging. Whole-brain group differences were analyzed by performing comparisons between co-twins and control twins. Correlations with cognitive flexibility were tested., Results: Compared with control twins, co-twins showed no differences in striatal regions, but increased signal in the dorsolateral prefrontal cortex (DLPFC) during missed target contrast was observed. In co-twins, increased DLPFC signal was associated with lower intra-extra dimensional set-shifting scores indicative of higher cognitive flexibility., Conclusions: Unaffected co-twins did not have decreased striatal activity during anticipation as previously reported for patients with schizophrenia. Instead, they showed increased activity in the DLPFC during evaluation of missed target contrast, which correlated with their level of cognitive flexibility. Unaffected co-twins had no diagnosis at a mean age of 40 years. This could indicate that greater cognitive flexibility and increased activity in the right DLPFC during processing of unexpected negative outcome represents a compensatory resilience mechanism in predisposed twins., (© 2022 The Authors.)

The increasing vulnerability of coastal areas to climate change and hazards has the authorities seeking the best way to protect the population. Because it is essential for citizens to contribute to their own protection, it is increasingly necessary to take their knowledge and perceptions into account. Two studies have been conducted to identify the factors that could explain protective behavior regarding coastal erosion and flooding risks: a qualitative study using interviews with 32 inhabitants of municipalities exposed to coastal risks, and a quantitative study using questionnaires with 237 participants exposed to coastal erosion (N = 116) and coastal flooding (N = 121). Thematic content and statistical analysis (multiple linear regression) have been used. The results indicate that flooding is considered a personal risk with heightened involvement, and thus necessitates personal action for self-protection. Erosion appears to be a collective risk, impacting all inhabitants, as well as being slower and easier to anticipate. The physical characteristics of the phenomenon will thus determine the dynamics and functioning of social knowledge on risk assessment and on the motivation to protect oneself. [ABSTRACT FROM AUTHOR]