Assessment of Wet Season Precipitation in the Central United States by the Regional Climate Simulation of the WRFG Member in NARCCAP and Its Relationship with Large-Scale Circulation Biases

Assessment of past-climate simulations of regional climate models(RCMs)is important for understanding the reliability of RCMs when used to project future regional climate.Here,we assess the performance and discuss possible causes of biases in a WRF-based RCM with a grid spacing of 50 km,named WRFG,from the North American Regional Climate Change Assessment Program(NARCCAP)in simulating wet season precipitation over the Central United States for a period when observational data are available.The RCM reproduces key features of the precipitation distribution characteristics during late spring to early summer,although it tends to underestimate the magnitude of precipitation.This dry bias is partially due to the model’s lack of skill in simulating nocturnal precipitation related to the lack of eastward propagating convective systems in the simulation.Inaccuracy in reproducing large-scale circulation and environmental conditions is another contributing factor.The too weak simulated pressure gradient between the Rocky Mountains and the Gulf of Mexico results in weaker southerly winds in between,leading to a reduction of warm moist air transport from the Gulf to the Central Great Plains.The simulated low-level horizontal convergence fields are less favorable for upward motion than in the NARR and hence,for the development of moist convection as well.Therefore,a careful examination of an RCM’s deficiencies and the identification of the source of errors are important when using the RCM to project precipitation changes in future climate scenarios.

Natural Disaster Risk Monitoring for Immovable Cultural Relics Based on Digital Twin

Natural disaster risk monitoring is an important task for disaster prevention and reduction.In the case of immovable cultural relics,however,the feedback mechanism,risk factors,monitoring logic,and monitoring indicators of natural disaster risk monitoring are complex.How to achieve intelligent perception and monitoring of natural disaster risk for immovable cultural relics has always been a focus and a challenge for researchers.Based on the analysis of the concepts and issues related to the natural disaster risk of immovable cultural relics,this paper proposes a framework for natural disaster risk monitoring for immovable cultural relics based on the digital twin.This framework focuses on risk monitoring,including the physical entities of natural disaster risk for immovable cultural relics,monitoring indicators,and virtual entity construction.A platform for monitoring the natural disaster risk of immovable cultural relics is proposed.Using the Puzhou Ancient City Site as a test bed,the proposed concept can be used for monitoring the natural disaster risk of immovable cultural relics at different scales.

Local Volunteerism and Resilience Following Large-Scale Disaster: Outcomes for Health Support Team Volunteers in Haiti

In-depth interviews with local Haitian volunteers trained in a psychological disaster recovery program called Health Support Team(HST) provide insight into the psychosocial outcomes resulting from their engagement with the program. Qualitative interviews were conducted with four male Haitian participants who had survived the January 2010 Haiti earthquake and had worked as HST volunteers for at least 6 months. Interviews were analyzed using narrative inquiry analysis, which allows individuals to discover and disclose a deeper meaning in their experience and enables the researchers to access more detailed data. Previous research supports the claim that volunteerism provides many important psychological benefits, and the results of the present study suggest that among survivors of large-scale disasters, volunteerism is beneficial as a means of increasing psychological resilience and facilitating personal recovery. Results and themes of our analysis included a reported increase in both hope and purpose for the respondents. Findings suggest that volunteerism on the part of members of the surviving community following large-scale disaster increases resilience among the volunteers and further contributes to their recovery.

Overview on Mangrove Forest Disaster Prevention and Mitigation Functions

This paper provides a comprehensive overview on coastal protection and hazard mitigation by mangroves.Previous stud-ies have made great strides to understand the mechanisms and influencing factors of mangroves’protection function,including wave energy dissipation,storm surge damping,tsunami mitigation,adjustment to sea level rise and wind speed reduction,which are sys-tematically summarized in this study.Moreover,the study analyzes the extensive physical models,based on indoor flume experi-ments and numerical models,that consider the interaction between mangroves and hydrodynamics,to help our understanding of mangrove-hydrodynamic interactions.Additionally,quantitative approaches for valuing coastal protection services provided by man-groves,including index-based and process-resolving approaches,are introduced in detail.Finally,we point out the limitations of previous studies,indicating that efforts are still required for obtaining more long-term field observations during extreme weather events,to create more real mangrove models for physical experiments,and to develop numerical models that consider the flexible properties of mangroves to better predict wave propagation in mangroves having complex morphology and structures.

A Two-Layer Encoding Learning Swarm Optimizer Based on Frequent Itemsets for Sparse Large-Scale Multi-Objective Optimization

Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.

Enhancing Evolutionary Algorithms With Pattern Mining for Sparse Large-Scale Multi-Objective Optimization Problems

Sparse large-scale multi-objective optimization problems(SLMOPs)are common in science and engineering.However,the large-scale problem represents the high dimensionality of the decision space,requiring algorithms to traverse vast expanse with limited computational resources.Furthermore,in the context of sparse,most variables in Pareto optimal solutions are zero,making it difficult for algorithms to identify non-zero variables efficiently.This paper is dedicated to addressing the challenges posed by SLMOPs.To start,we introduce innovative objective functions customized to mine maximum and minimum candidate sets.This substantial enhancement dramatically improves the efficacy of frequent pattern mining.In this way,selecting candidate sets is no longer based on the quantity of nonzero variables they contain but on a higher proportion of nonzero variables within specific dimensions.Additionally,we unveil a novel approach to association rule mining,which delves into the intricate relationships between non-zero variables.This novel methodology aids in identifying sparse distributions that can potentially expedite reductions in the objective function value.We extensively tested our algorithm across eight benchmark problems and four real-world SLMOPs.The results demonstrate that our approach achieves competitive solutions across various challenges.

Large-scale model testing of high-pressure grouting reinforcement for bedding slope with rapid-setting polyurethane

Bedding slope is a typical heterogeneous slope consisting of different soil/rock layers and is likely to slide along the weakest interface.Conventional slope protection methods for bedding slopes,such as retaining walls,stabilizing piles,and anchors,are time-consuming and labor-and energy-intensive.This study proposes an innovative polymer grout method to improve the bearing capacity and reduce the displacement of bedding slopes.A series of large-scale model tests were carried out to verify the effectiveness of polymer grout in protecting bedding slopes.Specifically,load-displacement relationships and failure patterns were analyzed for different testing slopes with various dosages of polymer.Results show the great potential of polymer grout in improving bearing capacity,reducing settlement,and protecting slopes from being crushed under shearing.The polymer-treated slopes remained structurally intact,while the untreated slope exhibited considerable damage when subjected to loads surpassing the bearing capacity.It is also found that polymer-cemented soils concentrate around the injection pipe,forming a fan-shaped sheet-like structure.This study proves the improvement of polymer grouting for bedding slope treatment and will contribute to the development of a fast method to protect bedding slopes from landslides.

Challenges in Implementing DVI Best Practice Guidelines Due to the Brumadinho Disaster

The broke of a tailings dam from Vale S.A. destroyed a gigantic area killing 270 persons in Brumadinho/Brazil. Organizing activities after a mass disaster is a complex process that requires the involvement of many people and resources in the laboratory. To DNA identification of the victims, a daunting work had to be made by DNA laboratory staff in a collaborative effort with many partners. Efforts to implement good practice guidelines in Disaster Victim Identification (DVI) have revealed several important aspects that need to change in the forensic DNA laboratory. This article highlights the challenges of implementing DVI best practice guidelines in resource-poor settings, but with professionals from different sectors engaged in the same goal of briefly identifying victims and helping families and society.

Risk assessment of coastal flooding disaster by storm surge based on Elevation-Area method and hydrodynamic models:Taking Bohai Bay as an example

The future inundation by storm surge on coastal areas are currently ill-defined.With increasing global sealevel due to climate change,the coastal flooding by storm surge is more and more frequently,especially in coastal lowland with land subsidence.Therefore,the risk assessment of such inundation for these areas is of great significance for the sustainable socio-economic development.In this paper,the authors use Elevation-Area method and Regional Ocean Model System(ROMS)model to assess the risk of the inundation of Bohai Bay by storm surge.The simulation results of Elevation-Area method show that either a 50-year or 100-year storm surge can inundate coastal areas exceeding 8000 km^(2);the numerical simulation results based on hydrodynamics,considering ground friction and duration of the storm surge high water,show that a 50-year or 100-year storm surge can only inundate an area of over 2000 km^(2),which is far less than 8000 km^(2);while,when taking into account the land subsidence and sea level rise,the very inundation range will rapidly increase by 2050 and 2100.The storm surge will greatly impact the coastal area within about 10-30 km of the Bohai Bay,in where almost all major coastal projects are located.The prompt response to flood disaster due to storm surge is urgently needed,for which five suggestions have been proposed based on the geological background of Bohai Bay.This study may offer insight into the development of the response and adaptive plans for flooding disasters caused by storm surge.

Climate Change-Related Disaster Risk Events in Togo: A Systematic Review

Togo is facing significant climate challenges that have profound consequences for its environment, economy, and population. This study provides an overview of various climate phenomena affecting Togo and highlights potential adaptation strategies. We used the inclusion and exclusion criteria (PRISMA) to search both French and English articles on climate change-related disaster risk events in Togo through Google Scholar, Directory of Open Access Journals (DOAJ), and PubMed databases using the keywords “Climate Change”, “Floods”, “Drought”, “Coastal erosion”, “High winds”, “Epidemy”, Heatwaves”, and “Air pollution”. Twenty-five articles from 2000-2023 were included in this study after applying different criteria. Droughts, floods, coastal erosion, food and crop productivity loss, heatwaves, spread of vector-borne diseases, air pollution, and high winds are among the climate phenomena discussed. These challenges are driven by climate change, altering precipitation patterns, increasing temperatures, and rising sea levels. Drought, floods, coastal erosion, loss of food and crop productivity, spread of vector-borne diseases, air pollution and heatwaves are the most climate risks experienced by Togo. Drought contributes to decreased plant cover, water scarcity, and changes in the water and energy balance. Floods cause property damage, health risks, and disruptions to livelihoods. Coastal erosion threatens coastal communities, infrastructure, and ecosystems. Adaptation strategies include early warning systems, improved water management, sustainable agriculture, urban and health planning, and greenhouse gas emissions reduction. Drought-resistant crops, mosquito control, and clean energy adoption are essential.

A semantic vector map-based approach for aircraft positioning in GNSS/GPS denied large-scale environment

Accurate positioning is one of the essential requirements for numerous applications of remote sensing data,especially in the event of a noisy or unreliable satellite signal.Toward this end,we present a novel framework for aircraft geo-localization in a large range that only requires a downward-facing monocular camera,an altimeter,a compass,and an open-source Vector Map(VMAP).The algorithm combines the matching and particle filter methods.Shape vector and correlation between two building contour vectors are defined,and a coarse-to-fine building vector matching(CFBVM)method is proposed in the matching stage,for which the original matching results are described by the Gaussian mixture model(GMM).Subsequently,an improved resampling strategy is designed to reduce computing expenses with a huge number of initial particles,and a credibility indicator is designed to avoid location mistakes in the particle filter stage.An experimental evaluation of the approach based on flight data is provided.On a flight at a height of 0.2 km over a flight distance of 2 km,the aircraft is geo-localized in a reference map of 11,025 km~2using 0.09 km~2aerial images without any prior information.The absolute localization error is less than 10 m.

Online identification and extraction method of regional large-scale adjustable load-aggregation characteristics

This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online identification method is a computer-involved approach for data collection,processing,and system identification,commonly used for adaptive control and prediction.This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration,aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods.The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics,economic efficiency,and comfort.The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes,the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57,indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term.Overall,the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.

Assessment and control of the mine tremor disaster induced by the energy accumulation and dispersion of thick-hard roofs

In order to solve the problem that current theory models cannot accurately describe thick-hard roof(THR)elastic energy and assess the mine tremor disasters,a theoretical method,a Timoshenko beam theory on Winkler foundation was adopted to establish the THR’s periodic breaking model.The superposition principle was used for this complex model to derive the calculation formulas of the elastic energy and impact load on hydraulic supports.Then,the influence of roof thickness h,cantilever length L_(1),and load q on THR’s elastic energy and impact load was analyzed.And,the effect of mine tremor disasters was assessed.Finally,it is revealed that:(1)The THR’s elastic energy U exhibits power-law variations,with the fitted relationships U=0.0096L_(1)^(3.5866^),U=5943.9h^(-1.935),and U=21.049q^(2).(2)The impact load on hydraulic supports F_(ZJ) increases linearly with an increase in the cantilever length,thickness,and applied load.The fitted relationships are F_(ZJ)=1067.3L_(1)+6361.1,F_(ZJ)=125.89h+15100,and F_(ZJ)=10420q+3912.6.(3)Ground hydraulic fracturing and liquid explosive deep-hole blasting techniques effectively reduce the THR’s cantilever length at periodic breakages,thus eliminating mine tremor disasters.

Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage.

A Large-Scale Group Decision Making Model Based on Trust Relationship and Social Network Updating

With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.

Mapping Climate Services for Disaster Risk Management: A Systematic Review and Research Gaps from a Policy Process Perspective

Climate services (CS) are crucial for mitigating and managing the impacts and risks associated with climate-induced disasters. While evidence over the past decade underscores their effectiveness across various domains, particularly agriculture, to maximize their potential, it is crucial to identify emerging priority areas and existing research gaps for future research agendas. As a contribution to this effort, this paper employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to review the state-of-the-art in the field of climate services for disaster risk management. A comprehensive search across five literature databases combined with a snowball search method using ResearchRabbit was conducted and yielded 242 peer-reviewed articles, book sections, and reports over 2013-2023 after the screening process. The analysis revealed flood, drought, and food insecurity as major climate-related disasters addressed in the reviewed literature. Major climate services addressed included early warning systems, (sub)seasonal forecasts and impact-based warnings. Grounded in the policy processes’ theoretical perspective, the main focus identified and discussed three prevailing policy-oriented priority areas: 1) development of climate services, 2) use-adoption-uptake, and 3) evaluation of climate services. In response to the limitations of the prevalent supply-driven and top-down approach to climate services promotion, co-production emerges as a cross-cutting critical aspect of the identified priority areas. Despite the extensive research in the field, more attention is needed, particularly pronounced in the science-policy interface perspective, which in practice bridges scientific knowledge and policy decisions for effective policy processes. This perspective offers a valuable analytical lens as an entry point for further investigation. Hence, future research agendas would generate insightful evidence by scrutinizing this critical aspect given its importance to institutions and climate services capacity, to better understand intricate facets of the development and the integration of climate services into disaster risk management.

Mechanism,prevention,and control of mining-induced dynamic disasters in underground metal mines in China:Challenges and solutions

Metal mineral resources play an indispensable role in the development of the national economy.Dynamic disasters in underground metal mines seriously threaten mining safety,which are major scientific and technological problems to be solved urgently.In this article,the occurrence status and grand challenges of some typical dynamic disasters involving roof falling,spalling,collapse,large deformation,rockburst,surface subsidence,and water inrush in metal mines in China are systematically presented,the characteristics of mining-induced dynamic disasters are analyzed,the examples of dynamic disasters occurring in some metal mines in China are summarized,the occurrence mechanism,monitoring and early warning methods,and prevention and control techniques of these disasters are highlighted,and some new opinions,suggestions,and solutions are proposed simultaneously.Moreover,some shortcomings in current disaster research are pointed out,and the direction of efforts to improve the prevention and control level of dynamic disasters in China’s metal mines in the future is prospected.The integration of forward-looking key innovative theories and technologies in the abovementioned aspects will greatly enhance the cognitive level of disaster prevention and mitigation in China’s metal mining industry and achieve a significant shift from passive disaster relief to active disaster prevention.

Study on Temperature Disaster Indicators of Passion Fruit in Southwest Fujian, China

In China, meteorological forecasting relies on meteorological data obtained from regional and national stations. However, there were discrepancies between the data collected from the meteorological station at the passion fruit growing base and the data from regional and national stations. Consequently, the high and low temperature disaster indicators determined by the meteorological station at the passion fruit growing base cannot be applied to meteorological forecasting. To address this issue and facilitate the monitoring and early warning of high and low temperature disasters in passion fruit cultivation in Fujian, China, we used multi-source hourly temperature data (including the data from meteorological observation stations in passion fruit growing bases, the nearest regional stations, and national surface conventional meteorological observation stations) in three cities in southwestern Fujian (Longyan, Sanming, and Zhangzhou) spanning the years 2020 to 2022. By employing comprehensive statistical analysis methods (0.5 interval division and Cumulative frequency), we identified that passion fruit in southwestern Fujian was susceptible to high temperature disasters during the blooming-fruiting period, as well as low temperature disasters during the sprouting period. Consequently, we developed high and low temperature disaster indicators based on data from regional and national stations for different phenological periods of passion fruit in this region.

Citizens’Mental Health Issues and Psychological Trauma Experience due to a Crowd-Crush Disaster in Korea

This study evaluated the state of anxiety, depression, post-traumatic stress disorder, general mental health, and mental well-beingamong citizens after a crowd-crush disaster in Korea. Individuals who experienced the crowd crush had significantly higheranxiety, depression, and post-traumatic stress disorder (PTSD) scores than those who did not (p < 0.001). Additionally,people who avoided the disaster area had significantly higher depression and PTSD scores than those who did not avoid thearea (p < 0.001). Those who directly witnessed the Seoul Halloween crowd crush had a significant difference in PTSD levels ineither group than those who experienced it indirectly (p = 0.005). There was a significant difference in PTSD scores in cases ofdirect damage or death of an acquaintance (p < 0.001). The Seoul Halloween crowd crush caused psychological damagethrough indiscriminate exposure to the public, and symptoms of PTSD appeared over a long period. It is crucial to provideessential resources for ongoing treatment and case management.

Disaster Risk Reduction (DRR) Education from Social Disciplinary Theoretical Perspectives

Disaster is a social phenomenon. The occurrence and impacts of disasters including the education sector can be studied through a social problem lens. This paper draws meaning and understanding of DRR education using the sociological disciplinary framework in a detailed qualitative case study of three schools as they responded to the devastating Gorakha earthquake in 2015 and other disasters in Nepal. This paper considers the three sub-disciplines of sociology: the sociology of disaster, the sociology of education and the sociology of education governance in a development context. These sub-disciplines are nested together to analyse social, political and historical factors and their relationships which are helpful to identify risks and vulnerabilities in the education sector in Nepal. These are the major areas to explore the disaster context and needs of context-specific education acts (hereafter DRR education) to minimise the potential risks of disasters. The article concludes that the social disciplinary framework is significantly useful to analyse DRR education provisions and implications of education governance to mobilise school in disaster preparedness, response and recovery.