Occurrence Of Hazards Research Articles

Assessing and mitigating building fire risk based on the scenario clusters in Bangladesh

The increasing occurrence of fire hazards in residential buildings in Bangladesh has led to severe property damage, physical injuries, and fatalities, underscoring a critical research gap in effective fire risk management. This study aims to address this gap by identifying key fire risks and proposing mitigation strategies using BIM technology. The research employs a comprehensive survey and scenario clustering methodology to identify these risks and assess the efficacy of various fire-resistant materials. Data collection involved questionnaires distributed to 200 respondents, including building owners, occupants, engineers, and firefighters. These clusters are based on the metrics of fatality numbers and property damage to quantify fire risk. Findings highlight the significant role of wall and ceiling lining materials (RII = 0.919) in contributing to fire risk. Further analysis was conducted using a residential building model developed in Autodesk Revit and simulated in Pyrosim software, assessing various lining materials. Quantitative results demonstrated that fire brick is the most effective material, exhibiting the lowest heat release rate (968 kW/m2), minimum adiabatic surface temperature (28°C), minimum heat transfer coefficient (1.75 W/m2/K), and lowest surface burning rate (7.5e−08 Kg/m2/s). These results suggest that fire brick is the optimal choice for enhancing fire resistance in residential buildings.

Research on an identification model for mine water inrush sources based on the HBA-CatBoost algorithm

Accurate and efficient identification of water inrush sources, as one of the three critical elements of mine water hazards, is crucial for mine water management. To identify the sources of mine water inrush effectively, a model named HBA-CatBoost is introduced. This model is established on the hybrid bat algorithm (HBA)-optimized category feature gradient boosting tree (CatBoost), and the shapley additive explanation (SHAP) method is employed to elucidate the model’s decision-making process. Given the prevalent occurrence of water hazards in coal seam roofs and floors in the northern Guizhou coalfield, coupled with the challenges in pinpointing water inrush sources in mines, the HBA-CatBoost model is tested at the Longfeng Coal Mine in northern Guizhou to validate its practicality. Comparative analysis with the HBA-RF, HBA-XGBoost, CatBoost, RF, and XGBoost models demonstrates that the hybrid bat algorithm significantly enhances the classification performance of the CatBoost model, resulting in improved convergence speed and classification accuracy. The HBA-CatBoost model outperforms the aforementioned models in terms of classification effectiveness, achieving accuracy, recall, precision, and F1 scores of 96.43%, 97.22%, 96.43%, and 96.61%, respectively. The SHAP method elucidates the decision mechanism of the optimal HBA-CatBoost model, highlighting the significance of sample features and bolstering the model’s credibility. These outcomes underscore the superior performance of the HBA-CatBoost model and its potential for effectively identifying water inrush sources in mines.

Risk management technology in the public sector: challenges and opportunities

The paper examines the importance of using effective risk management methods in the public sector, emphasizing the need for an integrated approach to this problem. The author emphasizes that public management mechanisms must be supported by appropriate technologies to ensure sustainable functioning. The use of ISO 31000 and ISSAI 9100 standards helps structure the risk management process and increase its effectiveness. Risk management should be based on the identification, assessment and response to risks and take into account the influence of various factors on the risk management process. The author also notes that risk management in the public sector is complicated by certain factors (frequent management changes, division of operating budgets and programs, lack of clear risk indicators, etc.). The analysis of the issue demonstrates that the system of assessment, management and risk reduction should play a key role in public administration. The main task of risk management as an element of public management is to make effective decisions, create the necessary conditions and plan actions taking risks into account to minimize their negative impact on planned results. As a result of the use of risk management technologies, it is possible to identify risks, determine factors and sources of risk, assess the degree and probability of occurrence of hazards and, ultimately, develop an optimal plan of management actions. The results of the study demonstrate that the issue of forming a risk management mechanism in the public sector requires further scientific research on the application of theoretical developments in the practical activities of public management and administration bodies of Ukraine. The paper also focuses on the issue of risk management in public sector organizations, and concludes that in order to assess the value, type and impact of risk, it is necessary to review the internal part of the organization and its management processes, as well as its environment. Such a review of risks should be complemented by increased risk awareness at each level of the management hierarchy and in the day-to-day activities of all employees of the organization. Thus, the need to use modern risk management technologies in the public sector is outlined in order to increase the effectiveness of the decisions made and minimize the negative impact of potential risks on the results of the work of management bodies.

Monitoring the effects of climate, land cover and land use changes on multi-hazards in the Gianh River watershed, Vietnam

Abstract In recent decades, global rapid urbanization has exacerbated the impacts of natural hazards due to changes in Southeast Asia’s environmental, hydrological, and socio-economic conditions. Confounding non-stationary processes of climate change and global warming and their negative impacts can make hazards more complex and severe, particularly in Vietnam. Such complexity necessitates a study that can synthesize multi-dimensional natural-human factors in disaster risk assessments. This synthesis study aims to assess and monitor climate change and land-cover/land-use change impacts on flood and landslide hazards in Vietnam’s Gianh River basin. Three Deep Neural Network (DNN) and optimization algorithms, including the Adam, Tunicate Swarm Algorithm (TSA), and Dwarf Mongoose Optimization (DMOA) were used to determine the regions with the probability of the occurrence of flood and landslide and their combination. All efficiently evaluated hazard susceptibility based on a synthesis analysis encompassing 14 natural and anthropogenic conditioning factors. Of the three, the Deep Neural Network (DNN)-DMOA model performed the best for both flood and landslide susceptibility, with area-under-curve values of 0.99 and 0.97, respectively, followed by DNN-TSA (0.97 for flood, 0.92 for landslide), and DNN-Adam (0.96 for flood, 0.89 for landslide). Although the area affected by flooding is predicted to decrease, the overall trend for total hazard-prone areas increases over 2005–2050 due to the more extensive area affected by landslides. This study develop and demonstrate a robust framework to monitor multi-hazard susceptibility, taking into account the changes in climate and land-use influence the occurrence of multiple hazards. Based on the quantitative assessment, these findings can help policymakers understand and identify confounding hazard issues to develop proactive land-management approaches in effective mitigation or adaptation strategies that are spatially and temporally appropriate.

Multivariate analysis of compound hail, wind and rainfall extremes in Alberta's hail alley

Hailstorms are severe weather events with the potential for devastating impacts. The consequences can be significantly worsened when hail events are accompanied by strong winds, intensifying both hail momentum and damage to property sidings and windows. Additionally, rainfall extremes during hailstorms can disrupt the drainage systems, potentially leading to flash flooding. Therefore, understanding the inter-dependencies and joint behaviour of these hazards is crucial for developing effective risk mitigation strategies. In this study, we conduct a multivariate probabilistic assessment of concurrent hail, wind, and rainfall extremes over the Alberta's “hail alley” using radar and ground-based observations. The analysis comprehensively explores individual hazards, as well as bivariate and trivariate scenarios using a vine copula approach. We quantify individual, conditional, and joint return periods (JRPs) for the various scenarios. Findings indicate that in both wind-driven hail and hail-rainfall extreme hazards, the joint occurrences based on JRP, can be underestimated by 20% and 70% when assuming independence, respectively, which has substantial implications for risk assessment and management, as well as infrastructure design and maintenance. The analysis of the trivariate case suggests the potential for the concurrent occurrence of multiple hazards in the region. Furthermore, results show that Archimedean copula families outperform elliptical copulas in simulating extreme variables related to compound events associated with hailstorms. The study stresses the importance of assessing the joint behaviour of these hazard components in hailstorms, with the objective of mitigating potential impacts on vulnerable regions.

Multi-hazard assessment for flood and Landslide risk in Kalimantan and Sumatra: Implications for Nusantara, Indonesia's new capital

Situated within the Ring of Fire and characterized by a tropical climate and high seismic activity, Indonesia is uniquely vulnerable to natural disasters such as floods and landslides. These events pose significant threats to both the population and infrastructure. This study predicts areas exposed to flood and landslide risk by considering various environmental factors related to climate, topography, and land use. The predictive performance of three machine learning models—naïve Bayes, k-nearest neighbors, and random forest (RF)—was evaluated by comparing the AUC, RMSE, and R2 values of each model. Ultimately, the RF model, which demonstrated the highest accuracy, was used to prioritize disaster impact factors and generate hazard maps. The results identified the interaction of rainfall, land use, and slope aspect as the most critical determinants of hazard occurrence. The predicted hazard maps revealed that approximately 26.7 % of the study area was vulnerable to either floods or landslides, with 16.8 % of the area experiencing both. The new capital of Nusantara showed a relatively higher multi-hazard risk than did the overall study area and protected zones, with 22.1 % of the hazard area vulnerable to both flooding and landslides. In single hazard zones, areas classified as at risk for floods had a higher mean probability of experiencing both hazards (43 %), as compared to areas classified as at risk for landslides (22 %). As a result, urban planners and relevant stakeholders can now utilize the hazard maps developed in this study to prioritize infrastructure reinforcement and disaster risk areas, integrating land use planning with risk assessment to mitigate the impact of disasters. By employing these strategies, Indonesia and other countries facing similar challenges can now enhance their disaster preparedness and response capabilities in new capital regions and other areas, ultimately planning for more sustainable urban development.

Experimental investigation of permeability evolution and progressive damage of sandstone in the complete process of direct shear

Permeability evolution and damage behavior under shearing conditions are critical for reliable predictions of rock seepage characteristics during excavation and a correct assessment of their impacts on the occurrence of natural or induced hazards. However, publications concerned with this problem have been poorly issued so far. The primary objective of this study is to innovatively investigate permeability evolution and damage behaviors of sandstone in the complete process of direct shear. Several groups of red sandstone specimens are deformed in direct shearing using a newly modified servo-controlled direct shear apparatus to measure permeability evolution and monitor acoustic emission (AE) activities with damage under various normal stress conditions. The morphology of the rupture surfaces is scanned using a 3D laser scanner after tests. According to our experimental results, the locus of the shear stress-displacement curve is found to be closely associated with microcrack progressivity. The initial permeability in advance of shearing test and the peak permeability appearing after the peak shear stress decrease with the increasing normal stress. The minimal and maximum permeability values exhibit a lag-effect with respect to the turning and the peak points on the shear stress-displacement curve. Furthermore, a palpable surge is found in the interevent time function during shearing, which can be a potential indicator for early warning of catastrophic rupture of brittle rock materials under shearing or compression. The joint roughness coefficient (JRC) values in the direction of shearing deformation for each normal stress case are more significant than those in the direction perpendicular to the shear displacement. The impacts of our obtained results are potential ramifications for permeability control in rock engineering projects.

Investigating the effective consequences on the assets of an urban system in facing scenario-based hazards

Urban assets are generated through physical elements in a city as part of critical infrastructure. To ensure a sustainable urban future, innovative businesses and people are also essential urban assets. In the possible occurrence of natural and manborne hazards, architecture, population density, gathering locations, and connected infrastructure systems in cities are efficient. Manmade hazards are accidents caused by people that happen in or close to human settlements. These are things that have a major impact on the quality of life, health status, and even mortality. The consequences of such hazards in the town will have to be analyzed. To this end, causal relationships between a town's assets against man-made and natural hazards have been analyzed using the Fuzzy DEMATEL method. According to the results of this research, physical elements have a more important effect on other assets in cities at risk from man-made hazards than any other factor. In the face of man's hazards, economic activity can be more closely linked to population and physical elements. Physical elements have a higher impact on the population and economic activities when confronted with environmental hazards. Physical elements interact more with population and economic activities. In line with the results, more important than physical elements are population and economic activities. Lastly, it is suggested that the risks of dangerous accidents should be assessed and their consequences determined by taking into account the worst-case scenario.

Culinary Hygiene and Sanitation: A Systematic Literature Review

Healthy food refers to food that is both sanitary and nourishing. Edible sustenance Hygienic food refers to food that is free from pathogens and contaminants that can pose a health risk. Food processed products typically exhibit vulnerabilities in terms of their safety, including biological or microbiological, chemical, and physical dangers. The occurrence of hazards or pollution, typically resulting from substandard raw materials, processing techniques, and insufficient sanitation and hygiene practices, has raised concerns regarding the awareness of workers or producers involved in handling processed food. This research aims to investigate the topic of Culinary Hygiene and Sanitation through a systematic review of existing literature. This review was conducted utilizing the latest and most advanced methodologies, following the recommended reporting items for reviews and meta-analyses (PRISMA) standards. The findings of this study clarify that food plays a multifaceted role and serves various purposes, including promoting health, reflecting political and social status, exerting power and fostering cooperation, influencing social and economic standing, embodying cultural values, responding to population density, reflecting changing times, and adhering to principles of hygiene and sanitation. The key elements of the facility are the physical location and structure, the sanitary facilities, the kitchen area, the dining room, and food storage area, the handling of food ingredients and prepared food, the food processing area, the storage area for food ingredients and prepared food, the food serving area, the presentation of food, the equipment used, and the labor involved.

Meat products in human nutrition and health – About hazards and risks

Meat processing has a long history and involves a wide and ever-increasing range of chemical and physical processes, resulting in a heterogeneous food category with a wide variability in nutritional value. Despite the known benefits of meat consumption, observational epidemiological studies have shown associations between consumption of red and processed meat - but not white meat - and several non-communicable diseases, with higher relative risks for processed meat compared to unprocessed red meat. This has led global and regional nutrition and health organisations to recommend reducing consumption of unprocessed red meat and avoiding processed meat. A plethora of potentially implicated hazardous compounds present in meat or formed during processing or gastrointestinal digestion have been reported in the literature. However, our mechanistic understanding of the impact of meat consumption on human health is still very incomplete and is complicated by the simultaneous occurrence of multiple hazards and interactions with other food compounds and host factors. This narrative review briefly discusses hazards, risks and their assessment in the context of dietary guidelines. It is argued that more mechanistic studies of the interactive effects of meat products with other foods and food compounds in different dietary contexts are needed to refine and increase the evidence base for dietary guidelines. Importantly, the great diversity in the composition and degree of processing of processed meats should be better understood in terms of their impact on human health in order to develop a more nuanced approach to dietary guidelines for this food category.

Risk Assessment of Multi-Hazards in Hangzhou: A Socioeconomic and Risk Mapping Approach Using the CatBoost-SHAP Model

As the global push for sustainable urban development progresses, this study, set against the backdrop of Hangzhou City, one of China’s megacities, addressed the conflict between urban expansion and the occurrence of urban geological hazards. Focusing on the predominant geological hazards troubling Hangzhou—urban road collapse, land subsidence, and karst collapse—we introduced a Categorical Boosting-SHapley Additive exPlanations (CatBoost-SHAP) model. This model not only demonstrates strong performance in predicting the selected typical urban hazards, with area under the curve (AUC) values reaching 0.92, 0.92, and 0.94, respectively, but also, through the incorporation of the explainable model SHAP, visually presents the prediction process, the interrelations between evaluation factors, and the weight of each factor. Additionally, the study undertook a multi-hazard evaluation, producing a susceptibility zoning map for multiple hazards, while performing tailored analysis by integrating economic and population density factors of Hangzhou. This research enables urban decision makers to transcend the “black box” limitations of machine learning, facilitating informed decision making through strategic resource allocation and scheduling based on economic and demographic factors of the study area. This approach holds the potential to offer valuable insights for the sustainable development of cities worldwide.

Do typhoon disasters foster climate change concerns? Evidence from public discussions on social media in China

Combating the adverse impacts of extreme climate events through adaptation depends on climate change awareness from both governments and the public. It has long been discussed that historical experiences of climatic hazards might help to foster and raise public concerns about climate change, and consequently contribute to climate actions. However, current studies have reached no consensus on the impacts of the occurrence of extreme climatic hazards on the public's climate change awareness. This study provides a national-level analysis of climate change-related posts accompanying extreme typhoon events in China from 2011 to 2022, based on social media big data and machine learning, to discern the relationship between occurrences of typhoon disasters and climate change concerns on Weibo—in terms of the volume, contents and sentiment expressed in climate change-related posts. Our analysis found that the volume of climate change-related posts generally did not significantly increase during or after typhoon disasters. In terms of contents, though, the proportion of contents about climate change impacts did significantly increase during typhoon events, from 41.74 % to 48.97 %, and these disasters also significantly provoked negative sentiment toward climate change, which increased from 62.47 % to 70.18 %. Contents about climate change impacts and negative sentiment, however, were observed to bounce back to pre-disaster levels within about twelve days after typhoon events. Our study emphasizes the importance of learning from previous climatic disasters, to foster public concerns about climate change and thereby to promote climate actions.

Early detection of Tonga volcanic-eruption from internal gravity wave effects on ionosphere, using satellite geodetic techniques

The occurrence of some natural hazards in the troposphere may lead to creation of Internal Gravity Waves (IGWs). These waves transfer energy from the lower troposphere to upper layers, and to the ionosphere. When these IGWs reach the ionosphere, they create significant variations in the ionospheric parameters. Therefore, they have considerable effects on performance of Global Navigation Satellite Systems (GNSS) receivers. In this study, we used double-frequency measurements of GNSS ground-based stations from GEONET network in New Zealand to detect the IGWs created by the tsunami induced from the 2022 Tonga volcanic eruption. In addition to GNSS measurements, FORMOSAT-7/COSMIC-2 (F7/C2) data, and SWARM data were also used to study these IGWs. It is known that many of the IGWs have horizontal phase speeds faster than that of the tsunami. As the volcanic-originated IGWs spread in cone-shape pattern, it is possible to detect these fast IGWs in the ionosphere earlier than the tsunami waves, reaching the tide gauges or DART buoys. In our study, we could detect the first IGWs at the New Zealand GNSS stations, 2 h earlier than the first registration of the tsunami waves at tide gauges and DART buoys near the New Zealand peninsula, which is located approximately 1.600 km from the Tonga Volcano. It can be concluded that IGWs can be used to warn tsunamis faster than the current early-warning systems, which make use of tide gauges and DART buoys. Furthermore, the spatial variations in ionospheric electron density (IED) were investigated using F7/C2 RO data. The results show that the volcanic-originated IGWs cause reduction in the IED peak value and altitude. The results of IED derived from F7/C2 and SWARM were in good agreement.

Natural Disaster Mitigation Strategies: A Comprehensive Review

The world is often rocked by the devastating effects of natural disasters, presenting a formidable challenge to communities everywhere. In order to address these challenges and minimize their impact, it becomes imperative for proactive measures to be taken. This comprehensive review delves into the wide array of strategies put in place to mitigate the vulnerability of both populations and infrastructure when faced with these hazardous occurrences. Taking into account an extensive range of literature, case studies, and expert opinions, this review strives to synthesize crucial findings that shed light on effective approaches towards mitigation. It commences by classifying natural disasters according to their distinct characteristics and geographical spread, bringing attention to their individual traits. Furthermore, it embarks on an analysis aimed at uncovering the root causes and driving factors behind these calamities - underlining how essential it is for us all to comprehend their dynamics in order to craft tailored mitigation plans. The significance of pre-disaster preparedness is emphasized through an exploration of risk assessment procedures, early warning systems implementation practices as well as land-use planning strategies. The critique emphasizes the importance of proactive measures, such as fortifying infrastructure and incorporating resilience principles into city planning. It also discusses the vital role of community engagement, education, and capacity building in bolstering disaster preparedness at a local level. The review delves into innovative methods for promoting community resilience, including participatory decision-making processes and initiatives focused on knowledge exchange. Furthermore, it evaluates the impact of technological advancements like remote sensing, geographic information systems (GIS), and predictive modeling on enhancing early warning systems, risk assessment protocols, and disaster response efforts. The analysis suggests various opportunities for utilizing technology to strengthen mitigation strategies effectively Post-disaster Response and Recovery: Analyzing the challenges and opportunities in postdisaster response and recovery efforts, emphasizing the importance of rapid and coordinated interventions to restore essential services, infrastructure, and livelihoods. The review explores strategies for promoting resilient reconstruction and long-term recovery. Policy Frameworks and International Cooperation: Evaluating the role of policy frameworks, international cooperation, and funding mechanisms in supporting effective disaster mitigation efforts. The review identifies policy gaps and barriers to implementation, along with recommendations for enhancing policy coherence and coordination at the national and international levels. In conclusion, this comprehensive review underscores the multidimensional nature of natural disaster mitigation and the importance of adopting integrated approaches that combine technical expertise, community engagement, and policy support. By synthesizing existing knowledge and best practices, this review provides valuable insights for policymakers, practitioners, and researchers striving to build more resilient and sustainable communities in the face of natural hazards. By synthesizing existing knowledge and best practices, this review provides valuable insights for policymakers, practitioners, and researchers striving to build more resilient and sustainable communities in the face of natural hazards.

Cultural and Spiritual Adaptations to Extreme Weather Events of Ethnic Farming Communities of the Three Watersheds Situated at Different Altitudinal Locations along Agno River Basin, Benguet and Pangasinan, Philippines

Ethnic farming communities in Agno River Basin face climate stressors that threaten their agricultural production. This paper investigated and assessed the cultural/spiritual adaptations to climate variabilities and extremes of ethnic farming communities in Agno River Basin, Philippines. How the presence of these cultural/spiritual practices moderate or mitigate the effects of climate variabilities and extremes were likewise investigated. The climate-related hazards considered in this study which are pronounced in the river basin are prolonged and extreme rainfall intensity, typhoons and extreme winds, drought, flood and occurrence of pests and diseases. These hazards are becoming more frequent and intense resulting to limited agricultural production. This paper considered only the sectors of agriculture/socioeconomic, water and health. The descriptive method employed in this study was based on the works of Oppenheimer et al (2014); Saroar, Routray and Leal Filho (2015) and Brooks and Adgers(undated). The data-gathering activities were household survey using pre-tested questionnaires, focused group discussions and key informant interviews. The ethnic affiliations of the farming communities were I-Karao, Ibaloi, Kankanaey and Kalanguya tribes. Secondary data on changing rainfall and erratic temperature characteristics were consistent with the general perception of the ethnic farming communities. Cultural/ spiritual traditions of Ethnic farming communities in the Agno River Basin form part of their adaptations to climate extremes and variabilities. The greatest number of rituals and activities (5) such as childless couples raising plant materials, burning of rice straws and Alay, Leppas, Maepdesan, and Ibkas or maboteng. are dedicated to ensuring a good harvest. Canyao, Libot, and Begnas are undertaken to prevent the occurrence of hazards. On the other hand, Ubob, Bayanihan, and Sakdo are adaptations to mitigate the impact of climate extremes and variabilities. Both Canyao and Leppas are also thanksgiving activities while Tagainep or dream interpretation serves as a coping mechanism.

Spatial Mapping for Multi-Hazard Land Management in Sparsely Vegetated Watersheds Using Machine Learning Algorithms

This study breaks new ground by developing a multi-hazard vulnerability map for the Tensift watershed and the Haouz plain in the Moroccan High Atlas area. The unique juxtaposition of flat and mountainous terrain in this area increases sensitivity to natural hazards, making it an ideal location for this research. Previous extreme events in this region have underscored the urgent need for proactive mitigation strategies, especially as these hazards increasingly intersect with human activities, including agriculture and infrastructure development. In this study six advanced machine learning (ML) models were used to comprehensively assess the combined probability of three significant natural hazards: flooding, gully erosion, and landslides. These models rely on causal factors derived from reputable sources, including geology, topography, meteorology, human activities, and hydrology. The research's rigorous validation process, which includes metrics such as specificity, precision, sensitivity, and accuracy, underlines the robust performance of all six models. The validation process involved comparing the model's predictions with actual hazard occurrences over a specific period. According to the outcomes in terms of the area under curve (AUC), the XGBoost model emerged as the most predictive, with remarkable AUC values of 93.41% for landslides, 91.07% for gully erosion and 93.78% for flooding. Based on the overall findings of this study, a multi-hazard risk map was created using the relationship between flood risk, gully erosion, and landslides in a geographic information system (GIS) architecture. The innovative approach presented in this work, which combined ML algorithms with geographical data, demonstrates the power of these tools in sustainable land management and the protection of communities and their assets in the Moroccan High Atlas and regions with similar topographical, geological, and meteorological conditions that are vulnerable to the aforementioned risks.

VISUALIZATION OF THREATS IN CIVIL SECURITY USING THREE-DIMENSIONAL SPATIAL MODELING

The article analyzes the main concepts and methods of modeling used in civil security to solve the issues of analyzing the prerequisites for the occurrence of hazards, by forecasting, preventing and managing their components. On-site research, in particular, direct measurements, can only be carried out on an already existing object. Modeling today is one of the important methods, the main methodology of which is to learn the essence of natural and social phenomena of various levels of complexity. It provides an opportunity to demonstrate various systems related to civil security using methods of applied mathematics, mathematical statistics and optimization. The task of analysis and forecasting on any object can be reduced to building a model that reflects the behavior of a natural object. The main tasks of this article are to establish modeling principles and methods for their use in civil security using the example of modeling noise maps from various sources, one of which is construction in a dense residential area. The task of analysis and forecasting on almost any object can be reduced to building a model that adequately reflects the behavior of a natural object. The basic principles on which modeling is based are defined. The principle of information sufficiency – building its model is impossible in the complete absence of information about the object. Feasibility principle – the model should ensure the achievement of the research goal with a non-zero probability value. The principle of multiplicity of models - the model should reflect exactly those properties of a real object or system that are of interest to the researcher. The principle of aggregation - the system is better decomposed as a set of subsystems, for the description of which standard schemes can be used. Principle of parameterization – the model must be built in the form of a known system, the parameters of which are unknown. Crisis simulation training is a common organizational tool for improving civil security preparedness. Although today its general advantages cannot be denied, the conditions under which these simulations offer opportunities for improvement often remain unclear.

Study and Action Plan on the Key Factors Influencing Unsafe Behaviors by Construction Workers

As front-line workers on construction projects, construction workers have always had the highest accident rate among workers in various industries, a statistic that significantly impacts society. In this study, the 2–4 Model was used to identify 14 influencing factors affecting workers’ unsafe behaviors, and a safety management index for construction workers was established. The fuzzy decision-making laboratory analysis method (Fuzzy-DEMATEL) and the interpretative structural model (ISM) were used to analyze the hierarchical structure, internal relations, and key influencing factors behind the unsafe behaviors in question, and the 14 factors affecting them were divided into five dimensions, including the technological environment, insufficient safety knowledge, and the corporate safety culture. The three most direct influencing factors (comprising eight key nodes in the constructed hierarchy) were found to be poor safety awareness, the violation of operating procedures, and skill errors. Therefore, the main paths toward reducing the level of unsafe behavior among construction workers include making changes in process equipment, addressing skill-related errors, ensuring adherence to operating procedures, improving safety awareness, and enhancing safety knowledge. In this study, we identify and classify the factors influencing unsafe behaviors by construction workers and propose scientific interventions with which to prevent the continued occurrence of safety hazards among them.

Enhancing natural disaster analysis and waste classification: a novel VGG-FL approach.

The study of natural disasters is a crucial field that involves analyzing the occurrence, impact, and aftermath of various natural hazards that can cause significant harm to communities and the environment. Efficient waste management and environmental protection require proper classification of waste. Analyzing natural disasters and categorizing waste can be a time-consuming task, and conventional methods often struggle with it. However, a new approach called Visual Geometry Group with Federated Learning (VGG-FL) has been introduced to address these challenges. This methodology uses the golden search optimization (GSO) algorithm for feature selection and leverages VGG with federated learning for feature extraction and classification. To test the effectiveness of this method, a disaster image dataset was used to train the VGG-FL model. The results showed that the VGG-FL model attained exceptional accuracy in discerning and categorizing various disaster scenarios. The waste classification dataset simultaneously trains the VGG-FL model to categorize waste based on its characteristics and potential hazards. To measure the model's performance, several evaluation metrics such as accuracy, specificity, precision, F1-score, and recall are utilized to assess the effectiveness of the proposed VGG-FL method. These results are then compared with existing methodologies. The VGG-FL method performs exceptionally well, achieving 98.52% accuracy, 97.48% precision, 97.83% recall, 97.58% F1-score, and 97.12% specificity. These experimental findings demonstrate the efficacy of the VGG-FL method in analyzing natural disasters and classifying waste materials.

Can’t ignore surrounding shapes: Neural processing mechanisms of safety sign designs

Effective safety sign design is one of the keys to accident prevention. However, the role of one design element, namely the surrounding shape, has been questioned. It may boil down to that the previous research of surrounding shapes did not reveal the net effect of shape per se. Meanwhile, the researches mostly use subjective measurement methods, such as questionnaires, which do not allow for studying the unconscious and conscious responses of people to the abstract shapes, resulting in less understanding of the role of the surrounding shape. In this study, we designed four tasks to reveal the neural processing mechanisms of pure surrounding shapes and shapes combined with pictorial symbols by using the neuroscience method (event-related potentials, ERPs). The results demonstrate that surrounding shape can convey emotional information without any context, and triangles convey negative emotion more and earlier than circles. Besides, incorporating neutral pictorial symbols into the shapes can make the cognitive processing of the combined signs earlier while not influence the degree of processing compared to pure shapes. Furthermore, the combined signs (shapes + symbols) exhibit effects on processing at two stages: early emotion perceptual processing of the surrounding shape and subsequent cognitive processing guided by neutral pictorial symbols. Therefore, the current study can deepen our understanding of surrounding shape effect and provide effective solutions for safety sign designers to facilitate the efficiency of safety signs and reduce the occurrence of hazards and accidents.