Human Casualties Research Articles

The Scars of Civil War: The Long-Term Welfare Effects of the Salvadoran Armed Conflict

This paper estimates the long-term effects on human capital accumulation and subsequent labor market outcomes of in utero and early childhood exposure to the civil war in El Salvador (1980–92), the second longest and deadliest civil conflict in Central America. Identification is obtained from spatial and intertemporal variation in the intensity of the conflict drawn from historical archive data comprising records of human casualties, disappearances, and refugees. The results show that people born in highly violent areas during the civil war saw a reduction in their probability of being employed by 6 percentage points, and of getting a high-skilled job by 5 percentage points, 20 to 30 years hence. The civil war also reduced their education by 0.8 year, as well as their enrollment and literacy rates. Subgroup analysis indicates that exposed males and indigenous groups experienced the largest losses in human capital and had weaker performance in the labor market. JEL Classification Codes: D31, I00, J13

Continuous burned area monitoring using bi-temporal spectral index time series analysis

Extreme wildfires are a major agent of disturbance in present-day forest ecosystems and, have serious impacts on society in terms of human casualties and economic losses. Prioritizing and implementing relief and recovery measures after fires require explicit spatial information on the disaster extent. Information for detecting and monitoring ecosystem disturbance due to wildfires is obtainable using time series analysis of remote sensing data. However, the accuracy of this information depends on many factors, such as the input spectral features and the scene characteristics. In this study, we appraise the use of three spectral indices for continuous burned area monitoring in mainland Greece using the Breaks For Additive Seasonal and Trend (BFAST) Monitor unsupervised algorithm. In addition to the commonly employed Normalized Burn Ratio (NBR) we evaluated the use of differenced Normalized Burn Ratio (dNBR) and Relativized Burn Ratio (RBR) generated from a 16-day Landsat-8 time series. The spatiotemporal Overall Accuracy (OA) is assessed using Copernicus Emergency Mapping Service (EMS) grading products and an independent visual interpretation procedure. The latter approach suggests that the using the dNBR (OA = 87.0 %) and RBR (OA = 86.9 %) indices is more efficient than using the NBR time series (OA = 66.4 %). Copernicus EMS validation data show that the RBR-based approach attains a higher OA (80.5 %) -higher than either dNBR (OA = 75.0 %) or NBR (OA = 72.1 %). Evaluating the temporal agreement with Copernicus EMS products, bi-temporal indices achieved mean time lags of 0.023 (dNBR), 0.019 (RBR) and −0.015 (NBR) years. Overall, RBR usage provides the most reliable input for consistent continuous burned area monitoring.

A Multi-Stream Attention-Aware Convolutional Neural Network: Monitoring of Sand and Dust Storms from Ordinary Urban Surveillance Cameras

Sand and dust storm (SDS) weather has caused several severe hazards in many regions worldwide, e.g., environmental pollution, traffic disruptions, and human casualties. Widespread surveillance cameras show great potential for high spatiotemporal resolution SDS observation. This study explores the possibility of employing the surveillance camera as an alternative SDS monitor. Based on SDS image feature analysis, a Multi-Stream Attention-aware Convolutional Neural Network (MA-CNN), which learns SDS image features at different scales through a multi-stream structure and employs an attention mechanism to enhance the detection performance, is constructed for an accurate SDS observation task. Moreover, a dataset with 13,216 images was built to train and test the MA-CNN. Eighteen algorithms, including nine well-known deep learning models and their variants built on an attention mechanism, were used for comparison. The experimental results showed that the MA-CNN achieved an accuracy performance of 0.857 on the training dataset, while this value changed to 0.945, 0.919, and 0.953 in three different real-world scenarios, which is the optimal performance among the compared algorithms. Therefore, surveillance camera-based monitors can effectively observe the occurrence of SDS disasters and provide valuable supplements to existing SDS observation networks.

Quantitative assessment of flooding risk based on predicted evacuation time: A case study in Joso city, Japan

Flooding is a frequent natural hazard, threatening people's lives and properties worldwide. Even though completely preventing damage from flooding is impossible, an appropriate evacuation can help minimize its impact and mitigate human casualties. However, analyzing various flooding information to make quick evacuation judgments is challenging for decision-makers. Therefore, a quantitative flooding risk assessment is needed to support appropriate evacuation decisions. This study introduces the travel time under ideal evacuation conditions to quantify evacuation vulnerability, with Dijkstra's algorithm finding the evacuation route and the Random Forest algorithm predicting the evacuation time. The evacuation vulnerability is then integrated with time-varying flooding hazards and estimated congestion levels to assess evacuation risk. Herein, a case study in Joso, Japan is used as an example to demonstrate the practicality and applicability of the proposed approach. As a result, the prediction model achieved high accuracy with an RMSE of 0.88min and a MAPE of 5.88 %. The proposed approach clearly illustrated the vulnerable areas and times that may hinder an efficient evacuation process. Also, this approach identified and visualized the individual evacuation risk distribution during a given flooding scenario: 49.38 % of buildings were assessed as having the highest-risk: no evacuation ability, while a maximum of 11.86 % and 3.83 % of the buildings were assessed as middle-risk and high-risk. Furthermore, the regional evacuation deadline and priorities were specified to ensure timely and effective evacuation. Overall, this study develops an approach for assessing evacuation vulnerability and risk that could facilitate awareness among the public and provide judgmental bases for evacuation preparedness and decision-making.

Remote sensing and optimized neural networks for landslide risk assessment: Paving the way for mitigating Afghanistan landslide damage

Landslides caused by mega earthquakes and other extreme climate change pose a major threat to lives and infrastructure. However, the lack of a detailed and timely landslide inventory and relevant risk assessment attributable to ongoing conflicts limits the effective prevention measures in Afghanistan. This study presents the first landslide inventory covering the whole nation of Afghanistan from 2015 to the present utilizing Google Earth Pro imagery and manual interpretation. Based on this inventory of 3,260 mapped landslides, we analyzed the distributional characteristics of landslides in Afghanistan and conducted a risk assessment that included landslide susceptibility and hazard, and vulnerability of the bearing areas. The existing regional studies attest to the accuracy and reliability of the inventory, and the results of the risk assessment using the optimized neural network method in this study are well validated. This study can provide a good database for the Afghan government to carry out relevant pre-disaster warnings and post-disaster reconstruction, which can help to delineate hotspots where landslides may occur, and reduce potential economic losses and human casualties from future landslides.

A FRAM-based Case Study on Leakage Accidents in Cathode Material Manufacturing Process

In this study, the interrelationships among accident prevention functions targeting leakage accidents in cathode material manufacturing process were analyzed using the FRAM (Functional Resonance Analysis Method) method. 17 system functions of accident prevention were derived from the target accidents. Each function was connected and developed into six aspects such as input, output, prerequisites, resources, control, and time. And, the causes of leakage accidents in complex interactions were analyzed in detail. Results showed that the complex interaction and ripple effects of functional variability were analyzed as the cause of the accident unlike the existing root cause analysis (RCA) that deals with simple cause-effect relationships. Specific response plans were derived, including systematic management of operation plans, continuous monitoring of facilities, and strengthening of worker training and information sharing mechanisms. The FRAM method confirmed that it can serve as an effective tool for accident prevention in considered new chemical processes. It is expected to be used as an important tool that can significantly reduce human casualties, property damage, and environmental accidents in the cathode active material manufacturing process.

Development of a Data-Based Machine Learning Model for Classifying and Predicting Property Damage Caused by Fire

Large fires in factories cause severe human casualties and property damage. Thus, preparing more economical and efficient management strategies for fire prevention can significantly improve fire safety. This study deals with property damage grade prediction by fire based on simplified building information. This paper’s primary objective is to propose and verify a framework for predicting the scale of property damage caused by fire using machine learning (ML). Korean public datasets are collected and preprocessed, and ML algorithms are trained with only 15 input data using building register and fire scenario information. Four models (artificial neural network (ANN), decision tree (DT), k-nearest neighbor (KNN), and random forest (RF)) are used for ML. The RF model is the most suitable for this study, with recall and precision of 74.2% and 73.8%, respectively. Structure, floor, causes, and total floor area are the critical factors that govern the fire size. This study proposes a novel approach by utilizing ML models to accurately and rapidly predict the size of fire damage based on basic building information. By analyzing domestic fire incident data and creating fire scenarios, a similar ML model can be developed.

On-screen framing of the Amhara protests in 2016 by journalists in Ethiopia

The main purpose of this research was to investigate the ways Amhara Television (ATV) framed the Amhara Protest and the challenges of protest framing. More specifically, the study is geared toward examining the ways the ATV journalists implemented media framing during the 2016 Amhara protest. The study was designed to a) identify the key frames utilized by ATV while covering public protests across the region, and b) spot the main internal and external obstacles to practising public protest framing in ATV. To attain these goals, a qualitative research design was employed. Media texts, individual in-depth interviews, and focus group discussions (FGD) were used to gather the data, and the data were analysed thematically. The findings indicate that ATV used the cause of the protest, the consequence of the protest, the government's response to the protest, and the solution to the protest as key frames. The station framed that a lack of good governance and development were the main factors that caused the protest, which is different from the quest of street demonstrators [6]. ATV portrayed the protest in a negative light, emphasizing the significant economic damage and human casualties it caused. Declaring a state of emergency, deep government renewal, and setting up command posts are portrayed as measures taken by the government. Furthermore, the approach to ending the public protest is portrayed as avoiding violent measures and peacefully presenting protestors' questions. To frame solutions, ATV soug ht the help of government officials, religious leaders, and Amhara region residents who support the perspective of the government and oppose the continuance of public demonstrations. Finally, Amhara's protest framing by ATV was heavily hampered by professional constraints, a lack of self-confidence, inconsistencies in institutional training, media ownership, political influence, emotional reactions from the public against ATV journalists, and a lack of collaboration from political leaders.

Threat modeling of industrial control systems: A systematic literature review

Threat modeling is the process of identifying and mitigating potential threats to a system. It was originally developed to enhance software security during the design phase but has since been adapted for Industrial Control Systems (ICSs). ICSs are complex and interconnected systems that control critical infrastructure, such as power plants, water treatment facilities, and manufacturing plants. As such, they are major targets for cyberattacks, which may lead to human casualties, severe national security impacts, and financial instability. This systematic literature review explores the existing threat modeling methodologies for ICSs and emphasizes the importance of employing methodical frameworks that cover safety, security, and privacy aspects with clear procedural guidelines. The review reveals that ICSs threat modeling often lacks validation to ensure that the used methodologies are effective in identifying and mitigating threats. This study emphasizes the need to develop and apply better validation metrics in case studies. The main goal of this review is to help cyber security researchers and practitioners in selecting a suitable threat modeling approach that facilitates the creation of ICSs with an acceptable level of security.

Evaluation of disaster-bearing capacity for natural gas pipeline under third-party damage based on optimized probabilistic neural network

Third-party damage has become a significant factor leading to pipeline failures and leaks, resulting in severe ecological damage and human casualties. Traditional safety risk analysis methods in the past have focused on severing the causal chain of accidents, while neglecting the unique characteristics of different pipeline segments and environmental factors on their ability to bearing disasters. This paper presents a framework for evaluating the disaster-bearing capacity of natural gas pipeline third-party damage based on probabilistic neural networks. A comprehensive disaster-bearing capacity index system for evaluating third-party damage to natural gas pipelines has been constructed by analyzing the causal factors and potential consequences of third-party damage risk incidents. Next, the cluster analysis and indicator weighting information is used for achieving the comprehensive disaster-bearing capacity assessment. Finally, the data samples are constructed with the index levels and comprehensive disaster-bearing capacity levels of each evaluation unit. An optimization algorithm, Particle Swarm Optimization (PSO), is utilized to improve the parameter selection process of the Probabilistic Neural Network (PNN) model, in order to evaluate the disaster-bearing level of each unit. A typical long-distance natural gas pipeline in Zhejiang Province was selected as the case study, and a total of 200 research units were obtained through pipeline analysis. Furthermore, 14 indicator data were obtained by leveraging multiple sources of data surrounding the pipeline. With the utilization of cluster analysis, it was determined that Gaussian mixed model clustering method ultimately identified a total of 5 different levels of disaster-bearing capacity. The optimal smoothing factor can be accurate from 0.5 to 0.588 by using the PSO-PNN model, and the final accuracy rate is improved from 88% to 98%, which is obviously the introduction of PSO optimization algorithm in the PNN model can improve the accuracy of the model. This represents a significant improvement in accuracy when compared to traditional PNN models. The proposed evaluation method can provide scientific basis for the prevention and control of third-party damage in oil and gas pipelines and pipeline planning.

Стійкість економік ЄС та України в умовах російської агресії

The brutal and inexplicable military aggres­sion of Russia against Ukraine has caused the disruption of macroeconomic stability in our country. The war has led to immense human casualties and the loss of means of livelihood, placing a heavy burden on the economy and forcing millions of people to leave their homes. The longer the armed conflict is persisting, the more severe the consequences are becoming, not only for Ukraine but also for its partners, with the European Union being a key one. The functioning of the economy during wartime exacerbates the question of ensuring its resilience. The aim of this article is to identify macroeconomic shocks in the context of war, analyse the level of economic resilience in the EU and Ukraine, and justify directions for supporting the Ukrainian eco­nomy. General scientific methods were used, including theoretical generalization, comparative systemic analysis, systematization, statistical analysis, grouping, and scientific abstraction. In this research, the properties of economic resilience are described, specifically its capacity to avoid, withstand, and recover from the impact of shocks. Existing macroeconomic shocks of the war are identified, which have determined the trajectory of economic development for both Ukraine and the EU and influenced the level of resilience in their economies. Using approaches from the European Commission and the international company FM Global, the current level of economic resilience in the EU and Ukraine was determined. The war exhausts the resources of the national economy, making Ukraine incapable of independently and sufficiently mobilizing financial resources to counteract shock impacts. Conversely, the EU possesses significant resources, allowing it not only to maintain a sufficient level of economic resilience but also to assist Ukraine. Financial assistance from the EU remains a key source of ensuring the resilience of Ukraineʼs eco­nomy, enabling the implementation of measures to counter the current crisis and the shock impacts associated with it.

A noval approach based on TCN-LSTM network for predicting waterlogging depth with waterlogging monitoring station.

As a result of climate change and rapid urbanization, urban waterlogging commonly caused by rainstorm, is becoming more frequent and more severe in developing countries. Urban waterlogging sometimes results in significant financial losses as well as human casualties. Accurate waterlogging depth prediction is critical for early warning system and emergency response. However, the existing hydrological models need to obtain more abundant hydrological data, and the model construction is complicated. The waterlogging depth prediction technology based on object detection model are highly dependent on image data. To solve the above problem, we propose a novel approach based on Temporal Convolutional Networks and Long Short-Term Memory networks to predicting urban waterlogging depth with Waterlogging Monitoring Station. The difficulty of data acquisition is small though Waterlogging Monitoring Station and TCN-LSTM model can be used to predict timely waterlogging depth. Waterlogging Monitoring Station is developed which integrates an automatic rain gauge and a water gauge. The rainfall and waterlogging depth can be obtained by periodic sampling at some areas with Waterlogging Monitoring Station. Precise hydrological data such as waterlogging depth and rainfall collected by Waterlogging Monitoring Station are used as training samples. Then training samples are used to train TCN-LSTM model, and finally a model with good prediction effect is obtained. The experimental results show that the difficulty of data acquisition is small, the complexity is low and the proposed TCN-LSTM hybrid model can properly predict the waterlogging depth of the current regional. There is no need for high dependence on image data. Meanwhile, compared with machine learning model and RNN model, TCN-LSTM model has higher prediction accuracy for time series data. Overall, the low-cost method proposed in this study can be used to obtain timely waterlogging warning information, and enhance the possibility of using existing social networks and traffic surveillance video systems to perform opportunistic waterlogging sensing.

Storage Earth Dam Failure due to Liquefaction Caused by Earthquakes

Aims: We are researching causes and criteria for the liquefaction dam failure by analyzing the safety of the dam under static and dynamic loads against shear failure using the finite element technique, which is used to simulate stability assessment for selected earth dams under different loading conditions. Background: Storage Massive earth dams are vulnerable to collapse during earthquakes, which can have severe effects ranging from direct human casualties to indirect economic losses. How seismically fragile earth dams are and what issues may arise from a failure depend on how they respond to earthquakes. Slope failure, piping, displacement, and/or settlement are examples of seismic responses that are caused by weak soil and/or the liquefaction of loose sands. Earth dam failure can be caused by a variety of factors, including seepage through the dam body, hydraulic issues, structural instability, and liquefaction failure brought on by earthquakes. Objective: The objective of this study is to find a way to design of earth-fill dams. Methods: The finite element method is a numerical solution. This method is based on a grid pattern (not necessarily rectangular) which divides the flow region into discrete elements and provides N equations with N unknowns. Material properties, such as permeability, are specified for each element, and boundary conditions (heads and flow rates) are set. The finite element method has several advantages over the finite difference method for more complex seepage problems. Results: The Lower San Fernando Dam is dangerous under dynamic loads, and the F.O.S. values for the upstream and downstream directions are 0.264 and 0.183, respectively. 1350 m2 is the Lower San Fernando Dam's liquefaction area. 40.67% of the Lower San Fernando Dam's overall foundation area is represented by that figure. Tapar (India) dam is hazardous due to slope failure under dynamic loads, and the F.O.S. values for the upstream and downstream directions are 0.5 and 0.109, respectively. Tapar Dam in India has a liquefaction area of 457 m2. This amount equals 52.33 percent of the Tapar (India) dam's entire foundation area. The slope failure under dynamic loads and the F.O.S. values of 0.313 and 0.548 for the slopes of the river upstream and downstream of Fatehgadh dam (India), respectively, lead to the conclusion that it is dangerous. 333.5 m2 is the size of the liquefaction area of the Fatehgadh dam in India. The foundation area of the Fatehgadh (India) dam as a whole is represented by that figure at 78.75%. Saluda Dam in Columbia is an unsafe slope failure under dynamic loads, and the F.O.S. values for the upstream and downstream directions are 0.102 and 0.101. Saluda Dam in Columbia has a 32095 m2 liquefaction area. This value represents 32.96% of the Saluda Dam's total foundation area (Columbia). Conclusion: Conclusions state that 32.96% of the minimum liquefaction zone area is what causes liquefaction failure. Under the effect of seismic stresses, a safe design standard for storage earth dams is produced. The evaluation must also take into account the specifications for safety limitations based on global norms, regulations, and codes. examining the dam safety requirements for dynamic loads.

Earthquake Environmental Effects and Building Properties Controlling Damage Caused by the 6 February 2023 Earthquakes in East Anatolia

On 6 February 2023, East Anatolia was devastated by two major earthquakes resulting in hundreds of thousands of collapses and tens of thousands of human casualties. This paper investigates the factors related to building properties and earthquake environmental effects (EEEs) that contributed to the building damage grade and distribution in southeastern Turkey. In regards to the building construction properties, the loose enforcement of the building code, the random urban planning solutions and the poor construction standards are the main construction deficiencies that led to one of the largest disasters in Turkey’s recent history. Regarding geological factors, the triggering of primary and secondary EEEs largely shaped the grade and distribution of damage. Where coseismic surface ruptures intersected with the built environment, heavy to very heavy structural damage was observed. This was evident in many cases along the ruptured segments of the East Anatolian Fault Zone (EAFZ). Liquefaction observed close to waterbodies caused damage typical of building foundation load-bearing capacity loss. The earthquake-triggered landslides affected mainly mountainous and semi-mountainous settlements characterized with pre-earthquake high related susceptibility. The high susceptibility to generation of EEEs was extensively confirmed in many cases resulting in extensive damage. The provided information highlights the importance of such studies for hazard mitigation and disaster risk reduction.

Gas-dynamic processes and causes of accidents at mines

Geodynamic phenomena that occur during the exploitation of a coal-bearing massif lead to human casualties, reduce the speed of penetration, and significantly complicate the operation of the mine.The article analyzes existing views on geodynamic processes occurring within mining fields located in coal-bearing formations. A safe technology for identifying potential outburst locations during coal mining is characterized. The technology has been tested during prospecting and environmental studies within the mining workings at active mines and abandoned mining fields. The results of profile and planar investigations in he areas exhibiting geodynamic outbursts in coal deposits and mines are presented.When sampling subsoil air using the technology of structural-thermo-atmospheric-hydrogeochemical research (STAHGS), which is carried out on the surface of the earth in profile and planar versions, the authors came to the conclusion about the possibility of predicting fault zones of increased permeability and zones of low fluid permeability. The first affect the rate of destruction of rocks under pressure, and in the second case, the possibility of gas emissions into the mine, which can be accompanied by an explosion and fire. Such zones are dangerous, because the most destructive events occur in places of collision. Early identification of places of geodynamic stress of rocks, fault zones of increased permeability, accumulation of gases – all this will allow to make a decision in advance about changing the conditions of conducting mining operations and degassing of the coal massif.In addition, the authors draw attention to hydrogen gas as one of the causes of initiation of explosions and fires during underground mining operations in mines, which is not taken into account at the current stage of safety control of mining operations. The possible cause of a hydrogen explosion is not taken into account in coal mine accidents. The appearance of hydrogen and helium in coal samples taken from the mine may indicate the deformation of the massif, which leads to the opening of cracks and the outflow of gas from deeper horizons. And then an increase in the concentration of hydrogen, not methane, creates the conditions for explosions and fires.

International environmental policy processes that dispossessed developing societies of public land resources: A case study of Nepal

AbstractPublic lands including forests and community pastures are still crucial means of local livelihood, social security, and environmental conservation in many developing countries including Nepal. However, these resources are increasingly managed primarily to offset greenhouse gas emissions of developed countries. The new management has exacerbated many local problems: livelihood constriction, social crises, human casualties (deaths and serious injuries), biodiversity degradation, and water scarcity including cryosphere retreating. Drawing data from multiple sources, this study attempted to explain the international political objectives and processes that dispossessed developing societies of public land resources for the benefit of developed countries. It shows that representatives of the developed countries were proactively and strategically involved in agenda formation, solutions negotiations, and decision-making while developing international environmental policies, and succeeded to structure the policies for managing the resources of developing countries for the best benefit of their own countries. The developed countries provided funds and experts, as strategic tools, through international aid agencies to implement the policies of their interest in institutionally weak countries. In Nepal, the aid agencies influenced the thinking of the public and the decisions of the government and other stakeholders through a series of strategic measures. They propagandized false crises, worked with a coalition of powerful international agencies, offered free technical support, and changed national policies proactively to manage the land resources for achieving their missions. Active involvement in policy implementation also helped the agencies to monitor implementation hurdles and apply other tactics to resolve them. Lucrative flash incentives were provided to motivate and get the support of communities, powerful stakeholders, and politicians to implement the policies. Psychosocial pressures were also applied to persuade local communities and their leaders for getting local cooperation in making and practicing new legal institutions (government authority rules or orders, user group rules, and forest management plans) that bind and control local communities for forest protection. The institutions obliged local communities to contribute free labor or cash for developing, modifying, and protecting the forests. These two levels of interventions led to the further development of reinforcing institutions, resource conditions, and social-ecological systems that secured benefits for developed countries and deprived local communities of power to control, produce and access the public land resources in their own backyard for years. This study also showed that international environmental policies and aid agencies have respectively served as institutional weapons and vehicles for materially and institutionally powerful countries to colonize the land resources of weaker countries, without using of physical coercion or deplyment of military forces.

Vulnerability assessment of debris flow in the central Peruvian rainforest – An intercultural approach

For several decades, calamitous debris flows have inflicted profound negative impact on Peruvian rainforest society, encompassing both economic losses and human casualties. To address this concern, this study proposes a methodological tool to assess vulnerability while focusing on intercultural nuances. The contextual analysis of the incident reports identifies La Merced (Junín, Perú) as a severely affected locality, thereby justifying its selection for a detailed case study on the Pampa del Carmen sector. The study conducted a thorough systematic review of parameters such as diglossia, poverty, and origin that are crucial for vulnerability assessment. Moreover, these parameters aided in developing a structured digital survey. The integration of survey data into the analytic hierarchy process revealed high levels of vulnerability in the sector, emphasizing the imminent need for targeted interventions. The intercultural approach is significant as it facilitates future risk mitigation strategies based on effective integration and genuine acknowledgment of social dynamics and individual freedoms within the region for devising impactful risk management policies and plans.

IMPROVEMENT OF TECHNICAL MEASURES FOR PROCEDURES IN ACCIDENTS ON GAS SYSTEM

Due to properties as flammability, explosiveness, suffocation (decreased oxygen content), toxicity (incomplete combustion), large lengths of gas pipelines, risk of leakage, etc., natural gas is media that requires special care during transport and use. There are many examples in the world where unprofessional and negligent use of gas led to unimaginable and tragic consequences with human casualties and huge material damages. This paper analyzes management of accidental situations at the Sarajevo Canton gas system. Problematic aspects of management will be presented through two case studies instead of presenting idealized models that integrate all structures responsible for dealing with such accidental situations. The methodological approach in this paper is presented through selection of accidents on gas network that had a significant impact and required a coordinated response from the competent services. The goal is to consider possibilities of improving technical measures by installing valves with remote control, which would raise safety to a higher level. With this step forward in technical terms, it would be possible to close the valve on gas system in the shortest period of time by controlling it from the dispatch center, which would stop the uncontrolled gas release. The paper will suggest locations where it would be optimal to install valves with remote control, in order to close the necessary section, and at the same time, the least number of consumers would have a gas supply interruption. Key words: gas system, accident, technical measures

The Use of Fear Appeal to Promote Safe Behaviors Among People in the Event of an Earthquake

Earthquake, an unpredictable natural disaster, poses a significant threat to public health, causing physical, human, social, cultural, and economic damages [1]. Iran is among the world’s top 10 earthquake-prone countries—remembering earthquakes cause the greatest human casualties. Preparedness and safe behavior before and during earthquakes are crucially important to reduce the damage of earthquakes and prevent life-threatening incidents and property damage [2]. Each year, unexpected events affect 200 million people, resulting in economic losses of up to 3% of the affected countries’ GDP (gross domestic product). By observing safe behaviors during earthquakes, it is possible to reduce risks and prevent casualties and damages [3].

Multiweek Prediction and Attribution of the Black Saturday Heatwave Event in Southeast Australia

Abstract Southeastern Australia experienced an extreme heatwave event from 27 January to 8 February 2009, which culminated in the devastating “Black Saturday” bushfires that led to hundreds of human casualties and major economic losses in the state of Victoria. This study investigates the causes of the heatwave event, its prediction, and the role of anthropogenic climate change using a dynamical subseasonal-to-seasonal (S2S) forecast system. We show that the intense positive temperature anomalies over southeastern Australia were associated with the persistent high pressure system over the Tasman Sea and a low pressure anomaly over southern Australia, which favored horizontal warm-air advection from the lower latitudes to the region. Enhanced convection over the tropical western Pacific and northern Australia due to weak La Niña conditions appear to have played a role in strengthening the high pressure anomalies over the Tasman Sea. The observed climate conditions are largely reproduced in the hindcast of the Australian Community Climate and Earth System Simulator–Seasonal prediction system version 1 (ACCESS-S1). The model skillfully predicts the spatial characteristics and relative intensity of the heatwave event at a 10-day lead time. A climate attribution forecast experiment with low atmospheric CO2 and counterfactual cold ocean–atmospheric initial conditions suggests that the enhanced greenhouse effect contributed about 3°C warming of the predicted event. This study provides an example of how a S2S prediction system can be used not only for multiweek prediction of an extreme event and its climate drivers, but also for the attribution to anthropogenic climate change.