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Typhoons are recurring meteorological phenomena in the southeastern coastal area of China, frequently triggering debris flows and other forms of slope failures that result in significant economic damage and loss of life in densely populated and economically active regions. Accurate prediction of typhoon-triggered debris flows and identification of high-risk zones are imperative for effective risk management. Surprisingly, little attention has been devoted to the construction of physical vulnerability curves in typhoon-affected areas, as a basis for risk assessment. To address this deficiency, this paper presents a quantitative method for developing physical vulnerability curves for buildings by modeling debris flow intensity and building damage characteristics. In this study, we selected the Wangzhuangwu watershed, in Zhejiang Province of China, which was impacted by a debris flow induced by Typhoon Lekima on August 10, 2019. We conducted detailed field surveys after interpreting remote sensing imagery to analyze the geological features and the mechanism of the debris flow and constructed a comprehensive database of building damage characteristics. To model the 2019 debris flow initiation, entrainment, and deposition processes, we applied the Soil Conservation Service-Curve Number (SCS-CN) approach and a two-dimensional debris flow model (FLO-2D). The reconstructed debris flow depth and extent were validated using observed debris flow data. We generated physical vulnerability curves for different types of building structures, taking into account both the degree of building damage and the modeled debris flow intensity, including flow depth and impact pressure. Based on calibrated rheological parameters, we modeled the potential intensity of future debris flows while considering various recurrence frequencies of triggering rainfall events. Subsequently, we calculated the vulnerability index and economic risk associated with buildings for different frequencies of debris flow events, employing diverse vulnerability functions that factored in uncertainty in both intensity indicators and building structures. We observed that the vulnerability function utilizing impact pressure as the intensity indicator tends to be more conservative than the one employing flow depth as a parameter. This comprehensive approach efficiently generated physical vulnerability curves and a debris flow risk map, providing valuable insights for effective disaster prevention in areas prone to debris flows. [ABSTRACT FROM AUTHOR]

A situation of emergency involving the whole population introduces changes in the dynamics of the health services that are provided. The magnitude of these shifts should be also linked to the medical speciality within which the health benefits are delivered. The aim of the paper was to identify changes in tendencies in inpatient medical service delivery during the COVID-19 pandemic by medical specialties. On the basis of a database extracted for in-patient treatment received from the public payer, a retrospective analysis was carried out. Comparing the values of the dynamics of the services provided in each medical speciality, the period before the COVID-19 pandemic was collated to the years of the pandemic (2020–2021). In the period before COVID-19, positive patient dynamics were observed in more than half of the specialities. Between 2020 and 2021, virtually all specialties reversed the trend and negative dynamics were recorded. The dynamics in 2021 indicate a process of return to the values from 2015 to 2019. Emergency situation has affected the dynamics of healthcare provision in different specialities to various extent. The most resistant to the negative impulses of the state of emergency were the areas that are strictly organisationally and financially defined (e.g. the group of "therapeutic and drug programmes"). [ABSTRACT FROM AUTHOR]

Background: According to the legal definition healthcare systems and their components (e.g., hospitals) are part of the critical infrastructure of modern industrial nations. During the last few years hospitals increasingly became targets of cyber attacks causing severe impairment of their operability for weeks or even months. According to the German federal strategy for protection of critical infrastructures (KRITIS strategy), hospitals are obligated to take precautions against potential cyber attacks or other IT incidents. Objective: This article describes the process of planning, execution and results of an advanced table-top exercise which took place in a university hospital in Germany and simulated the first 3 days after a cyber attack causing a total failure of highly critical IT systems. Material and methods: During a first stage lasting about 8 months IT-dependent processes within the clinical routine were identified and analyzed. Then paper-based and off-line back-up processes and workarounds were developed and department-specific emergency plans were defined. Finally, selected central facilities such as pharmacy, laboratory, radiology, IT and the hospitals crisis management team took part in the actual disaster exercise. Afterwards the participants were asked to evaluate the exercise and the hospitals cyber security using a questionnaire. On this basis the authors visualized the hospitalʼs resilience against cyber incidents and defined short-term, medium-term and long-term needs for action. Results: Of the participants 85% assessed the exercise as beneficial, 97% indicated that they received adequate support during the preparations and 75% had received sufficient information; however, only 34% had the opinion that the hospitalʼs and their own preparedness against critical IT failures were sufficient. Before the exercise took place, IT-specific emergency plans were present only in 1.7% of the hospital facilities but after the exercise in 86.7% of the clinical and technical departments. The highest resilience against cyber attacks was not surprisingly reported by facilities that still work routinely with paper-based or off-line processes, the IT department showed the lowest resilience as it would come to a complete shutdown in cases of a total IT failure. Conclusion: The authors concluded that the planning phase is the most important stage of developing the whole exercise, giving the best opportunity for working out fallback levels and workarounds and through this strengthen the hospitals resilience against cyber attacks and comparable incidents. A meticulous preparedness can minimize the severe effects a total IT failure can cause on patient care, staff and the hospital as a whole. [ABSTRACT FROM AUTHOR]

In recent years, coal and gas outburst disasters are still occurring and difficult to prevent, seriously endangering the safety of coal mine production. It is well known that the transporting and crushing of outburst coal is the main pathway of energy dissipation during the coal and gas outburst process. However, a consensus regarding how much gas involves in outburst and affects energy dissipation is still lacking. Quantitative study on the gas effect on migration and fragmentation characteristics of outburst coal in restricted roadway space can improve the energy model and guide the prevention and control of gas outburst. In this paper, an improved visual coal and gas outburst dynamic effect simulation experiment system was used to conduct outburst simulation experiments at different gas pressure conditions. The results showed that the movement of outburst coal in the roadway has experienced various flow patterns. In the initial stage of the outburst, under low gas pressure condition, the motion of the outburst coal was dominated by stratified flow. However, as the gas pressure increases, the initial acceleration increases, and the outburst coal mainly move forward rapidly in the form of plug flow. The average velocity at 0.3, 0.5, and 0.8 MPa gas pressure condition were 6.75, 22.22 and 35.81 m/s, respectively. Gas also has a crushing effect on outburst coal. With increasing gas pressure, the number of coal powder particles of the same mass increased significantly, and the range of the particle size distribution of the particles decreaed, and the median particle size decreased. As the gas pressure increases, the outburst intensity gradually increases, and the total energy involved in the outburst work also increases. However, the energy dissipation pathways are different. At 0.3 MPa, the energy dissipation is dominated by crushing energy, which is about six times the ejection energy. As the gas pressure increased to 0.8 MPa, the proportion of the ejection energy gradually increases to about twice that of the crushing energy. Under the experimental conditions, 2.71–13.43% of the adsorbed gas involves in the outburst (AGIO) through rapid desorption, and the proportion increases with increasing gas pressure. This paper improves the energy model of coal and gas outburst, which is applicable to risk assessment and prevention of outburst disasters. [ABSTRACT FROM AUTHOR]

Large-scale disasters occur worldwide, with a continuing surge in the frequency and severity of disruptive events. Researchers have developed several optimization models to address the critical challenges of disaster relief supply chains (e.g., emergency material reserving and scheduling inefficiencies). However, most developed algorithms are proven to have low fault tolerance, which makes it difficult for disaster relief supply chain managers to obtain optimal solutions and meet the emergency distribution requirements within a limited time frame. Considering the uncertainty and ambiguity of disaster relief information and using Interval Type-2 Fuzzy Set (IT2TFS), this paper presents a collaborative optimization model based on an integrative emergency material supplier evaluation framework. The optimal emergency material suppliers are first selected using a multi-attribute group decision-making ranking method. Multi-objective fuzzy optimization is then run in three emergency phases: early -, mid-, and late-disaster relief stages. Focusing on a massive flash flood disaster event in Yunnan Province as a case study, a comprehensive numerical analysis tests and validates the developed model. The results revealed that the proposed optimization method can optimize emergency material planning while ensuring that reserve material safety inventory is always maintained at a reasonable level. The presented method suggests a fuzzy interval to prevent emergency materials' safety inventory shortage and minimize continuous life/property losses in disaster-affected areas. [ABSTRACT FROM AUTHOR]

Landslide susceptibility mapping (LSM) is a major measure for disaster prevention and control. This paper proposes an incremental learning random forest model that uses random forests for incremental training of landslides. The 1652 landslides in Ganzhou, China from year 2015 to 2020 were divided into three equal parts (T1, T2 and T3) considering time series. And the data of T1 part is used to construct incremental learning random forest model ILRF_1, T2 and T3 part as the new part of incremental training. Landslide susceptibility maps were plotted using ILRF, batch learning random forests (BLRF), batch learning support vector machines (BLSVM), respectively. And the accuracy is verified using the receiver operating curve (ROC). The results showed that the ILRF was the most promising model(AUC = 0.937) for the LSM, followed by BLRF(AUC = 0.905), BLSVM(AUC = 0.865). ILRF has better predictive ability in the susceptibility evaluation of this region. It is very high-prone area accounts for the smallest proportion, mainly concentrated in the mountain front area in the northeast of Ganzhou City. The multi-year average rainfall, population density, and normalized difference vegetation index are the first three important factors that control the spatial possibility of landslides. [ABSTRACT FROM AUTHOR]

The regional seismic risk assessment plays an important role in the disaster prevention and mitigation. The accurate time-history analysis for a region provides an important data base for regional seismic risk assessment. However, an accurate calculation method is limited, then, its accuracy still lacks validation. Meanwhile, there is also a lack of risk assessment methods and indicators applicable to the regional accurate time-history analysis, both of which hinder the seismic risk assessment. In this paper, a regional-scale time history analysis-driven method is proposed for the quantitative evaluation of regional seismic damage risk and verified with a real earthquake damage survey. First, the regional time history analysis method is introduced, including the automatic modeling, response calculating, result analysis and 3-D visualization modules. Second, a calculation method for seismic damage degrees (DD) of buildings is introduced. Subsequently, the probabilistic models of each DD under the ground motions with a specific intensity are calculated based on regional-scale nonlinear time history analysis. Next, the concept of the three-level seismic damage risk is offered, then a determining workflow of seismic damage risks is proposed based on the code-abiding three-level design requirement. Afterward, the regional seismic damage index is suggested for the quantitative evaluation of earthquake-induced damage risk. Finally, the actual seismic damage database of 192 buildings in epicenter of Ludian Earthquake is established through on-site investigations, then used to verify the regional simulation's accuracy. Consequently, it could be an aid to accurately present seismic damage distribution assessments and effectively develop seismic risk mitigation strategies. [ABSTRACT FROM AUTHOR]

The data revolution transforms operations, innovation, and society through artificial intelligence and advanced analytics. Data-driven innovations (DDI) have the most potential to tackle global challenges, including poverty, healthcare, climate actions, disaster management, gender inequality, peace and justice and others. This paper identifies the sources of DDI capabilities to address various global challenges. The findings show three major foundations of DDI capabilities: market orientation, infrastructure orientation, and talent orientation. Theoretically, these findings highlight the role of dynamic DDI capabilities to sense, seize and transform global challenges. Practically, we present guidelines for developing DDI in an agile and efficient manner that is fair and inclusive. [ABSTRACT FROM AUTHOR]

The dynamic effect is a very important issue widely debated by scholars when studying the genetic and disaster-causing mechanisms of earthquake-triggered landslides. First, the dynamic effect mechanism and phenomena of earthquake-triggered landslides were summarized in this paper. Then, the primary types of dynamic effects were further used to interpret the Mogangling landslide in Moxi Town of Luding County, China. A field investigation, remote sensing, numerical calculation and theoretical analysis were carried out to illustrate the failure mechanism of slope rock masses affected by earthquakes. The interaction between seismic waves and slope rock masses and the induced dynamic effect of slope rock masses were primarily accounted for in the analysis. The slope topography, rock mass weathering and unloading characteristics, river erosion, regional seismogenic structure, and rock mass structure characteristics were also discussed. The results showed that the formation of the Mogangling landslide was mainly related to the high amplification effect of seismic acceleration and back slope effects, interface dynamic stress effects, and double-sided slope effects of seismic waves caused by the catastrophic Ms 7.75 Moxi Earthquake in 1786. The principles for the site and route selection of large-scale infrastructure in the planning stage and the scientific prevention of seismic geological disasters were proposed on the basis of the dynamic effect of earthquake-induced landslides. [ABSTRACT FROM AUTHOR]

Mission-critical applications refer to the real-time applications, which require fast and secure service provisioning, such as defense sector and disaster management. This paper proposes a delay-aware and secure service provisioning model for such types of applications. As a use-case, we have considered the defense sector, which is a vital sector for a country's all-round well-being including security, safety, society, and economy. In the conventional sensor-cloud model, the sensor data is stored and processed in the cloud. However, the sensor nodes have small coverage and the use of the long distant cloud servers increases the delay. Therefore, the conventional sensor-cloud model may not be efficient for defense application. Moreover, data hiding for security purposes is another important aspect of this field. To address these challenges, this paper proposes a mobility-aware sensor-fog paradigm for mission-critical applications based on network coding and steganography, referred to as Mobi-Sense. In Mobi-Sense, steganography is used for hiding the data during transmission. The theoretical results demonstrate that Mobi-Sense outperforms the existing frameworks with respect to delay and power consumption by ∼ (40 - 80) %. The simulation results present that Mobi-Sense reduces the delay by ∼ (18 - 40) % than the conventional sensor-cloud framework for mission-critical applications. An optimal path finding algorithm based on deep learning has been deployed in the context of disaster scenario. The experimental analysis shows that the proposed optimal path finding method achieves precision and accuracy above 90%. This is observed that our proposed modules have outperformed existing baselines in terms of accuracy, delay, and power consumption. [ABSTRACT FROM AUTHOR]

Agro-pastoral ecotone of northern China is the prominent area for agricultural production, but it is also the most typical ecological fragile area with frequent drought disasters. Taking Yulin City at Shaanxi Province in China as the case area, the paper aims to investigate the spatio-temporal changes of agricultural vulnerability to drought in China's agro-pastoral ecotone in the period 2000 to 2020. The results show that: 1) the agricultural vulnerability to drought in Yulin City has shifted from high vulnerability in the period 2000–2010 to low vulnerability in the period 2011–2020. 2) There exist obvious spatio-temporal differences of the agricultural vulnerability to drought in Yulin City during the research period. 3) Four sensitive events and 14 resilient events were identified in the research and the crops of Yulin had become more resilient to drought. Finally, the paper put forward with policy implications to make adaptive strategies of agriculture to climate change in China's agro-pastoral ecotone in the future, e.g., carrying out agricultural zoning based on agricultural production conditions, intensifying the construction of disaster prevention and relief system, and integrating with modern agricultural technology to develop new type agriculture. [ABSTRACT FROM AUTHOR]

This paper focuses on the role of local community organizations in disaster management following the Beirut explosion of August 4, 2020. It answers the following question: How can interorganizational relationships be effective in their response to disaster in a developing context? The main argument is that a lack of communication and collaboration made non-profit organizations' interventions unsuccessful, and governmental support is necessary for successful interorganizational coordination. This paper develops a framework for disaster response that can be adapted in developing countries. Its theoretical importance is in adding to scarce literature on interorganizational relationships in developing contexts. [ABSTRACT FROM AUTHOR]

Nowadays, logistics is one of the most important tools in disaster relief operations. The logistic planning is essential and a key component in covering the initial needs in the immediate aftermath of any disaster. Planning is both necessary and practical, as it is generally possible to predict the types of disasters that should affect a given location and the needs that such disasters will be likely to cause. Transport planning, reception and distribution of emergency supplies, type and quantity of the resources, the way of procurement and storage of the supplies, the tools of the tracking and means transportation to the stricken area, the specialization of teams participating in the operation and plan of cooperation between these teams, are some vital life-saving coordination roles after natural disasters are connected directly to logistic planning. Turkey is located in one of the most active earthquake and volcanic regions which causes to many major earthquake-prone, in the world with a majority of the population living in these earthquake-prone areas. Earthquakes are one of the major disasters that require emergency logistic planning strategies due to their devastating effects, the large-scale natural disasters could cause major problem on commodities such as food, medicine etc. In this paper, a game theoretical model for emergency logistic planning is developed. To do this a cooperative game model is constructed from a flow problem which occurred after an earthquake in Istanbul. Several solution concepts for maximizing the transferred commodity are given. The paper ends with a conclusion and outlook to future studies. [ABSTRACT FROM AUTHOR]

With the emergence of GPS-equipped portable devices and Online Social Networks, geo-tagged textual data have been highly produced on a continuous basis, which can provide important information for various applications, such as marketing, disaster response, and so on. Therefore, processing continuous spatial-keyword queries over streaming data is a hot topic for the research community nowadays. However, applying such queries to moving objects is computationally expensive due to the frequent updates of objects' information that will continuously change the queries' answers. Few research works focus on processing spatial-keyword queries over moving objects, so this problem demands more exploration by research. This paper proposes Lagic; a cloud-based solution scheme to process continuous spatial-keyword range queries over moving objects. Lagic is the first model that provides an exact solution to the problem and minimizes the overhead on users' devices. A parallelized in-memory indexing structure is proposed to ensure the efficiency and scalability of Lagic. Short-term Safe Regions and a new approach for Buffer Regions are presented to reduce the number of required computations to update queries' answer sets in an incremental manner. Evaluations show that Lagic can reduce the total processing time to seven folds less than a baseline model. It also provides better computational scalability and efficiency. Furthermore, Lagic shows stability in continuous running time against variations of queries' and objects' attributes. [ABSTRACT FROM AUTHOR]

The stadium is a wind-sensitive structure, and the tornado has a greater damage to the stadium. The Ward tornado generating device was used to simulate the wind load characteristics of the stadium during tornado action in order to investigate the wind load characteristics of the stadium. The results of this paper are compared with the experimental results to verify the accuracy of the simulation method. The wind load characteristics of the stadium and its most vulnerable location during a tornado are ascertained by examining the wind pressure coefficient and wind speed distribution law of the stadium's upper and lower surfaces with different swirl ratio. The influence of uniform wind and wind profile on the wind load of the stadium is explored by comparing and analyzing the wind pressure coefficient of the stadium surface with and without wind profile at the entrance of the wind field. The impact of varying ground roughness on the stadium is compared in order to ascertain how it affects the wind speed, stadium wind pressure, and vortex core radius. The vorticity of the stadium in the tornado wind field with different swirl ratios was compared and analyzed, and the influence of the stadium on the vorticity development of the wind field was explored. This study reveals the effects of swirl ratio, ground roughness and wind profile on the wind pressure, wind speed and vorticity of the stadium under tornadoes, and determines the most vulnerable location and the most unfavorable factors. The above research is helpful to provide reference for disaster prevention and mitigation of stadium structure design. [ABSTRACT FROM AUTHOR]

Numerical models are used for detailed and site-specific tailings dam breach analyses (TDBAs) to estimate the downstream inundation and deposition resulting from a potential breach at a tailings dam. The results of TDBAs are key inputs into risk assessments, consequence classification, and emergency planning. This paper describes the research and development of a database of 12 tailings dam breach events with a specific focus on observations that are needed for numerical modelling, in conjunction with an assessment of existing dam breach conventions to improve consistency in reporting. The characteristics relevant to modelling include outflow volumes, breach processes, breach geometries, and runout observations local to the downstream area. This study and the new database shed light on the diversity of outflow materials, facility arrangements, breach processes, and downstream environments that affect the breach development and tailings runout. Familiarity with case studies is a crucial element of expert judgement for forward-analysis TDBAs, which this database supports. The database can also be used to define model inputs for back-analysis of additional tailings dam breach events, and simultaneously provides calibration or validation constraints with the runout observations. Continued review and critical assessments are needed to reduce uncertainties and to enhance case history data availability and quality in this database. [ABSTRACT FROM AUTHOR]

Static formations of swarms of rotorcraft drones, used for example in disaster management, are subject to intrusions, and must bear the cost of holding the formation while avoiding collisions which leads to their increased energy consumption. While the behaviour of the intruder is unpredictable, the formation can have its parameters set to try to balance the cost of avoidance with its functionality. The novel model presented in this paper assists in the selection of parameter values. It is based on multivariate linear regression, and provides an estimate of the average disturbance caused by an intruder as a function of the values of the parameters of a formation. Cross-entropy is used as a metric for the disturbance, and the data based are generated through simulations. The model explains up to 54.4% of the variability in the value of the cross-entropy, providing results that are twice as good as the baseline estimator of the mean cross-entropy. [ABSTRACT FROM AUTHOR]

The ever-increasing environmental complexity makes strategizing a difficult multidimensional task. In this paper, we conducted a corporate foresight case study in an SME in packaging industry in Iran. The case study offers a detailed procedure of implementing corporate foresight (CF) and how it can reshape traditional strategic planning. A multimethodological approach was taken in this case study. Once an intraorganizational team in studied company was formed, archival document analysis, PESTEL and weak signal analysis, importance/uncertainty matrix, cross-impact balanced (CIB) analysis, scenario construction, wind tunneling, robust decision-making, and premortem session were used to create foresight intelligence. This paper presents a detailed description of how CF can be linked to conventional strategizing and reshape it. Key variables, driving forces, critical uncertainties, and 4 plausible scenarios are presented. The case study illustrates that as alternative realities challenged the foresight teams ingrained presuppositions, they found the dialectic between "weight of history" and "pull of future" both revelatory and indigestible. The CF intervention illuminated the fragility of preexisting strategic objectives, the implicit optimism bias underlying them, and an overflowing-plate syndrome of formulating too many strategic objectives. Consequently, studied company decided to revisit their strategic objectives, prepare a contingency plan for worst-case scenarios, and begin developing a crisis-ready culture. The comprehensive case study demonstrates how CF can enhance and contradict traditional strategizing, presents a rich know-how of added value of scenarios, and provides some subtleties and complexities of CF interventions. [ABSTRACT FROM AUTHOR]

Estimating the number of buildings in any geographical region is a vital component of urban analysis, disaster management, and public policy decision. Deep learning methods for building localization and counting in satellite imagery, can serve as a viable and cheap alternative. However, these algorithms suffer performance degradation when applied to the regions on which they have not been trained. Current large datasets mostly cover the developed regions and collecting such datasets for every region is a costly, time-consuming, and difficult endeavor. In this paper, we propose an unsupervised domain adaptation method for counting buildings where we use a labeled source domain (developed regions) and adapt the trained model on an unlabeled target domain (developing regions). We initially align distribution maps across domains by aligning the output space distribution through adversarial loss. We then exploit counting consistency constraints, within-image count consistency, and across-image count consistency, to decrease the domain shift. Within-image consistency enforces that the building count in the whole image should be greater than or equal to the count in any of its sub-image. Across-image consistency constraint enforces that if an image contains considerably more buildings than the other image, then their sub-images shall also have the same order. These two constraints encourage the behavior to be consistent across and within the images, regardless of the scale. To evaluate the performance of our proposed approach, we collected and annotated a large-scale dataset consisting of challenging South Asian regions having higher building densities and irregular structures as compared to existing datasets. We perform extensive experiments to verify the efficacy of our approach and report improvements of approximately 7–20% over the competitive baseline methods. The dataset and code are available here: https://github.com/intelligentMachines-ITU/domain-Adaptive-Building-Counting. [ABSTRACT FROM AUTHOR]

Shopping malls are crowded areas which makes the evacuation routes unable to meet personnel evacuation needs. Therefore, this paper proposes the idea of using escalators evacuation to increasing evacuation routes. In addition, the effects of escalator fire shutters on the use of escalator evacuation paths, and the efficiency of mall personnel evacuation under different conditions were simulated. The results show that the different states of fire shutters have different effects on the emergency evacuation. When the fire shutter is not lowered, it will result in 1 person not being evacuated to complete. However, when the fire shutter is lowered to 1.8 m from the ground or sprinklers are set, escalators can be used for evacuation routes are verified. And compared to the normal descent of the fire shutter (only the staircase evacuation, 2 people not evacuate completed), the evacuation of people is completed and the efficiency of evacuation is improved. This result can improve the new evacuation model for emergency evacuation plans of shopping malls with escalators. [ABSTRACT FROM AUTHOR]

On December 29, 2020, nine months after the March Mw 5.4 Zagreb earthquake and amidst the COVID-19 lockdown, a devastating Mw 6.4 earthquake struck near the town of Petrinja, about 50 km SE from the country's capital Zagreb. It was preceded by the Mw 4.9 foreshock from the day before. The main shock claimed 7 fatalities and caused widespread damage. Historical centers of nearby cities with invaluable heritage buildings were significantly affected as were the many residential buildings, built mainly of unreinforced masonry. Damage was observed as far as 60 km from the epicenter. This paper summarizes the seismological aspects of the Mw 6.4 Petrinja earthquake, the emergency response and the main impacts to people and buildings. The description and findings are based on the field observations and a series of post-earthquake activities led by the team of the Faculty of Civil Engineering, University of Zagreb. Typical damage to buildings and usability data are presented with examples based on 50,000 inspection results. By far the most affected were the unreinforced masonry buildings, followed by confined masonry, whereas reinforced concrete buildings were the least affected. The total direct and indirect losses are estimated to 4.8 billion EUR. The provided information represents a useful basis and impetus for improving emergency action and long-term disaster reduction plans in other regions with similar building exposure and seismotectonic settings. [ABSTRACT FROM AUTHOR]

The paper presents an exploratory case study on the operation of intergovernmental relations (IGR) in Mexico during the management of the COVID-19 pandemic to identify and explain the main conflicts faced in the management of the emergency. The main findings are: Mexico is undergoing an aggressive re-centralization process encouraged by inequalities among states and their lack of professional public administrative systems; 2) formal rules for balancing IGR lose value versus informal rules based on transitory political-partisan agreements; 3) the weakness of the rule of law, power imbalances, lack of clear rules, and communication problems difficult intergovernmental collaboration in emergencies management. [ABSTRACT FROM AUTHOR]

Static formations of swarms of rotorcraft drones, used for example in disaster management, are subject to intrusions, and must bear the cost of holding the formation while avoiding collisions which leads to their increased energy consumption. While the behaviour of the intruder is unpredictable, the formation can have its parameters set to try to balance the cost of avoidance with its functionality. The novel model presented in this paper assists in the selection of parameter values. It is based on multivariate linear regression, and provides an estimate of the average disturbance caused by an intruder as a function of the values of the parameters of a formation. Cross-entropy is used as a metric for the disturbance, and the data based are generated through simulations. The model explains up to 54.4% of the variability in the value of the cross-entropy, providing results that are twice as good as the baseline estimator of the mean cross-entropy. [ABSTRACT FROM AUTHOR]

Background: Simulation training is indispensable in emergency medicine, especially for enhancing patient safety. Methods and technologies used include a wide spectrum ranging from simple skill trainers to complex full-scale simulated environments integrating standardized patient actors. Limitations include the simulation of dynamic changes of clinical symptoms, the depiction of emotions and patient movements as well as complex environments, such as lively traffic. Extended reality (XR) holds the potential to overcome these limitations. Methods/aims: Starting with the technological basis and the didactic considerations in the field of XR, the paper reflects the potentials and limitations of this new technology in the domain of medical simulation training. Further focus is put on the integration of XR into existing training curricula. Results: The XR covers various technologies, ranging from PC-based applications that are similar to conventional computer games, over virtual realities enabling spatially freely navigable 3‑dimensional simulation (using closed 3D glasses: head mounted displays, HMD), to mixed-reality applications that combine virtual elements and real physical objects; however, technology alone does not stimulate learning. As with other simulation methods, it is crucial with XR to implement learning objectives, methods and technologies in a suitable teaching-learning arrangement and to familiarize teachers and students with the new technology. Evidence in the literature with respect to learning success is limited by the heterogeneity of technologies, target groups, teaching-learning arrangements and learning outcomes. Overall, significant increases can be shown for the intrinsic motivation of learners, and for high emotional participation (measured as perceived presence in the virtual environment). Discussion: Technological developments and the increasing use of digital media in emergency medical education and training favor the leap from XR-based pure demonstration projects to educational practice. Decisive for the educational success are the clear orientation towards concrete learning goals and a thorough familiarization with the new technology. Conclusion: Simulation training based on XR expands the spectrum of existing simulation methods to integrate new dimensions of learning objectives. Further research on the effectiveness of this method is needed. [ABSTRACT FROM AUTHOR]

The WHO (World Health Organization) study reports that, between 1998-2017, 4.8 million people have been affected by landslides with more than 18000 deaths. The prevailing climate change and erratic high intensity rainfall are expected to trigger more landslides, which can increase the death rates per year in future. Therefore, evolving successful mechanisms to identify and predict landslides is critical in risk reduction and post-disaster management activities. With the applications of Machine Learning , the success rates of landslide identification have been improved significantly. This review paper presents the results of data analysis on the papers published for the last three decades on varying degrees of reliability and success rate on the theme "Machine Learning for landslide identification, mitigation, and prediction". The analyses show how the reliability and accuracy of the landslide prediction model have improved considerably with the tools available in Machine Learning. Though many conventional tools such as statistical packages are available, the Machine Learning algorithms gave a robust dimension for a reliable landslide risk analysis, modeling prediction tools and post-disaster damage identification, This paper recommends a multi-modal framework for characterising landslides in all aspects using Machine Learning techniques that could outperform single modal approaches. Open research problems and future research dimensions by integrating landsat data and Machine Learning for landslide studies are also discussed in this work which would be beneficial for researchers in this field and also to the community at large across the globe. [ABSTRACT FROM AUTHOR]

This paper contains a large-scale seismic assessment of the downtown area of Skikda city in Algeria. This area was identified for the present work based on its importance regarding size and the legacy of older buildings for which information was available. Moreover, seismic microzonation studies for the city of Skikda based on the simulation of local effects are available. This paper aims to evaluate the seismic risk in the old part of Skikda using the Risk-UE LM1 methodology, namely, the empirical method LM1 based on an in situ survey. Damage probability matrices have been obtained for the deterministic (historical events) and probabilistic earthquake scenarios (i.e., characterized by a return period), in accordance with the city's microzoning study. The results of the seismic risk assessment in the city are represented by damage to buildings. Thus, physical damage was also used as a hazard-related variable to predict economic and human losses, and other quantities, such as restoration cost and quantities of debris, were studied. Despite the moderate seismic risk in Skikda, the results show a high risk due to the high exposure and vulnerability of the buildings, which are generally made up of buildings in unreinforced masonry. Important social and economic losses are expected in these areas as well. The seismic risk of Skikda was estimated as moderate with a predicted number of 83 million USD global economic impacts, including economic losses and restoration costs for a seismic intensity scenario of VII. To better manage and analyze the human and economic exposure. The seismic risk maps was elaborated to be used as a guide to decision-making to prepare emergency plans for earthquake risk mitigation in Skikda. [ABSTRACT FROM AUTHOR]

Microseismic (MS) monitoring is an effective and widely applied technology for early warning and prevention of rockburst disasters in deeply buried tunnels. The location of the microseismic source is the top priority of MS monitoring. However, an unknown complex and consistently changing velocity distribution of surrounding rocks is a major challenge for accurate MS source location. A real-time velocity inversion method of monitoring areas during tunnel drilling is urgently needed to provide a newly updated heterogenous velocity model. This paper develops a velocity inversion strategy for surrounding rocks using drilling and blasting seismic data. Inverted heterogeneous velocity models can be obtained at multiple construction stages with drilling. An L2 norm double-difference location method based on the simplex optimization method is provided for MS location using heterogeneous velocity models. Numerical studies test the accuracy of the proposed velocity inversion and MS location method. Noticeable improvement in location accuracy can be observed using updating inverted heterogeneous velocity models. Finally, we conducted a field experiment at two new tunnels with a burial depth of 2400 m in the China Jinping Underground Laboratory Phase II. Multiple blasting sources with known coordinates verified the performance of the proposed MS location method. [ABSTRACT FROM AUTHOR]

To establish and improve the theoretical framework and operation mechanism of emergency information sharing, and to establish the index system and evaluation method of the efficiency of emergency information sharing are the key work to improve the disaster prevention and mitigation capacity of coastal cities in China. In this paper, the synergetic theory is introduced into the construction of emergency information sharing framework. By clarifying the operation relationship between the participants and the information transmission, the framework and operation mechanism of emergency information sharing are constructed. Around the 3 dimensions of emergency information sharing mechanism construction, resource guarantee ability and collaborative driving force of emergency information sharing, the evaluation index system of emergency information sharing collaborative efficiency in coastal cities is proposed, and the evaluation method of emergency information sharing collaborative efficiency based on coupling degree model is proposed. The empirical results show that the efficiency of emergency information sharing in coastal cities in China is generally on the rise, and the contribution rate of the construction level of emergency information sharing mechanism is higher than that of the resource guarantee ability and the collaborative driving force of emergency information sharing, but the efficiency of emergency information sharing in coastal cities in China is still at the bottom level. The research results provide theoretical basis and methods for the emergency management departments of coastal cities in China. [ABSTRACT FROM AUTHOR]

As one of the most destructive geological disasters, a myriad of landslides has revived and developed in the Three Gorges Reservoir area under the combined action of various detrimental factors. Therefore, the pertinently regional landslide susceptibility mapping (LSM) is of great significance for disaster prevention and mitigation. In this study, LSM is prepared by using a boosting-C5.0 decision tree model. Under the landslide verification of on-site investigations, the study area is divided into accumulation and rock areas, and a total of 12 impact factors are selected. TOL and VIF are employed to determine the multicollinearity among the impact factors. The independent training (80%) and validation (20%) datasets are constructed by random sampling for LSM. ANN, C5.0, and SVM are selected for comparative analysis. The results show that there is no rigorous multicollinearity among the impact factors proposed in this paper. The landslide susceptibility in the study area is divided into low, moderate, high, and very high. The highest susceptibility area distributes along the riverside where the landslide ratio is 37.05% in boosting-C5.0 model. Then the ROCs are expropriated to infer the accuracy of each model. The boosting-C5.0 performs the best with the largest area under the curve in both accumulation and rock areas, reaching at 0.991 and 0.990 in the validation sets, respectively. Finally, the composite modification of the 5 validation sets shows that the uncertainty of boosting-C5.0 is concentrated in the intermediate probability areas of susceptibility. This study reveals the feasibility of machine learning in landslide susceptibility assessment, which could provide a basis for the risk management and control of geological disasters. [ABSTRACT FROM AUTHOR]

In this paper I offer the term "potato ethics" to describe a particular professional rural health sensibility. I contrast this attitude with the sensibility behind urban professional ethics, which often focus on the narrow doctor–patient treatment relationship. The phrase appropriates a Swedish metaphor, the image of the potato as a humble side dish: plain, useful, versatile, and compatible with any main course. Potato ethics involves making oneself useful, being pragmatic, choosing to be like an invisible elf who prevents discontinuity rather than a more visible observer of formal rules and assigned tasks. It also includes actively taking part in everyday disaster-prevention and fully recognizing the rural context as a vulnerable space. This intersectional argument, which emphasizes the ongoing, holistic responsibility of those involved in rural communities, draws on work from the domains of care ethics, relational ethics, pragmatic psychology, feminist ethics of embodiment, social location theory, and reflections on geographical narcissism. [ABSTRACT FROM AUTHOR]

Owing to the superelevation in the bend, debris flow rushed out of the channel and destroyed the engineering facilities near the bend, which brings challenges to the design of disaster prevention and mitigation projects. Under the assumption of a continuous homogeneous medium and rectangular channel section, the debris flow velocity in the bend is decomposed into a tangential velocity that causes superelevation and a normal velocity that generates runup. The calculation equations of superelevation, runup, and total superelevation of debris flow in the bend are derived based on the gravity center offset of the flow section in the debris flow movement. The proposed equation can determine and depict the evolution of total superelevation value along the bend, which is useful for engineers to carry out the targeted design in debris flow prevention and control engineering. Meanwhile, using the equation in this paper and two other representative equations, the total superelevation, superelevation, and runup height of debris flow in 37 groups of test data and bends in the case study are computed to compare with the test and measured results. The test findings showed that the runup height accounts for about 40% of the total superelevation, and the runup height of the five bend points in the case accounts for 43–48%, indicating that the runup is an essential part of the total superelevation and needs to be calculated in the design of the bend of the debris flow prevention and control project. [ABSTRACT FROM AUTHOR]

Automatically generating descriptions for disaster news images could effectively accelerate the spread of disaster message and lighten the burden of news editors from tedious news materials. Image caption algorithms are remarkable for generating captions directly from the content of the image. However, current image caption algorithms trained on existing image caption datasets fail to describe the disaster images with fundamental news elements. In this paper, we developed a large-scale disaster news image Chinese caption dataset (DNICC19k), which collected and annotated enormous news images related to disaster. Furthermore, we proposed a spatial-aware topic driven caption network (STCNet) to encode the interrelationships between these news objects and generate descriptive sentences related to news topics. STCNet firstly constructs a graph representation based on objects feature similarity. The graph reasoning module uses the spatial information to infer the weights of aggregated adjacent nodes according to a learnable Gaussian kernel function. Finally, the generation of news sentences are driven by the spatial-aware graph representations and the news topics distribution. Experimental results demonstrate that STCNet trained on DNICC19k could not only automatically creates descriptive sentences related to news topics for disaster news images, but also outperforms benchmark models such as Bottom-up, NIC, Show attend and AoANet on multiple evaluation metrics, achieving CIDEr/BLEU-4 scores of 60.26 and 17.01, respectively. [ABSTRACT FROM AUTHOR]

As natural disasters often cause catastrophic damages, disaster management must be considered. Relief logistics planning in disaster management is a dynamic process that includes deliberate processes taken before and after a disaster. This paper presents a robust optimization model for relief logistics planning covering before and after disaster actions to minimize the total cost. In this model, demand and traveling time between nodes are non-deterministic parameters, and scenario-based robust optimization is applied to handle uncertainty. The model is an integrated multi-echelon, multi-period, and multi-commodity vehicle routing problem considering the fair distribution of goods and services in a way that social costs are minimized. Vehicles can enter nodes and exit them several times. In this paper, a genetic algorithm (GA) is considered the solution approach. To estimate the GA parameters, an experimental design based on the Taguchi method is considered. A roulette wheel method is applied to select a child from parents for the next generation. Single-point crossover and swap mutation operators are used to generate the new child. Test problems in different scales are generated and solved using GAMS and GA. The results indicate that GA solutions have 3.75% gaps in average with optimal solutions. Finally, the model is applied with the data of a real case (Tehran). The results also indicate that it is better to consider the appropriate capacity for supply centers and distributors and select vehicles. [ABSTRACT FROM AUTHOR]

This paper explores the role of savings groups in resilience to urban climate-related disasters. Savings groups are a rapidly growing phenomenon in Africa. They are decentralized, non-institutional groups that provide millions of people excluded from the formal banking sector with a trusted, accessible, and relatively simple source of microfinance. Yet there is little work on the impacts of savings groups on resilience to disasters. In this paper, we use a combination of quantitative and qualitative evidence from Dar es Salaam (Tanzania) to shed new light on the role that savings groups play in helping households cope with climate-related shocks. Drawing on new data, we show that approximately one-quarter of households have at least one member in a group, and that these households recover from flood events faster than those who do not. We further argue that the structure of savings groups allows for considerable group oversight, reducing the high costs of monitoring and sanctioning that often undermine cooperative engagement in urban areas. This makes the savings group model a uniquely flexible form of financing that is well adapted to helping households cope with shocks such as repeated flooding. In addition to this, we posit that they may provide a foundation for community initiatives focusing on preventative action. [ABSTRACT FROM AUTHOR]

Natural disasters cause heavy casualties and financial losses annually. To reduce these damages, the rescue teams need to be planned effectively. In this regard, in this research, a mixed-integer programming model is offered to allocate and schedule rescue teams in a response phase of disaster management under uncertainty. The objective function minimizes the incident's total weighted completion times. The literature review shows that the uncertain condition and time windows have been less addressed in the previous studies. To cover these gaps, this paper investigates the problem under uncertainty and considers time windows for incidents. Besides, the fatigue effect is considered in this paper. Accordingly, within a planning horizon, incident processing times are not fixed. Since the considered problem is an NP-hard one and exact methods cannot solve it within a reasonable amount of time, this research develops a heuristic-based simulated annealing algorithm. The presented model is solved using the developed algorithm and three known meta-heuristic algorithms. Then, the results obtained by algorithms are compared and analyzed. Finally, the sensitivity analysis is carried out on some crucial parameters of the presented model, and the related results are reported. [ABSTRACT FROM AUTHOR]

In today's severe situation of the global new crown virus raging, there are still efficiency problems in emergency resource scheduling, and there are still deficiencies in rescue standards. For the happiness and well-being of people's lives, adhering to the principle of a community with a shared future for mankind, the emergency resource scheduling system for urban public health emergencies needs to be improved and perfected. This paper mainly studies the optimization model of urban emergency resource scheduling, which uses the deep reinforcement learning algorithm to build the emergency resource distribution system framework, and uses the Deep Q Network path planning algorithm to optimize the system, to achieve the purpose of optimizing and upgrading the efficient scheduling of emergency resources in the city. Finally, through simulation experiments, it is concluded that the deep learning algorithm studied is helpful to the emergency resource scheduling optimization system. However, with the gradual development of deep learning, some of its disadvantages are becoming increasingly obvious. An obvious flaw is that building a deep learning-based model generally requires a lot of CPU computing resources, making the cost too high. [ABSTRACT FROM AUTHOR]

Accurate and timely assessment of post-disaster building damage is of great significance for national development and social security concerns. However, due to the high timeliness requirements of disaster emergency response and the conflict that sufficient computing resources are not easily available in harsh environments, and therefore the lightweight AI-driven post-disaster building damage assessment model is highly needed. In this paper, we introduced a knowledge distillation-based lightweight approach for assessing building damage from xBD high-resolution satellite images with the purpose of reducing the dependence on computing resources in disaster emergency response scenarios. Specifically, an ensemble Teacher-Student knowledge distillation method was designed and compared with the xBD baseline model. The result has shown that, the knowledge distillation reduces the parameter number of the original model by 30%, and the inference speed is increased by 30%-40%. In the building localization task, the accuracy of teacher and student model are 0.879 and 0.832 (IOU) respectively. In the damage classification task, the accuracy of teacher and student are 0.798 and 0.775 respectively. In addition, we proposed a dual-teacher-student knowledge distillation strategy, which cannot use the pre-training skills of curriculum learning in student model training, but achieve the same effect through more direct knowledge transfer. In the experiment, our dual-teacher-student method improves the knowledge distillation baseline by 3.7% with 30 epoch training. With only 70% parameters, our student model performs close to the teacher model at a degradation within 5%.This study verifies the effectiveness and prospect of knowledge distillation method in building damage assessment for disaster emergency. [ABSTRACT FROM AUTHOR]

Roadways are critical infrastructure in our society, providing services for people through and between cities. However, they are prone to closures and disruptions, especially after extreme weather events like hurricanes. At the same time, traffic flow data are a fundamental type of information for any transportation system. In this paper, we tackle the problem of traffic sensor placement on roadways to address two tasks at the same time. The first task is traffic data estimation in ordinary situations, which is vital for traffic monitoring and city planning. We design a graph-based method to estimate traffic flow on roads where sensors are not present. The second one is enhanced observability of roadways in case of extreme weather events. We propose a satellite-based multi-domain risk assessment to locate roads at high risk of closures. Vegetation and flood hazards are taken into account. We formalize the problem as a search method over the network to suggest the minimum number and location of traffic sensors to place while maximizing the traffic estimation capabilities and observability of the risky areas of a city. [ABSTRACT FROM AUTHOR]

Bazhong City is located on stratum dominated by red-bed conditions. This type of weak geological condition with sand and mudstone interbedding is very easily affected by climatic conditions to produce rainfall-type landslides. On the basis of landslide data statistics collected in Bazhong City from 2011 to 2019, this paper uses ERA5-Land rainfall data to statistically analyze the correlation between rainfall and landslide events in Bazhong City. The landslide events in Bazhong City are greatly affected by rainfall events lasting for 10 days. Considering the influence of rainfall seepage and other processes, an effective cumulative rainfall-duration threshold curve for Bazhong City is obtained via median nonlinear fitting. Then, on the basis of the prediction interval, the rainfall threshold formula under different landslide occurrence probabilities is obtained and the critical threshold curve with a non-exceeding probability of 1% in Bazhong City is calculated and verified. Subsequently, a continuous probability distribution fitting function of landslide occurrence is established and a continuous probability distribution surface with a good fitting effect in Bazhong City is obtained. This allows a definite probability of whether future rainfall events will induce landslides to be obtained, providing an important basis for engineering disaster prevention and mitigation. [ABSTRACT FROM AUTHOR]

Smartphones are one of the most affordable and familiar devices available to people that are used for many scientific applications. Thus, implementing a dedicated mobile app for earthquake early warning (EEW) systems is one of the best options to save money and time to achieve dissemination of earthquake warnings to the public at large. This paper describes the development of a Warning Dissemination System comprising field sirens and mobile apps and presents details of developing a smartphone app for Android and iOS operating systems. The app is designed for the EEW system currently operational in the Northwest Himalayan State of Uttarakhand in India. The app named "Uttarakhand Bhookamp Alert" is freely available on the Google Play Store and Apple App Store and has already been downloaded more than 10,000 times. This app uses Firebase Cloud Messaging (FCM) service for sending earthquake early warnings to the users. The user's basic information and FCM token are saved at the warning server in a database during the user registration process. The information will be shared with the disaster response authorities after a damaging earthquake to rescue affected users requiring help. This app also provides online earthquake safety recommendations and guidance, including how to prepare yourself, find a safe hiding place, and do's and don'ts during an earthquake. [ABSTRACT FROM AUTHOR]

In this paper, the combined static-dynamic loading tests on refabricated parallel fissured granite were conducted with SHPB system at six confining pressures (0, 10, 20, 30, 50, and 60% of the UCS) and three dip angle (0, 45, and 90 °). Moreover, a non-contact monitoring device, the DIC, was used to fully detect the full-field strain of the specimen. Comprehensively the initiation, expansion, penetration process and failure mode of double-fractured specimens were investigated, and the deformation and failure characteristics and strength change law during the failure process were discussed. The fractures had a significant degradation effect on the dynamic compressive strength of the rock. Parallel double fractures showed the large principal and shear strains at and near the fissure tips firstly during damage and extended through each other, causing damage to the specimens. There were two main types of cracks: tensile cracks and shear cracks in the failure process. The displacements of key cracks in 45 and 90 ° specimens were analyzed, which showed that the slip displacement gradually increased with the increase in axial pressure, while the tension displacement showed an increase first and then a decrease. The angle of inclination of parallel double fissures is critical to the initial generation of tensile strain zones and the subsequent development and expansion of tensile strain zones. The research results will provide an important theoretical basis for the development and utilization of underground engineering structures and disaster prevention and mitigation. [ABSTRACT FROM AUTHOR]

Twitter is an intensely utilized platform for disaster events and emergencies. Therefore, Twitter is an important resource for providing the essential information. Named entity recognition (NER), which is the process of determining the elementary units in a text and classifying them with pre-defined categories, plays a significant role to extract essential and usefulness information. However, NER is a challenging task due to the utilized informal text in the Twitter platform such as grammatical errors and nonstandard abbreviations. In this paper, recurrent neural network (RNN)-based approaches considering diversity of activation functions and optimization functions with NER tools are utilized to extract named entities such as organization, person, and location from the tweets. Inputs for RNN models are provided via two different NER tools which are natural language toolkit (NLTK) and general architecture for text engineering (Gate). Then, pre-labeled data are trained via GloVe word embedding technique, and RNN model variants such as LSTM, BLSTM, and GRU are demonstrated. Therefore, outperforming models among RNN variants are presented for predicting named entities. Yellowbrick interpreter is used for evaluation of the proposed method and Wilcoxon signed-rank test are applied on results of two different data sets to demonstrate consistency of the proposed method. In addition, comparison is made with existing machine learning methods. The experiments by utilizing the Nepal earthquake Twitter data set show that the RNN-based approaches achieve good results in finding named entities. In emergencies, the results of this paper can help in reducing the efforts of event location detection and provide better disaster management. [ABSTRACT FROM AUTHOR]

Emergencies often occur irregularly, such as infectious diseases, earthquakes, wars, floods, the diffusion and leakage of chemically toxic and harmful substances, etc. These emergencies can bring huge disasters to people, even worse, the time left for people to make critical decisions is usually very limited. When an emergency occurs, the most important thing for people is to make reasonable decisions as soon as possible to deal with the current problems, otherwise, the situation may deteriorate further. The paper proposes an emergency decision-making algorithm under the constraints of the limited time and incomplete information, the research is mainly carried out from the following aspects, firstly, we use the data structure of the hesitant fuzzy probabilistic linguistic set to collect the basic data after careful comparison, which has three advantages, (1) considering the hesitation in the decision-making process, each evaluation information is allowed to contain multiple values instead of just one value; (2) each evaluation value is followed by a probability value, which further describes the details of the evaluation information; (3) the data structure allows some probability information to be unknown, which effectively expands the application scope of the algorithm. Secondly, the maximization gap model is proposed to calculate unknown parameters, the model can distinguish alternatives with small differences. Thirdly, all the evaluation information will be aggregated by the dynamic hesitant probability fuzzy weighted arithmetic operator. Subsequently, an instance is given to illustrate the effectiveness and the accuracy of the algorithm proposed in the paper. Finally, the advantages of the proposed algorithm are further demonstrated by comparing it with other outstanding algorithms. The main contribution of the paper is that we propose the maximization gap model to obtain the unknown parameters, which can effectively and accurately distinguish alternatives with small differences. [ABSTRACT FROM AUTHOR]

The growing need for humanitarian assistance has inspired an increasing amount of academic publications in the field of humanitarian logistics. Over the past two decades, the humanitarian logistics literature has developed a powerful toolbox of standardized problem formulations to address problems ranging from distribution to scheduling or locations planning. At the same time, the humanitarian field is quickly evolving, and problem formulations heavily rely on the context, leading to calls for more evidence-based research. While mixed methods research designs provide a promising avenue to embed research in the reality of the field, there is a lack of rigorous mixed methods research designs tailored to translating field findings into relevant HL optimization models. In this paper, we set out to address this gap by providing a systematic mixed methods research design for HL problem in disasters response. The methodology includes eight steps taking into account specifics of humanitarian disasters. We illustrate our methodology by applying it to the 2015 Nepal earthquake response, resulting in two evidence-based HL optimization models. [ABSTRACT FROM AUTHOR]

Every year, natural disasters such as earthquakes, floods, volcanos, etc. cause millions of victims. So a quick response to these disasters is vital to reduce their negative consequences. Vehicle routing models can make important contributions to faster response, and thus, save lives. This paper proposes a vehicle routing problem to deliver relief resources from origins to destinations in response to disasters. For this purpose, a multi-period, multi-depot, multi-trip mixed-integer linear programming model is developed. Minimizing the sum of arrival times is considered as a service-based objective function for the increase of the survival rate. For the first time, the problem is solved using a grouping metaheuristic algorithm. Then its performance is compared with two other grouping algorithms. To evaluate the solution method, the algorithms are implemented on various test problems and compared statistically. Additionally, to show the validity of the model, sensitivity analyses are performed and managerial insights are given. [ABSTRACT FROM AUTHOR]

The post-disaster humanitarian logistic operations deal with the supply of emergency relief materials to mitigate damages in the affected areas. Immediately after the disaster, it is challenging to estimate the demand for emergency relief materials. As a result, the demand for such materials at the point of demand and the corresponding transportation costs for the entire supply chain network becomes uncertain. This paper proposes a new probabilistic fuzzy goal programming model for making decisions to manage the post-disaster supply of emergency relief materials. A suggested procedure converts the proposed model to its deterministic equivalent when the demands for the relief materials follow uniform distributions. We implement the differential evolution, a metaheuristic technique, for analyzing demand satisfaction for relief materials under various scenarios. A case example based on the Nepal Earthquake in 2015 demonstrates the usefulness of the proposed approach. The solution of the model will help the Disaster Management Agency coordinate with the humanitarian organizations and foreign countries to provide the required emergency relief materials so that an adequate level of supply can be assured to the affected areas with the least possible transportation cost. [ABSTRACT FROM AUTHOR]

Quality management has been widely discussed in the literature, and recent special issues on humanitarian supply chains and relief operations have emphasized the increasing importance of quality management in this key emerging area. In this paper, we provide an extensive literature review in the field of quality management in humanitarian operations and disaster relief management. Our comprehensive review, comprising 61 articles published from 2009 to 2018, leads to the identification of enablers (e.g., transparency, policy framework), challenges (e.g., financial services, identity protection), and theory development approaches, as well as numerous research gaps that must be addressed. [ABSTRACT FROM AUTHOR]

The presence of geological hazards on the Sakhalin shelf creates difficulties in the design, construction, and maintenance of offshore oil and gas facilities. Identification and assessment of geological hazards in the coastal area of the northeastern shelf of Sakhalin Island will allow avoiding environmental disasters, minimizing the risks of accidents, and developing recommendations for ensuring safety in the development of oil and gas fields. The paper presents the results of an analysis of data from expeditionary surveys carried out at two sites using geophysical surveys and bathymetric surveys. The zones of ice gouging in the coastal part of site no. 1 caused by gouging of the seabed by stamukhas have been identified. Gas zones and paleo-incisions were found and mapped in the bottom part of the section at site no. 2. [ABSTRACT FROM AUTHOR]

Emergency responders require accurate and comprehensive data to make informed decisions. Moreover, the data should be acquired and analyzed swiftly to ensure an efficient response. One of the tasks at hand post-disaster is damage assessment within the impacted areas. In particular, building damage should be assessed to account for possible casualties, and displaced populations, to estimate long-term shelter capacities, and to assess the damage to services that depend on essential infrastructure (e.g. hospitals, schools, etc.). Remote sensing techniques, including satellite imagery, can be used to gathering such information so that the overall damage can be assessed. However, specific points of interest among the damaged buildings need higher resolution images and detailed information to assess the damage situation. These areas can be further assessed through unmanned aerial vehicles and 3D model reconstruction. This paper presents a multi-UAV coverage path planning method for the 3D reconstruction of postdisaster damaged buildings. The methodology has been implemented in NetLogo3D, a multi-agent model environment, and tested in a virtual built environment in Unity3D. The proposed method generates camera location points surrounding targeted damaged buildings. These camera location points are filtered to avoid collision and then sorted using the K-means or the Fuzzy C-means methods. After clustering camera location points and allocating these to each UAV unit, a route optimization process is conducted as a multiple traveling salesman problem. Final corrections are made to paths to avoid obstacles and give a resulting path for each UAV that balances the flight distance and time. The paper presents the details of the model and methodologies, and an examination of the texture resolution obtained from the proposed method and the conventional overhead flight with the nadir-looking method used in 3D mappings. The algorithm outperforms the conventional method in terms of the quality of the generated 3D model. [ABSTRACT FROM AUTHOR]

Search and rescue are essential applications of disaster management in which people are evacuated from the disaster-prone area to a safer place. This overall process of search and rescue can be more efficient if an automated system can quickly locate the human or area where rescue is required. To provide a faster and accurate search of those places, this paper proposes a novel approach to search and rescue using automated drone surveillance. In this paper, a complex scene classification problem is solved using the proposed 3DCNN model. The proposed model uses spatial as well as temporal features of the video for the classification of the scene as help or non-help in the natural disaster. Due to the unavailability of such kind of dataset, it is impossible to train the model. Therefore, it is essential to develop a dataset for search and rescue. The proposed dataset is a first and unique dataset for scene classification using drone surveillance. The major contribution of this paper is (1) a novel 3DCNN powered model for scene classification in drone surveillance, (2) to develop the required dataset for the training of scene classification model, and (3) particular swarm optimization (PSO)-based hyper-parameter tuning for getting the best value of multiple parameters used for training the model. Our hybridization of parameter tuning with PSO helps for the convergence of parameter values of proposed 3DCNN model, and the proposed scene classification model (3DCNN+PSO) is applied to the dataset. The proposed model gives an impressive performance to help situation identification with 98% training and 99% validation accuracy. [ABSTRACT FROM AUTHOR]