Response Plan Research Articles

Evaluation of Seasonal Prediction of Extreme Wind Resource Potential over China Based on a Dynamic Prediction System SIDRI-ESS V1.0

Wind resources play a pivotal role in building sustainable energy systems, crucial for mitigating and adapting to climate change. With the increasing frequency of extreme events under global warming, effective prediction of extreme wind resource potential can improve the safety of wind farms and other infrastructure, while optimizing resource allocation and emergency response plans. In this study, we evaluate the seasonal prediction skill for summer extreme wind events over China using a 20-year hindcast dataset generated by a dynamical seamless prediction system designed by Shanghai Investigation, Design and Research Institute Co., Ltd. (Shanghai, China) (SIDRI-ESS V1.0). Firstly, the hindcast effectively simulates the spatial distribution of summer extreme wind speed thresholds, even though it tends to overestimate the thresholds in most regions. Secondly, high prediction skills, measured by temporal correlation coefficient (TCC) and normalized root mean square error (nRMSE), are observed in northeast China, central east China, southeast China, and the Tibetan Plateau (TCC is about 0.5 and the nRMSE is below 0.9 in these regions). The highest skills emerge in southeast China with a maximum TCC greater than 0.7, and effective prediction skill can extend up to a 5-month lead time. Ensemble prediction significantly enhances predictive skill and reduces uncertainty, with 24 ensemble members being sufficient to saturate TCC and 12–16 members for nRMSE in most key regions and lead times. Furthermore, we show that the prediction skill for extreme wind counts is strongly related to the prediction skill for summer mean wind speeds, particularly in southeast China. Overall, SIDRI-ESS V1.0 shows promising performance in predicting extreme winds and has great potential to provide services to the wind industry. It can effectively help to optimize wind farm operating strategies and improve power generation efficiency. However, further improvements are needed, particularly in areas where prediction skills for extreme winds are influenced by smaller-scale weather phenomena and areas with complex underlying surfaces and climate characteristics.

Application of different earthquake-induced landslide hazard assessment models on the 2022 Ms 6.8 luding earthquake

Following the earthquake, prompt evaluation of the distribution of coseismic landslides and estimation of potential disaster losses are crucial for emergency response and resettlement planning. The Luding earthquake of 2022 offers a valuable opportunity to conduct a rapid assessment of coseismic landslides using various models. In this study, we utilize the Logistic Regression (LR)-based Xu2019 model, a new-generation model developed in China, alongside the Newmark model to perform the rapid hazard assessment of coseismic landslides. Assessing the accuracy and applicability of these two models based on the coseismic landslides from the Luding earthquake, we find that within intensity area of IX, the high probability area identified by the Newmark model aligns closely with the actual distribution of landslides. However, the Newmark model’s prediction is overestimated in the intensity area of VIII. For the Xu2019 model, the prediction results are in good agreement with the distribution of actual landslides. Most landslides are located in high probability areas, such as Detuo town, Wandong, and Xingfu villages, indicating that the model has a higher prediction accuracy. Overall, two models have good practical utility in emergency hazard assessment of coseismic landslides. However, the Newmark model requires multi-input parameters and the assignment of these parameters will increase the uncertainty and subjectivity in the practical application of the modeling assessment.

A conceptual framework for integrating volunteers in emergency response planning and optimization assisted by decision support systems

In disaster response, the overwhelming amount of time-sensitive information and response options, combined with the dynamic nature of disasters, makes decision-making challenging for emergency service providers. Furthermore, it is often not economically feasible for countries to maintain a large number of full-time emergency responders. As such, many countries rely heavily on volunteer emergency responders during major disasters. This means that the success of disaster response often hinges on the efficient use of this volunteer workforce. We propose a framework for a Decision Support System (DSS) designed to optimize the use of volunteers by emergency services. This framework includes the data management layer, integrating necessary inputs and information; the analytical layer, which serves as the system's processing core; the user interface layer; and the decision-making layer. We argue that, while significant academic focus has been on the analytical layer, practical implementation requires the integration of all four components. Additionally, we emphasize the need for coordination with a broad spectrum of stakeholders involved in data provision, decision-making, and resource deployment for operationalizing this DSS. We also explore and analyze existing methodologies for developing the analytical layers, the requirements of these models, and the current methodological gaps. The proposed framework establishes a clear roadmap for adopting emergency response approaches that are human-centric, but at the same time, effectively utilize advancements in modeling, optimization, machine learning, and data integration.

Forecasting road network functionality states during extreme rainfall events to facilitate real-time emergency response planning

Rapid prediction of Road Network Functionality (RNF) during extreme rainfall-induced flooding is crucial for supporting proactive and real-time emergency planning, such as rescue, evacuation planning, and emergency supply distribution. Unlike normal operational conditions, extreme rainfall events introduce complex non-stationary, non-Euclidean characteristics to RNF due to intricate meteorological and hydrological processes, as well as the role of a community's road network in emergency response planning. Conventional physics-based flood simulations and flow-based road network analyses typically lack the computational efficiency required for real-time RNF predictions, hindering timely risk mitigation decisions. This study leverages the accuracy of physics-based simulations and the efficacy of deep-learning technologies to develop a deep learning-based surrogate model for Rain-to-RNF (R2R) predictions. This model couples Long Short-Term Memory (LSTM) networks with Spatial-Temporal Graph Convolutional Networks (ST-GCNs) to uniquely capture the spatiotemporal dynamics of RNF under extreme rainfall events. The predictive accuracy, stability, and versatility of the R2R surrogate model are demonstrated in four flood-prone communities in Zhejiang Province. Its implementation during Typhoon Fitow (2013) over a 30-hour intense rainfall showcases its promising predictive capacity and unparalleled computational efficiency. This research advances disaster management, enhancing the resilience and responsiveness of community infrastructure during extreme weather events.

Challenges in the Treatment of Coinfection of Visceral Leishmaniosis and Hepatitis B/C Viruses

There is a significant research gap in India due to incomplete investigation of seroprevalence of co-infection with visceral leishmaniasis (VL), hepatitis B (HBV) and hepatitis C (HCV). This study attempts to fill this gap by providing an in-depth analysis of the prevalence and consequences of these co-infections. The study of 32 VL patients revealed that 28 (8.88%) tested positive for HBV, 1 (0.317%) for HCV and 3 (0.95%) for both HBV and HCV. These results highlight how difficult it is to treat people with two diseases at the same time. An example of how co-infection makes treatment much more difficult is the fact that a patient with both VL and HBV required six courses of anti-leishmaniasis medication. The study shows how important it is for VL patients to be tested regularly for HBV and HCV to improve treatment response and support India's plan to get rid of kala-azar. Accurate identification of co-infections is necessary to reduce the overall burden of the disease and develop effective treatment regimens. Systematic testing of VL patients for HBV and HCV not only helps in controlling and perhaps even eliminating kala-azar, but also makes it easier to treat each patient as an individual, improving their health. In areas where viral hepatitis is prevalent, this strategy is particularly important as these diseases represent a double burden on public health. Implementing integrated treatment regimens and routine screening can reduce the impact of co-infection and advance the public health goal of kala-azar elimination. To improve patient outcomes and advance public health goals, the results of this study strongly support the integration of comprehensive screening programs into existing healthcare systems to improve the treatment of VL patients.

A hybrid surrogate model for real-time coastal urban flood prediction: An application to Macao

Tropical cyclones could cause co-occurrences of storm tides and rainfall and, therefore, induce flooding hazards, posing high risks to coastal cities. Accurate and timely prediction of flood hazards in time–space domain is essential for flood risk management. In this study, a hybrid surrogate model is proposed for real-time flooding prediction by considering the compound effects of storm tides, rainfall, and drainage outflows. The hybrid surrogate model combines a long short-term memory (LSTM) neural network for predicting drainage outflows with a one-dimensional convolutional neural network (1D CNN) for predicting water depths. The model is tested using performance metrics, and the simulation results agree well with the historical measurements. Moreover, a sensitivity analysis of the drainage outflows using the modified Morris screening method reveals the importance of considering drainage systems in flood modelling, particularly when rainfall return periods are relatively small. The sensitivity indices vary over the computation domain in terms of different driving factors, with positive values corresponding to rainfall-dominated area while negative values corresponding to storm tide-dominated area. The computation cost of the model is quite low, and the fast prediction speed could provide a sound basis for early warning and real-time flood management. The proposed hybrid surrogate model offers a promising function for rapid and accurate flood inundation prediction in coastal cities, enabling more effective flood risk management and emergency response planning.

Crossing river oil pipeline spill emergency response plan automatic association study based on Apriori‐Topsis

Crossing river oil pipeline spill emergency response plan automatic association study based on Apriori‐Topsis

A research on Fethiye Babadag commercial tandem paragliding participants within the scope of experiential tourism

The tourism sector has continuously evolved and restructured due to technological, economic, and social transformations. Experiential tourism is an approach that takes tourists from being mere observers to placing them at the center of the experience, encouraging them to learn new things and personally develop during their travels. The study explores the perceptions of tourists who participate in paragliding regarding their experiences with adventure and experiential tourism. Paragliding involves uncertainty, risk, adrenaline, adventure, and experience, as opposed to the traditional understanding of travel as safe and guided. This study aims to uncover the relationships between risk, benefit, evaluation, co-creation, trust, satisfaction, and future intention among local and foreign tourists participating in paragliding activities within an experiential context. Therefore, the study examines the relationships of the experiential perceptions of tourists participating in paragliding. In the data collection phase, researchers conducted face-to-face questionnaires with 381 local and foreign tourists who experienced tandem paragliding activity in Fethiye Babadag. After eliminating incomplete or incorrectly filled surveys, 321 surveys were analyzed. The data were processed using SPSS 27 and AMOS 23 statistical package programs. The research focuses on the perceptions of paragliders’ experiential risk, benefits, value, co-creation, trust, satisfaction, and future intentions. Structural equation modeling was applied to examine these perceptions. The results suggest that there are positive correlations between various aspects of experiential tourism, such as benefits and satisfaction, benefits and trust, satisfaction and future intentions, and trust and future intentions. However, the study did not find any connection between experiential evolution and satisfaction, or between experiential evolution and trust. Similarly, there were no observed relationships between experiential co-creation and satisfaction, or between experiential co-creation and trust. Additionally, the study revealed that risk perception does not impact satisfaction and that trust does not influence risk perception. These results align with the adventurous nature of paragliding. Management implicationsParagliding is a thrilling sport that offers participants a unique and adrenaline-filled experience. The study results provide insights for tourism operators, particularly those involved in adventure and experiential tourism. In today's experience-driven economy, the insights gained from those who take part in this activity are extremely valuable to the tourism sector. Research findings have provided valuable insights into the impact of paragliding on participants' overall experience.Tourism operators should prioritize providing clear and transparent information about the risks and safety measures associated with paragliding. This transparency helps participants make informed decisions and can enhance their overall satisfaction. Understanding these insights, particularly for providers such as pilots, travel agencies, tour operators, and site managers, can help the industry create exceptional paragliding experiences.The study's results showed that participants' perception of risk did not have a negative impact on their satisfaction. This indicates that individuals engaging in risk-involved activities like paragliding consciously acknowledge the risks and that these risks do not diminish their overall satisfaction. Within the tourism sector, providing customers with precise and transparent information can help them better comprehend the risks and make informed decisions about participating in such activities. Specifically, in paragliding, the perception of managed risk may significantly contribute to overall satisfaction.The study's findings indicate that enhancing the experiential value of paragliding activities influences participant satisfaction and trust. Therefore, businesses should focus on improving the quality of the paragliding experience through high-quality services and engaging experiences. By emphasizing unique and meaningful experiences, operators can increase tourist interest and contribute to the local economy. Strategic marketing and promoting paragliding as part of experiential tourism can further attract tourists seeking authentic and memorable experiences. Focusing on these areas can lead to higher satisfaction, trust, and future intentions for participation by tourists. In the tourism industry, providing customers with more precise and transparent information can encourage them to fully understand the risks and make informed decisions to participate in such activities.Local, national, and international regulations related to paragliding activities should be organized from a managerial perspective. These regulations encompass licensing, certifications, operational standards, and training programs for staff and customers. Regularly updating and certifying instructors will help maintain high safety standards. Regular inspection and maintenance of equipment ensure safety standards. Decision-makers should implement a strict protocol for the repair and replacement of gear.Conducting thorough risk assessments is essential for all paragliding activities. Emergency response plans should be practiced. Regular drills must be available to prepare for potential incidents. Customer experience, customer education, personalized experiences, and customer feedback are considerable issues with marketing, operational efficiency, financial management, sustainability, and community engagement. Addressing these managerial implications can ensure safe, efficient, and profitable operations for a paragliding business because they provide customers with a memorable and enjoyable experience.

College President Departure After No-Confidence Votes: An Event History Analysis

ABSTRACT Faculty-led no-confidence votes against college presidents have increased over the past decade. These votes often aim to create conditions that lead to the president’s departure. In this study, I used event history analysis to investigate the influence of faculty-led no-confidence votes on the tenure of college presidents. The analysis reveals that nearly 70% of presidents leave their positions within four semesters of a no-confidence vote. The study further explores the influence of various factors, including institutional and presidential characteristics, on the likelihood of presidential departure after no-confidence votes. The results indicate that student enrollment size is the only significant predictor, with smaller institutions more likely to experience leadership turnover following a vote. The study concludes that no-confidence votes are a powerful tool for facilitating presidential change across diverse institutional types. These findings have important implications for faculty governance bodies and boards of trustees, highlighting the need for strategic planning in response to such votes.

URBANIZATION AND SOCIAL CRIME: A STUDY ON SOCIO-ECONOMIC DIMENSIONS OF THE MANIPUR CENTRAL VALLEY

Geography is concerned to provide accurate, orderly, and rational description and interpretation of the earth's surface. The study of population dynamics and its impact on social crime is crucial for understanding regional development and planning. The present study examines the dynamics of population growth, urbanization, and social crime in Manipur's Central Valley. Utilizing, the data from the primary and secondary sources (such: NCRB, Superintendent of Police, Manipur central Valley, and Census of India). The research adopts technique, such as Decadal Growth method, Principal component Analysis. Over a 70-year period from 1951 to 2021, the region witnessed a consistent rise in population, growing from 3,87,523 to 18,77,416. Despite this growth, the pace has decelerated, reflecting a shift from high to moderate growth rates due to factors such as birth rates, mortality rates, and migration patterns. Urban population data from the 2011 Census reveals significant regional disparities, with Imphal West being the most urbanized and Bishnupur the least. Principal Component Analysis (PCA) of social crime data identifies three key patterns: "Anomie Violence," "Marginalized Deviance," and "Economic Strain Deviance," each associated with different regions and types of crime. A comparison of social crime patterns with urban population sizes highlights varying correlations, suggesting complex interactions between demographic changes and crime rates. This analysis underscores the need for targeted urban planning and crime prevention strategies in response to evolving socio-economic dimensions.

South Sudanese Refugee Higher Education Access in Uganda: An Intersectionality Enquiry

This study was guided by the intersectionality theory, originally developed by Kimberlé Crenshaw. According to Crenshaw, the theory assumes that people have multiple biological, social and cultural identity markers that intersect, resulting in experiences of privilege or disadvantage, inclusion or discrimination. However, the theory maintains that marginalized individuals are not homogeneous and they experience discrimination and inclusion in different ways. The intersectionality theory also recognizes power dynamics and how structural, political and representational domains, intersect and produce complex patterns of discrimination, inequality and disadvantage. Ultimately, the intersectionality theory asserts that effective activism and social change require recognizing and addressing the intersections of various forms of oppression, and advocating for justice across multiple fronts. This exposes discrimination and exclusion that would have otherwise gone unnoticed, also giving voice to the disadvantaged and excluded. The study adopted a qualitative approach, an exploratory case study design, and an advocacy/ participatory philosophical lens. Twenty-seven purposively sampled participants took part in the study. They included 12 undergraduates from two private Ugandan universities, 13 government and non-governmental organization (NGO) officials, two officials from public and private universities, all involved in refugee higher education. Data was collected through a literature review, in-depth interviews with key informants and students, and a students’ focus group discussion. The study established that higher education access for South Sudanese refugees in Uganda involves multiple intersectional prohibitive and also supportive factors. The study therefore recommends that through situational analysis and needs assessment, Uganda develops clear objectives, activities and outcomes in the Education Response Plan, to establish the divergent supportive and prohibitive factors and cater for the various refugee student needs. The study also designed the refugee access and resilience (RARE) model to assist in the aforementioned regard

River Surface Velocity Measurement for Rapid Levee Breach Emergency Response Based on DFP-P-LK Algorithm.

In recent years, the increasing frequency of climate change and extreme weather events has significantly elevated the risk of levee breaches, potentially triggering large-scale floods that threaten surrounding environments and public safety. Rapid and accurate measurement of river surface velocities is crucial for developing effective emergency response plans. Video image velocimetry has emerged as a powerful new approach due to its non-invasive nature, ease of operation, and low cost. This paper introduces the Dynamic Feature Point Pyramid Lucas-Kanade (DFP-P-LK) optical flow algorithm, which employs a feature point dynamic update fusion strategy. The algorithm ensures accurate feature point extraction and reliable tracking through feature point fusion detection and dynamic update mechanisms, enhancing the robustness of optical flow estimation. Based on the DFP-P-LK, we propose a river surface velocity measurement model for rapid levee breach emergency response. This model converts acquired optical flow motion to actual flow velocities using an optical flow-velocity conversion model, providing critical data support for levee breach emergency response. Experimental results show that the method achieves an average measurement error below 15% within the velocity range of 0.43 m/s to 2.06 m/s, demonstrating high practical value and reliability.

Optimal aggregation and disaggregation for coordinated operation of virtual power plant with distribution network operator

Virtual power plants (VPPs) offer an effective approach for managing distributed energy resources (DERs), including microturbines, distributed generators, demand response aggregators, and energy storage systems. This technology significantly enhances the economic efficiency and flexibility of distribution network systems. This study aims to facilitate a flexible power exchange between the distribution network system and the upper-level grid by aggregating the power flexibility of heterogeneous DERs via a VPP. Additionally, it introduces a methodology for optimal aggregation and disaggregation within a coordinated operation framework between the VPP and distribution network operator (DNO). On one hand, the VPP can determine its day-ahead feasible region and real-time flexible regulation power based on operational constraints of DERs and representative data provided by the DNO, circumventing the need for detailed network information. On the other hand, day-ahead and real-time correction procedures for the DER cost functions are proposed, effectively neutralizing the impact of network operational constraints on these cost functions. Consequently, precise cost functions for both the active power and the flexible regulation power aggregated by the VPP are derived. Employing this aggregated cost function enables the determination of a cost-minimized optimal scheduling solution in real-time by solving a fundamental economic dispatch problem, significantly alleviating computational demands. Finally, case studies demonstrate that the proposed method achieves an error in aggregated power of only 0.77%, compared to the precise computation method that requires comprehensive system information. The proposed optimal VPP disaggregation scheme exhibits power discrepancies of 0.63% and cost discrepancies of 0.91% relative to the precise method. Additionally, when implementing the most cost-effective demand response plan based on the proposed cost function, the average costs for aggregators are reduced by 19.7%.

Assessment Of Hazard Problems In Workplace Related To Emergency Response Plan For Employees In Primary Health Care Centers At Babylon City.

Assessment Of Hazard Problems In Workplace Related To Emergency Response Plan For Employees In Primary Health Care Centers At Babylon City.

Analyzing the worldwide progression of COVID-19 cases and deaths using nonlinear mixed-effects model.

COVID-19 is an infectious disease that continues to spread worldwide. A precise estimation of the cases and deaths due to COVID-19 would allow for appropriate consideration of healthcare resource allocation, public health response, and vaccination and economic planning, to minimize social damage. In this study, we analyzed the progression of COVID-19 cases and deaths until January 2022 in 156 countries using a nonlinear mixed-effect model based on the SIR framework. Given the major changes in mortality from infection, risk of re-infection and social responses, the analysis was limited to the period before the emergence of the Omicron variant. The impact of infection prevention measures in various countries was assessed, with a specific focus on estimating the effectiveness of lockdowns, where the effect was assumed to change over time. By accounting for excess mortality, our analysis allowed the estimation of unreported cases and deaths, and thus providing a more comprehensive understanding of the impact of pandemic. In the analysis, we identified gross domestic product (GDP), proportion of people aged 65 years or older, latitude of the capital city on transmissibility of infection, and city population and cardiovascular death rate on mortality rate as significant influencing factors. Furthermore, the differences in transmissibility and mortality rates by variants and the effect of vaccination on the mortality rate were assessed. The transmissibility has increased by odds ratios of 1.2 to 1.4 in Beta, Gamma, and Delta variants; mortality rate has increased by odds ratios of 1.7, 2.2, and 1.4 in Beta, Gamma, and Delta variants, respectively; and vaccination decreased the mortality rate by odds ratios of 0.4 and 0.1 in Delta and other variants, respectively.

Dam Break Analysis of Batu Dam Using Hec-Ras

In Malaysia, dams are commonly built because they benefit the local community in several ways, especially through agricultural production and flood control. Nevertheless, an unexpected containment breach could result in the loss of life and property in a populated area downstream due to its huge potential energy reservoir. Dam break event simulations are essential for identifying and mitigating risks associated with possible dam breaches. Batu Dam has the highest percentage of the population at risk (PAR) and most likely exposed to the flood disaster due to dam break. From flood boundary maps, it was estimated that 78% of residential areas would be affected if a dam break disaster occurs. From the simulation, the maximum depth can reach up to 18 m with an estimated flow speed between 0.2 m/s – 3 m/s. At these depth and flow speed, people may lose strength and unable to control themselves in flood and would cause death and injury. Flood arrival time took 15 to 22 hours for flooding to arrive downstream with a maximum flood depth of 5.07 m at Persiaran Jasa Utama and the highest depth of 12.62 m at Kampung Baru Batu Caves with flood velocity between 1.60 m/s to 1.46m/s. Analysis and simulation of embankment dam breach events and the resulting floods are crucial for distinguishing and mitigating dangers from potential dam failures. Accurate forecast of inundation levels and the time of flood wave arrival at downstream key places is required for the development of effective emergency response plans.

Rural Natural Disaster Stress: A Survey of Community Resource Use and Effect

ABSTRACT Objective This study identified rural community experiences and preferences related to available resources and their effects on stress following tornadoes and floods. Methods The Rural Natural Disaster Stress and Recovery survey was distributed from December 2021 to February 2022 in rural disaster-affected communities. Within the analysis sample (N = 159) of self-selected participants representing both agricultural and non-agricultural occupations, 125 responded to the Resource Use and Effect survey component that evaluated the effects of 22 resources on post-disaster stress. Additional qualitative questions provided further data. Results The most frequently used resources post-disaster were found to be talking about the event (98.3%), friends and neighbors (97.6%) and family (97.6%). Using a derived Impact per Use score, groups from the neighboring community and personal faith activities most often reduced stress. Resources that increased stress were identified as FEMA or other government organizations; repairing, replacing, or rebuilding property; and following news or social media. Participants reported help from their community (35.8%) or things they did for themselves (31.2%) most effectively decreased disaster stress. Family, friends, faith, neighbors, and community were top choices to decrease stress in a future disaster. Conclusion Rural residents identified available resources for reducing disaster stress, but some common post-disaster activities were found to increase stress. Rural emergency management should adapt response and recovery plans and activities to leverage readily accessible people, groups, and activities to mitigate negative mental and emotional health effects.

Reliability in mass alert system for dam emergencies: Case study of Vale S/A

AbstractIn its iron ore operations, Vale, the largest mining company in Brazil and the fourth largest mining company in the world by market value, has 77 dams considered with high potential damage in case of a failure in the stability controls. This classification follows the criteria of the sector regulatory agency in Brazil and determines that, for this class, a mass alert system is necessary for the resident population and workers immediately downstream of the dam within a region preset by legislation, considering a simulated flood area. For Vale, the mass alert system for dam emergencies is part of a critical mitigating control for a business risk analysis considering the hypothetical dam break as an unwanted material event. Therefore, both the probability of failure on demand, spurious failure, and availability of the assets of this protection barrier are important elements for this purpose, as well as the emergency response plan and the evacuation simulation of flood areas, which are part of a risk management system that require continuous verification and continuous improvement. Currently, Vale has 375 sirens, and it is planned to reach 420 sirens by the end of 2024. The purpose of this article is to present how the safety concepts applied to these systems were developed, the good practices applied from this study, and the lessons learned from the dam emergency management arising from the break of the Brumadinho dam in 2019. The reliability analyses of the systems involved and the main contributors to the Safety Critical Unavailability are presented, as well as the results of the reduction in the maintenance time and increase in system availability.

Evaluating Bereavement Response Plans in Daycare: A Comparative Analysis of Support Systems in Denmark and Norway

This study examines the implementation and use of bereavement response plans (bereavement plans) in Danish and Norwegian daycare institutions. Two large-scale surveys were conducted to explore the implementation rates, satisfaction levels, and source of the plans. The data collected was analyzed using frequency analyses and Chi-square tests of independence. The findings suggest that 87% of Danish institutions have bereavement plans compared to 98% in Norway. Furthermore, the study highlights the differences in the development of bereavement plans between the two countries, as Danish institutions trust their individually tailored plans more than their Norwegian counterparts, while the Norwegian plans seem to be of higher quality. Despite these differences, bereavement plans provide a sense of safety for staff when supporting bereaved children in both countries. This study contributes to the ongoing efforts to improve bereavement support in daycare institutions and emphasizes the importance of continued research and development in this area.

Lessons from Iceland: Natural Disaster Planning and Response

Summary This case study evaluates Iceland’s emergency response plans for mitigating the impact of natural disasters on tourists. Recent natural disasters, including earthquakes and volcanic eruptions from late 2023 to early 2024, are discussed to illustrate the implementation of these plans. The study outlines the main components of Iceland’s emergency response plan, highlighting the roles of government agencies, police districts, and tourism-related groups. It emphasizes the plan’s effectiveness in prioritizing the safety of tourists through evacuation procedures, mitigation measures, and coordination among stakeholders. Recommendations for improvement include the implementation of disaster aid insurance plans and regular reviews and adjustments to the emergency response plan. These recommendations aim to enhance preparedness and resilience in the face of future disasters. Information © The Authors 2024