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The available studies on contingency planning in projects lack an effective method for accurately estimating time contingencies because they (1) do not account for historically unprecedented disruptions, (2) produce estimation errors for low-probability and high-impact events and (3) do not consider the disproportionate impact of disruptive events on project resources. With this in mind, this research posits that the notion of resilience is central to contingency planning because understanding disruptions is directly interconnected with modelling and measuring resilience to disruptions. As a result, a method for estimating time contingency is presented in which resilience is used to describe the disruption and recovery profiles of project activities. The proposed method is applied in a real-life construction project to demonstrate its capability. In addition, a case study from the literature is selected to compare the proposed method with a critical chain project management solution. The results show that an accurate forecast of time contingency requires precise information about the specific impact of a disruptive event on project resources across various stages of disruptions. From a practical standpoint, the proposed method assists project managers and key stakeholders in the delay analysis of disrupted projects, as well as in developing cost-effective resource management plans. [ABSTRACT FROM AUTHOR]

We study household credit responses to Hurricane Harvey using new, geographically granular data on credit cards, mortgages, and flooding. Estimates from a differences-in-differences design that exploits the flooding gradient show that affected households only borrow at low-interest rates, often using promotional (zero interest) cards and that they quickly pay down balances. We also document that take-up of forbearance (borrowing by missing mortgage payments without penalty) increases with flooding. These results are attenuated in floodplains, particularly in structures subject by code to physical hardening. Our results indicate that credit acts as a substitute for the lack of physical hardening. [ABSTRACT FROM AUTHOR]

Relief supply inventories are important in disaster response operations. To address the problem of dead stocks and avoid relief supply expiration, such inventories must be renewed periodically. Different from the traditional shelf-life rotation mechanism (i.e. renewal of the entire inventory at the expiration date), which pays more attention to decreasing the quantity of dead stocks, the novel period rotation mechanism that we design in this study will gradually rotate relief supplies according to their shelf life. We propose a mathematical programming model to minimise the total cost of relief organisations and conduct a case study and analyse the sensitivity of key parameters using real-world data. Results show that our rotation mechanism can substantially reduce the financial burden on relief organisations and improve the quality of relief supplies for victims. The novelty of our study includes: (1) We introduce a new approach that can rotate multiple times, through which we consider the quality of relief supplies; (2) We incorporate recycling and remanufacturing into the rotation process; and (3) We adopt an inductive reasoning method to develop the model with demand and time uncertainties. We prove the feasibility of our model and contributions of our study to humanitarian logistics practice and research. [ABSTRACT FROM AUTHOR]

The Indian subcontinent is highly susceptible to natural disasters. To abate the effect of the disasters, the government emanates institutional mechanisms and strategies for disaster preparedness, mitigation and response. Although there is well-established institutional framework in India for disaster management, there is no study that proposes a disaster supply chain structure under the institutional framework and integrates information and digital technologies (IDT) of industry 4.0 within it. The present study overcomes this research gap. It introduces an IDT structure within the institutional framework of India for multi-agency information sharing, coordination and decision making. It formulates a mathematical model and analyses the impact of IDT on the total response time through three scenarios. The results of scenario analysis reveal the significance of IDT on the disaster supply chains and its capability to handle information delays and IDT failure. The present study can be adopted by disaster management institutions to construct and implement better response practices at the operational, planning and strategic levels. Furthermore, the proposed model in the study can be used for vaccination planning, which includes vaccine distribution, monitoring, regulation and effective implmentation as a response against the current pandemic situation caused due to SARS-CoV-2. [ABSTRACT FROM AUTHOR]

Risk management is an essential component of effective campus recreational sport programming. However, research on risk management policies, procedures, and practices within recreational sports is both limited and outdated. As such, this study provides an overview of current risk management policies, procedures, and practices across North American collegiate recreational sports departments. Specifically, the results emphasize current training practices for both part-time and full-time staff, practices in establishing and updating emergency action plans, and contents covered in emergency action plans. The results suggest that while the vast majority of recreational sport programs have established risk management procedures and engage in risk management training, they diverge in best practices related to emergency action plans and in terms of contents covered in such plans. Applications of these results for practitioners and institutions are emphasized, including discussions around best practices in risk management. [ABSTRACT FROM AUTHOR]

In real-world complex group decision-making problems, preference inconsistency and opinion conflict are common and crucial challenges that need to be tackled. To promote consensus reaching, a novel group consensus reaching model is constructed considering individual satisfaction and group fairness. This study focuses on managing the group consensus reaching process based on flexible and adaptable information, modelled as distributed linguistic preference relations (DLPRs). First, a building process for DLPRs is discussed by integrating cumulative distribution functions converted from single linguistic term sets, hesitant fuzzy linguistic term sets, and comparative linguistic expressions. Furthermore, a two-stage consistency improvement method is proposed, which makes a trade-off between the frequency and magnitude of adjustments. Finally, we establish an improved group consensus model to balance individual satisfaction and group fairness, where individual satisfaction is measured by the deviation between the original and revised preferences and group fairness is measured by the deviation between individual satisfactions. The emergency response plan selection is conducted to show the validity and advantages of the proposed approach. [ABSTRACT FROM AUTHOR]

This research aimed to analyze the level of flood vulnerability in Masamba urban area, using quantitative and qualitative descriptive approaches. Data analysis techniques were carried out by weighting, scoring, and overlaying for each flood-causing parameter map, including; slope map, rainfall, land elevation, soil type, and land use. Delineation of flood-prone areas is determined based on spatial analysis between the detailed spatial plan map of Masamba urban area and the maps of these parameters, obtaining the area and classification of the level of vulnerability to flood disasters in the Masamba urban area. The results of the analysis show that the low flood vulnerability level is located in the Kamiri village, Masamba village and part of the Kappuna village with an area of 83.39 Ha or 7.22%, the medium flood vulnerability level covers the Radda and Baliase villages with an area of 824.75 Ha or 71.58%, while the Bone and Bone Tua Villages are included in the high vulnerability classification category with an area of 244.07 Ha or 21.18% of the Masamba urban detailed spatial plan (RDTR) area. On the basis of the research findings, mitigation efforts are needed in spatial planning in North Luwu Regency. Directions for spatial utilization in the flood-prone Masamba urban area, including; development of a flood disaster mitigation system with structural and non-structural methods. Recommendations for spatial utilization in Masamba urban area to minimize similar flash flood events in the future are reviewing the RDTR of Masamba urban area, revising the urban drainage system master plan, and modeling the classification of flood-prone areas. With the availability of flood vulnerability data in North Luwu Regency, it can be an input for the government and the community as a basis for decision making in spatial planning, spatial utilization and spatial control to create a resilient and sustainable Masamba City. [ABSTRACT FROM AUTHOR]

This study investigates the relationship between the implementation of Psychosocial Support Activities (PSA) and the psychosocial readiness and well-being of learners participating in in-person classes in the Second Congressional District of Bohol for the 2022-2023 school year. A total of 114 teachers and 114 learners were selected through simple random sampling. Utilizing a descriptive survey design, data were collected via questionnaires to assess PSA implementation levels, learners' psychosocial readiness, and psychosocial well-being. Spearman's Rank Correlation Coefficient was employed to analyze the relationships among these variables. Results indicated a very high level of PSA implementation, suggesting effective school practices. Furthermore, learners exhibited very high psychosocial readiness, indicating strong psychological, social, and emotional preparedness for the learning environment. While PSA implementation did not significantly influence learners' psychosocial readiness or well-being, a substantial positive correlation was found between the two latter variables. Based on these findings, the study advocates for integrating Basic Psychosocial Support into the School Disaster Risk Reduction and Management (DRRM) Contingency Plan, enabling educators to support students facing distress effectively. This research suggests the importance of psychosocial support in enhancing students' overall educational experiences and well-being institutionalizing psychological interventions and integrating Basic Psychosocial Support into School DRRM Contingency Plans to foster a holistic learning environment to enhance students' capacity. [ABSTRACT FROM AUTHOR]

This study aimed to asses disaster awareness and preparedness among teachers and learners of Amado M. Pitogo Memorial School, Upper Panul-iran, Abuno, Iligan City for the School Year 2023-2024. The output of the study was the school disaster risk reduction and management plan. It examined the socio-demographic profiles of respondents and their correlation with disaster awareness, preparedness, and awareness on school disaster capability building. Using a descriptive-correlational method, data were gathered from 116 learners and 10 teachers and an administrator. Results indicated that learners aged 11-12 years old represent the highest frequency, with males slightly outnumbering females. Grade 5 have the highest frequency among learners, while the majority of teachers were aged 41 and above, female, holding Teacher I positions, and with Master's level education. Regarding disaster awareness, respondents were extremely aware that disasters can happen anytime but showed less preparedness in terms of emergency kits. Similarly, while there were extremely aware regarding the presence of fire extinguishers, there were less confidence in knowing what to do during a fire incident. Notably, awareness of school disaster capability building is high, but participation in emergency planning was low. Statistical analysis revealed significant associations between disaster awareness, preparedness, and grade level of learners, indicating a need for targeted interventions. These findings highlighted the importance of considering demographic factors in disaster preparedness education and suggest avenues for improving school-based disaster management strategies and plans. [ABSTRACT FROM AUTHOR]

The scientific article studied and explained the content of the legal normative bases of the activity of the state border protection bodies of the State Border Guard Service of Ukraine during the implementation of measures of the legal regime of emergency. This problem is understudied. With the help of general and special methods of scientific knowledge, the authors researched the laws of Ukraine, current subordinate legal acts and scientific sources that regulate the content of the activities of the state border protection bodies of the State Border Guard Service of Ukraine regarding the implementation of measures of the legal state of emergency. The researchers found out the content and features of the legal provision that determines the type and limits of the official actions of the state border protection bodies of the State Border Guard Service of Ukraine aimed at implementing the measures of the legal emergency regime. It has been established that the measures of the legal state of emergency, in the implementation of which the state border protection authorities take part, determine the possibility, necessity and grounds for the exercise of the powers of the State Border Guard Service of Ukraine. During emergency situations, in the event of the introduction of appropriate modes of operation of the system of protection of the population and territories within the areas of responsibility, the state border protection authorities carry out various forms and methods of official measures. Depending on the situation, the degree of spread of a predicted or an emergency situation that has arisen within a specific territory, in accordance with the procedure established by law, the appropriate mode of operation of the population and territory protection system is introduced, which determines the share of the state border protection authorities' participation in their elimination. The authors come to the conclusion that the provisions of the regulatory legal acts that determine the duties of the state border protection authorities during the implementation of measures of the legal state of emergency and the general procedure for their actions in emergency situations are implemented in the event of the introduction of the appropriate modes of operation of the system of protection of the population and territories within areas of responsibility of state border protection authorities. Their activities are closely related to the performance of the functions of the State Border Guard Service of Ukraine. [ABSTRACT FROM AUTHOR]

Hurricane Ian in 2022 was a destructive category 4 Atlantic hurricane striking the state of Florida, which caused hundreds of deaths and injuries, catastrophic property damage, and an economic loss of more than $112 billion. Before the landfall of Ian in Florida, the state government issued evacuation orders in high-risk zones to reduce casualties and injuries. However, there is limited data available to monitor the actual evacuation patterns and compliance with the evacuation orders at a large geographic scale. This study utilizes human mobility data (i.e. SafeGraph Weekly Pattern) to analyse the spatial patterns of evacuation during Hurricane Ian in 2022. The objectives of the study include three key aspects: 1) proposing an analytical workflow that utilizes human mobility data to detect mobility patterns in disasters and other emergency events; 2) identifying significant evacuation patterns, and 3) revealing the spatial variations in the compliance with evacuation orders in the affected areas. Using data science and spatial analysis techniques, this study detected notable changes in population movements, both within Florida and nationwide, which are potentially linked to the hurricane-induced population evacuation. The distance decay pattern of population flows from Florida demonstrates a propensity for individuals to relocate to nearby areas during the hurricane. Furthermore, the increase in population outflows from the impacted areas suggests the effectiveness of mandatory evacuation orders. A more pronounced increase in outflows from designated mandatory evacuation areas points to the public awareness of the evacuation zone designation. This study provides large-scale, fine-resolution analysis of evacuation behaviours in natural disasters which cannot be easily detected in traditional data sources. The analytical workflows provide actionable tools for government agencies and policymakers to evaluate the effectiveness of evacuation orders and improve evacuation plans in future disasters. [ABSTRACT FROM AUTHOR]

The Taihang Mountains in China span numerous cities, where landslide disasters occur frequently in the mountainous areas, jeopardizing the lives and properties of residents. Consequently, it is of great significance to focus on prevention and control of landslide disasters in the region. Currently, a single model is commonly employed to analyze landslide susceptibility mapping (LSM), but the accuracy of the results fails to meet the demands of early warning, prevention, and control. This paper focuses on the Taihang Mountain area as the research area, organizes the collection of landslide disaster potential points and related influence factor data, and employs the information quantity method to derive a composite machine learning model by coupling with Random Forest (RF) and Extreme Gradient Boosting (XGB), subsequently utilizing the Genetic Optimization Algorithm (GA) to optimize the model. The performance of the composite model is enhanced using the Genetic Algorithm (GA), employing accuracy, regression rate, precision, F1 score, AUC value, and Taylor diagram to evaluate the comprehensive accuracy of the model results, with a susceptibility map generated for comparative analysis. The results demonstrate that the IV-GA-RF model performs optimally (accuracy = 0.956, precision = 0.96, recall = 0.953, F1 score = 0.957, AUC = 0.946 for the testing set, AUC = 0.929 for the training set), with all-around improvement in performance metrics compared to the unoptimized composite model, with metric values improving by 0.044, 0.051, 0.046, 0.044, 0.021 and 0.020, respectively. The IV-GA-RF model exhibits a significant advantage over the IV-GA-XGB algorithm, also optimized using the GA algorithm. The accuracy of the susceptibility map produced by the IV-GA-RF model is superior, as assessed by the Seed Cell Area Index (SCAI) method. The four factors of slope, rainfall, seismicity, and stratigraphic lithology are crucial in determining the occurrence of landslides in the study area. In summary, the IV-GA-RF model can be utilized as an effective model for analyzing landslide disasters, providing a reference for research in this field and contributing scientific insights to disaster prevention and control efforts in the study area; simultaneously, the concept of the composite optimization model introduces new perspectives into this field. [ABSTRACT FROM AUTHOR]

Many landslides occurred every year, causing extensive property losses and casualties in China. Landslide susceptibility mapping is crucial for disaster prevention by the government or related organizations to protect people's lives and property. This study compared the performance of random forest (RF), classification and regression trees (CART), Bayesian network (BN), and logistic model trees (LMT) methods in generating landslide susceptibility maps in Yanchuan County using optimization strategy. A field survey was conducted to map 311 landslides. The dataset was divided into a training dataset and a validation dataset with a ratio of 7:3. Sixteen factors influencing landslides were identified based on a geological survey of the study area, including elevation, plan curvature, profile curvature, slope aspect, slope angle, slope length, topographic position index (TPI), terrain ruggedness index (TRI), convergence index, normalized difference vegetation index (NDVI), distance to roads, distance to rivers, rainfall, soil type, lithology, and land use. The training dataset was used to train the models in Weka software, and landslide susceptibility maps were generated in GIS software. The performance of the four models was evaluated by receiver operating characteristic (ROC) curves, confusion matrix, chi-square test, and other statistical analysis methods. The comparison results show that all four machine learning models are suitable for evaluating landslide susceptibility in the study area. The performances of the RF and LMT methods are more stable than those of the other two models; thus, they are suitable for landslide susceptibility mapping. [ABSTRACT FROM AUTHOR]

Background: The COVID‐19 pandemic has taken a significant toll on the US population, with birthing people having special clinical needs. The Pregnancy Risk Assessment Monitoring System (PRAMS) is a population‐based surveillance system for monitoring birthing people's experiences. Comment data from the PRAMS survey can provide further insight into birthing people's experiences. This study aims to use PRAMS data to describe pandemic‐related experiences in a representative sample of birthing people in Wisconsin to help inform future emergency preparedness planning. Methods: This study analyzed 2020 Wisconsin PRAMS data for births in March or later. Content analysis of pandemic‐related comment data was conducted, and quantitative data on demographic characteristics and pandemic‐related experiences were examined. Results: Findings from 1406 respondents indicated that many birthing people were affected by the pandemic, including changes in healthcare visits and employment. One hundred respondents commented on pandemic‐related experiences; four interrelated themes emerged from content analysis: changed nature and quality of healthcare, limited social support, increased anxiety, stress, or fear, and employment or financial burden. Most comments discussed negative impacts; some expressed positive aspects. Discussion: Findings suggest opportunities for improving support for birthing people during public health emergencies, for example, through developing healthcare policies and public health guidelines that prioritize the protective mechanisms of social support for birthing people, identifying additional and immediately accessible policy protections to support birthing and postpartum people (e.g., insurance and paid leave) during public health emergencies, and implementing additional screening and support to help address increased mental health needs during public health emergencies. [ABSTRACT FROM AUTHOR]

Introduction: Epistaxis is commonly referred to as nosebleeds. The age range in which it most frequently manifests itself is three to eight years old. One of the primary settings for pediatric epistaxis is schools since kids can get accidental injuries while playing. The security and welfare of the pupils in school are the teacher's concerns. So, the teachers must be aware of the correct basic first-aid protocols. This study aims to assess the awareness of handling of epistaxis first aid in schools. Methodology: The study participant's responses were gathered using a pre-tested semi-structured questionnaire. Statistical analysis was done and the significance of the results was assessed Results & Conclusion: The majority of participants felt confident when it came to managing epistaxis, despite the fact that they had not received any formal training and simply possessed only an average understanding of the subject, lacking detailed insights and intricacies related to its first aid treatment. This underscores the fact that they were overestimating their knowledge and expertise in this domain. Therefore, educators, who are typically the initial responders to pediatric epistaxis in schools, have a limited or inadequate understanding and familiarity with the appropriate first aid protocols. Further studies scaling larger populations and propagating awareness in institutes related to children especially through means of live demonstrations and workshops can advance first aid management during emergencies. [ABSTRACT FROM AUTHOR]

In recent years, the landscape of disasters, conflicts and terror events has become more frequent and complex. Climate change, armed conflicts, terrorism, disinformation, cyber‐attacks, inequality and pandemics now present significant challenges to humanity. Emergency physicians today are likely to encounter ideologically motivated violent extremism or terrorist actions by radicalised lone actors. Terror medicine, distinct from disaster medicine, addresses the unique and severe injuries caused by terrorist incidents, including explosions, gunshots and chemical agents. The chaotic aftermath of such attacks demands rapid triage, prioritisation and strict adherence to scene safety protocols. Moreover, terrorist events have profound psychological impacts on victims and responders alike. Understanding the broader public health implications of these attacks is crucial for emergency physicians to enhance community safety and resilience. Terror medicine also brings unique ethical and legal challenges, such as patient confidentiality, mandatory reporting and mass casualty management. Effective responses to terror incidents necessitate close collaboration between healthcare providers and law enforcement. Familiarity with terror medicine principles fosters better communication and coordination, ultimately improving response efficiency and patient outcomes. This review offers a comprehensive approach to understanding terror medicine, defining the concept of 'terror', its significance for emergency physicians, and the known health impacts on patients, healthcare workers and responders. By delving into these aspects, the review aims to equip medical professionals with the knowledge and skills needed to navigate the complexities of terror‐related emergencies effectively. [ABSTRACT FROM AUTHOR]

Landslides are among the most dangerous hazards in Asia, posing a significant threat to human lives, infrastructure, and sustainable development. Landslide susceptibility maps provide useful insights into hazard potential but lack quantitative exposure assessments to develop targeted mitigation strategies and resource allocation. This study aims to propose an integrated approach for landslide hazards and exposure evaluation using machine learning models on the Google Earth Engine environment and geospatial analysis techniques. A geospatial database was established to predict hazards and evaluate exposure, including data on topography, geology, hydrology, climate features, land use, and building data. The landslide susceptibility map was created with advanced machine-learning algorithms, including Classification And Regression Tree (CART), Random Forest (RF), Gradient Boosting (GB), and Support Vector Machine (SVM). The ROC curve and the Wilcoxon signed-rank test were employed to evaluate the performance differences among the CART, GB, RF, and SVM models. The results indicated that the RF model demonstrated the highest performance, leading to its selection for creating a landslide susceptibility map. Landslide exposure was evaluated by overlaying the landslide susceptibility map with building data to quantify the number of affected houses by landslides across districts and communes. The analysis results identified the Cho Don district as the most exposed, with 46,237 households located in high and very high landslide susceptibility zones, followed by Ba Be district (39,631 households), Bac Kan city (37,266 households), Bach Thong district (28,495 households), Cho Moi district (28,436 households), Na Ri district (17,723 households), Ngan Son district (14,142 households), and Pac Nam district (13,034 households). These findings enable the identification of the potential consequences of landslides on infrastructure, human settlements, and livelihoods, contributing to the promotion of disaster reduction and prevention strategies. [ABSTRACT FROM AUTHOR]

• The future-oriented approach considers climate change impacts and LULC change. • Integrated AHP & bivariate analysis for precise flood susceptibility mapping. • Crucial insights for policymakers to enhance disaster preparedness. Flood susceptibility maps provide invaluable information for assessing and managing flood-prone areas, aiding in proactive planning, risk reduction strategies, and safeguarding vulnerable communities. The current research concentrates on advancing sustainable development practices by undertaking a comprehensive assessment of flood susceptibility in the Upper Vellar basin, with a projection for 2050. Employing an integrative methodology, this study utilizes an Analytical Hierarchy Process (AHP) and Bivariate Analysis. Nine critical parameters were used: elevation, distance from the river, distance from the road, drainage density, predicted LULC, projected precipitation, slope, soil type, and Topographic Wetness Index (TWI). The Modules of Land Use Change Evaluation (MOLUSE) plugin, which uses Cellular Automata-Artificial Neural Network (CA-ANN), was employed to predict the LULC map for the year 2050. Furthermore, bias-corrected Coupled Model Intercomparison Project 6 (CMIP 6) EC EARTH 3 Model (GCM) RCP 4.5 and 8.5 projected precipitation data were employed. The resulting flood susceptibility zones are classified into three categories: low, moderate, and high, with proportions of 32.64%, 55.52%, and 11.84% for RCP 4.5, and 34.63%, 53.46%, and 11.91% for RCP 8.5, respectively, concerning the total area. In both scenarios, nearly 38% of the settlement area is at high flood risk. This study provides essential insights for policymakers and stakeholders, facilitating the formulation of sustainable strategies to address projected changes in land use, precipitation patterns, and flood susceptibility in the Upper Vellar region up to 2050. [ABSTRACT FROM AUTHOR]