Emergency Scenarios Research Articles

Inductive reasoning with type-constrained encoding for emerging entities

Knowledge graph reasoning, vital for addressing incompleteness and supporting applications, faces challenges with the continuous growth of graphs. To address this challenge, several inductive reasoning models for encoding emerging entities have been proposed. However, they do not consider the multi-batch emergence scenario, where new entities and new facts are usually added to knowledge graphs (KGs) in multiple batches in the order of their emergence. To simulate the continuous growth of knowledge graphs, a novel multi-batch emergence (MBE) scenario has recently been proposed. We propose a path-based inductive model to handle multi-batch entity growth, enhancing entity encoding with type information. Specifically, we observe a noteworthy pattern in which entity types at the head and tail of the same relation exhibit relative regularity. To utilize this regularity, we introduce a pair of learnable parameters for each relation, representing entity type features linked to the relation. The type features are dedicated to encoding and updating the features of entities. Meanwhile, our model incorporates a novel attention mechanism, combining statistical co-occurrence and semantic similarity of relations effectively for contextual information capture. After generating embeddings, we employ reinforcement learning for path reasoning. To reduce sparsity and expand the action space, our model generates soft candidate facts by grounding a set of soft path rules. Meanwhile, we incorporate the confidence scores of these facts in the action space to facilitate the agent to better distinguish between original facts and rule-generated soft facts. Performances on three multi-batch entity growth datasets demonstrate robust performance, consistently outperforming state-of-the-art models.

Influence of the first wave of COVID-19 on Chinese students' psychology and behavior: a case study approach.

During the first wave of COVID-19, China demonstrated a strong commitment to epidemic prevention and control. This case study focuses on Z University, which adopted closed management when the epidemic was serious, and examines the influence of COVID-19 on students' psychology and behavior through interviews with 10 students. The research reveals that while students perceive closed management during the epidemic as enhancing safety and promoting learning engagement to some extent, the epidemic also has adverse effects on their physical health, psychology, and social life. These impacts included deteriorating physical health, feelings of rebellion and depression regarding college life, alongside concerns and aspirations regarding future job stability. In the discussion, we suggest that higher education institutions can utilize this information to shape policies and procedures, particularly concerning mental health and risk communication, not only during the current pandemic but also in future emergency or disaster scenarios.

Enhancing unmanned ground vehicle performance in SAR operations: integrated gesture-control and deep learning framework for optimised victim detection.

In this study, we address the critical need for enhanced situational awareness and victim detection capabilities in Search and Rescue (SAR) operations amidst disasters. Traditional unmanned ground vehicles (UGVs) often struggle in such chaotic environments due to their limited manoeuvrability and the challenge of distinguishing victims from debris. Recognising these gaps, our research introduces a novel technological framework that integrates advanced gesture-recognition with cutting-edge deep learning for camera-based victim identification, specifically designed to empower UGVs in disaster scenarios. At the core of our methodology is the development and implementation of the Meerkat Optimization Algorithm-Stacked Convolutional Neural Network-Bi-Long Short Term Memory-Gated Recurrent Unit (MOA-SConv-Bi-LSTM-GRU) model, which sets a new benchmark for hand gesture detection with its remarkable performance metrics: accuracy, precision, recall, and F1-score all approximately 0.9866. This model enables intuitive, real-time control of UGVs through hand gestures, allowing for precise navigation in confined and obstacle-ridden spaces, which is vital for effective SAR operations. Furthermore, we leverage the capabilities of the latest YOLOv8 deep learning model, trained on specialised datasets to accurately detect human victims under a wide range of challenging conditions, such as varying occlusions, lighting, and perspectives. Our comprehensive testing in simulated emergency scenarios validates the effectiveness of our integrated approach. The system demonstrated exceptional proficiency in navigating through obstructions and rapidly locating victims, even in environments with visual impairments like smoke, clutter, and poor lighting. Our study not only highlights the critical gaps in current SAR response capabilities but also offers a pioneering solution through a synergistic blend of gesture-based control, deep learning, and purpose-built robotics. The key findings underscore the potential of our integrated technological framework to significantly enhance UGV performance in disaster scenarios, thereby optimising life-saving outcomes when time is of the essence. This research paves the way for future advancements in SAR technology, with the promise of more efficient and reliable rescue operations in the face of disaster.

Impact of large-scale renewable energy integration on the grid voltage stability

Nowadays, the production of renewable energy is increasing rapidly due to its enormous potential and environmental advantages. Still, the addition of renewable energy to the grid has resulted in some volatility in the electrical system. In this work, the national grid of Ethiopia is used as an example to examine the impact of significant wind power integration on grid stability. In particular, issues with voltage stability in both steady-state and emergency scenarios are taken into account for the network. The dynamic modeling of the wind farm is taken into consideration for contingency-based analysis, and the whole nation's power network, including the largest wind farm in the nation, Adama-II, is modeled for base-case analysis using MATLAB's built-in PSAT software. The result indicates that the bus voltages are within an acceptable standard voltage range for the base-case system. Nevertheless, the wind farm will be disconnected from the network in compliance with the global manufacturer's guideline in the case of a three-phase fault at the network point of common coupling (PCC), where the voltage at PCC is equivalent to 0.045 per unit voltage. The results also demonstrate that the voltage profile throughout the whole nation power network is lower when there is a problem at PCC. As a result, as Ethiopia is building several huge wind farms, it is advised that fault detection and control methods be thoroughly designed before connecting the major wind farms to the grid.

Physiological and performance metrics during a cardiopulmonary real-time feedback simulation to estimate cognitive load

Multitasking is crucial for First Responders (FRs) in emergency scenarios, enabling them to prioritize and treat victims efficiently. However, research on multitasking and its impact on rescue operations are limited. This study explores the relationship between multitasking, working memory, and the performance of chest compressions during cardiopulmonary resuscitation (CPR). In this experiment, eighteen first-year residents participated in a CPR maneuver using a real-time feedback simulator to learn chest compressions. Different additional secondary tasks were developed and accomplished concurrently with the chest compressions. Heart rate, respiration rate, galvanic skin response, body temperature, eye gaze movements and chest compression performance data were collected. The findings of this study indicated that multitasking impacted chest compression quality for all secondary tasks, showing significance (p-value < 0.05) for the frequency of the chest compressions which worsened in all cases. Additionally, vital signs such as heart rate, respiration rate, and eye gaze speed were also affected during multitasking. Nevertheless, this change on vital signs was different depending on the type of secondary task accomplished. Therefore, as a conclusion, performing multiple tasks during chest compressions affects performance. Understanding cognitive load and its impact on vital signs can aid in training FRs to handle complex scenarios efficiently.

Antegrade long giant ileocolic intussusception via colon–cecum cancer lead points: case report with focus on the sign of the python colon

BackgroundIleocolic intussusception is a rare condition that is difficult to diagnose. A pathological lead point, usually malignant, is often the cause of adult intussusception. In emergency scenarios, radiologists must be able to identify this entity and its causes and express their opinions on associated complications.Case presentationThe hospital's emergency room admitted a 75-year-old woman who was suffering from intermittent abdominal pain and constipation. An abdominal contrast-enhanced computed tomography revealed a long giant ileocolic intussusception. Considering the clinical stability and the absence of signs of complications on CT, the patient did not undergo an urgent surgical procedure but underwent a colonoscopy the following day. Surprisingly, the colonoscopy highlighted the spontaneous resolution and revealed the presence of the three tumour lead points in the cecum and ascending colon. A CT was performed after the colonoscopy, confirming the intussusception's resolution. The surgeon performed a right oncological hemicolectomy. The malignant lead points on histological examination were moderate to poorly differentiated adenocarcinomas.ConclusionsA giant and long ileocolic intussusception with a long embedded visceral segment (the python colon sign) could suggest pathological lead points in the cecum and ascending colon. Despite the large size, a spontaneous resolution of the python colon is possible. In this scenario, even if, on imaging, it is impossible to macroscopically identify a lead point, in cases of giant and long ileocolic intussusceptions, every effort must be undertaken to search for the cause of the intussusception.

Emergent robot-to-open conversion – Multidisciplinary simulation training in crisis management

Anticipating and addressing unexpected intraoperative events and anatomies are some of the most challenging aspects of pediatric urologic practice; uncontrolled hemorrhage is one of the most anxiety provoking and precarious. The increasing application of the robotic platform in pediatric urology adds another layer of complexity as surgeons are not immediately at the patient's bedside. Should hemorrhage occur in robotic cases, clear communication and seamless coordination between members of the operating room team are paramount to optimize patient safety and minimize errors. This is especially important in pediatric cases for which the margin of error is narrow. Non-technical skills, including leadership, decision-making, situational awareness, stress management, and team-communication, become increasingly critical. While many programs have focused on robotic training, few prepare the operating room team and surgical trainees to manage these unforeseen, emergent intraoperative scenarios. This review discusses the role of a multidisciplinary, in situ robot-to-open conversion simulation program in addressing this educational gap, ways to approach establishing these programs, and potential barriers.

Construction of user-adaptive urban waterlogging emergency scenarios considering mapping concerns

Many current urban waterlogging emergency scenario constructions overlook the factors that influence users’ concerns, rendering the resulting scenarios less adaptable to various user types and hindering efficient comprehension of waterlogging risks. The objective of this study is to link user concerns with elements of waterlogging emergency scenarios and then proposes a model accommodating various user groups to enhance scenario applicability. Initially, we established a user classification system for urban waterlogging emergencies. Subsequently, we extracted relevant topic keywords from social media and emergency plans, aligning them with different user groups to address their specific concerns. Additionally, we expanded the geographical elements (time, location, people, things, events, and phenomena) of the scenario to encompass its constituent components. Utilising domain ontology, we developed a mapping model between user concerns and scenario elements. Finally, this study employed both prototype system development and questionnaire validation to assess the efficacy of the user model. The validation for mean values, include Accuracy 0.923, Completeness 0.945, Satisfaction 0.879, and model’s effectiveness is 91.7 %. The results indicate that the model has high reliability and could effectively expand the user universality of urban waterlogging emergency scenarios.

Rapid-Response Water-Shrink Films with High Output Work Density Based on Polyethylene Oxide and α-Cyclodextrin for Autonomous Wound Closure.

Conventional wound closure methods, including sutures and tissue adhesives, present significant challenges for self-care treatment, particularly in the context of bleeding wounds. Existing stimuli-responsive contractile materials designed for autonomous wound closure frequently lack sufficient output work density to generate the force needed to bring the wound edges into proximity or necessitate stimuli that are not compatible with the human body. Here, semi-transparent, flexible, and water-responsive shrinkable films, composed of poly(ethylene oxide) and α-cyclodextrin, are reported. These films exhibit remarkable stability under ambient conditions and demonstrate significant contraction (≈50%) within 6 s upon exposure to water, generating substantial contractile stress (up to 6MPa) and output work density (≈1028kJ m-3), which is 100 times larger than that of conventional hydrogel and 25 times larger than that of skeletal muscles. Remarkably, upon hydration, these films are capable of lifting objects 10000 times their own weight. Leveraging this technology, water-shrink tapes, which, upon contact with water, effectively constrict human skin and autonomously close bleeding wounds in animal models within 10 seconds, are developed further. This work offers a novel approach to skin wound management, showing significant potential for emergency and self-care scenarios.

Emergent cosmology in 4D Einstein Gauss Bonnet theory of gravity

In this paper, in an FLRW background and a perfect fluid equation of state, we explore the possibility of the realization of an emergent scenario in a 4D regularized extension of Einstein-Gauss-Bonnet gravity, with the field equations particularly expressed in terms of scalar-tensor degrees of freedom. By assuming non-zero spatial curvature (k = ± 1), the stability of the Einstein static universe (ESU) and its subsequent exit into the standard inflationary scenario is tested through different approaches. In terms of dynamical systems, a spatially closed universe rather than an open universe shows appealing behaviour to exhibit a graceful transition from the ESU to standard cosmological history. We found that under linear homogeneous perturbations, for some constraints imposed on the model parameters, the ESU is stable under those perturbations. Moreover, it is noted that for a successful graceful transition, the equation of state ω must satisfy the conditions −1 < ω < 0 and ω < − 1 for closed and open universes, respectively. Furthermore, the ESU is seen to be neutrally stable under matter perturbation in the Newtonian gauge.

Essential Review of Oncological Emergencies

Innovations in oncology have expanded treatment eligibility, leading to a rise in cancer patients requiring critical care. This necessitates that all critical care clinicians possess a fundamental knowledge of prevalent oncological conditions and identify emergent scenarios requiring immediate action. This article will explore key oncological complications and their management approaches.

The key role of Spain in the traffic of West Nile virus lineage 1 strains between Europe and Africa

Background West Nile Virus (WNV) is a zoonotic arbovirus worldwide spread. Seasonal WNV outbreaks occur in the Mediterranean basin since the late 1990’s with ever-increasing incidence. In Southern Spain WNV is endemic, as disease foci - caused by WNV lineage 1 (WNV-L1) strains - occur every year. On the contrary, WNV-L2 is the dominant lineage in Europe, so most European WNV sequences available belong to this lineage, WNV-L1 sequences being still scarce. Methods To fill this gap, this study reports the genetic characterisation of 27 newly described WNV-L1 strains, involved in outbreaks affecting wild birds and horses during the last decade in South-Western Spain. Results All strains except one belong to the Western Mediterranean-1 sub-cluster (WMed-1), related phylogenetically to Italian, French, Portuguese, Moroccan and, remarkably, Senegalese strains. This sub-cluster persisted, spread and evolved into three distinguishable WMed-1 phylogenetic groups that co-circulated, notably, in the same province (Cádiz). They displayed different behaviours: from long-term persistence and rapid spread to neighbouring regions within Spain, to long-distance spread to different countries, including transcontinental spread to Africa. Among the different introductions of WNV in Spain revealed in this study, some of them succeeded to get established, some extinguished from the territory shortly afterwards. Furthermore, Spain’s southernmost province, Cádiz, constitutes a hotspot for virus incursion. Conclusion Southern Spain seems a likely scenario for emergence of exotic pathogens of African origin. Therefore, circulation of diverse WNV-L1 variants in Spain prompts for an extensive surveillance under a One Health approach.

Reflections on co-managing investigative praxis in the context of a health emergency.

This article deals with the interest of the scientific field in systematizing the co-management investigative praxis, in a health emergency scenario, based on the analysis of a research with a multicentric qualitative approach, using the framework of the Support Research and the analysis of critical hermeneutics. As a result, it was identified that the creation of a map guide contributed as a guiding document, aiming at organizing different techniques for the organization and formation of field researchers, as well as an instrument of data analysis. The training of researchers for the theoretical framework of Support Research, as well as their co-management and involvement in the different stages of research, proved to be a differential for the production of subjects and collectives with investigative praxis, allowing a dialogic exchange between coordinators and researchers and regular sharing of the results. It is concluded that the way in which the methodology was proposed, allowed the expansion of the reflective capacity and understanding of reality, contributing to the formation of researchers as active and critical subjects in the process of data collection, analysis and discussion, encouraging sensitive and attentive actions while seeking to identify the particularities of each context.

Reflexões sobre práxis investigativa cogestora em contexto de emergência sanitária

Abstract This article deals with the interest of the scientific field in systematizing the co-management investigative praxis, in a health emergency scenario, based on the analysis of a research with a multicentric qualitative approach, using the framework of the Support Research and the analysis of critical hermeneutics. As a result, it was identified that the creation of a map guide contributed as a guiding document, aiming at organizing different techniques for the organization and formation of field researchers, as well as an instrument of data analysis. The training of researchers for the theoretical framework of Support Research, as well as their co-management and involvement in the different stages of research, proved to be a differential for the production of subjects and collectives with investigative praxis, allowing a dialogic exchange between coordinators and researchers and regular sharing of the results. It is concluded that the way in which the methodology was proposed, allowed the expansion of the reflective capacity and understanding of reality, contributing to the formation of researchers as active and critical subjects in the process of data collection, analysis and discussion, encouraging sensitive and attentive actions while seeking to identify the particularities of each context.

The difficulty of nuclear emergency prevention measures during natural disasters: ongoing challenges in Japan

It is crucial to anticipate nuclear emergency scenarios and implement effective measures. Japan’s climate and topography make it vulnerable to natural disasters; thus, it is necessary to address compounding and cascading disaster scenarios involving the simultaneous occurrence of natural and nuclear disasters. On 1 January 2024, an earthquake hit the Noto region of Ishikawa Prefecture, resulting in damage to the area around the Shika Nuclear Power Plant, located 90 km from the epicenter. This earthquake revealed that, in the event of a complex disaster, it is possible that residents living within 30 km of the Shika Nuclear Power Plant will be completely unprepared for a nuclear disaster. In the event of a complex disaster, it is crucial to implement appropriate countermeasures while balancing responses to both nuclear and natural disasters and optimizing radiation disaster prevention measures.

Multi-scale population analysis unit construction method considering scene feature variability and long/short-term patterns in spatiotemporal population activities

ABSTRACT Differences in the spatial scale and component elements of urban scenes affect the analysis of spatiotemporal changes in population distribution (PD) patterns. Fixed scales and geometric forms constrained previous analyses of PD and spatiotemporal changes, which neglected the realistic characteristics of urban scenarios. These limitations hinder the ability to capture the diversity and spatiotemporal representation of PD patterns across multiple spatial scales. This study developed a multi-spatial scale population analysis unit (PAU) construction method considering the scenario heterogeneity and long/short-term patterns of spatiotemporal population changes (PC). Initially, we decompose the multi-scale changes in population time series patterns and their relationships with scene features along the temporal dimension to analyze the primary and secondary factors. Starting from the temporal characteristics of the PD and PC, we established a validation and correction method for the factors. Finally, combined with a multi-feature clustering method, a multi-scale PAU construction method driven by scene feature factors is proposed. Experiments were conducted at different spatial scales in both routine and emergent scenarios. The results indicated that this method can help to obtain more homogeneous analysis regions and enhance the stability and phase pattern representations of changes in PDs, thereby improving the interpretability of analytical results.

Chemical Gas Source Localization with Synthetic Time Series Diffusion Data Using Video Vision Transformer

Gas source localization is vital in emergency scenarios to enable swift and effective responses. In this study, we introduce a gas source localization model leveraging the video vision transformer (ViViT). Utilizing synthetic time series diffusion data, the source grid is predicted by classifying the grid with the highest probability of gas occurrence within the diffusion data coverage. Through extensive experimentation using the NBC-RAMS simulator, we generate large datasets of gas diffusion under varied experimental conditions and meteorological environments, enabling comprehensive model training and evaluation. Our findings demonstrate that the ViViT outperforms other deep learning models in processing time series gas data, showcasing a superior estimation performance. Leveraging a transformer architecture, the ViViT exhibits a robust classification performance even in scenarios influenced by weather conditions or incomplete observations. Furthermore, we conduct an analysis of accuracy and parameter count across various input sequence lengths, revealing the ability of the ViViT to maintain high computational efficiency while achieving accurate source localization. These results underscore the effectiveness of the ViViT as a model for gas source localization, particularly in situations demanding a rapid response in real-world environments, such as gas leaks or attacks.

Envisioning the Future of Emergency Management focused on Artificial Intelligence

Artificial Intelligence (AI) in Emergency Management integration has become crucial to improving crisis response efficiency and effectiveness. AI presents new paradigms for responding to emergencies but poses technical and operational challenges, ethical considerations, and the need for robust regulatory frameworks. The study examines various AI applications in emergency scenarios and employs a systematic literature review methodology to identify solutions and future directions for ethical and effective AI integration. The findings highlight the critical balance between leveraging AI capabilities and ensuring safety, privacy, and ethical compliance. Understanding AI's full scope and potential in this critical field is essential as we navigate technological advancements. The article contributes to understanding AI's potential to transform emergency management practices and emphasizes the importance of ethical considerations and regulatory frameworks.

Research on electrical capacitance tomography (ECT) detection of cerebral hemorrhage based on symmetrical cancellation method

Currently, there is an urgent need for a fast and portable intracerebral hemorrhage (ICH) detection technology for pre-hospital emergency scenarios. Owing to the disproportionately elevated permittivity of blood compared to other brain tissues, Electrical Capacitance Tomography (ECT) offers a viable modality for mapping the spatial distribution of permittivity within the brain, thus facilitating the imaging-based identification of ICH. Currently, ECT is confined to time-differential imaging due to limited sensitivity, and this methodology requires non-hemorrhagic measurements for comparison, data that are frequently inaccessible in clinical contexts. To overcome this limitation, in accordance with the natural bilateral symmetry of the cerebral hemispheres, a symmetrical cancellation scheme is introduced. In this method, electrodes are uniformly arrayed around the cranial periphery and strategically positioned in a symmetrical manner relative to the sagittal suture. Subsequently, the measured capacitances for each electrode pair are subtracted from those of their symmetrical counterparts aligned with the sagittal suture. As a result, this process isolates the capacitance attributable solely to hemorrhagic events within a given hemisphere, permitting the absolute imaging of ICH. To assess the feasibility of this method, simulation and empirical imaging were conducted respectively on a numerical hemorrhage model and three physical models (a water-wrapped hemorrhage model, an isolated porcine fat-wrapped hemorrhage model, and an isolated porcine brain tissue-wrapped hemorrhage model). Traditional absolute imaging, time-differential imaging and symmetrical cancellation imaging were performed on all models. The results substantiate that the proposed imaging modality is capable of obtaining absolute imaging of ICH. But a mirrored artifact, symmetrical to the site of the actual hemorrhage image appeared in each of the imaging results. This mirror artifact was characterized by identical dimensions and an inverted pixel-value schema, an intrinsic consequence of the symmetrical cancellation imaging algorithm. The real image of hemorrhage can be ascertained through pre-judgment with the symptoms of the patient. Additionally, the quality of this imaging is seriously dependent on the precise alignment between the electrodes and the sagittal suture of the brain; even a minor deviation in symmetry could introduce excessive noises. Thus, the complicated operational procedures remain as challenges for practical application.

Analyzing factors causing deadlock events of bi-directional pedestrian flow when moving on stairs using a personal space model

Comprehending crowd dynamics on staircases is imperative for preventing accidents, particularly in emergency scenarios. In this context, our study delves into bi-directional pedestrian flow. When confronted with limited staircase space, the occurrence of two distinct lanes—one for upstairs and another for downstairs—is a common observation. However, there has been no quantitative investigation conducted to understand this phenomenon. To facilitate such an analysis, we employ a velocity-based personal space model that accurately reproduces the formation of upstairs and downstairs lanes in bi-directional staircases. The study of lane formation mechanisms and the exploration of factors influencing deadlock are essentially two sides of the same coin. This is due to the fact that, the occurrence of deadlock signifies a disruption in the anticipated lane behavior during counter flow. As a result, we have devised various scenarios to meticulously analyze the factors contributing to both deadlock formation and its subsequent performance. This involves manipulating parameters such as speed, speed dispersion, pedestrian count, initial density, right-hand preference weight, minimum personal space size, same-direction following intensity, and time step. The findings hold the potential to enhance the overall quality of service in stairway movement and offer significant contributions to the understanding and management of pedestrian behavior in such settings.