Disaster Rescue Research Articles

The core competencies in disaster nursing of new graduate nurses in Guangdong, China: A cross-sectional study

AimThis study aimed to assess the level of core competencies in disaster nursing of New Graduate Nurses (NGNs) and explore its influencing factors. BackgroundIn recent years, the overall frequency of disasters around the world has been on the rise. As the emerging workforce in clinical settings, NGNs play an integral role in future disaster relief efforts. NGNs’ level and influencing impact of core competencies in disaster nursing need to be understood. DesignA cross-sectional design. MethodsFrom June to September 2023, the Core Competencies in Disaster Nursing Scale - General Professional Nurse (CCDNS-GPN), designed according to the CCDN V2.0, was used to collect data from NGNs of six nursing schools and 15 hospitals in Guangdong, China. Descriptive analysis was conducted to examine the scores of core competencies in disaster nursing. Furthermore, one-way analysis of variance and multivariate linear regression analysis were used to explore the influencing factors. ResultsA total of 607 NGNs participated in this study. The scores of CCDNS-GPN of NGNs were 90.23 (SD 15.09) (score ratio: 51.56%), indicating a low level of core competencies in disaster nursing. The highest competency was the recovery (score ratio: 55.00%), while the lowest competency was the communication (score ratio: 45.44%). The predictor for core competencies in disaster nursing of NGNs were male (β = 0.091, p < 0.05), below bachelor’s degree (β = −0.109, p < 0.05), had received disaster nursing education at school (β = 0.087, p < 0.05), had participated in a disaster rescue drill at school (β = 0.140, p < 0.05), had been to the ICU during internship (β = 0.135, p < 0.05) and had the intention to be a disaster specialized nurse (β = 0.114, p < 0.05). ConclusionsThe NGNs exhibited insufficient core competencies in disaster nursing in Guangdong, China. Nursing schools and clinical institutions should collaborate and play their respective roles to enhance nurses' core competencies in disaster nursing, ensuring they can timely, safely and efficiently participate in disaster medical relief efforts.

Disaster literacy in disaster emergency response: a national qualitative study among nurses

BackgroundAs the largest group of healthcare professionals, nurses play an indispensable and crucial role in disaster response. The enhancement of nurses’ disaster literacy is imperative for effective disaster emergency management. However, there is currently a lack of knowledge regarding nurses’ disaster literacy. This study represents the first attempt to explore the key components and characteristics of disaster literacy among nurses.MethodsA qualitative descriptive design was employed, and the reporting followed the Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines (refer to File S1). The purposive sampling method was utilized. Thirty-one rescue nurses from 31 medical institutions across 25 provinces and regions in China were recruited to participate in the study. The respondents were requested to share their experiences and insights regarding disaster rescue operations. Inductive content analysis was employed for data examination.ResultsThe results indicated that rescue nurses universally recognized that there was a pressing need to enhance the level of disaster literacy among nurses. The disaster literacy of nurses encompasses nine dimensions: physical and mental quality, disaster rescue general knowledge, professional and technical competence, professional ethics, teamwork, emotional ability, information literacy, leadership, and knowledge transformation.ConclusionsTo ensure national sustainability, government departments, healthcare organizations, and hospital administrators can accurately evaluate the disaster literacy of individual clinical nurses, groups, and the workforce as a whole through nine dimensions, which also can provide evidence to support the development of precision strategies to strengthen the disaster literacy of nurses.

Ultra-Fast Preparation of High-Strength Polymer Concrete via Frontal Polymerization at Room Temperature.

Concrete, in particular application scenarios, such as crack repair, disaster rescue and relief, and 3D printing, needs to be cured quickly. In this work, a novel synthesis strategy, UV-initiated frontal polymerization (FP), for fast preparation of high-strength polymer concrete at room temperature is proposed, and the whole fabrication progress within several minutes, in which coarse aggregates and a small amount of short carbon fibers (CF) are used to enhance mechanical properties and heat conduction and reduce the amounts of chemical adhesives. SEM, DSC, MIP, Ultra depth of field microscope, and Rheometer are employed to characterize the properties of chemical adhesives, polymer concrete, and polymerization process. In addition, the finite element method is used to investigate the internal temperature field status of the FP process. The results indicate that the compressive and flexural strengths of the polymer concrete with 1.0wt%-1mm-CF-50vol.%-quartz sand exceeded 70 and 20MPa, respectively, in which the average self-propagation velocity reached 58mmmin-1. The investigation provides a promising approach for the rapid construction of structural facilities, emergency repair after disasters, 3D printing concrete, etc.

Optimal decision-making of post-disaster emergency material scheduling based on helicopter–truck–drone collaboration

In the last decades, natural disasters, such as earthquakes and landslides, have occurred frequently, seriously threatening the safety of people’s lives and property. How emergency material is scheduled and delivered efficiently to the affected sites after a disaster has become a critical issue in emergency management. Current studies on emergency material scheduling mainly focus on truck or helicopter transport. Inspired by the success of employing drones in commercial logistics, this work investigates the emergency material scheduling issue based on the cooperative transport of drones, helicopters, and trucks. Specifically, this paper considers the limited transport capacity, road conditions in the early stage of the disaster rescue, and affected sites restricted by road conditions that can only be served by helicopters or drones. The studied problem is formulated as a mixed integer programming model, and a two-stage heuristic algorithm is developed to solve the model. For the proposed model, instances of different sizes are generated, and extensive experiments are performed to test the efficiency of the proposed algorithm. The comparison between the solutions obtained by the two-stage algorithm and Gurobi Solver for the small instances validates the effectiveness of the proposed heuristic algorithm. Experimental results for the larger instances show that the proposed two-stage algorithm can effectively solve the problem of emergency material scheduling. Sensitivity analysis of ten typical instances is performed to provide managerial insights. Finally, a case study of the Sichuan earthquake and the visualization of transport routes are presented. The model and solving approach proposed in this work can provide essential decision references for emergency management decision-making.

Development of an Attention Mechanism for Task-Adaptive Heterogeneous Robot Teaming

The allure of team scale and functional diversity has led to the promising adoption of heterogeneous multi-robot systems (HMRS) in complex, large-scale operations such as disaster search and rescue, site surveillance, and social security. These systems, which coordinate multiple robots of varying functions and quantities, face the significant challenge of accurately assembling robot teams that meet the dynamic needs of tasks with respect to size and functionality, all while maintaining minimal resource expenditure. This paper introduces a pioneering adaptive cooperation method named inner attention (innerATT), crafted to dynamically configure teams of heterogeneous robots in response to evolving task types and environmental conditions. The innerATT method is articulated through the integration of an innovative attention mechanism within a multi-agent actor–critic reinforcement learning framework, enabling the strategic analysis of robot capabilities to efficiently form teams that fulfill specific task demands. To demonstrate the efficacy of innerATT in facilitating cooperation, experimental scenarios encompassing variations in task type (“Single Task”, “Double Task”, and “Mixed Task”) and robot availability are constructed under the themes of “task variety” and “robot availability variety.” The findings affirm that innerATT significantly enhances flexible cooperation, diminishes resource usage, and bolsters robustness in task fulfillment.

Training nurses in an international emergency medical team using a serious role-playing game: a retrospective comparative analysis

BackgroundAlthough game-based applications have been used in disaster medicine education, no serious computer games have been designed specifically for training these nurses in an IEMT setting. To address this need, we developed a serious computer game called the IEMTtraining game. In this game, players assume the roles of IEMT nurses, assess patient injuries in a virtual environment, and provide suitable treatment options.MethodsThe design of this study is a retrospective comparative analysis. The research was conducted with 209 nurses in a hospital. The data collection process of this study was conducted at the 2019-2020 academic year. A retrospective comparative analysis was conducted on the pre-, post-, and final test scores of nurses in the IEMT. Additionally, a survey questionnaire was distributed to trainees to gather insights into teaching methods that were subsequently analyzed.ResultsThere was a significant difference in the overall test scores between the two groups, with the game group demonstrating superior performance compared to the control group (odds ratio = 1.363, p value = 0.010). The survey results indicated that the game group exhibited higher learning motivation scores and lower cognitive load compared with the lecture group.ConclusionsThe IEMT training game developed by the instructor team is a promising and effective method for training nurses in disaster rescue within IEMTs. The game equips the trainees with the necessary skills and knowledge to respond effectively to emergencies. It is easily comprehended, enhances knowledge retention and motivation to learn, and reduces cognitive load.

Drones in Human Aid and Disaster Relief using Thermal Imaging Technology

Disaster Rescue is a very dangerous job to carry out the process without risking more lives in the process as the environment plays a major role. To find the people, requires the system to identify the movement and recognize the human by using the Thermal imaging system in the place of low-temperature areas such as forests, flood areas, Dark places, and so on. Not just thermal but also RGB cameras, stereo cameras and Infrared cameras are used to gain the surrounding situational inference, and by using the intelligent system to identify humans by their body shape from a distance and using the CV pipeline the movement can be identified and confirmed for victims condition in the disaster area. By mounting the optical systems in the Drone, the process makes it safe for the people, and a quick rescue response can be implemented. Using the heavy payload carrying Drone to supply emergency kit and food supplies to the Victims on time. It as well can also act as an emergency communication Network establishment with the GPS to gain the location of a specific point of Disaster regions.

Türkiye's Health Diplomacy Activities: A Qualitative Research

Health diplomacy is a field that facilitates the implementation and governance of global health policies. Assisting, such as transferring expertise, experience, and human and medical equipment to different countries, constitutes the content of health diplomacy. The main argument of this study is that Türkiye provides official development assistance abroad, establishes and operates health enterprises, and provides medical rescue and emergency health services in disasters and emergencies. For this reason, it has been tried to reveal Türkiye's perception of health diplomacy, how the health service process is carried out across the border, and the possible contributions of these services. A case study design was used to examine Türkiye's health diplomacy process. In this context, interviews were conducted with the public (n=1) and non-governmental organization personnel (n=8). The research findings were evaluated within the framework of four main themes: perception of health diplomacy, difficulties experienced in the health diplomacy process, solutions to the difficulties encountered in the health diplomacy process, and contributions of health diplomacy. The perception of health diplomacy is fieldwork, experience transfer, support, joint action, foreign policy, soft power, and health priority. In the process of health service delivery, it has been determined that there are difficulties in terms of medical equipment, service delivery, personnel, and politics, and these difficulties are tried to be solved through cooperation, communication, and other means. It has been concluded that health diplomacy contributes to the health of the recipient country or region and supports employment with newly opened health institutions. In addition, it was determined that Turkish health personnel gained experience, sympathy for Türkiye developed, and prejudices were broken.

Hierarchical RNNs with Graph Policy and Attention for Drone Swarm

Abstract In recent years, the drone swarm has experienced remarkable growth, finding applications across diverse domains such as agricultural surveying, disaster rescue, and logistics delivery. However, the rapid expansion of drone swarm usage underscores the necessity for innovative approaches in the field. Traditional algorithms face challenges in adapting to complex tasks, environmental modeling, and computational complexity, highlighting the need for more advanced solutions like multi-agent deep reinforcement learning to enhance efficiency and robustness in drone swarm. Our proposed approach tackles this challenge by embracing temporal and spatial. In terms of the temporal, the proposed approach builds upon historical data, it enhances the predictive capabilities regarding future behaviors. In the spatial, the proposed approach leverage graph theory to model the swarm's features, while attention mechanisms strengthen the relationships between individual drones. The proposed approach addresses the unique characteristics of drone swarms by incorporating temporal dependencies, spatial structures, and attention mechanisms. Extensive experiments validate the effectiveness of the proposed approach.

A national survey on current state and development needs of clinical and academic emergency medicine in China

BackgroundTo characterize the current state of emergency medicine (EM) and the requirements for advancing EM clinical practice, education and research in China.MethodsAn anonymous electronic survey was conducted by Chinese Society of Emergency Medicine during September to October 2021. The survey contained 30 questions divided into 2 sections: the current state of EM development and the requirements for EM growth.Results722 hospitals were included, of 487 were Level III and 235 were Level II hospitals. We found that after 40 years of development, EM had established a mature disciplinary system and refined sub-specialties including critical care, cardiopulmonary resuscitation, toxicology, disaster and emergency rescue. In Level III hospitals, 70.8% of EDs were standardized training centers for EM residents, but master’s degree program, Doctor Degree program and post-doctoral degree program was approved in only 37.8%, 8.4% and 2.9% of EDs respectively and postgraduate curriculum was available in 1/4 of EDs. Only 8% have national or provincial key laboratories. In addition to advance clinical practice, there was also a high demand to improve teaching and research capacities, mainly focusing on literature review, research design and delivery, paper writing, residency training.ConclusionsEM has built a mature discipline system and refined sub-specialties in China. Teaching and research developed parallel with clinical practice. However, there was still a lack of EM master’s and doctoral programs and research capacities need to be improved. More outstanding clinical and academic training should be provided to promote the rapid growth of EM in China.

A multi‐objective optimization approach for beam pattern synthesis ofUAVvirtual rectangular antenna array

AbstractVirtual antenna array (VAA) formed by unmanned aerial vehicle (UAV) antenna units using collaborative beamforming (CB) technology plays an important role in the air communication system, and can be used in radar, military, disaster rescue and other places. However, there are still some issues with the beam pattern formed by this method, such as high sidelobe level (SLL), high cost and low efficiency. In this article, each UAV carries an omnidirectional antenna unit, and a large number of UAVs form a UAV virtual rectangular antenna array (UVRAA) to communicate with the ground base station (BS). We formulate an overhead minimization and efficient communication multi‐objective optimization problem (OMECMOP) which jointly optimize the excitation current weights of the UVRAA and reduce the number of UAVs in operation to improve the beam pattern, enhance the communication efficiency and decrease the overhead of UVRAA. In addition, we also propose an improved multi‐objective multi‐verse optimization algorithm based on the inverse decline curve type (ISDT‐MOMVO) which introduces a strategy optimization initialization solution with quasi‐opposition based learning (QBL) and a hybrid solution updating operators to solve the OMECMOP. The simulation results show that compared with other traditional swarm intelligence (SI) optimization algorithms the ISDT‐MOMVO algorithm produces better beam pattern and the thinning rate can reach 50%.

A Three-Stage Stochastic Model to Improve Resilience with Lateral Transshipment in Multi-Period Emergency Logistics

Driven by the growing threat of natural disasters caused by climate change, there is an urgent need to strengthen the emergency rescue logistics network. However, insufficient research has been conducted on optimizing both pre-disaster preparation and post-disaster response, resulting in lower resilience and inefficiency of emergency logistics management. To this end, this study explores the optimization of emergency rescue resource allocation and transportation network design, considering the uncertainty and multi-period nature of natural disaster rescue. By employing a lateral transshipment strategy, a three-stage stochastic programming model is established, which aims to balance economic benefits with the need for devastations, thereby enhancing the resilience of the logistics network. Numerical experiments verify the effectiveness of the proposed model with different instances and the performance of the lateral transshipment strategy by comparing it with a two-stage stochastic programming model. Sensitivity analysis is performed on the costs of constructing a depot and the penalties for unmet needs. The analysis yielded valuable insights that can be used to enhance emergency rescue operations, supply chain network design, and logistics network design. The research outcome can benefit emergency responders and logistics professionals in optimizing their operations.

Online Optimization of Pickup and Delivery Problem Considering Feasibility

A pickup and delivery problem by multiple agents has many applications, such as food delivery service and disaster rescue. In this problem, there are cases where fuels must be considered (e.g., the case of using drones as agents). In addition, there are cases where demand forecasting should be considered (e.g., the case where a large number of orders are carried by a small number of agents). In this paper, we consider an online pickup and delivery problem considering fuel and demand forecasting. First, the pickup and delivery problem with fuel constraints is formulated. The information on demand forecasting is included in the cost function. Based on the orders, the agents’ paths (e.g., the paths from stores to customers) are calculated. We suppose that the target area is given by an undirected graph. Using a given graph, several constraints such as the moves and fuels of the agents are introduced. This problem is reduced to a mixed integer linear programming (MILP) problem. Next, in online optimization, the MILP problem is solved depending on the acceptance of orders. Owing to new orders, the calculated future paths may be changed. Finally, by using a numerical example, we present the effectiveness of the proposed method.

Individually optimized estimation of energy expenditure in rescue workers using a tri-axial accelerometer and heart rate monitor.

Objectives: This study aimed to provide an improved energy expenditure estimation for heavy-load physical labor using accelerometer data and heart rate (HR) measured by wearables and to support food preparation and supply management for disaster relief and rescue operations as an expedition team. Methods: To achieve an individually optimized estimation for energy expenditure, a model equation parameter was determined based on the measurements of physical activity and HR during simulated rescue operations. The metabolic equivalent of task (MET), which was measured by using a tri-axial accelerometer and individual HR, was used, where two (minimum and maximum) or three (minimum, intermediate, and maximum) representative reference points were selected for each individual model fitting. In demonstrating the applicability of our approach in a realistic situation, accelerometer-based METs and HR of 30 males were measured using the tri-axial accelerometer and wearable HR during simulated rescue operations over 2days. Results: Data sets of 27 rescue operations (age:34.2 ± 7.5years; body mass index (BMI):22.9 ± 1.5kg/m2) were used for the energy expenditure estimation after excluding three rescue workers due to their activity type and insufficient HR measurement. Using the combined approach with a tri-axial accelerometer and HR, the total energy expenditure increased by 143% for two points and 133% for three points, compared with the estimated total energy expenditure using only the accelerometer-based method. Conclusion: The use of wearables provided a reasonable estimation of energy expenditure for physical workers with heavy equipment. The application of our approach to disaster relief and rescue operations can provide important insights into nutrition and healthcare management.

Novel Acid-Driven Bioinspired Self-Resettable Bilayer Hydrogel Actuator Mimicking Natural Muscles.

Soft robots have great potential applications in manufacturing, disaster rescue, medical treatment, etc. Artificial muscle is one of the most important components of a soft robot. In previous years, hydrogel actuators that can be controllably deformed by the stimuli of external signals have been developed as good candidates for muscle-like materials. In this article, we successfully prepared a chemical fuel-driven self-resettable bilayer hydrogel actuator mimicking natural muscles with the aid of a new negative feedback reaction network. The actuator can temporarily deform upon the addition of H+ (chemical fuel). Subsequently, H+ accelerated the reaction between BrO3- and Fe(CN)64-, which consume H+. It resulted in the spontaneous recovery of the pH as well as the shape of the actuator. Such an actuator exhibits a great similarity with natural muscles in actuation mechanisms and automaticity in the manipulation compared to the widely reported stimuli-responsive hydrogel actuators. This illustrates that fuel-driven self-resettable hydrogel is a promising dynamic material for mimicking the functions of living creatures.

A Planar Multi-Inertial Navigation Strategy for Autonomous Systems for Signal-Variable Environments.

The challenge of precise dynamic positioning for mobile robots is addressed through the development of a multi-inertial navigation system (M-INSs). The inherent cumulative sensor errors prevalent in traditional single inertial navigation systems (INSs) under dynamic conditions are mitigated by a novel algorithm, integrating multiple INS units in a predefined planar configuration, utilizing fixed distances between the units as invariant constraints. An extended Kalman filter (EKF) is employed to significantly enhance the positioning accuracy. Dynamic experimental validation of the proposed 3INS EKF algorithm reveals a marked improvement over individual INS units, with the positioning errors reduced and stability increased, resulting in an average accuracy enhancement rate exceeding 60%. This advancement is particularly critical for mobile robot applications that demand high precision, such as autonomous driving and disaster search and rescue. The findings from this study not only demonstrate the potential of M-INSs to improve dynamic positioning accuracy but also to provide a new research direction for future advancements in robotic navigation systems.

Development and Review of Group Rescue Robots Based on Artificial Intelligence Technology

Rescue robots can perform rescue missions in dangerous and complex environments, protect humans from harm, and improve the efficiency and effectiveness of rescue, thus playing an increasingly important role in disaster management and rescue. This article reviews the technologies and methods required to apply artificial intelligence to rescue robot teams. Firstly, the feasibility of motion control for swarm robots was explored from the perspective of biomimetic robots. Through the analysis of animal biomimetics and the comparison of commonly used topological structures, the nature of team rescue robot rescue is emphasized, and based on this, a scheme for optimizing topological networks by combining environmental intelligence is proposed. Secondly, several existing micro robots were introduced and their data loading capabilities were evaluated. On this basis, the process of robot vision and motion commands was outlined. At the meanwhile, researchers focus on the current mainstream robot motion trajectory algorithms, and study the algorithm optimization process from extending the motion path planning of a single robot to group coordinated motion. This includes traditional cell decomposition algorithms and algorithms combined with machine learning to improve path planning efficiency. Finally, the above methods were summarized, and the impact of other possible feasible methods in the field of artificial intelligence was explored and analyzed.

Retracted: Earthquake Disaster Rescue Model Based on Complex Adaptive System Theory

Retracted: Earthquake Disaster Rescue Model Based on Complex Adaptive System Theory

Application Effect of the China Association For Disaster and Emergency Rescue Medicine - Cardiopulmonary Resuscitation and Automatic Extracorporeal Defibrillation (CADERM-CPR·D) Training in Medical Teaching.

Objective In China, the penetration rate of cardiopulmonary resuscitation training is not high and the effect of traditional teaching methods is not good. In this study, the case-guided cardiopulmonary resuscitation training mode was introduced to provide cardiopulmonary resuscitation training to medical students with a certain medical background, using the 2018 technical specifications for cardiopulmonary resuscitation and automatic extracorporeal defibrillation of the China Association for Disaster and Emergency Rescue Medicine. Compared with traditional teaching methods, the application effect of this training method in clinical probation teaching was analyzed. Methods 120 medical students with a certain medical background were randomly divided into the experimental group and the control group, with 60 students in each group. The knowledge, skills, and attitude of the subjects were assessed by questionnaire survey. Results A total of 120 students were included in the study and randomly divided into an experimental group and a control group. The test scores of knowledge, skill, and attitude in the experimental group (38.40±2.775, 19.07±1.118, 14.92±0.962)were significantly higher than those in the control group (32.47±3.615, 14.65±1.338, 12.68±0.930)(P<0.05). Conclusion Case-guided cardiopulmonary resuscitation training of the China Association for Disaster and Emergency Rescue Medicine specifications can improve medical students' knowledge and skills of cardiopulmonary resuscitation, enhance their confidence in treatment, and can be further applied in medical teaching.

Current Status and Prospects of Robot Dog Application

In the era of rapid development in computer science, artificial intelligence (AI), and the Internet of Things (IoT), robotics has undergone continuous updates and iterations. People have been exploring various application areas for robots, with robotic dogs standing out prominently. These robotic dogs are highly anticipated due to their remarkable attributes of high flexibility, adaptability, and stability. They have become a powerful magnet, attracting numerous technology companies worldwide. The market prospects for robotic dogs are extremely broad, as they can be applied in areas such as companionship, rescue operations, reconnaissance, and logistics. Furthermore, they also generate significant social discourse, making research on robotic dogs highly valuable. This paper provides a brief overview of the application scenarios of robotic dogs in the domains of home companionship and natural disaster rescue. It summarizes the relevant technologies and current developments utilized in these two fields and offers a glimpse into the future applications of robotic dogs.