Rescue Missions Research Articles

IMMERSIVE VIRTUAL REALITY FOR DARK HERITAGE INTERPRETATION: THE CASE OF ŽANIS LIPKE MEMORIAL

The article will focus on the use of immersive virtual reality experiences in the interpretation of the dark heritage for young audiences. Here, through a case study of the Žanis Lipke Memorial, a new virtual reality experience was tested with young people, evaluating the benefits and shortcomings of this tool in conveying the story of the largest Jewish rescue mission at the time of the Holocaust in Latvia. Given that every heritage institution aims to pass on the heritage to future generations, it is also essential for them to keep up with the practices of the new generation of the digital natives in their information-seeking, education and leisure-time habits, given that technology plays a pivotal role in their daily life. While virtual reality is often associated with the entertainment and gaming industries, it is also increasingly used in educational processes, in this case – as an interpretive tool in educating people about the Jewish rescue mission and Holocaust at the Memorial. Young people in the focus groups acknowledged the emotional, embodied and implicit learning aspects of the virtual reality experience, but also pointed out some spatial and technological shortcomings of it. While some had relatively low initial expectations about the innovativeness of the Memorial, in the end, most recognised that the high-quality, realistic and historically accurate immersive virtual reality experience, combined with a physical tour at the Memorial, provided a deeper understanding of the historical event in question.

Perspectives of College Students on the Filipino Rescue Mission to the Jews

This research explored the participant’s sensemaking capacity using their written narratives on the documentary on the Filipino rescue mission to the Jews during the Nazi holocaust. The study used the framework point of view (POV) and perspective as these two are considered the main cognitive processes that help shape a person’s narrative. Through the lenses of POV, the research sought how the participants positioned themselves to understand the character’s stance in the documentary and the type of POV they used as a point of angle in writing their narrative. More than organizing their thoughts, the concept of perspective gave a background of how the participants attached personal meanings to the character’s story during the rescue mission, and weaved these to draw their interpretations of this historical event. The study, with 37 participants, applied frequency counting, percentage calculation, and thematic analysis for the data analysis. The findings revealed that participants primarily used the first-person POV as a point of angle to express their thoughts about the rescue mission. On the other hand, their narratives revealed different themes that emphasize how Filipinos accepted the Jews, the endearing Filipino trait that surpasses hospitality, the humanitarian legacy etched in history by President Manuel Quezon, and their reflections on the event which most of the participants does not know about. The study also highlights the importance of incorporating emotional cues to effectively shape the writer’s perspective in a narrative to vivify not only the understanding of historical events but also the personal meanings the writer attaches to them.

Bayesian reinforcement learning for navigation planning in unknown environments.

This study focuses on a rescue mission problem, particularly enabling agents/robots to navigate efficiently in unknown environments. Technological advances, including manufacturing, sensing, and communication systems, have raised interest in using robots or drones for rescue operations. Effective rescue operations require quick identification of changes in the environment and/or locating the victims/injuries as soon as possible. Several techniques have been developed in recent years for autonomy in rescue missions, including motion planning, adaptive control, and more recently, reinforcement learning techniques. These techniques rely on full knowledge of the environment or the availability of simulators that can represent real environments during rescue operations. However, in practice, agents might have little or no information about the environment or the number or locations of injuries, preventing/limiting the application of most existing techniques. This study provides a probabilistic/Bayesian representation of the unknown environment, which jointly models the stochasticity in the agent's navigation and the environment uncertainty into a vector called the belief state. This belief state allows offline learning of the optimal Bayesian policy in an unknown environment without the need for any real data/interactions, which guarantees taking actions that are optimal given all available information. To address the large size of belief space, deep reinforcement learning is developed for computing an approximate Bayesian planning policy. The numerical experiments using different maze problems demonstrate the high performance of the proposed policy.

Novel ways of applying artificial intelligence in emergency medicine - literature review

Artificial intelligence (AI) holds immense promise for revolutionizing emergency medicine, expediting diagnosis and treatment decisions. This review explores AI’s wide-ranging applications in emergency care, ranging from managing out-of-hospital cardiac arrest (OHCA) to diagnosing fractures, spine injuries, stroke, and pulmonary embolisms, and even assisting in search and rescue missions with snake robots. In OHCA cases, AI aids in early detection, survival prediction, and ECG waveform classification, bolstering prehospital care efficiency. AI-powered digital assistants like the AI4EMS platform optimize diagnosis and patient prioritization, reducing overlooked cases of cardiac arrest and improving response times. Furthermore, AI algorithms enhance the diagnosis of conditions such as pneumothorax, pulmonary emphysema, and fractures by analysing medical images with exceptional accuracy, often outperforming human experts. In stroke and pulmonary embolism, AI expedites diagnosis through automated imaging analysis, enabling swift treatment. AI may enhance triage methods with independent systems, improving patient sharing and treatment quality while minimizing infection risks, especially during pandemics. Medical professionals generally welcome AI triage systems, acknowledging their potential to enhance healthcare efficiency. It is important to understand the scope of development of AI in order to make its application beneficial.

Improving human activity classification based on micro-doppler signatures of FMCW radar with the effect of noise.

Nowadays, classifying human activities is applied in many essential fields, such as healthcare, security monitoring, and search and rescue missions. Radar sensor-based human activity classification is regarded as a superior approach in comparison to other techniques, such as visual perception-based methodologies and wearable gadgets. However, noise usually exists throughout the process of extracting raw radar signals, decreasing the quality and reliability of the extracted features. This paper presents a novel method for removing white Gaussian noise from raw radar signals using a denoising algorithm before classifying human activities using a deep convolutional neural network (DCNN). Specifically, the denoising algorithm is used as a preprocessing step to remove white Gaussian noise from the input raw radar signal. After that, a lightweight Cross-Residual Convolutional Neural Network (CRCNN) with adaptable cross-residual connections is suggested for classification. The analysis results show that the denoising algorithm with a range-bin interval of 3 and a cut-threshold value of 3 achieves the best denoising effect. When the denoising algorithm was applied to the dataset, CRCNN improved the right classification rate by up to 10% compared to the recognition results achieved with the original noise-added dataset. Additionally, a comparison of the CRCNN with the denoising algorithm solution with six cutting-edge DCNNs was conducted. The experimental results reveal that the proposed model greatly outperforms the others.

The Rescue Mission: From Confucian Corruption to Protestant Conscience at the Turn of Nineteenth Century Korea

Abstract: This article is an account of the changing conceptualization of Confucianism by Protestant missionaries and Korean Protestant believers at the turn of the twentieth century. The Koreans and Anglo-American missionaries each identified different moral and political reasons for religious conversion. Key to the missionary notion was a proper religion. A religion proper is premised in the Revelation in the Scripture and verifiable in history and is distinct from a set of abstract ethical precepts. To this end, the missionaries deployed various catechisms with reference to comparative chronology and the rational basis of souls. On the other hand, the converts were attracted to the political possibilities of Protestant religion. The converting Koreans saw potential in reforming and regenerating public and private morality, arguing that Confucianism had exhausted its ethical resources. However, the closure of the political possibilities by the 1900s prompted the missionaries to explore other avenues such as Christian moral psychology. This reflected the missionaries' emphasis on post-mortem individual salvation, leading to a renewed conceptual focus on inner conscience rooted in Christian moral and natural science.

Human Detection From Unmanned Aerial Vehicles’ Images for Search and Rescue Missions: A State-of-the-Art Review

Human Detection From Unmanned Aerial Vehicles’ Images for Search and Rescue Missions: A State-of-the-Art Review

Application of 3D-LiDAR Mounted Drone for Search and Rescue Missions of Disaster Victims

Application of 3D-LiDAR Mounted Drone for Search and Rescue Missions of Disaster Victims

A Lightweight Man-Overboard Detection and Tracking Model Using Aerial Images for Maritime Search and Rescue

Unmanned rescue systems have become an efficient means of executing maritime search and rescue operations, ensuring the safety of rescue personnel. Unmanned aerial vehicles (UAVs), due to their agility and portability, are well-suited for these missions. In this context, we introduce a lightweight detection model, YOLOv7-FSB, and its integration with ByteTrack for real-time detection and tracking of individuals in maritime distress situations. YOLOv7-FSB is our lightweight detection model, designed to optimize the use of computational resources on UAVs. It comprises several key components: FSNet serves as the backbone network, reducing redundant computations and memory access to enhance the overall efficiency. The SP-ELAN module is introduced to ensure operational speed while improving feature extraction capabilities. We have also enhanced the feature pyramid structure, making it highly effective for locating individuals in distress within aerial images captured by UAVs. By integrating this lightweight model with ByteTrack, we have created a system that improves detection accuracy from 86.9% to 89.2% while maintaining a detection speed similar to YOLOv7-tiny. Additionally, our approach achieves a MOTA of 85.5% and a tracking speed of 82.7 frames per second, meeting the demanding requirements of maritime search and rescue missions.

A Navigation Algorithm to Enable Sustainable Control of Insect-Computer Hybrid Robot with Stimulus Signal Regulator and Habituation-Breaking Function.

The insect-computer hybrid soft robots are receiving increasing attention due to their excellent motor capabilities, small size, and low power consumption. However, the effective control of insects is limited to minutes since the response from insects is reduced as the number of stimulus signal increase. This phenomenon is known as habituation, which causes the loss of control of robots and hinders their application in practical tasks such as search and rescue missions that require several hours. It has been shown that constantly switching the pattern of stimulus signals can slow down the onset of habituation. Moreover, when habituation occurs, applying a different stimulus signal can break the habituation. Based on this, we have designed a navigation algorithm that can extend the control time of insects to several hours. The algorithm is composed of a stimulation decision-making core responsible for deciding on the type of stimulus signal (left, right, acceleration), a stimulation parameters adjustment (SPA) core responsible for adjusting the stimulus signal voltage constantly to delay the occurrence of habituation, and a reactivation function (RF), as a different stimulus signal from the normal stimulus signal, is used to break insects' habituation to the normal stimulus signal. Experiments have shown that our SPA regulator and RF can significantly extend the control time of insects. Navigation experiments demonstrating effective control of the insects for up to 3 h verified the effectiveness of the navigation algorithm, which strikes a balance between control accuracy and control time.

Development and implementation of a new approach for posture control of a hexapod robot to walk in irregular terrains

AbstractThe adaptability of hexapods for various locomotion tasks, especially in rescue and exploration missions, drives their application. Unlike controlled environments, these robots need to navigate ever-changing terrains, where ground irregularities impact foothold positions and origin shifts in contact forces. This dynamic interaction leads to varying hexapod postures, affecting overall system stability. This study introduces a posture control approach that adjusts the hexapod’s main body orientation and height based on terrain topology. The strategy estimates ground slope using limb positions, thereby calculating novel limb trajectories to modify the hexapod’s angular position. Adjusting the hexapod’s height, based on the calculated slope, further enhances main body stability. The proposed methodology is implemented and evaluated on the ATHENA hexapod (All-Terrain Hexapod for Environment Adaptability). Control feasibility is assessed through dynamic analysis of the hexapod’s multibody model on irregular surfaces, using computational simulations in Gazebo software. Environmental complexity’s impact on hexapod stability is tested on both a ramp and uneven terrain. Independent analyses for each scenario evaluate the controller’s effect on roll and pitch angular velocities, as well as height variations. Results demonstrate the strategy’s suitability for both environments, significantly enhancing posture stability.

The Sound of Swarm. Auditory Description of Swarm Robotic Movements

Movements of robots in a swarm can be mapped to sounds, highlighting the group behavior through the coordinated and simultaneous variations of musical parameters across time. The vice versa is also possible: Sound parameters can be mapped to robotic motion parameters, giving instructions through sound. In this article, we first develop a theoretical framework to relate musical parameters such as pitch, timbre, loudness, and articulation (for each time) with robotic parameters such as position, identity, motor status, and sensor status. We propose a definition of musical spaces as Hilbert spaces and musical paths between parameters as elements of bigroupoids, generalizing existing conceptions of musical spaces. The use of Hilbert spaces allows us to build up quantum representations of musical states, inheriting quantum computing resources, already used for robotic swarms. We present the theoretical framework and then some case studies as toy examples. In particular, we discuss a 2D video and matrix simulation with two robo-caterpillars; a 2D simulation of 10 robo-ants with Webots; a 3D simulation of three robo-fish in an underwater search-and-rescue mission.

Context matters: rescuing allied civilians interned in the japanese-controlled areas of China, 1944–1947

Around 13,500 Allied civilians were interned by Japan throughout China during the Second World War. This article explores how these Allied internees were rescued after the war. It finds that the Allied and Japanese governments made painstaking efforts to make the rescue mission a success. The complex military, diplomatic, and political context in China around the end of the war caused numerous difficulties for the rescue mission. This context also facilitated the mission to a certain degree by creating the conditions for the Allied internees to become increasingly valuable, which, in turn motivated the Allied and Japanese governments to work actively towards their relief.

2022: Are We Back to the pre-COVID-19 Pandemic Period in the Management of out-of-Hospital Cardiac Arrest?

Introduction: The COVID-19 pandemic caused a significant strain on the Emergency system, particularly for time-dependent diseases like Out of Hospital Cardiac Arrest (OHCA). Studies have shown an increase in the incidence of OHCA during different waves of the pandemic, but there is limited evidence on how survival rates and rescue efforts have been affected in the post-pandemic period. Methods: We performed a retrospective observational cohort study of all OHCA rescues by AREU (Agenzia Regionale Emergenza Urgenza), in the Lombardy region in March in three different years (2019, 2021 and 2022). We used rescue mission data collected in AREU’s database, where logistic information of patient rescue missions managed by the Lombardy Region’s 112 system is recorded.Results: This study was an epidemiology analysis of OHCA after the pandemic. The results showed no significant changes in the probability of receiving bystander cardiopulmonary resuscitation (22.5% vs 24.0%; p=0.41) and public access defibrillation (3.6 vs 3.2; p=0.50) compared to pre-pandemic period. However, there was a decrease in the probability of ROSC (11.5% vs 6.2%; p<0.01).Conclusion: According to our analysis, there appears to be a return to the pre-pandemic phase with regard to the OHCA network. However, it remains to be pointed out that a careful study of disease networks is essential to understand the resilience of our health system and to understand whether we have returned to a system similar to the pre-pandemic phase after the COVID-19 pandemic.

Application of invasive medical and pharmacological measures by German emergency paramedics

IntroductionGerman emergency paramedics are to avert serious damage to the patient's health in the event of acute illness or accidents. For this purpose, they have a variety of invasive medical and pharmacological measures at their disposal in addition to their three-year professional training. The aim of this thesis is to investigate the use of invasive medical and pharmacological measures by emergency paramedics in the German rescue service by means of instruments of professional scientific research. In doing so, the motivations of emergency paramedics to refrain from using these measures will be investigated for the first time.MethodsFor this study, rescue missions were analyzed during the period July 1, 2022, to June 30, 2023. The chosen method involved a quantitative document analysis of the deployment protocols to investigate the use of invasive and pharmacological measures by emergency paramedics and the reasons against their use.ResultsDuring the period, N = 10,102 (100%) rescue missions could be evaluated. Analysis of the data revealed that invasive medical measures were used by emergency paramedics in n = 1,211 (11.9%) missions and pharmacologic measures were used in n = 1,403 (13.8%) missions. Most frequently, emergency paramedics concluded that invasive medical measures were not required.DiscussionIn summary, the use of invasive medical measures is not a standard part of the emergency medical services mission. Rather, the focus is on diagnostics and transport of patients to an appropriate destination hospital. An adjustment of prehospital care structures based on evidence-based data and an update of the catalog of indications for emergency physician interventions would be elementary for this goal. Further studies of professional fields of action in emergency and rescue medicine are necessary in order to analyze and optimize care structures. This would not only allow conclusions to be drawn about the future education, further education and training of rescue service personnel, but also allow care structures to be adapted to the current state of science and the care needs of patients.

Comprehensive risk management of health, safety and environment for social emergency rescue organization

During the rescue and relief work of social emergency rescue organizations, the rescue team members face significant responsibilities and risks. Social rescue organizations need to improve funding, available equipment and other aspects compared with professional emergency rescue organizations. Moreover, the development of rescue levels among emergency rescue organizations is unbalanced, and rescue teams’' comprehensive quality and skills are uneven. To understand the safety risks of these organizations before the implementation of rescue and relief tasks, the task situation and its characteristics must be assessed timely, and safety must be ensured under the premise of efficient completion of the rescue missions. Based on the theory of safety system engineering and health, safety, and environment risk management, a risk management model is established to achieve a closed-loop risk management. The risk factors in rescue and relief tasks of social rescue organizations were identified, and a health, safety and environment risk assessment index system and grading standard were established. A gray cloud model was applied for the evaluation method, the problems of information randomness, risk-level boundary fuzziness and randomness of the evaluation index data were effectively solved. Subsequently, a risk hierarchical early warning and control strategies were proposed to allocate emergency resources rationally. The proposed method was verified and found to have universal applicability and strong practicability.

Navigating Rover Using Arduino

Abstract: The objective of the proposed system is to use Arduino to guide a rover. The connectivity will be the necessary electronic circuit. The PWM signal of a microcontroller powers the DC motors on the rover. The rover may be maneuvered along a straight path or any other path it chooses by manipulating each motor separately or in tandem. The rover is programmed with Arduino. The results of the system's implementation and testing in a real-world setting demonstrate its ability to precisely navigate through a variety of terrains and avoid obstacles. The suggested method may find use in search and rescue missions, environmental monitoring, and autonomous farming. Overall, navigating rover shows that creating a reliable and efficient rover navigation system is possible even with a commonly used programming language like Embedded C.

Design and Analysis of an Adaptive Obstacle-Overcoming Tracked Robot with Passive Swing Arms

This paper presents a novel adaptive tracked robot equipped with passive swing arms for overcoming obstacles. First, the paper introduces the overall composition of the robot and focuses on the adaptive mechanism of the passive swing arms. Second, analyzing the single-step obstacle-overcoming process of the robot reveals the relationship between the obstacle height and the geometric parameters of the passive swing arms, establishing a kinematic model. Then, a dynamic model of the robot’s obstacle-overcoming process is established by simplifying the robot into a crank–slider linkage, and the time range for the robot to overcome obstacles is analyzed. Finally, through virtual simulation and a physical prototype, the feasibility and maneuverability of the robot’s design are verified. These findings demonstrate the potential of the robot in various applications, such as search and rescue missions and homeland security.

Design and Development of swarm AGV’s alliance for Search and Rescue operations

Rapid response is essential for saving lives in search and rescue operations since the amount of time is critical. In this project, a swarm of autonomous ground vehicles (AGVs) equipped with ROS-based software architecture will be designed and built for rapid search and rescue missions. The swarm of AGVs will function autonomously to navigate through challenging areas and be outfitted with a variety of sensors, including cameras, and LIDAR. The proposed system will be capable of performing 2D mapping, live video surveillance, autonomous navigation, victim/object detection, and two-way audio communication. The goal of the project is to reduce the risk to human life in dangerous areas by providing a quick and efficient response system for search and rescue operations. A centralized management system with a de-centralized module will be created as part of the project to keep an eye on and manage the AGV horde. However, there will be a functionality to take control of a specific robot in the swarm network when needed. In difficult areas where it might not be safe for humans to operate, the suggested method will enable quick and efficient search and rescue operations.

Robust Multiagent Reinforcement Learning for UAV Systems: Countering Byzantine Attacks

Multiple unmanned aerial vehicle (multi-UAV) systems have gained significant attention in applications, such as aerial surveillance and search and rescue missions. With the recent development of state-of-the-art multiagent reinforcement learning (MARL) algorithms, it is possible to train multi-UAV systems in collaborative and competitive environments. However, the inherent vulnerabilities of multiagent systems pose significant privacy and security risks when deploying general and conventional MARL algorithms. The presence of even a single Byzantine adversary within the system can severely degrade the learning performance of UAV agents. This work proposes a Byzantine-resilient MARL algorithm that leverages a combination of geometric median consensus and a robust state update model to mitigate, or even eliminate, the influence of Byzantine attacks. To validate its effectiveness and feasibility, the authors include a multi-UAV threat model, provide a guarantee of robustness, and investigate key attack parameters for multiple UAV navigation scenarios. Results from the experiments show that the average rewards during a Byzantine attack increased by up to 60% for the cooperative navigation scenario compared with conventional MARL techniques. The learning rewards generated by the baseline algorithms could not converge during training under these attacks, while the proposed method effectively converged to an optimal solution, proving its viability and correctness.