Rescue Systems Research Articles

The ribosome-associated quality control pathway supports survival in the absence of non-stop ribosome rescue factors.

In bacteria, it is estimated that 2%-4% of all translation reactions terminate with the ribosome stalled on a damaged mRNA lacking a stop codon. Mechanisms that rescue these ribosomes are essential for viability. We determined the functional overlap between the ribosome quality control pathway and the classical non-stop rescue systems [alternative rescue factor (ArfA) and trans-translation] in a representative Firmicute and Proteobacterium, phyla that are evolutionarily distinct. Furthermore, we used a bioinformatics approach to examine the conservation and overlap of various ribosome rescue systems in >15,000 species throughout the bacterial domain. These results provide key insights into ribosome rescue in diverse phyla.

Advances and Challenges in Automated Drowning Detection and Prevention Systems

Drowning is among the most common reasons for children’s death aged one to fourteen around the globe, ranking as the third leading cause of unintentional injury death. With rising populations and the growing popularity of swimming pools in hotels and villas, the incidence of drowning has accelerated. Accordingly, the development of systems for detecting and preventing drowning has become increasingly critical to provide safe swimming settings. In this paper, we propose a comprehensive review of recent existing advancements in automated drowning detection and prevention systems. The existing approaches can be broadly categorized according to their objectives into two main groups: detection-based systems, which alert lifeguards or parents to perform manual rescues, and detection and rescue-based systems, which integrate detection with automatic rescue mechanisms. Automatic drowning detection approaches could be further categorized into computer vision-based approaches, where camera-captured images are analyzed by machine learning algorithms to detect instances of drowning, and sensing-based approaches, where sensing instruments are attached to swimmers to monitor their physical parameters. We explore the advantages and limitations of each approach. Additionally, we highlight technical challenges and unresolved issues related to this domain, such as data imbalance, accuracy, privacy concerns, and integration with rescue systems. We also identify future research opportunities, emphasizing the need for more advanced AI models, uniform datasets, and better integration of detection with autonomous rescue mechanisms. This study aims to provide a critical resource for researchers and practitioners, facilitating the development of more effective systems to enhance water safety and minimize drowning incidents.

Assessment of the Seismic Vulnerability of Florence (Italy) Through Macro-Seismic Methods

In recent years, the assessment of damage scenarios in urban communities has become one of the central themes in local government policies, aimed at promoting effective seismic risk mitigation and improving the efficiency of rescue systems to manage emergencies. In Italy, the seismic hazard has become a topical issue since the 1982 Irpinia earthquake, and several ventures have been promoted to face the seismic mitigation of complex residential districts. The objective of this research is to define the damage scenarios of the city of Florence, where 97% of the building stock is designed without anti-seismic prescriptions. The urban vulnerability of Florence has been assessed based on the current approaches available in the technical literature, combined with the knowledge provided by the recent investigation on the subsoil. Once the possible damage scenarios have been defined, the resilience of the area has been determined, and the population involved in the evacuation has been estimated.

RqcH supports survival in the absence of non-stop ribosome rescue factors.

Ribosomes frequently translate truncated or damaged mRNAs due to the extremely short half-life of mRNAs in bacteria. When ribosomes translate mRNA that lacks a stop codon (non-stop mRNA), specialized pathways are required to rescue the ribosome from the 3' end of the mRNA. The most highly conserved non-stop rescue pathway is trans-translation, which is found in greater than 95% of bacterial genomes. In all Proteobacteria that have been studied, the alternative non-stop ribosome rescue factors, ArfA and ArfB, are essential in the absence of trans-translation. Here, we investigate the interaction between non-stop rescue pathways and RqcH, a ribosome quality control factor that is broadly conserved outside of Proteobacteria. RqcH does not act directly on non-stop ribosomes but adds a degron tag to stalled peptides that obstruct the large ribosomal subunit, which allows the stalled peptide to be cleared from the ribosome by peptidyl-tRNA hydrolase (PTH). We show that Bacillus subtilis can survive without trans-translation and BrfA (Bacillus ArfA homolog), due to the presence of RqcH. We also show that expression of RqcH and its helper protein RqcP rescues the synthetic lethality of ΔssrAΔarfA in Escherichia coli. These results suggest that non-stop ribosome complexes can be disassembled and then cleared because of the tagging activity of RqcH, and that this process is essential in the absence of non-stop ribosome rescue pathways. Moreover, we surveyed the conservation of ribosome rescue pathways in >14,000 bacterial genomes. Our analysis reveals a broad distribution of non-stop rescue pathways, especially trans-translation and RqcH, and a strong co-occurrence between the ribosome splitting factor MutS2 and RqcH. Altogether, our results support a role for RqcH in non-stop ribosome rescue and provide a broad survey of ribosome rescue pathways in diverse bacterial species.

AI-powered trustable and explainable fall detection system using transfer learning

Accidental falls pose a significant public health challenge, especially among vulnerable populations. To address this issue, comprehensive research on fall detection and rescue systems is essential. Vision-based technologies, with their promising potential, offer an effective means to detect falls. This research paper presents a cutting-edge fall detection methodology aimed at enhancing individual safety and well-being. The proposed methodology utilizes deep neural networks, leveraging their capabilities to drive advancements in fall detection. To overcome data limitations and computational efficiency concerns, this study employ transfer learning by fine-tuning pre-trained models on large-scale image datasets for fall detection. This approach significantly enhances model performance, enabling better generalization and accuracy, especially in real-time applications with constrained resources. Notably, the methodology achieved an impressive test accuracy of 98.15%. Additionally, the incorporation of Explainable Artificial Intelligence (XAI) techniques is used to ensure transparent and trustworthy decision-making in fall detection using deep learning models, especially in critical healthcare contexts for vulnerable individuals. XAI provides valuable insights into complex model architectures and parameters, enabling a deeper understanding of fall identification patterns. To evaluate the effectiveness of this approach, a rigorous experimentation was conducted using a diverse dataset containing real-world fall and non-fall scenarios. The results demonstrate substantial improvements in both accuracy and interpretability, confirming the superiority of this method over conventional fall detection approaches.

РОЗВИТОК АВАРІЙНИХ ЗАСОБІВ ПОРЯТУНКУ ЕКІПАЖУ ГЕЛІКОПТЕРА

The subject of the research of this article is the study of the methods used to ensure the rescue of helicopter crews in emergency situations. The purpose of the study is to analyze the advantages and disadvantages of the developed variants of emergency equipment for helicopters. In the process of research, an analysis of modern developments aimed at ensuring the rescue of crews in cases of accidents was carried out. In particular, technologies that help reduce the risk of damage and improve the survival conditions of the crew after a helicopter accident have been considered. The tasks of the research include the analysis of existing rescue systems, the determination of the efficiency of their functioning and the possibilities of their further improvement. Scientific novelty consists in a systematic approach to the analysis of existing developments and the determination of promising directions for further research in the field of safety of helicopter crews. This approach to the study of emergency rescue in the helicopter industry is aimed at the development of technologies that ensure a high level of safety for crews during emergency situations. Alternative approaches to the development of emergency equipment for helicopters have been also considered. Special attention was paid to innovative approaches to the development of emergency equipment. Such a comprehensive approach makes research unique and relevant in the context of modern challenges in aviation security. The obtained results indicate the need for further research in the field of helicopter crew safety. During the research, it was found that the existing technologies for the rescue of helicopter crews have their limitations and require further improvements. One of the areas of improvement is the improvement of emergency exit systems and automation of rescue processes. New integrated systems can speed up the response to emergency situations and ensure a quick and safe process for the crew to exit the helicopter. Therefore, the application of the latest technologies in the field of helicopter emergency rescue will make it possible to improve the general standard of safety for crews. New developments are aimed not only at preventing accidents, but also at ensuring the effective and safe exit of the crew in case of unforeseen circumstances. This approach makes the research more complete and comprehensive, taking into account the modern requirements and opportunities of technological development in the field of safety of helicopter operations.

Culturally-informed for designing motorcycle fire rescue: Empirical study in developing country

Fire must be responded to quickly and effectively. Difficulties in reaching the fire location may arise due to high traffic. Motorcycle fire rescue can be an alternative to first aid in challenging areas. However, these areas, characterized by diverse cultural backgrounds, require careful consideration. Culture significantly impacts how such rescue systems are perceived, accepted, and utilized. This study delves into the efficiency and implementation of motorcycle fire rescue systems across different contexts, focusing on their alignment with regional cultural norms. Objectives include assessing operational challenges, identifying cultural implications, proposing culturally sensitive design recommendations, evaluating acceptance and effectiveness, and suggesting strategies and policy enhancements to strengthen emergency response capabilities. A preliminary trial was conducted to validate the survey instrument, followed by statistical analysis such as structural equation modeling, confirmatory factor analysis, and satisfaction level assessments. Findings highlight how cultural attitudes shape design preferences, the need for adaptable approaches to overcome operational hurdles, and the importance of adhering to culturally relevant design principles for system efficacy. Field tests also suggest potential improvements in emergency response effectiveness. Recognizing cultural considerations is essential in designing motorcycle fire rescue systems to enhance emergency response in developing nations. Furthermore, aligned with sustainable urban development goals, this research provides policymakers with valuable insights and practical approaches for engaging stakeholders. Equipping designers with information to address motorcycle fire incidents using environmentally friendly solutions underscores the importance of crafting emergency response plans sensitive to cultural differences across diverse landscapes.

DESIGN AND FABRICATION OF EMERGENCY FIRE RESCUE MACHINE

Building fires pose a serious risk to people, property and the environment. Numerous factors, such as human mistakes, electrical issues, heating systems, combustible items, might cause fire accidents. The effects of fire can be disastrous, resulting in accidents, fatalities, and significant building damage. Compared to fires in low-rise structures, high-rise building fire incidents pose numerous difficulties and a greater risk. High-rise building's vertical structures, greater resident capacity, and intricate infrastructure make fire fighters and evacuating residents more difficult to escape. It is essential to comprehend the causes, effects, and preventive strategies unique to high-rise fires in order to ensure occupant safety and reduce property damage. The disturbingly high rate of fire events, which are frequently brought on by inadequate fire prevention measures and damaged infrastructure, represent a serious threat to people's lives, their property, and the health of the economy. India can reduce the dangers connected with fire incidents in high-rise structures and guarantee the safety and well-being of its residents in the urban environment by giving priorityto the implementation of appropriate rescue systems. So in this project we are going to develop such machine which can help humans to get escape in the case of emergency situation in high rise building.

Метод экспоненциального программирования в задачах управления организационными системами поиска и спасания на водном транспорте

Обеспечение безопасности, в том числе на водном транспорте, является одной из приоритетных задач государства, которая направлена на снижение вероятности возникновения чрезвычайных ситуаций. Активное развитие научно – технологического прогресса влечёт за собой увеличение количества и тяжести чрезвычайных ситуаций, ликвидация последствий которых осуществляется силами и средствами Единой государственной системы предупреждения и ликвидации чрезвычайных ситуаций. Своевременное реагирование сил и средств и правильность принимаемых решений позволяет минимизировать социальные и материальные ресурсы на проведение аварийно-спасательных и других неотложных работ. Оптимизация процесса управления в подобных системах возможна с помощью соответствующих математических методов и моделей. В статье рассмотрена возможность применения метода экспоненциального программирования для решения задач оптимального распределения неоднородных ресурсов между частными задачами организационной системы для повышения эффективности реагирования на чрезвычайные ситуации на объектах водного транспорта. Представлен пример решения задачи экспоненциального программирования симплекс-методом дополненным правилом ограниченного ввода в базис. Применение разработанного метода позволит принимать оптимальные управленческие решения и снизить величину материального и социального ущерба с целью минимизации последствий чрезвычайных ситуаций. Ensuring safety, including on water transport, is one of the priorities of the state, which is aimed at reducing the likelihood of emergency situations. The active development of scientific and technological progress entails an increase in the number and severity of emergency situations, the elimination of the consequences of which is carried out by the forces and means of the Unified State Emergency Prevention and Response System. The timely response of forces and means and the correctness of decisions made allows minimizing social and material resources for emergency rescue and other urgent work. Optimization of the management process in such systems is possible with the help of appropriate mathematical methods and models. The article considers the possibility of using the exponential programming method to solve problems of optimal distribution of heterogeneous resources between particular tasks of the organizational system to improve the effectiveness of emergency response at water transport facilities. An example of solving the exponential programming problem by the simplex method supplemented by the rule of limited input into the basis is presented. The application of the developed method will make it possible to make optimal management decisions and reduce the amount of material and social damage in order to minimize the consequences of emergency situations.

Title

Abstract

The Structural and Molecular Mechanisms of Mycobacterium tuberculosis Translational Elongation Factor Proteins.

Targeting translation factor proteins holds promise for developing innovative anti-tuberculosis drugs. During protein translation, many factors cause ribosomes to stall at messenger RNA (mRNA). To maintain protein homeostasis, bacteria have evolved various ribosome rescue mechanisms, including the predominant trans-translation process, to release stalled ribosomes and remove aberrant mRNAs. The rescue systems require the participation of translation elongation factor proteins (EFs) and are essential for bacterial physiology and reproduction. However, they disappear during eukaryotic evolution, which makes the essential proteins and translation elongation factors promising antimicrobial drug targets. Here, we review the structural and molecular mechanisms of the translation elongation factors EF-Tu, EF-Ts, and EF-G, which play essential roles in the normal translation and ribosome rescue mechanisms of Mycobacterium tuberculosis (Mtb). We also briefly describe the structure-based, computer-assisted study of anti-tuberculosis drugs.

Mapping Tools for Flood Risk Rescue and Assistance Management

The assessment of vulnerability to the danger of flooding, with a focus on the need for rescue and assistance from the population, is still in an exploratory scientific phase. The main objective of this research is to propose a methodology based on the issues of rescue and assistance in the face of the risk of floods and to provide a tool for its management. A series of maps is presented, indicating those territorial elements that require rescue and surveillance in a prioritized manner in a visual and accessible way for public administration. Four methodological cartographic proposals have been designed as follows: (1) a map of territory sectors with special rescue needs (dependent population and/or buildings without shelter); a map of the impact on transportation infrastructure and vulnerable areas of buildings (2); a map of vulnerability to possible disorder and looting caused by flooding events (3); and a map of the increase in the cost of deployment for rescue and assistance systems (4). As an experimental zone to test the effectiveness of these proposals, a peri-urban area of the municipality of Málaga (Spain) is chosen, which has an extensive history of severe floods. The results confirm the applied and preventive nature of the tool, which can be incorporated into flood risk management plans and local flood risk action plans developed by public administrations. The main finding of the research is the technical advancement that comes with a precise understanding of vulnerability and its resulting issues for better flood risk management.

IoT-Enabled Systems for Automated Detection and Rescue of Vehicle Accidents

Accident Detection and Rescue Systems (ADRSs) are crucial in mitigating the consequences of accidents by promptly responding to incidents, thereby averting injuries, losses, damages, and property destruction. With the persistent occurrence of fatal and costly accidents in daily life, the necessity for efficient ADRSs cannot be overstated. This study presents a novel approach to augment existing ADRSs by integrating advanced technologies and optimizing communication channels. By incorporating state-of-the-art embedded systems and leveraging cloud technology, the proposed system aims to expedite recovery efforts and enhance overall effectiveness. The integration of cutting-edge embedded systems enhances the capability of the ADRS to swiftly detect and report accidents. These systems can utilize various sensors such as accelerometers, gyroscopes, and GPS modules to accurately identify incidents. Once an accident is detected, the system triggers an automated response mechanism to alert emergency services and relevant authorities. This rapid notification facilitates quicker response times, thereby reducing the severity of injuries and minimizing property damage. Furthermore, by harnessing cloud technology, the proposed ADRS can store and process vast amounts of data collected from accident scenes in real-time. This enables comprehensive analysis of the situation, aiding emergency responders in making informed decisions and deploying appropriate resources effectively. Moreover, cloud-based storage ensures that critical information is accessible remotely, allowing for seamless coordination between different stakeholders involved in the rescue operation. One of the key features of the system is its seamless integration with medical evacuation services. Upon detecting an accident, the ADRS automatically initiates a request for medical assistance, providing precise location data and relevant information about the nature of the incident. This ensures that injured individuals receive prompt medical attention, potentially saving lives in critical situations. By integrating advanced technologies and optimizing communication channels, the proposed ADRS aims to significantly improve response times, mitigate the impact of accidents, and ultimately save lives.

Collaborative Target Search With a Visual Drone Swarm: An Adaptive Curriculum Embedded Multistage Reinforcement Learning Approach.

Equipping drones with target search capabilities is highly desirable for applications in disaster rescue and smart warehouse delivery systems. Multiple intelligent drones that can collaborate with each other and maneuver among obstacles show more effectiveness in accomplishing tasks in a shorter amount of time. However, carrying out collaborative target search (CTS) without prior target information is extremely challenging, especially with a visual drone swarm. In this work, we propose a novel data-efficient deep reinforcement learning (DRL) approach called adaptive curriculum embedded multistage learning (ACEMSL) to address these challenges, mainly 3-D sparse reward space exploration with limited visual perception and collaborative behavior requirements. Specifically, we decompose the CTS task into several subtasks including individual obstacle avoidance, target search, and inter-agent collaboration, and progressively train the agents with multistage learning. Meanwhile, an adaptive embedded curriculum (AEC) is designed, where the task difficulty level (TDL) can be adaptively adjusted based on the success rate (SR) achieved in training. ACEMSL allows data-efficient training and individual-team reward allocation for the visual drone swarm. Furthermore, we deploy the trained model over a real visual drone swarm and perform CTS operations without fine-tuning. Extensive simulations and real-world flight tests validate the effectiveness and generalizability of ACEMSL. The project is available at https://github.com/NTU-UAVG/CTS-visual-drone-swarm.git.

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.

Rescue device concept for the ARGO profiling float

Abstract The material presents the development work carried out by the ARGO-Poland scientific consortium related to the design of rescue systems for ARGO profiling floats used for in situ exploration of the world oceans. The article describes the principle of operation of the Argo float and the theoretical basis of the rescue system and six alternative design solutions for such a system.

A method to accelerate the rescue of fire-stricken victims

For individuals trapped in a fire, early detection can significantly increase their chances of survival. Presently, firefighters employ a systematic search method along the walls, ensuring a comprehensive and thorough exploration of the environment to prevent missing any trapped victims. However, this approach has its drawbacks, such as time wastage in areas or rooms without victims. The goal of this study is to propose a search method that can expedite the discovery of trapped victims. Our method relies primarily on prior knowledge, crowd information and the temperature within the fire scene, utilizing fuzzy logic to calculate the probability of the presence and survival of victims in each distinct space. The resulting search sequence not only accelerates victim discovery but also safeguards firefighters from unnecessary risks. We conducted various simulation experiments to validate our approach. In comparison to the sequential search method currently employed by firefighters, the experimental results indicate that our method reduces the time to locate trapped victims by 25 %. Compared to other path planning algorithms based on approximate optimization, our method offers a shorter computation time, making it suitable for integration into fire rescue systems to provide real-time assistance.

Support for the state emergency medical system in the fight against COVID -19 by airborne search and rescue teams of the ASAR service

The COVID-19 pandemic, which undoubtedly left significant imprint in public consciousness,necessitated unprecedented support for medical rescue systems bothin Poland and around the world. During the pandemic, the State Emergency MedicalService in Poland was bolstered with legally available support measures, includingassistance from the Airborne Search and Rescue Teams of ASAR.

Research on Comprehensive Evaluation Model of Metal Mine Emergency Rescue System Based on Game Theory and Regret Theory

In view of the complexity, vagueness, and systematization of metal mine emergency rescue system evaluation, regret theory was introduced, and a comprehensive evaluation model of the metal mine emergency rescue system was established. Firstly, from four perspectives, including pre-emergency prevention, pre-emergency preparation, emergency rescue during an event, and post-disaster recovery and reconstruction, 26 influencing factors were selected to build a comprehensive evaluation index system of metal mine emergency rescue systems. Secondly, the G1 method and the anti-entropy weight method were used to determine the subjective weight and objective weight of the evaluation indicators, respectively, and the comprehensive weight of the indicator was determined based on game theory. The final evaluation level was determined by calculating the total evaluation value of the object to be evaluated. Finally, the established comprehensive evaluation model of a metal mine emergency rescue system based on game theory and regret theory was applied to Chengchao Iron Mine in Ezhou City, Hubei Province, and the evaluation results of this model were compared with those of fuzzy comprehensive evaluation and set pair analysis. The results showed that the calculation results of the evaluation model are reasonable and reliable, which can provide a new means of evaluating emergency rescue systems in metal mines.

Body-UAV Near-Ground LoRa Links Through a Mediterranean Forest

LoRa low-power wide-area network protocol has recently gained attention for deploying ad-hoc search and rescue (SaR) systems. They could be empowered by exploiting body-UAV links that enable communications between a body-worn radio and a UAV-mounted one. However, to employ UAVs effectively, knowledge of the signal's propagation in the environment is required. Otherwise, communications and localization could be hindered. The radio range, the packet delivery ratio (PDR), and the large- and small-scale fading of body-UAV LoRa links at 868 MHz when the radio wearer is in a Mediterranean forest are here characterized for the first time with a near-ground UAV having a maximum flying height of 30 m. A log-distance model accounting for the body shadowing and the wearer's movements is derived. Over the full LoRa radio range of about 600 m, the new model predicts the path loss (PL) better than the state-of-the-art ones, with a reduction of the median error even by 10 dB. The observed small-scale fading is severe and follows a Nakagami-m distribution. Extensions of the model for similar scenarios can be drawn through appropriate corrective factors.