Efficient Rescue Research Articles

Improvement of embryo rescue efficiency in haploid melon (Cucumis melo L.) via irradiated pollen pollination

Improvement of embryo rescue efficiency in haploid melon (Cucumis melo L.) via irradiated pollen pollination

Research on Submersible Position Prediction Based on Kalman Filter Algorithm

The global situation of the world's submersible technology is experiencing a boom, with countries competing to develop advanced submersible equipment to explore the deep sea, undersea resources, and uncharted territories. With the wide range of applications of submersibles in various fields, the risks of communication loss and mechanical failures faced during their missions require the establishment of accurate predictive models of submersible positions to ensure safe and efficient rescue. In this study, a model containing equations of motion is developed from the motion characteristics of a submersible on the seafloor. Uncertainties such as sea currents, topography, and seawater density are considered, and a random walk model is used to simulate these effects to ensure that the model is closer to reality. Subsequently, combined with the Kalman filter algorithm, a time-dependent position prediction model is constructed in this study. The model can predict the position of the submersible in real-time, and after error analysis, it shows that the average variance and root mean square error are at a low level, which proves that its prediction is accurate and reliable, and provides strong technical support for the safe rescue of the submersible.

Modeling and optimization of two-stage emergency supply chain network under uncertain environment

In order to formulate an effective emergency supply chain network planning scheme, improve the efficiency of emergency organization rescue and realize the reasonable allocation of resources and materials, considering the uncertainty of supply and demand, a distribution mode combining multiple transportation modes is adopted, and the optimization objectives are to minimize network response time, cost and carbon emissions. A two-stage emergency supply chain mixed integer programming model is constructed. At the same time, an adjustable robust optimization model is constructed based on robust optimization theory to enhance the network's ability to cope with uncertain factors, and the constraints with uncertain parameters are transformed through linear duality theory. In order to improve the solution effect of the model, an optimize cuckoo search (OCS) algorithm is proposed, and a benchmark example is introduced to verify the superiority and applicability of the OCS algorithm in solving multi-objective functions. Finally, the emergency material distribution data during the COVID-19 epidemic in Wuhan is used to study the emergency supply chain network decision-making problem with uncertain parameters, and the effective suppression of the robust control coefficient on uncertain disturbances is proved through sensitivity analysis.

701P AAV.U7.ex44 mediates efficient exon skipping, protein restoration & phenotype rescue – pre-clinical intramuscular and dose escalation study for a mutational hotspot of the Duchenne muscular dystrophy (DMD)

701P AAV.U7.ex44 mediates efficient exon skipping, protein restoration & phenotype rescue – pre-clinical intramuscular and dose escalation study for a mutational hotspot of the Duchenne muscular dystrophy (DMD)

Firefighting Robot Extinguishment Decision‐Making Based on Visual Guidance: A Novel Attention and Scale U‐Net Model and Genetic Algorithm

ABSTRACTAt present, rescue firefighting relies mainly on firefighting robots, and robots with perception and decision‐making functions are the key elements for achieving intelligent firefighting. However, traditional firefighting robots often lack the ability for autonomous perception and decision‐making when extinguishing multiple fire sources, leading to low rescue efficiency and increased risk for rescue personnel, especially when making firefighting decisions in extreme fire scenes, which poses a challenge. To effectively handle firefighting tasks and ensure operational efficiency, a robot firefighting decision‐making method based on drone visual guidance is proposed. First, we introduce a novel Attention and Scale U‐Net (ASUNet) model to accurately capture crucial target information, including fire location and size, in a fire scene. The ASUNet model adopts an effective multiscale fusion strategy and attention mechanism to enhance the model's performance. Subsequently, based on the results of the ASUNet model, through pixel segmentation clustering and a genetic optimization algorithm, we obtain the robot's firefighting decision results, thereby guiding the robot to carry out firefighting operations systematically. Finally, through numerical experiments, it is verified that the proposed ASUNet model is superior and effective, as the model can perceive important information in a fire scene and extract it well. The use of improved genetic optimization can further accelerate algorithm convergence. To our knowledge, this study is the first to use drone‐based monocular vision guidance for firefighting decision‐making, providing significant engineering value.

Efficient Rescue of Retinal Degeneration in Pde6a Mice by Engineered Base Editing and Prime Editing

AbstractRetinitis pigmentosa (RP) is a complex spectrum of inherited retinal diseases marked by the gradual loss of photoreceptor cells, ultimately leading to blindness. Among these, mutations in PDE6A, responsible for encoding a cGMP‐specific phosphodiesterase, stand out as pivotal in autosomal recessive RP (RP43). Unfortunately, no effective therapy currently exists for this specific form of RP. However, recent advancements in genome editing, such as base editing (BE) and prime editing (PE), offer a promising avenue for precise and efficient gene therapy. Here, it is illustrated that the engineered BE and PE systems, particularly PE, exhibit high efficiency in rescuing a target point mutation with minimal bystander effects in an RP mouse model carrying the Pde6a (c.2009A > G, p.D670G) mutation. The optimized BE and PE systems are first screened in N2a cells and subsequently assessed in electroporated mouse retinas. Notably, the optimal PE system, delivered via dual adeno‐associated virus (AAV), precisely corrects the pathogenic mutation with average 9.4% efficiency, with no detectable bystander editing. This correction restores PDE6A protein expression, preserved photoreceptors, and rescued retinal function in Pde6a mice. Therefore, this study offers a proof‐of‐concept demonstration for the treatment of Pde6a‐related retinal degeneration using BE and PE systems.

Fire Safety Disparities in Sweden: Sociodemographic Influences and the Impact of Societal Protection on Personal Fire Prevention Measures

AbstractPrevious research has identified sociodemographic inequalities in fire prevention measures. This study examined whether sociodemographic differences persist in the Swedish population concerning fire prevention measures and particularly whether there remains an inverted u-curve related to age in protection habits. Additionally, it investigated whether fire protection practices are influenced by the level of societal protection. The research utilised survey data and register data from The Swedish Civil Contingencies Agency and Statistics Sweden. A latent class analysis was conducted, dividing respondents into four latent classes, followed by two binomial regression analyses. The study revealed three key findings regarding fire protection measures. First, certain demographic groups, namely the young, women, single and childfree households, low-income and low-education individuals, immigrants, and urban residents, are disproportionately lacking optimal fire safety measures. Second, although a safety maturity curve is still observed, older adults in Sweden today are considerably more protected compared to 15–20 years ago, indicating that safety practices employed during middle age continue into old age. Third, a trend is observed where individuals living in areas with more efficient professional rescue services tend to have lower levels of personal fire protection, suggesting a rational choice based on the perceived level of societal protection.

Disaster Rescue Drone Based on YOLOv4 Algorithm

Abstract With the rapid development of artificial intelligence technology, the application of unmanned aerial vehicles (UAV) in disaster relief is becoming more widespread. This article presents a disaster relief UAV based on the YOLOv4 algorithm, aimed at improving the speed and efficiency of emergency response and rescue. The article designs and implements a UAV integrated with the YOLOv4 object detection algorithm, used for real-time identification and location of people within disaster areas and for deploying rescue materials using a mechanical claw. Through experiments and comparative verification, the system has demonstrated high-efficiency in target detection and tracking in various disaster environments.

Optimization of embryo rescue technique for development of hybrid plants in stenospermic grapes

The development of seedless cultivars is a primary goal in grapevine breeding. Since grapes are stenospermic and tend to abort their embryos before development, traditional breeding methods often yield seedless cultivars at a low frequency. Therefore, embryo rescue has emerged as a promising approach for creating seedless grape cultivars. This study aimed to optimize the ideal sampling time and protocol for efficient embryo rescue in grapes. Ovules from immature berries collected at various days after pollination (20DAP, 30DAP, 40DAP, and 50DAP) were cultured on Nitsch and Nitsch (NN) medium with different concentrations of Benzyl aminopurine (BAP) (0.1, 0.2, and 0.5 mg/L) and Activated Charcoal (AC) (1.5, 2, and 2.5 g/L). Several parameters were assessed, including maximum ovule growth, the percentage of enlarged ovules, percentage of collapsed ovules, callus formation percentage, and embryo germination percentage. The study’s results indicated that berries collected at 40 DAP yielded the best outcomes across all parameters. Regarding treatments, the most favorable results were achieved when ovules were cultured on NN medium supplemented with 0.5 mg/L BAP and 2 g/L AC. In conclusion, the study underscores the significance of choosing the right sampling time and treatments to ensure efficient embryo rescue in grapes. The protocol standardized from this research is recommended for effectively rescuing embryos and developing seedless hybrids.

Synergistic rescue of temperature-sensitive p53 mutants by hypothermia and arsenic trioxide.

The p53 tumor suppressor is inactivated by mutations in about 50% of tumors. Rescuing the transcriptional function of mutant p53 has potential therapeutic benefits. Approximately 15% of p53 mutants are temperature sensitive (TS) and regain maximal activity at 32°C. Proof of concept study showed that induction of 32°C hypothermia in mice restored TS mutant p53 activity and inhibited tumor growth. However, 32°C is the lower limit of therapeutic hypothermia procedures for humans. Higher temperatures are preferable but result in suboptimal TS p53 activation. Recently, arsenic trioxide (ATO) was shown to rescue the conformation of p53 structural mutants by stabilizing the DNA binding domain. We examined the responses of 17 frequently observed p53 TS mutants to functional rescue by temperature shift and ATO. The results showed that ATO only rescued mild p53 TS mutants with high basal activity at 37°C. Mild TS mutants showed a common feature of regaining significant activity at the semi-permissive temperature of 35°C and could be further stimulated by ATO at 35°C. TS p53 rescue by ATO was antagonized by the cellular redox mechanism and was rapidly reversible. Inhibition of glutathione (GSH) biosynthesis enhanced ATO rescue efficiency and sustained p53 activity after ATO washout. The results suggest that mild TS p53 mutants are uniquely responsive to functional rescue by ATO due to small thermostability deficits and inherent potential to regain active conformation. Combining mild hypothermia and ATO may provide an effective and safe procedure for targeting tumors with p53 TS mutations.

Design & Development of Rescue Assistance Rover for Land-Based Operations

In the face of natural disasters like earthquakes and landslides, timely and efficient rescue operations are paramount to saving lives. However, these operations are often hindered by hazardous conditions and inaccessible terrain. This research focuses on the design and development of a Rescue Assistance Rover (RAR) aimed at enhancing the capabilities of rescuers in challenging environments. The RAR integrates advanced robotics, sensory systems, and communication technologies to navigate through debris, assess hazards, and provide crucial assistance to rescue teams. By leveraging autonomy and remote operation capabilities, the RAR aims to augment human efforts, improving overall response effectiveness and minimizing risks to both rescuers and survivors. This paper outlines the conceptualization, design considerations, development stages, and potential applications of the RAR, highlighting its potential to revolutionize disaster response efforts and save lives in critical situations. Key Words: Additive Manufacturing, 3D Printing, Rocker Bogie Mechanism, Arduino Programming, Rescue Operations, Robotics

Mediatized governance in environmental disasters: self-organizing community rescue through collaborative online documents

The frequency of risks and disasters in contemporary society has created new challenges for rescue governance. Through content analysis and participant observation, this study examined the development of an online collaborative and self-organizing community during the 2021 rainstorm rescue effort in Zhengzhou, China. The study shows that this community provided and optimized major public goods and services, adapted rescue resources to victim needs, facilitated streamlined information transfer, protected privacy, and connected a multitude of actors. The findings illustrate the significance of a new mode of mediatized governance that emphasizes the continuous process of reconciling conflicting interests and encouraging joint action among multiple actors, thereby realizing a flexible and efficient rescue that could not have been achieved by a top-down government-led administration.

Firefighting Vehicle Dispatch and Route Planning Model Based on Priority Passage Rights

<div>Efficient fire rescue operations in urban environments are critical for saving lives and reducing property damage. By utilizing connected vehicle systems (CVS) for firefighting vehicles planning, we can reduce the response time to fires while lowering the operational costs of fire stations. This research presents an innovative nonlinear mixed-integer programming model to enhance fire rescue operations in urban settings. The model focuses on expediting the movement of firefighting vehicles within intricate traffic networks, effectively tackling the complexities associated with collaborative dispatch decisions and optimal path planning for multiple response units. This method is validated using a small-scale traffic network, providing foundational insights into parameter impacts. A case study in Sioux Falls shows its superiority over traditional “nearest dispatch” methods, optimizing both cost and response time significantly. Sensitivity analyses involving clearance speed, clearance time, minimum rescue force, and fire loss parameters contribute to the enhancement of urban fire rescue operations and the refinement of practical decision support systems.</div>

USVs Path Planning for Maritime Search and Rescue Based on POS-DQN: Probability of Success-Deep Q-Network

Efficient maritime search and rescue (SAR) is crucial for responding to maritime emergencies. In traditional SAR, fixed search path planning is inefficient and cannot prioritize high-probability regions, which has significant limitations. To solve the above problems, this paper proposes unmanned surface vehicles (USVs) path planning for maritime SAR based on POS-DQN so that USVs can perform SAR tasks reasonably and efficiently. Firstly, the search region is allocated as a whole using an improved task allocation algorithm so that the task region of each USV has priority and no duplication. Secondly, this paper considers the probability of success (POS) of the search environment and proposes a POS-DQN algorithm based on deep reinforcement learning. This algorithm can adapt to the complex and changing environment of SAR. It designs a probability weight reward function and trains USV agents to obtain the optimal search path. Finally, based on the simulation results, by considering the complete coverage of obstacle avoidance and collision avoidance, the search path using this algorithm can prioritize high-probability regions and improve the efficiency of SAR.

The spatial overlay effect of urban waterlogging risk and land use value

Urban waterlogging poses a severe threat to lives and property globally, making it crucial to identify the distribution of urban value and waterlogging risk. Previous research has overlooked the heterogeneity of value and risk in spatial distribution. To identify the overlay effect of urban land value and risk, this study employs the Entropy Weighting Method (EM) to assess urban value, Principal Component Analysis (PCA) to determine waterlogging risk and key areas (RK), local Moran's I (SC) to identify key areas (HK), and finally Bivariate local Moran's I (DC) to comprehensively evaluate urban value and waterlogging risk to delineate key areas (BH). The results indicate that waterlogging risk is primarily influenced by proximity to water systems (PCA coefficient: 0.567), population density (0.550), and rainfall (0.445). There is a positive correlation between urban value and waterlogging risk, with a global Moran's I of 0.536, indicating that areas with higher urban value also face greater waterlogging risk. The DC method improved identification precision, reducing the BH area by 6.42 and 3.51 km2 compared to RK and HK, accounting for 25.50 % and 15.76 % of the RK and HK identified areas, respectively. At present, rescue resources can access less than one-third of the area within 5 min, but with the DC method, during the centennial rainfall scenario, the accessibility rate within 5 min for the BH area reaches 63 %, and all BH key areas can be covered within 15 min. This study provides a new methodology for identifying key areas of waterlogging disasters and can be used to enhance urban rescue efficiency and the precision management of flood disasters.

Emergency Material Prediction in South China Based on Case Analysis Method

After the occurrence of flood disasters, timely and rapid emergency response is one of the important tasks to improve the efficiency of emergency rescue and reduce disaster losses, and the prediction of material demand, which is the premise and basis of emergency response in disaster areas after disasters, is one of the key problems that need to be solved urgently. Considering the characteristics of poor information in the post-disaster disaster area, a post-disaster material demand forecasting technology based on fuzzy case reasoning was introduced. Firstly, on the basis of the analysis and summary of the existing case database, the key feature attributes of flood disasters that affect the demand for materials after the disaster are extracted, and the fuzzy set of flood disaster characteristics is established by introducing the concept of fuzzy sets, and then the membership degree of the specific feature attribute values of the new and old cases to the fuzzy set is calculated, in order to measure the similarity between the new and old cases, and the corrected measure closeness based on the weight of the feature attributes of the new and old cases is calculated, and the one with the largest closeness represents the best match between the new and old cases. Finally, an actual case is used to show the specific application process of the technology, and the existing reference cases that are closest to the new case are obtained, which can provide reference for post-disaster emergency rescue. The results show that the proposed method can provide a scientific prediction of the demand for emergency supplies and provide a basis for decision-making for the relevant emergency management departments in an actual flood disaster case in South China.

Legal System Research on Humanitarian Rescue by Mainland Government Vessels Blockading in the Taiwan Strait During Wartime

This paper explores the legal system concerning humanitarian rescue by mainland government vessels blockading in the Taiwan Strait during wartime. Firstly, the concept of government vessels is defined, and their role in the humanitarian rescue system is analyzed. Subsequently, the concept of wartime maritime blockade and its conception in the Taiwan Strait are discussed. Furthermore, the feasibility of government vessels conducting humanitarian rescue in blockaded areas is analyzed from aspects such as legal basis, third-party intervention issues, and military-civil coordination. The legal nature of humanitarian rescue by government vessels and the analysis of national supervisory responsibilities are then discussed. Finally, the paper points out the deficiencies in the current legal system regarding rescue provisions, such as unclear power regulations, risks in relief channels, and risks in the choice of review agencies, and proposes improvement suggestions, including refining administrative regulations, adopting pre-review procedures, and specifying review agencies, to enhance rescue efficiency and protect the rights of distressed individuals. Through these measures, the government can better fulfill its responsibility to protect the lives of citizens and enhance the legal effectiveness of maritime rescue.

EMR-YOLO: A Study of Efficient Maritime Rescue Identification Algorithms

Accurate target identification of UAV (Unmanned Aerial Vehicle)-captured images is a prerequisite for maritime rescue and maritime surveillance. However, UAV-captured images pose several challenges, such as complex maritime backgrounds, tiny targets, and crowded scenes. To reduce the impact of these challenges on target recognition, we propose an efficient maritime rescue network (EMR-YOLO) for recognizing images captured by UAVs. In the proposed network, the DRC2f (Dilated Reparam-based Channel-to-Pixel) module is first designed by the Dilated Reparam Block to effectively increase the receptive field, reduce the number of parameters, and improve feature extraction capability. Then, the ADOWN downsampling module is used to mitigate fine-grained information loss, thereby improving the efficiency and performance of the model. Finally, CASPPF (Coordinate Attention-based Spatial Pyramid Pooling Fast) is designed by fusing CA (Coordinate Attention) and SPPF (Spatial Pyramid Pooling Fast), which effectively enhances the feature representation and spatial information integration ability, making the model more accurate and robust when dealing with complex scenes. Experimental results on the AFO dataset show that, compared with the YOLOv8s network, the EMR-YOLO network improves the mAP (mean average precision) and mAP50 by 4.7% and 9.2%, respectively, while reducing the number of parameters and computation by 22.5% and 18.7%, respectively. Overall, the use of UAVs to capture images and deep learning for maritime target recognition for maritime rescue and surveillance improves rescue efficiency and safety.

Optimal Allocation Model for Maritime Emergency Resource Considering the Spatial Correlation between Accident Hotspots

For effective maritime traffic emergency rescue (MTER) operations in the event of maritime traffic accidents (MTAs) and to improve rescue efficiency, it is necessary to analyze the MTER synergy problem and the cooperation between port states. First, the spatial information of accidents under the geographic information system data structure is clarified from the global integrated shipping information system of the International Maritime Organization, and the density-based spatial clustering of applications with noise algorithm is used to conduct hotspot mapping analysis of MTAs to establish the clustering and classification of accident characteristics in key areas. Second, the classification characteristics of accident samples are extracted based on spatial information, and the correlation attributes between MTA hotspots are analyzed. Furthermore, by introducing complex network measurement technology, a topological model of the MTER network is established considering the correlation of accident hotspots, and this model is combined with the sample data of MTAs in Southeast Asian waters from 1990 to 2022. Third, the MTER topological network model is quantitatively analyzed under the accident space of Southeast Asia, and the degree of correlation of traffic accidents in key areas is obtained to reveal the inevitable demand for MTER between regions. The results of the analysis show that there is a network correlation between inter-regional accident hotspots, and thus the degree of correlation between accident hotspots needs to be considered for MTER in key areas. Countries in densely connected regions would set up joint rescue exercises and consider rescue assistance between port country stakeholders, thus improving protection for accident emergency responses. The method of complex network topology based on spatial correlation between accident hotspots suggests a new approach for solving the MTER problem.

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.