Emergency Tasks Research Articles

System design and flight control of a flying wheel-legged humanoid robot (FWLR)

Purpose The purpose of this paper is to design a flying wheel-legged humanoid robot (FWLR), endowing the robot with flight capability to improve the obstacle-crossing ability of the wheel-legged humanoid robot. A flight control method using thrust-vector-control (TVC) under constant thrust strength is proposed, which reduces the performance requirements on the response speed of thrusters. Design/methodology/approach To endow the robot with flight capability, three sets of thrusters are installed at the robot’s back and two arm ends to provide flight lift and the direction of thrust can be changed through the arm swing. According to the robot configuration, this paper established a linearized dynamic model and proposed a constant-strength-thrust-vector-control (CSTVC) framework enabling the robot to achieve flight without thrust intensity change. Findings With the proposed modeling method and CSTVC framework, FWLR can inhibit attitude and position drift during takeoff and hovering, and has certain adaptability to takeoff attitude. Finally, FWLR reached a flying height up to 1 m under a 30 kg large self-weight with fixed thrust strength. Originality/value The design, modeling and flight control method proposed in this paper enables a human-sized wheel-legged humanoid robot to achieve takeoff and hovering for the first time. The movement range of wheel-legged humanoid robot is extended to the air, thereby enhancing its application value in emergency tasks such as disaster search-and-rescue.

Augmented Intelligence of Things for Emergency Vehicle Secure Trajectory Prediction and Task Offloading

Augmented Intelligence of Things for Emergency Vehicle Secure Trajectory Prediction and Task Offloading

A High-Concurrency Tasks Emergency Offloading Framework for Industrial Edge Networks

In the envisioned scenario of smart factories, large-scale distributed intelligent industrial devices generate a substantial number of concurrent heterogeneous industrial tasks. A single device is inadequate to meet the demanding requirements of complex tasks, which necessitate high computational capabilities and low latency in the system. Typically, multi-access edge computing (MEC) is employed to address this challenge. However, industrial edge networks face threats such as network attacks, natural disasters, and hardware failures, posing security risks to the industrial edge network. Therefore, we have designed an emergency offloading framework to enhance its resilience. Based on a task path analysis method with multiple priority levels, we developed an emergency task scheduling pre-classification (ETSPC) algorithm. Experimental results demonstrate that the proposed algorithm significantly reduces the delay of emergency offloading for high-concurrency tasks, with a maximum reduction of up to 19.2%. In extreme scenarios, the task completion rate of the ETSPC algorithm increases by 45.3% compared to the control algorithm.

Research on the collaborative relationship of task-driven urban earthquake emergency organizations

Earthquake emergency response is a complex process, and a large number of tasks are involved in the process of urban earthquake emergency response. To further study the collaborative relationship between organisations in the city-level emergency collaboration network driven by different emergency tasks, this study first constructs a task–organisation relationship framework and explores the collaborative mechanism of organisations driven by tasks. Then, the co-expression analysis was used to analyse the degree of correlation between task-driven emergency organisations. Finally, considering the influence of tasks on the degree of inter-organisational collaboration, this study constructs a task-driven weighted directed emergency organisation collaboration network. Social network analysis was used to reveal the structural attributes of the task-driven organisational collaboration network, the collaborative characteristics among organisations in the network, and the structural positions of organisations in the collaborative network. It is found that the task-driven earthquake emergency collaboration network was relatively loose, and there were small groups with close collaboration in the network. Most organisations in these small groups are in the same task module, and their tasks are similar. Commanders in the task execution process are usually at the core of the network, and these organisations are usually the main coordinators of the overall emergency collaboration and task module networks. These findings can improve our understanding of earthquake emergency organisational collaboration relationships and inspire the optimisation of urban earthquake emergency management.

Increasing resilience and selection of a strategy for restoring transport networks in extreme natural processes

Aim. The development of an approach to increasing resilience by selecting strategies for restoring transport networks affected by extreme natural processes. Methods. This study evaluates the dynamics of extreme natural processes, specifically exogenous geological processes that can that can disrupt transport networks. It includes as framework for assessing the sustainability and restoration of transport networks under climate risk factors. Strategies for restoring the transport network were formulated. Results. The formulated strategies enable network modeling of transport network topology, which can be represented as an undirected weighted graph with a set of nodes and edges. The proposed model allows determining the most effective strategy for quickly restoring the connectivity of the transport network by determining the optimal sequence of restoration for repairing road sections, considering restoration time. The efficiency of restoring damaged sections of the transport network is expected to decrease as the share of the restored network increases. Therefore, it is crucial to estimate the necessary extend of network restoration to perform the necessary extent of network restoration to support emergency and urgent tasks by RSChS formations in specific areas. Conclusion. The analysis and assessment of alternative solutions for restoring the sustainability of transport networks considers the complexity of tasks under climate risk factors, such as extreme natural processes. In some cases, the RSChS problems do not require complete network restoration, unlike the tasks solved by the transport industry. This work aims to develop a framework for assessing restoration strategies, identifying the features of each of the considered strategies under uncertainty, and increasing operational sustainability. The proposed approach is flexible, allowing decision makers to assess various priorities during a specific natural emergency in a certain area, such as average recovery time, process efficiency, and uncertainty levels, when choosing the most desirable strategy. It is assumed that the average recovery time does not differ significantly among strategies for full network restoration. However, for partial restorations necessary for RSChS tasks, the average restoration time depends on the chosen strategy.

Perceptions of Key Responsibilities and Professional Development Interests of Senior Educational Leaders: A Needs Assessment.

High-ranking educational leaders in academic medicine oversee multiple clinical programs. This requires them to prioritize dozens of emergent tasks and responsibilities daily, from educational policy and strategy to staff management, financial planning, onboarding of trainees, and facility planning and management. Identifying their key responsibilities and frequently used skills and competencies may clarify the educational needs of senior educational leaders and facilitate targeted professional development to promote effective and efficient performance. In August 2022 researchers interviewed 12 designated education officers (DEOs) from U.S. Department of Veterans Affairs (VA) Veterans Health Administration medical centers about their daily work and most challenging responsibilities. Content analysis of interview transcripts identified key responsibilities and activities identified by participants and prioritization of the perceived skills needed to complete them. Participants emphasized 4 key areas of responsibility: fiscal, administrative, affiliate partnership, and educational duties. They identified 12 skills as baseline requirements for effective performance for which additional professional development would be useful and suggested that both new and more established educational leaders receive targeted professional development and mentoring to foster these capacities. The key skills participants identified by area of perceived responsibility are relevant to VA DEOs, designated institutional officers, and senior academic leaders who develop health professions education programs, oversee clinical training, and manage educational change. Structured orientation programs and ongoing professional development for senior educational leaders could emphasize these areas of responsibility, potentially enriching DEOs' performance and reducing burnout.

A new priority aware routing protocol for efficient emergency data transmissions in MANETs

This paper introduces a new priority-aware routing protocol for mobile Ad-hoc networks to be utilized in emergencies, which is based on AODV. Mobile Ad-hoc networks find extensive use in various domains including military operations, environmental monitoring, healthcare, disaster response, smart transportation systems, unmanned aerial vehicles, and smart homes. During emergencies, communication can be severely restricted or even impossible due to the congestion of physical communication channels and unexpected technical failures in the infrastructure. Mobile Ad-hoc networks offer a solution to maintain continuous and reliable communication under such challenging conditions. In emergency scenarios, it is crucial for any node in the network to promptly deliver urgent messages to the intended destination, especially when certain nodes require ongoing active communication. The proposed routing protocol effectively addresses this requirement through its priority-aware mechanisms. The protocol ensures that nodes not involved in emergency tasks select the least congested route to prevent any delays or disruptions in the transmission of critical emergency data. This approach guarantees seamless communication for emergency nodes while allowing non-emergency nodes to communicate with each other as well. The study proposed in this paper introduces a new priority-aware routing protocol based on AODV for mobile Ad-hoc networks in emergencies. The packet transmission ratio of emergency nodes within the network is improved while maintaining the overall network performance unaffected. The adoption of proposed mechanisms to enhance performance does not necessitate an expansion in the size of data and control packets. These mechanisms do not inflict any supplementary latency or incur packet loss expenses on the network. The proposed protocol has been implemented and evaluated using ns-3 simulation software across various emergency scenarios. The results show that emergency nodes using the proposed protocol, achieve better packet delivery ratios compared to the original AODV, DSR, P-AODV, and AOMDV protocols, with improvements of 10.8%, 15.9%, 6.2%, and 5.9% respectively. This improvement in the packet delivery ratio for emergency data traffic is achieved without causing any disruptions in the overall network communication flow.

Emergent task allocation and incentives: an agent-based model

In recent times, organizations have increasingly adopted structures in which decision making is distributed rather than centralized. This approach often leads to task allocation emerging from the bottom up, moving away from strict top-down control. This shift raises a key question: How can we guide this emergent task allocation to form an effective organizational structure? To address this question, this paper introduces a model of an organization where task assignment is influenced by agents acting based on either long-term or short-term motivations, facilitating a bottom-up approach. The model incorporates an incentive mechanism designed to steer the emergent task allocation process, offering rewards that range from group-based to individual-focused. The analysis reveals that when task allocation is driven by short-term objectives and aligned with specific incentive systems, it leads to improved organizational performance compared to traditional, top-down organizational designs. Furthermore, the findings suggest that the presence of group-based rewards reduces the necessity of mirroring, i.e., for a precise matching of the organizational structure to task characteristics.

A multi-agent reinforcement learning approach for ART adaptive control in automated container terminals

Intelligent transportation equipment represented by Artificial Intelligence Robot of Transportation (ART) has been gradually applied to automated container terminals (ACT). ARTs have higher intelligence and distributed autonomous decision-making capability compared to AGVs. The production and operation of ACT require strict loading plans based on container types and destination ports, which puts strict requirements on the ART loading task sequence. ART travel time from the yard to the quay is highly uncertain due to special scenarios such as traffic flow convergence and turning in narrow areas, which will lead to long waits for ART due to out-of-sequence arrivals at the quay. Therefore, it is necessary to regulate the speed of ART to adjust the timing of ART arriving at the quay. However, existing scheduling systems tend to ignore the impact of ART speed and loading sequence constraints on terminal operations. To optimize the multi-ART control problem of ACT, the waiting time of ART at the quay is taken as the optimization goal, and the speed regulation of ART during each task is defined as a Markov decision process. Then, by incorporating reinforcement learning and a multi-agent approach, an adaptive decision-making method is developed to generate the optimal speed regulation strategy. This method interacts with the multi-agent simulation environment to obtain real-time status information and learns strategies online to improve the intelligence of individual agents and the collaborative capabilities of the agent group. Finally, a multi-agent simulation model embedded with a reinforcement learning agent is established on AnyLogic software. Simulation results show that this method can effectively reduce the waiting time of ART, improve the coherence and efficiency of loading operations, and exhibit good performance in online scenarios that consider emergency tasks. The results indicate that the speed of ART in ACTs with parallel layout of the yard can impact the operational efficiency of the system. Furthermore, by appropriately defining the reward function and training method, collaborative behaviour among equipment can be enhanced, ultimately optimizing the overall performance of the system.

Vulnerability Analysis of UAV Swarm Network with Emergency Tasks

With the rapid development of Unmanned Aerial Vehicle (UAV) technology, UAV swarms are used for emergency tasks in various scenarios such as area detection, fire rescue, logistics, and transportation. However, for complex scenarios, UAV swarms are prone to environmental interference that damages their equipment or disrupts their communication links, affecting the normal execution of tasks. In this paper, an information–communication interdependent network model is designed for the vulnerability analysis of UAV swarm networks with emergency tasks. Firstly, from the perspective of network functions of a UAV swarm, we introduce the theory of interdependent networks to abstract the relationship between the UAV swarm’s communication network and its information network, where the communication network represents its communication topology and the information network is related to the function of each UAV individual in the UAV swarm. Then, the vulnerability of the UAV swarm is analyzed according to the relationship between network construction costs and network connectivity under environmental interference. Finally, the effectiveness of the vulnerability analysis method is verified through simulation.

A Multi-satellite Scheduling Method for Emergency Observation Mission Based on Hierarchical Planning Strategy

Emergency observation tasks involve Earth imaging activities by satellites to aid in emergency scenarios like disaster relief, emphasizing promptness and unpredictability. Multi-satellite collaborative planning for several emergency tasks faces issues like high computational complexity, slow processing, and numerous optimization considerations. This paper introduces a hierarchical planning approach and designs an algorithm for collaborative task planning across multiple emergencies and satellites. The algorithm’s computational efficiency and planning effectiveness are later confirmed through a case study.

Extraction of Emergency Elements and Business Process Model of Urban Rail Transit Plans

The emergency plan of the urban rail transit (URT) system is a guiding document for dealing with emergencies and formulating emergency plans. However, the emergency plan text described in natural language has some problems, such as poor visibility and enforceability. Effective help is difficult to provide for the rapid implementation of emergency response. Therefore, obtaining key emergency task information from the emergency response plan and visualizing the emergency disposal workflow are the main challenges. In this article, we propose a method of extracting emergency elements (EELs) from emergency plans and constructing a business process model. First, a nested entity extraction model incorporating adversarial training is proposed to extract EELs from the complex sentences in the emergency plan text. Second, the EELs are combined into emergency task units, and then the relations between emergency task units are identified to form the emergency task sequence flow, which is stored in matrix form. Finally, the emergency disposal workflow model is generated based on the emergency task sequence flow and the BPMN modeling method. Taking the actual emergency plan text as an example, the process from the extraction of EELs to the construction of the disposal workflow model is demonstrated. Experimental results prove that this method has advantages in comprehensively extracting EELs, visualizing the emergency disposal workflow, and improving the enforceability of emergency plans.

Analyzing Korean Public Health Centers' Infectious Disease Disaster Response Experiences with a Focus on Business Continuity.

This study aims to provide basic data for establishing strategies to maintain the core functions of health centers, and enable an effective response to emergency tasks in the event of future infectious disease disasters. The participants were 41 workers from two public health centers in Seoul. They all had prior experience in responding to the early and middle stages of the COVID-19 pandemic. Data were collected through Focus Group Discussions, and then analyzed using the deductive method of content analysis. The participants' experiences during the infectious disease disaster crisis were examined through ten categories: governance and coordination, information management, human resources, essential medical supplies and equipment, infrastructure, administration, finance and logistics, community engagement and risk communication, delivery of essential services, security, and additional considerations for vulnerable populations. The analysis of the results made it apparent that new systems and policies were imperative for responding appropriately to the concerns and experiences of the public healthcare center staff, and for improving the response to future epidemics. We found that to prepare for infectious disaster situations in the future, it is necessary for health centers to establish a mid- to long-term business continuity plan to ensure the continuation and stability of their operations. Additionally, it was found that health professionals in public health centers also believe in the necessity of education and training programs on disaster preparedness, based on Business Continuity Planning proposed by the World Health Organization. They deem these essential to sustain routine tasks for the management of the health of local community residents during outbreaks of novel infectious diseases in the future.

Youth and Community Work for Climate Justice: Towards an Ecocentric Ethics for Practice

ABSTRACT This paper traces an expanded ethical perspective for youth and community work (YCW) practice in response to the climate and biodiversity crises. Discussing ecological ethics, we problematise the liberal humanist emphasis on utilitarianism and reject it as inappropriate for YCW in these times. Instead, we argue for an ecocentric practice ethic which intrinsically values the non-human world. To advance an ecocentric ethical perspective for YCW we draw on decolonial and posthuman theory. Inspired by a Freirean dialogical approach, we apply these theoretical domains as lenses to problematise YCW practice, seeking a generative dialogue between perspectives. Through this, we identify three emergent tasks for ecocentric YCW: (a) thinking and acting beyond the individual; (b) cultivating reciprocal care and connection and (c) practicing critical pedagogies of place. This third element builds on YCW's social pedagogic tradition and provides a practical means to incorporate ecocentric ideas into practice. We conclude that, given the unprecedented implications of climate crises and biodiversity collapse, a YCW ethics that does not consider these implications for young people is insufficient for the context of practice today. Enacting an ecocentric YCW ethics requires ongoing collective praxis and dialogue between young people, practitioners, educators, managers and students.

Research on data mapping and fusion method of ship production workshop based on digital twins

Aiming at the problems existing in discrete manufacturing workshops of ships, such as the lack of real-time management and control ability of on-site production status, and the long time delay of real-time interaction and fusion between physical entities and virtual entities data, this paper proposes ship production workshop data mapping and fusion method based on digital twins. Taking a ship machining workshop as an application research example, the integrated business process and data mapping and fusion method of the machining workshop based on digital twins are studied. On this basis, the dynamic scheduling optimization algorithm of ship machining workshops based on digital twins is studied. The results show that the data mapping and fusion method in this study can significantly reduce the time delay of data interaction between the virtual and real objects. It can effectively improve the real-time and production flexibility of dynamic and complex tasks such as emergency insertion and task rework. The ability of real-time data interaction and synchronous real-time mapping between physical entities and virtual entities in dynamic scenes such as production line reconstruction and customized production is improved.

Investigating the Human Reliability in the Healthcare Sector Using the Fuzzy Analytic Network Process and the Success Likelihood Index Method.

We aimed to identify the factors contributing to human error in hospital emergency departments using scientific methods. We used the Fuzzy Analytical Network Process (FANP) and Success Likelihood Index Method (SLIM) to investigate human reliability in 54 hospital emergency departments in 15 provinces of Iran from 2021 to 2022. The study classified 17 general factors affecting human errors in hospital emergency departments. Organizational (0.349), occupational (0.330), and personal factors (0.320) had the most significant impact on human error. Based on a matrix of paired comparisons for nine emergency tasks using the probability of success index method, "checking test results and diagnosis" had the highest probability of error when referring patients to intensive care or discharge. Although the study prioritized patients, there was still a cumulative probability of human error before disease diagnosis at 0.01332, highlighting the need for further training to minimize these risks. The FANP and SLIM were effective in identifying the factors contributing to human error in hospital emergency departments. Doctors and nurses working in these departments require more knowledge, experience, and responsibility to avoid errors. By identifying factors influencing the occurrence of human error and finding solutions to reduce risks, hospitals can improve the quality of their care and prevent errors.

Bidirectional Progressive Neural Networks With Episodic Return Progress for Emergent Task Sequencing and Robotic Skill Transfer

Bidirectional Progressive Neural Networks With Episodic Return Progress for Emergent Task Sequencing and Robotic Skill Transfer

Modeling and simulation of complex emergency dispatch based on BIPSO

In emergency task scheduling, this study proposes a complex model for emergency scheduling. It is based on the particle swarm algorithm and improves upon the traditional version. Additionally, the study recommends the use of the binary particle swarm optimization algorithm (PSO). The study proposes applying the multi-objective task scheduling-particle swarm optimization algorithm (MOTS-PSO) to the complex emergency scheduling model by combining it with the multi-objective function. Compared to other algorithms, the proposed improved algorithm exhibited the lowest average number of iterations, which consistently fell within the range of 130, and achieved a 100% success rate for optimization searches on the majority of functions. When compared with other models, the proposed research model demonstrated superior performance, exhibiting the lowest total scheduling cost, total execution time, and data transfer time of 280 and 900, respectively, for the task quantity of 5000. Furthermore, the proposed model exhibited the lowest maximum execution cost for a single node, which remained within the range of 0.45S. The outcomes of the experiments demonstrate that the proposed research model adequately satisfies the requirements for complex scheduling and its simulability has been confirmed.

Integrated Emergency Vehicle Scheduling Model in Urban Emergency Rescue within a Time-Limited Period

Considering the uncertainty of urban emergency rescue time, it is difficult for emergency vehicles to maintain a 100% service level continuously. The biggest challenge is to minimize driving distance and costs when the number of vehicles available for deployment is uncertain. In this study, considering the limitation of emergency vehicles, a new integrated emergency vehicle scheduling model for large-scale urban emergency rescue was developed, which is able to tackle multiple emergency tasks at the same time, achieving the shortest path and the lowest cost. Then, the emergency scheduling was simulated in seven urban areas of H City in China to verify the applicability of the proposed model, during which a fast non-dominated sorting multi-objective genetic algorithm with elite strategy was used to solve this model. Moreover, the corresponding algorithm test was conducted to verify the feasibility of the Pareto solution and demonstrate its effectiveness. Moreover, the simulation results show that the used algorithm can reduce computing time by 14.27%, compared with the genetic algorithm. Finally, the optimal scheduling scheme was selected according to the decision-makers’ preferences. This study solved various emergency vehicle scheduling problems under uncertain emergency times, which was beneficial for multilateral cooperation emergency scheduling and provided decision support for emergency command departments for choosing the best emergency scheme.

Collaborative computation offloading for scheduling emergency tasks in SDN-based mobile edge computing networks

Due to the growing demand for time-intensive tasks, several tasks coexist, some of which last for a long time or only occur once in a while, such as emergencies. Emergencies such as natural disasters, accidents or medical emergencies require immediate attention and often demand real-time execution. Such tasks are the most difficult to plan for and foresee. Planning, resource allocation, and clear communication between all involved parties and network layers are crucial for scheduling emergency tasks. In the extensive literature, when an emergency task triggers, it will be handled like any other regular task. If the deadline is missed, a catastrophe will ensue. They ignored the impact of insufficient mobile edge computing (MEC) capacity and network congestion, which might result in execution delays. Collaborative offloading, which involves distributing computation tasks across multiple servers, shows promise in enhancing MEC network performance, particularly in emergency scenarios. In this context, efficient task scheduling plays a vital role in minimizing the total execution time of regular tasks while meeting the deadlines of emergency tasks. Our proposed scheme utilizes a collaborative offloading approach to schedule emergency tasks in MEC networks, leveraging the computing capacity of edge-deployed MEC servers. By utilizing the software-defined networking (SDN)’s global view of the network, task requests are collected and allocated to suitable MEC servers capable of meeting the demands. To address key challenges, our scheme propose four scheduling algorithms to address the following issues: (i) ensuring tasks are assigned to the nearest MEC server with sufficient computational resources, (ii) controlling a threshold to prevent network congestion, (iii) selecting an optimal collaborative MEC server for executing overloaded tasks based on collaborative offloading decisions, and (iv) allocating resources for emergency tasks when triggered to meet urgent deadlines by stealing resources from regular tasks without compromising their deadlines. Extensive simulations were conducted to assess the effectiveness of the proposed scheme. The results clearly illustrate its enhanced performance in terms of the total execution time of regular tasks and the ability to meet deadlines for emergency tasks.