Emergency Tasks Research Articles

Age of Information in UAV Aided Wireless Sensor Networks Relying on Blockchain

Nowadays, the freshness of status update data is critical for emergent tasks in wireless sensor networks (WSNs). This paper analyzes the peak age of information (PAoI) in the transaction-confirmation process of blockchain technology in the context of unmanned aerial vehicle (UAV) aided wireless sensor networks. Each access node transmits the latest packets to the associated access node, which processes the packets relying on blockchain technology. We investigate the transaction-confirmation process based on a finite-buffer batch-service <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\bf GI/M^{[b]}/1/N}$</tex-math></inline-formula> queueing system. Additionally, the embedded Markov chain techniques are utilized to obtain the state probabilities of the system. Moreover, we deduce the closed-form expressions of average PAoI, which regards as the worse case of the update packet's freshness. Numerical results verify that reducing the block size and the queue length leads to a lower PAoI. Besides, fixed interval network traffic and Markov property network traffic based situations display much better aging behavior than other situations.

Emergency task offloading strategy based on cloud-edge-end collaboration for smart factories

The realization of smart factories is becoming increasingly possible due to the rapid development of edge computing technology and 5G communication technology. Smart devices in smart factories can perform tasks such as environmental monitoring, production scheduling, and autonomous mobility. These tasks cannot be completed on time depending only on the computing power of smart devices. Therefore, some tasks need to be performed on edge servers and cloud servers. However, events such as natural disasters, network attacks, and hardware failures will cause server computing resource failures. These threats bring hidden dangers to the normal operation of smart factories. This paper proposes an emergency offloading strategy based on cloud–edge-end collaboration for smart factories. In this strategy, the total task execution delay and the key task execution delay constitute an objective function, which is solved by a Fast Chemical Reaction Optimization (Fast-CRO) algorithm. The algorithm follows the principle of prioritizing the offloading of key tasks in emergency situations, and it quickly makes emergency offloading decisions for the system. Simulation results show that this strategy outperforms the other five baseline algorithms and existing priority based CRO algorithms.

Translator education as a collaborative quest for insights into the re-positioning of the human translator (educator) in the age of machine translation: the results of a learning experiment

ABSTRACT Motivated by the urgent need to investigate the possibilities for re-positioning the human translator and his/her educator in the machine translation (MT) age, this article explores the dynamics of the human-machine dance in the translation classroom. The article discusses the results of a collaborative learning experiment which was conducted in an MA course. In the experiment, the students carried out the emergent professional tasks of MT post-editing, pre-editing and error annotation. The results showed that the experiment helped the majority of the students re-position themselves as future experts of MT-related tasks by developing task-specific self-efficacy beliefs and raising their awareness of their self-concept as human agents and of the added value they could create. The educators, on the other hand, gained insights into the challenges faced by the students in the experiment, during which the boundary between teaching and learning became blurred. The educators further witnessed, first-hand, the need to transform translation classrooms into platforms of collaborative learning, where all involved can work together to discover ways of creating human added value in emergent MT workflows, which also requires the re-positioning of the translator educator in the MT age.

A Data-driven Agent-based Planning Strategy of Fast-Charging Stations for Electric Vehicles

Electric vehicles (EVs) are believed to play an important role in mitigating carbon emissions. To accommodate the increasing number of EVs, the co-planning of electrical distribution networks and the charging infrastructure, such as fast-charging stations (FCSs), becomes an emergent task. Both the charging demand of EVs and the stability of the electricity network should be satisfied. In this paper, we proposed a data-driven agent-based planning strategy for FCSs. Different from the conventional planning strategy, we utilized machine learning tools to consider EV behaviors at the microscopic level in a planning problem. First, a Partially Observable Markov Decision Process of EVs is established, and multi-agent deep reinforcement learning is utilized to learn the charging and driving decisions of EVs under different transportation network typologies and FCSs planning schemes. Second, a data-driven agent-based traffic assignment model (DA-TAM) is proposed to aggregate the atomic behaviors of EVs, which can present the sensitivity of the traffic flow and EV charging demand to the FCS planning schemes. Third, the DA-TAM is adapted to the proposed planning model to ensure the quality of service and prevent unbalanced traffic flow. Through the proposed method, microscopic behaviors of EVs can be reflected, and the impact of the planning scheme on the traffic condition can be revealed. The proposed methodologies are verified in the case studies. It can be concluded that compared with the baseline, the presented agent-based planning strategy can serve more EV charging demands (increased by 33%), cause less waiting time (decreased by 59%) in FCSs to enhance the quality of service, and encounter less severe traffic unbalance problems (increased by 17%).

Semantic Decision Internal-Attention Graph Convolutional Network for End-to-End Emotion-Cause Pair Extraction

Emotion-cause pair extraction is an emergent natural language processing task; the target is to extract all pairs of emotion clauses and corresponding cause clauses from unannotated emotion text. Previous studies have employed two-step approaches. However, this research may lead to error propagation across stages. In addition, previous studies did not correctly handle the situation where emotion clauses and cause clauses are the same clauses. To overcome these issues, the authors first use a multitask learning model that is based on graph from the perspective of sorting, which can simultaneously extract emotion clauses, cause clauses and emotion-cause pairs via an end-to-end strategy. Then the authors propose to convert text into graph structured data, and process this scenario through a unique graph convolutional neural network. Finally, the authors design a semantic decision mechanism to address the scenario in which there are multiple emotion-cause pairs in a text.

Toward Low Time Fragmentation of Equipment: A Double-DQN Based TT&amp;C Task Planning Approach

With the increase of the number of satellites in space, satellite tracking, telemetry, and command (TT&amp;C) is becoming more and more important for aerospace. This paper proposes a method for a low time fragmentation oriented. TT&amp;C task planning method based on Double Deep Q-Network (DDQN). This method mainly solves the problem of poor responses to emergency tasks caused by the large amount of time fragments of equipment under traditional TT&amp;C task-planning methods. Firstly, a multi-objective optimization model aiming at minimizing time fragments and maximizing task revenue is formulated. Then, according to the conflict characteristics of tasks, a task-planning conflict graph is proposed, based on which a TT&amp;C task-planning problem is transferred into an independent set problem. Finally, DDQN is combined with graph structure embedding to solve the transferred independent set problem. The experimental results demonstrate that the proposed method reduces the time fragment of TT&amp;C equipment by 32% and shortens the response time of emergency tasks by 36% compared to existing methods.

A Practical Study of a Nursing Manpower Deployment Program Under Temporary Tasks

<i>Objective</i> We aim to investigate the practical methods and effects of hospital nursing manpower allocation under large temporary tasks and emergency situations of public emergencies. <i>Methods</i> A TFF nursing manpower deployment plan based on the principle of a three-level nursing management system, fixed and dedicated shift scheduling, and the assistance of flexible nurses was constructed. Under the condition of ensuring the normal work of in-hospital treatment, 936 nursing staff was flexibly deployed from the whole hospital to support large temporary tasks outside the hospital and emergency tasks of public emergencies. <i>Results</i> During the period of facing large temporary tasks, our hospital managed human resources from April to August 2022 on the basis of ensuring the quality and safety of in-hospital nursing work, and efficiently completed all emergency medical treatment tasks issued by the superior. The nursing staff was 95% satisfied with the manpower deployment program, and the SRQ questionnaire after reasonable manpower deployment showed that the nursing staff had good mental health status. <i>Conclusion</i> The TFF nursing manpower deployment program, with good operational effect, can smoothly cope with large temporary tasks like COVID-19, optimize nursing manpower organization and management, and complete large temporary rescue tasks in an efficient and orderly manner.

The role of preschoolers' home literacy environment and emergent literacy skills on later reading and writing skills in primary school: A mediational model.

The importance of the quality of home literacy environment and practices (HLE&P) in the earliest years on children's reading and writing development is recognized in the literature. However, whether and to what extent this relationship between preschoolers' HLE&P on their later reading and writing skills in primary school is mediated by emergent literacy competence remains to be clarified. It may be that preschool constitutes a significant opportunity for children to develop notational awareness and phonological awareness which are emergent literacy skills that are fundamental for later reading and writing skills. Children who experience literacy-poor HLE&P with fewer opportunities to practice more complex language skills and diverse vocabulary might develop adequate reading and writing skills when their emergent literacy skills in preschool are high (notational and phonological awareness). This longitudinal study aimed to investigate the mediational role of preschoolers' emergent literacy skills in preschool (notational and phonological awareness) in the relationship between HLE&P and reading and writing skills shown by the same children in primary school using a large-scale dataset. A total of 115 children (mean-age at last year of preschool = 4.88 ± 0.36) took part in the research. In preschool, children performed emergent literacy tasks and their parents completed a home literacy questionnaire. Later, in primary school, children completed standardized assessments of spelling (orthographic accuracy and fluency in a dictation task) and reading decoding (accuracy and speed in a text reading task) skills. The results of mediational analyses showed that notational awareness totally mediates the relationship between HLE&P and reading speed (𝛽= - 0.17, p < 0.05) and writing accuracy (𝛽=0.10, p < 0.05), but not for reading accuracy in primary school. The mediational model with phonological awareness as mediator was not significant. The results are discussed in the light of the effect of preschool in contributing to filling children's home literacy gaps and disadvantages. In preschool, emergent literacy programs are essential to counterbalance the needs of preschoolers to develop adequate reading and writing skills when the family cannot provide enriched HLE&P from the early years of life.

A service-oriented collaborative approach to disaster decision support by integrating geospatial resources and task chain

The benefits of geospatial technologies in disaster management have reached a consensus in the international community. However, immediate integration and orchestration of the rich geospatial resources (data, algorithms, computing resources, etc.) in an emergency remain challenging due to the four gaps: encoding gap, source gap, knowledge gap, and network gap. Aiming to bridge the “resource islands”, we propose a service-oriented collaborative approach to disaster decision support by integrating geospatial resources and task chain as domain knowledge into a distributed environment. Data contributors, model contributors, GIS developers, and business experts collaboratively build a spatial decision support system (SDSS) that integrates various resources and knowledge. When disaster strikes, responders collaboratively perform emergency tasks to deliver the right information to the right person at the right time by leveraging the SDSS. The proposed approach is implemented as a Geospatial service platform for Disaster Response (GeoDR). Four joint exercises and the operation in relevant institutions in China demonstrate its capability to facilitate timely and effective responses to natural disasters.

Trans-UTPA: PSO and MADDPG based multi-UAVs trajectory planning algorithm for emergency communication.

Communication infrastructure is damaged by disasters and it is difficult to support communication services in affected areas. UAVs play an important role in the emergency communication system. Due to the limited airborne energy of a UAV, it is a critical technical issue to effectively design flight routes to complete rescue missions. We fully consider the distribution of the rescue area, the type of mission, and the flight characteristics of the UAV. Firstly, according to the distribution of the crowd, the PSO algorithm is used to cluster the target-POI of the task area, and the neural collaborative filtering algorithm is used to prioritize the target-POI. Then we also design a Trans-UTPA algorithm. Based on MAPPO 's policy network and value function, we introduce transformer model to make Trans-UTPA's policy learning have no action space limitation and can be multi-task parallel, which improves the efficiency and generalization of sample processing. In a three-dimensional space, the UAV selects the emergency task to be performed (data acquisition and networking communication) based on strategic learning of state information (location information, energy consumption information, etc.) and action information (horizontal flight, ascent, and descent), and then designs the UAV flight path based on the maximization of the global value function. The experimental results show that the performance of the Trans-UTPA algorithm is further improved compared with the USCTP algorithm in terms of the success rate of each UAV reaching the target position, the number of collisions, and the average reward of the algorithm. Among them, the average reward of the algorithm exceeds the USCTP algorithm by 13%, and the number of collisions is reduced by 60%. Compared with the heuristic algorithm, it can cover more target-POIs, and has less energy consumption than the heuristic algorithm.

Policy Guidelines to Mitigate the Effects of Covid-19 Pandemic towards the Reproductive Healthcare System in Kenya: A Review

The first case of COVID-19 emerged in Wuhan, China in December 2019. The virus was declared a global pandemic by the World Health Organization in March 11, 2020. In East Africa, Kenya was leading with 250,000 cases as at October 30th 2021. The Ministry of Health (MoH) was tasked with responding to the pandemic to contain the spread. This was a qualitative study and the study objectives included; determining how decision-making process occurs, strategies to mitigate the effects of COVID-19 to the reproductive health care system, exploring the role of the ad-hoc committee of experts and the COVID-19 Task Force in the response and finding out the factors that policy actors consider when formulating policies. Taguette Software was used to analyze the data qualitatively. The study findings from key informants and document analysis established that, leadership, coordination and collaboration from the different sectors for the COVID-19 response was key. Societal values influenced policy decisions. The study established that, where major political elites had most power, policy was actioned. The MoH should develop contextualized mitigation plans to sustain service provision; focusing on capacity strengthening of providers on emergency responses, task shifting and tele-health. Policy guidelines should be continuously updated to reflect the current status at the national and sub-national levels. In conclusion, the health policy triangle provided a framework for simplifying the complex and dynamic nature of policy making but little consideration to other aspects that describe how and why policies are modified were considered. This needs to be exhaustive. Keywords: COVID-19, Reproductive Healthcare System, Guidelines, Mitigation, Decision Making, Policy Action, Kenya &amp; Task Force

An effective technique to schedule priority aware tasks to offload data on edge and cloud servers

Recent advancements in the Internet of Things (IoT) have enhanced the quality of life globally. Billions of devices are brought under the ambit of IoT to make them smarter. IoT-based applications are generating voluminous data and managing this widespread amount of data in real-time through Cloud Technology, which offers high computational and storage facilities. However, sending all data to the cloud can bring serious concerns for applications, which are critical and require instant action without any delay. Edge computing has recently emerged as an effective technology to handle the instant processing of tasks of IoT-based applications locally. Additionally, an important concern in IoT networks is response to emergency tasks on time to increase the performance of large-scale IoT systems. As such, scheduling of tasks becomes vital, where emergency and non-emergency tasks can be prioritized to offload data to the nearby edge and cloud servers respectively and enhance Quality of Service (QoS). The execution order of tasks and allocating resources for computation to avoid delays are two of the most important factors that must be addressed during task scheduling in Edge Computing. With the aforementioned issues, we design a Priority aware Task Scheduling (PaTS) algorithm for sensor networks to schedule priority aware tasks to offload data on edge and cloud servers. The problem is formulated as a multi-objective function and the efficiency of the proposed algorithm is evaluated using the Bio-inspired NSGA-2 technique. The overall improvement for average queue delay, computation time, and energy obtained for 200 tasks is 17.2%, 7.08% and 11.4%, respectively. The results obtained show significant improvement when compared with the benchmark algorithms demonstrating the effectiveness of the proposed solution. Similarly, comparative results for tasks when increased from 200 to 1000 tasks also shows subsequent improvements.

Custom Grasping: A Region-Based Robotic Grasping Detection Method in Industrial Cyber-Physical Systems

Industrial Cyber Physical Systems can use data and information gained from across a variety of different environments to enable robots that are reconfigurable. Custom grasping is a basic operation a robot must be able to carry out for a given task, i.e., finding the best grasping point for emergent behaviors. However, environmental disturbance and limited data degrade the precision and speed of many tailored machine learning models on robot grasping detection. This paper proposes a region-based method to enable fast custom grasping through fewer RGB-D data. The grasping detection problem is simplified as a two-stage prediction problem. At the first stage, a robust grasp candidate generation strategy is proposed based on the Sobel operator. At the second stage, a region-based predictor is designed to locate the best grasping point-pair for an emergent task. The predictor is trained by a modified consistency based self-training method to realize semi-supervised learning. Experimental results show that the success rate of custom grasping of new emergent object can be increased by 3.4% on average using the proposed method. By introducing data augmentation strategies in training, the success rate is further increased by 9.2% on average. A robot is able to grasp new object with 91.5% success rate using less than 100 training samples. The number of training samples required for the proposed method is less than to 1% of which for the previous works. Note to Practitioners—This research was motivated by the problem of robot reconfigurability for various industrial automation processes and focuses mainly on the recognition of grasping point-pair of emergent object for different task. Existing approaches on robotic grasping detection are tailored to a given object and require expensive training with large amount of labeled data. This paper presents a region-based few shot learning approach that enables the robot to detect the best grasping point-pair autonomously and quickly. We show how to generate candidate point-pairs with image distortion and background disturbance. We then demonstrate how the best grasping point-pair can be located with much less training cost. Experiments suggest that this approach is feasible in robot automation for handling a class of objects. In future research, we will construct behavior learning module to enable evolving cyber-physical robotic system for more purposes.

COVID-19 news: vaccination and late effects

On March 21, 2023, the World Health Organization in­formed that more than 761 mil­lion confirmed cases of COVID-19 were registered, of which the number of deaths was 6,879,677, and the vaccine doses administered world­wide exce­eded 13,260 million. In Europe, the last wave of illnesses was in December 2022, but the most numerous confirmed cases were in the same period in the western Pacific area. Severe COVID-19 cases in people with comorbidities have determined the ap­pea­rance of long COVID, manifested more frequently in adults than in children and adolescents. Anti-COVID-19 vac­ci­na­tion is the only way to prevent severe illnesses. The bi­va­lent Wuhan/Omicron BA.4-5 Pfizer-BioNTech vaccine was recently approved by the European Medicines Agency (EMA) for booster doses in chil­dren and adults, and the Moderna vaccine is currently approved for boosting adults and adolescents. Emergency Task Force (ETF) considers that bi­va­lent vaccines authorized in the European Union can also be used for the primary series to support vaccination cam­p­aigns. Apart from the immediate systemic reactions or those at the administration site, the literature did not reveal adverse effects at more than four weeks after the inoculation.

Dispersal Foraging Strategy With Cuckoo Search Optimization Based Path Planning in Unmanned Aerial Vehicle Networks

Unmanned aerial vehicles (UAVs) are assumed to be a promising model of automatic emergency tasks in dynamic marine ecosystems. But, the real-time communication efficacy betwixt UAVs and base platforms is developing a serious challenge. The compact-sized powerful flying robots can be wirelessly controlled and accomplish end tasks with and without human involvement. UAVs still face severe challenges that limit the dream of completely autonomous unmanned flying machines. The main difficulties contain path planning and hindrance avoidance of such unmanned flying robots, which are mandatory but carry out the application-specific functionality in either indoor or outdoor environments. This study introduces a new Dispersal Foraging Strategy with Cuckoo Search Optimization based Path Planning (DFSCSO-PP) technique for UAV networks. In the presented DFSCSO-PP technique, the identification of optimal paths for data transmission is performed in the UAV network. In addition, the presented DFSCSO-PP technique involves the optimal allocation of resources while finding the optimal paths in the network. Moreover, the DFSCSO technique can be designed by integrating the DFS concept into the CSO method to avoid local optima problems. A widespread simulation analysis is performed to exhibit the enhanced outcome of the DFSCSO-PP approach. A detailed set of comparative studies assured the improved performance of the DFSCSO-PP technique over other approaches.

Surviving Multiple Pandemics. Socio-Economic Impact of COVID-19 among the Elderly in Botswana: A Call for a Holistic Pastoral Approach

Amid the COVID-19 and HIV and AIDS crises, elderly people have been identified as the most affected and at-risk population group. This paper uses existing studies and research from different disciplines to explore the socio-economic impact of these multiple pandemics among the elderly in Botswana. We propose an elderly-inclined Holistic Pastoral Care intervention in surviving multiple pandemics. The intervention incorporates the inclusion of older people on pandemic outbreaks emergency task forces or other inter-agency platforms and coordination mechanisms at the national level. The paper further calls for deliberate and intentional socio-economic equality and attention for the greatest benefit of the elderly people. In conclusion, the paper recommends to all stakeholders, sustainable, socio-economic, Inclusive Holistic Pastoral Care in favour and targeting the elderly for long-term survival of these Multiple Pandemics of COVID-19 and HIV and AIDS. Keywords: COVID-19, HIV and AIDS, The Elderly, Socio-Economic Impact, Holistic Pastoral Approach

Improving the emergency management of energy infrastructure using scenario construction

The safety of energy infrastructure is currently attracting more and more attention. Once accidents happened or improper incident handling, uncontrolled release of huge energy will lead catastrophic consequences. The manuscript takes the hydrogen refueling station as a typical representative of the energy infrastructure. Once the hydrogen in hydrogen refueling stations (HRSs) leaks, it easily forms a combustible cloud and can explode by encountering a heat source. At such a time, effective emergency management can prevent and reduce the impact of accidents. In this paper, the “scene-task-ability” scenario construction method is first applied to HRS leakage and explosive accidents and used to illustrate the risk evolution process during the different stages of an accident. This method separates the emergency tasks of various emergency responders and evaluates their emergency response ability. The values of prevention, preparation, response and recovery are coupled using the time-ordered weighted averaging and time-ordered weighted geometric averaging hybrid operator, in order to derive a systematic and comprehensive evaluation of the safe emergency management of HRSs. Finally, based on the results of the emergency response ability evaluation, targeted and effective suggested measures are proposed to improve the emergency management level of emergency responders.

Changes in gut microbiota and metabolism associated with phenotypic plasticity in the honey bee Apis mellifera.

Honey bees exhibit an elaborate social structure based in part on an age-related division of labor. Young workers perform tasks inside the hive, while older workers forage outside the hive, tasks associated with distinct diets and metabolism. Critical to colony fitness, the work force can respond rapidly to changes in the environment or colony demography and assume emergency tasks, resulting in young foragers or old nurses. We hypothesized that both task and age affect the gut microbiota consistent with changes to host diet and physiology. We performed two experiments inducing precocious foragers and reverted nurses, then quantified tissue-specific gut microbiota and host metabolic state associated with nutrition, immunity and oxidative stress. In the precocious forager experiment, both age and ontogeny explained differences in midgut and ileum microbiota, but host gene expression was best explained by an interaction of these factors. Precocious foragers were nutritionally deficient, and incurred higher levels of oxidative damage relative to age-matched nurses. In the oldest workers, reverted nurses, the oxidative damage associated with age and past foraging was compensated by high Vitellogenin expression, which exceeded that of young nurses. Host-microbial interactions were evident throughout the dataset, highlighted by an age-based increase of Gilliamella abundance and diversity concurrent with increased carbonyl accumulation and CuZnSOD expression. The results in general contribute to an understanding of ecological succession of the worker gut microbiota, defining the species-level transition from nurse to forager.

Spatial scales and urban greenspace types influence public conservation preferences

There are increasing concerns for urban greenspaces as critical sources for biodiversity. Urban greenspaces provide various ecosystem services essential for urban residents. The emergent task is to incorporate public opinion for implementing urban landscape management by increasing their preference for conservation of urban greenspaces. However, there is little available information about public conservation preferences. Here, we examined the public preference for conservation of urban greenspaces and their determinants through an online questionnaire using photographs taken across two spatial scales (landscape and community) and two greenspace types (satoyamas and urban parks). Participants were asked about their conservation preferences (measured by the extent of willingness to pay) in response to the photographs of grassland habitats under different management conditions across satoyamas and urban park sites. We showed that the public are willing to pay more for managed satoyama sites (on average 447.0 JPY) than abandoned ones (395.5 JPY) at landscape scales. Managed satoyama sites included more species richness than abandoned ones. However, conservation preference for satoyama sites was deemed scale-dependent since it remained unaffected by management conditions at community scales. We thus suggest the importance of conserving the satoyama landscapes as per public requirement. In urban parks, independent of spatial scales, we found greater conservation preference for intensively-managed lawns (on average 478.6 and 426.0 JPY at landscape and community scales, respectively) than moderately-managed meadows (447.5 and 380.7 JPY). Urban lawns had less species richness than meadows, suggesting that public conservation preference for urban greenspaces is not necessarily built on biodiversity conservation. Furthermore, participants with more ecological knowledge, richer natural experiences in their childhood, and a higher frequency of greenspace visits were more willing to pay for conservation of both satoyamas and urban park sites independent of spatial scales. Thus, public conservation preferences for urban greenspaces depend on spatial scales considered and greenspace types. We provided some realistic data to prove that the Japanese people are willing to pay for the conservation of satoyamas and urban park greenspaces. Environmental education efforts and ensuring nature experiences could prove to be crucial measures to increase the public acceptance of the societal costs for biodiversity conservation.

RISE: Rolling-Inspired Scheduling for Emergency Tasks by Heterogeneous UAVs

The multiple unmanned aerial vehicles (UAVs) system is highly sought after in the fields of emergency rescue and intelligent transportation because of its strong perception and extensive coverage. Formulating a reasonable task scheduling scheme is essential to raising the task execution efficiency of the system. However, the dynamics of task arrival and the heterogeneity of UAV performance make it more difficult for multiple UAVs to complete the tasks. To address these issues, this paper focuses on the multi-UAV scheduling problem and proposes a method of rolling-inspired scheduling for emergency tasks by heterogeneous UAVs (RISE). In order to ensure that emergency tasks can be allocated to UAVs in a real-time manner, a task grouping strategy based on a density peaks (DP) clustering algorithm is designed, which can quickly select UAVs with matching performance for the tasks arriving at the system. Furthermore, an optimization model with multiple constraints is constructed, which takes the task profit and UAV flight cost as the objective function. Next, we devise a rolling-based optimization mechanism to ensure that the tasks with shorter deadlines are executed first while maximizing the objective function, so as to obtain the optimal task execution order for each UAV. We conduct several groups of simulation experiments, and extensive experimental results illustrate that the number of tasks successfully scheduled and the utilization rate of UAVs by RISE are superior to other comparison methods, and it also has the fastest running time. It further proves that RISE has the capability to improve the completion rate of emergency tasks and reduce the flight cost of multiple UAVs.