Distributed collaborative complete coverage path planning based on hybrid strategy

Collaborative coverage path planning(CCPP) refers to obtaining the shortest paths passing over all places except obstacles in a certain area or space. A multi-unmanned aerial vehicle(UAV) collaborative CCPP algorithm is proposed for the urban rescue search or military search in outdoor environment.Due to flexible control of small UAVs, it can be considered that all UAVs fly at the same altitude, that is, they perform search tasks on a two-dimensional plane. Based on the agents’ motion characteristics and environmental information, a mathematical model of CCPP problem is established. The minimum time for UAVs to complete the CCPP is the objective function, and complete coverage constraint, no-fly constraint, collision avoidance constraint, and communication constraint are considered. Four motion strategies and two communication strategies are designed. Then a distributed CCPP algorithm is designed based on hybrid strategies. Simulation results compared with patternbased genetic algorithm(PBGA) and random search method show that the proposed method has stronger real-time performance and better scalability and can complete the complete CCPP task more efficiently and stably.

Energy-Efficient UAVs Coverage Path Planning Approach

Unmanned aerial vehicles(UAVs),commonly known as drones,have drawn significant consideration thanks to their agility,mobility,and flexibility features.They play a crucial role in modern reconnaissance,inspection,intelligence,and surveillance missions.Coverage path planning(CPP)which is one of the crucial aspects that determines an intelligent system’s quality seeks an optimal trajectory to fully cover the region of interest(ROI).However,the flight time of the UAV is limited due to a battery limitation and may not cover the whole region,especially in large region.Therefore,energy consumption is one of the most challenging issues that need to be optimized.In this paper,we propose an energy-efficient coverage path planning algorithm to solve the CPP problem.The objective is to generate a collision-free coverage path that minimizes the overall energy consumption and guarantees covering the whole region.To do so,the flight path is optimized and the number of turns is reduced to minimize the energy consumption.The proposed approach first decomposes the ROI into a set of cells depending on a UAV camera footprint.Then,the coverage path planning problem is formulated,where the exact solution is determined using the CPLEX solver.For small-scale problems,the CPLEX shows a better solution in a reasonable time.However,the CPLEX solver fails to generate the solution within a reasonable time for large-scale problems.Thus,to solve the model for large-scale problems,simulated annealing forCPP is developed.The results show that heuristic approaches yield a better solution for large-scale problems within amuch shorter execution time than the CPLEX solver.Finally,we compare the simulated annealing against the greedy algorithm.The results show that simulated annealing outperforms the greedy algorithm in generating better solution quality.

Complete Coverage Path Planning Based on Improved Area Division

It is difficult to solve complete coverage path planning directly in the obstructed area. Therefore, in this paper, we propose a method of complete coverage path planning with improved area division. Firstly, the boustrophedon cell decomposition method is used to partition the map into sub-regions. The complete coverage paths within each sub-region are obtained by the Boustrophedon back-and-forth motions, and the order of traversal of the sub-regions is then described as a generalised traveling salesman problem with pickup and delivery based on the relative positions of the vertices of each sub-region. An adaptive large neighbourhood algorithm is proposed to quickly obtain solution results in traversal order. The effectiveness of the improved algorithm on traversal cost reduction is verified in this paper through multiple sets of experiments. .

Deployment optimization for target perpetual coverage in energy harvesting wireless sensor network

Energy limitation of traditional Wireless Sensor Networks(WSNs)greatly confines the network lifetime due to generating and processing massive sensing data with a limited battery.The energy harvesting WSN is a novel network architecture to address the limitation of traditional WSN.However,existing coverage and deployment schemes neglect the environmental correlation of sensor nodes and external energy with respect to physical space.Comprehensively considering the spatial correlation of the environment and the uneven distribution of energy in energy harvesting WSN,we investigate how to deploy a collection of sensor nodes to save the deployment cost while ensuring the target perpetual coverage.The Confident Information Coverage(CIC)model is adopted to formulate the CIC Minimum Deployment Cost Target Perpetual Coverage(CICMTP)problem to minimize the deployed sensor nodes.As the CICMTP is NP-hard,we devise two approximation algorithms named Local Greedy Threshold Algorithm based on CIC(LGTA-CIC)and Overall Greedy Search Algorithm based on CIC(OGSA-CIC).The LGTA-CIC has a low time complexity and the OGSA-CIC has a better approximation rate.Extensive simulation results demonstrate that the OGSA-CIC is able to achieve lower deployment cost and the performance of the proposed algorithms outperforms GRNP,TPNP and EENP algorithms.

Evolutionary Decision-Making and Planning for Autonomous Driving Based on Safe and Rational Exploration and Exploitation

Decision-making and motion planning are extremely important in autonomous driving to ensure safe driving in a real-world environment.This study proposes an online evolutionary decision-making and motion planning framework for autonomous driving based on a hybrid data-and model-driven method.First,a data-driven decision-making module based on deep reinforcement learning(DRL)is developed to pursue a rational driving performance as much as possible.Then,model predictive control(MPC)is employed to execute both longitudinal and lateral motion planning tasks.Multiple constraints are defined according to the vehicle’s physical limit to meet the driving task requirements.Finally,two principles of safety and rationality for the self-evolution of autonomous driving are proposed.A motion envelope is established and embedded into a rational exploration and exploitation scheme,which filters out unreasonable experiences by masking unsafe actions so as to collect high-quality training data for the DRL agent.Experiments with a high-fidelity vehicle model and MATLAB/Simulink co-simulation environment are conducted,and the results show that the proposed online-evolution framework is able to generate safer,more rational,and more efficient driving action in a real-world environment.

Motion Planning for Autonomous Driving with Real Traffic Data Validation

Accurate trajectory prediction of surrounding road users is the fundamental input for motion planning,which enables safe autonomous driving on public roads.In this paper,a safe motion planning approach is proposed based on the deep learning-based trajectory prediction method.To begin with,a trajectory prediction model is established based on the graph neural network(GNN)that is trained utilizing the INTERACTION dataset.Then,the validated trajectory prediction model is used to predict the future trajectories of surrounding road users,including pedestrians and vehicles.In addition,a GNN prediction model-enabled motion planner is developed based on the model predictive control technique.Furthermore,two driving scenarios are extracted from the INTERACTION dataset to validate and evaluate the effectiveness of the proposed motion planning approach,i.e.,merging and roundabout scenarios.The results demonstrate that the proposed method can lower the risk and improve driving safety compared with the baseline method.

A Sensor Network Coverage Planning Based on Adjusted Single Candidate Optimizer

Wireless sensor networks(WSNs)are widely used for various practical applications due to their simplicity and versatility.The quality of service in WSNs is greatly influenced by the coverage,which directly affects the monitoring capacity of the target region.However,low WSN coverage and uneven distribution of nodes in random deployments pose significant challenges.This study proposes an optimal node planning strategy for net-work coverage based on an adjusted single candidate optimizer(ASCO)to address these issues.The single candidate optimizer(SCO)is a metaheuristic algorithm with stable implementation procedures.However,it has limitations in avoiding local optimum traps in complex node coverage optimization scenarios.The ASCO overcomes these limitations by incorporating reverse learning and multi-direction strategies,resulting in updated equations.The performance of the ASCO algorithm is compared with other algorithms in the literature for optimal WSN node coverage.The results demonstrate that the ASCO algorithm offers efficient performance,rapid convergence,and expanded coverage capabilities.Notably,the ASCO achieves an archival coverage rate of 88%,while other approaches achieve coverage rates below or equal to 85%under the same conditions.

Selection of media tools for community planning:A study from the perspective of communicative planning

In community planning,due to the lack of evidence regarding the selection of media tools,this study examines how a common but differentiated ideal speech situation can be created as well as how more appropriate media tools can be defined and selected in the community planning process.First,this study describes the concept and theoretical basis of media used in community planning from the perspectives of the multiple effects of media evolution on communicative planning.Second,the classification criteria and typical characteristics of media tools used to support community planning are clarified from three dimensions:acceptability,cost effectiveness,and applicability.Third,strategies for applying media tools in the four phases of communicative planning-namely,state analysis,problem identification,contradictory solution and optimization-are described.Finally,trends in the development of media tools for community planning are explored in terms of multistakeholder engagement,supporting scientific decision-making and multiple-type media integration.The results provide a reference for developing more inclusive,effective,and appropriate media tools for enhancing decision-making capacity and modernizing governance in community planning and policy-making processes.

Coverage and Factors Associated with Acceptance and Satisfaction of Seasonal Malaria Chemoprevention in Six Health Zones in Benin

Introduction: Seasonal malaria chemoprevention (SMC) was adopted in 2019 in two health zones in Benin where malaria transmission is very high. Positive results led to the extension of the intervention to other zones with additional financial support. Annual SMC campaigns from 2021 to 2023 were carried out in all six health zones in the Atacora and Alibori departments. In five years of implementation, various approaches have been developed on the basis of a communication plan facilitating buy-in and acceptance by all stakeholders. The aim of this study was to assess the effective coverage and acceptance of the SMC by their beneficiary populations in 2023. Methods: It was a cross-sectional study with an analytical focus. Data collection took place from November 30 to December 13, 2023. The study population consisted of children under 5 years of age residing in the departments of Atacora and Alibori in northern Benin. A total of 3573 children under 5 years of age were included in the study, and their parents or guardians were interviewed. Results: During the 2023 campaign, 87.7% of targets were reached by SMC administration and 100.00% of children had received at least one dose of SMC by the fourth visit. Effective therapeutic coverage of SMC was 70.55%, with 99.60% in BNK, 69.40% in KGS, 16.20% in MK, 56.10% in 2KP, 92.40% in NBT and 89.60% in TMC. This coverage was statistically related to child and respondent ages (p Conclusion: SMC is a strategy accepted by the population, and the main reasons for non-participation in SMC were dominated by the absence of mothers or babysitters when the agents visited.

Optimal search path planning of UUV in battlefeld ambush scene

Aiming at the practical application of Unmanned Underwater Vehicle(UUV)in underwater combat,this paper proposes a battlefield ambush scene with UUV considering ocean current.Firstly,by establishing these mathematical models of ocean current environment,target movement,and sonar detection,the probability calculation methods of single UUV searching target and multiple UUV cooperatively searching target are given respectively.Then,based on the Hybrid Quantum-behaved Particle Swarm Optimization(HQPSO)algorithm,the path with the highest target search probability is found.Finally,through simulation calculations,the influence of different UUV parameters and target parameters on the target search probability is analyzed,and the minimum number of UUVs that need to be deployed to complete the ambush task is demonstrated,and the optimal search path scheme is obtained.The method proposed in this paper provides a theoretical basis for the practical application of UUV in the future combat.

Models to Simulate Effective Coverage of Fire Station Based on Real-Time Travel Times

In recent years,frequent fire disasters have led to enormous damage in China.Effective firefighting rescues can minimize the losses caused by fires.During the rescue processes,the travel time of fire trucks can be severely affected by traffic conditions,changing the effective coverage of fire stations.However,it is still challenging to determine the effective coverage of fire stations considering dynamic traffic conditions.This paper addresses this issue by combining the traveling time calculationmodelwith the effective coverage simulationmodel.In addition,it proposes a new index of total effective coverage area(TECA)based on the time-weighted average of the effective coverage area(ECA)to evaluate the urban fire services.It also selects China as the case study to validate the feasibility of the models,a fire station(FS-JX)in Changsha.FS-JX station and its surrounding 9,117 fire risk points are selected as the fire service supply and demand points,respectively.A total of 196 simulation scenarios throughout a consecutiveweek are analyzed.Eventually,1,933,815 sets of valid sample data are obtained.The results showed that the TECA of FS-JX is 3.27 km^(2),which is far below the standard requirement of 7.00 km^(2) due to the traffic conditions.The visualization results showed that three rivers around FS-JX interrupt the continuity of its effective coverage.The proposed method can provide data support to optimize the locations of fire stations by accurately and dynamically determining the effective coverage of fire stations.

Hepatitis B Vaccination Coverage among University Students in Sub-Saharan Africa: Systematic Review and Meta Analysis

Introduction: Hepatitis B is an infectious disease that remains a real public health problem in Africa. Students represent a group at risk for this disease. The objective of this study was to estimate the hepatitis B vaccination coverage rate among students in sub-Saharan Africa. Methods: A systematic search of databases (PubMed, AJOL) and a manual search of Google Scholar was conducted to retrieve all published studies reporting hepatitis B vaccination coverage among students in sub-Saharan Africa. The pooled coverage rate was estimated with a 95% confidence interval (CI) in a random-effects meta-analysis. Results: A total of 35 studies were included and included 20,520 students. The mean age was 22.1 ± 5.1 years with a predominance of female sex (sex ratio F/M = 1.05). The vaccination coverage rate was 28.8% [95% CI: 22.9% - 34.7%]. Disaggregation allowed to estimate coverage rates of 29.8% [95% CI: 22.9% - 36.7%], 23.4% [95% CI: 9.4% - 37.4%] and 17.0% [95% CI: 14.4% - 19.5%] respectively in West Africa, East Africa and Central Africa. Conclusion: Less than a third of students in sub-Saharan Africa are protected against hepatitis B. However, the majority of this target group is at risk of infection. It would be relevant to screen and, if necessary, vaccinate all new students.

Efficient Route Planning for Real-Time Demand-Responsive Transit

Demand Responsive Transit (DRT) responds to the dynamic users’ requests without any fixed routes and timetablesand determines the stop and the start according to the demands. This study explores the optimization of dynamicvehicle scheduling and real-time route planning in urban public transportation systems, with a focus on busservices. It addresses the limitations of current shared mobility routing algorithms, which are primarily designedfor simpler, single origin/destination scenarios, and do not meet the complex demands of bus transit systems. Theresearch introduces an route planning algorithm designed to dynamically accommodate passenger travel needsand enable real-time route modifications. Unlike traditional methods, this algorithm leverages a queue-based,multi-objective heuristic A∗ approach, offering a solution to the inflexibility and limited coverage of suburbanbus routes. Also, this study conducts a comparative analysis of the proposed algorithm with solutions based onGenetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO), focusing on calculation time, routelength, passenger waiting time, boarding time, and detour rate. The findings demonstrate that the proposedalgorithmsignificantly enhances route planning speed, achieving an 80–100-fold increase in efficiency over existingmodels, thereby supporting the real-time demands of Demand-Responsive Transportation (DRT) systems. Thestudy concludes that this algorithm not only optimizes route planning in bus transit but also presents a scalablesolution for improving urban mobility.

Detecting the distribution of and public attitudes to charismatic alien species based on media coverage

Charismatic species are often reported by the media,providing information for detecting population status and public perception.To identify the number and distribution of free-living Black Swan(Cygnus atratus),a charismatic alien species in Chinese mainland and to detect the public and the media attitudes to the species,we analyzed the reports and emotional tendency from media coverage in 2000-2022 using manual reading,crawler extraction and latent Dirichlet allocation.A total of 6654 Black Swans were reported at 711 sites,including 147 individuals at 30 nature reserves.Successful breeding was reported at one-fourth of the total sites,including five nature reserves.The proportion of positive emotional tendency to Black Swans was overwhelming in the reports and was higher than that to alien species in general,suggesting that the public and the media are unaware of the risk of biological invasion.Effective management of invasive species requires the media clarifies the invasion risk of charismatic alien species.Promoting the unity between the harmfulness of abstract concept of alien species and the charisma of a specific alien species among the public help effective management.

Scheduling optimization for UAV communication coverage using virtual force-based PSO model

When the ground communication base stations in the target area are severely destroyed,the deployment of Unmanned Aerial Vehicle(UAV)ad hoc networks can provide people with temporary communication services.Therefore,it is necessary to design a multi-UAVs cooperative control strategy to achieve better communication coverage and lower energy consumption.In this paper,we propose a multi-UAVs coverage model based on Adaptive Virtual Force-directed Particle Swarm Optimization(AVF-PSO)strategy.In particular,we first introduce the gravity model into the traditional Particle Swarm Optimization(PSO)algorithm so as to increase the probability of full coverage.Then,the energy consumption is included in the calculation of the fitness function so that maximum coverage and energy consumption can be balanced.Finally,in order to reduce the communication interference between UAVs,we design an adaptive lift control strategy based on the repulsion model to reduce the repeated coverage of multi-UAVs.Experimental results show that the proposed coverage strategy based on gravity model outperforms the existing state-of-the-art approaches.For example,in the target area of any size,the coverage rate and the repeated coverage rate of the proposed multi-UAVs scheduling are improved by 6.9–29.1%,and 2.0–56.1%,respectively.Moreover,the proposed scheduling algorithm is high adaptable to diverse execution environments.©2022 Published by Elsevier Ltd.

Exploration of Detailed Planning Practice of Chemical Industrial Parks under the Background of Territorial Space Planning

With the gradual completion of the overall planning of city and county land space,the detailed planning will be prepared according to the requirements of transmitting and refining the upper planning.Industrial parks are one of the“main forces”of local economic development,and the preparation of their detailed planning will escort their development.The key points of the Control Indicators of Construction Land in Industrial Projectsissued in 2008 and 2023 were compared,and the new requirements for detailed planning under the background of territorial space and the contradictions between the detailed planning of industrial parks and the overall planning of the upper territorial space were sorted out based on the summary of the existing problems in the development of chemical parks.It provides some ideas for the practice of detailed planning of chemical industrial parks under the background of territorial space.

Postpartum Family Planning at the Timbuktu Hospital in Mali

Introduction: Postpartum family planning is the prevention of pregnancies during the 12 months following childbirth. Few studies have been devoted to postpartum family planning in Mali. Our work will contribute to reducing unmet need for family planning. Objective: To study the use of contraceptive methods in the postpartum period in the obstetrics and gynecology unit of Timbuktu hospital. Materials and Methods: This was a descriptive and analytical cross-sectional study with prospective collection of data from January 1, 2022 to December 31, 2023. All women who gave birth having chosen and benefited from a contraceptive method were included. The statistical test used was the Fisher test with a significance threshold fixed at 5%. Results: The frequency of contraception in the postpartum period was 17.03%. The average age of clients was 26.14% with extremes of 14 and 45 years. They were paupiparous at 56.4% with an inter-birth interval of less than 12 months at 12.3%. More than half of the counseling (58.5%) was done during postnatal visits. The methods chosen were implants at 48.1%, injectable progestins at 21.3%, intrauterine device at 14.7%, miro-progestin pills at 13.5%, tubal ligation at 1 .4% and condoms at 1%. The regular follow-up rate was 51.1% of cases and 25.6% had no follow-up. Conclusion: The overall rate of postpartum family planning of 17.08% remains low. Improving FP staff skills will reduce unmet needs and contribute to increasing contraceptive prevalence in Timbuktu.

Stochastic programming based coordinated expansion planning of generation,transmission,demand side resources,and energy storage considering the DC transmission system

With the increasing penetration of wind and solar energies,the accompanying uncertainty that propagates in the system places higher requirements on the expansion planning of power systems.A source-grid-load-storage coordinated expansion planning model based on stochastic programming was proposed to suppress the impact of wind and solar energy fluctuations.Multiple types of system components,including demand response service entities,converter stations,DC transmission systems,cascade hydropower stations,and other traditional components,have been extensively modeled.Moreover,energy storage systems are considered to improve the accommodation level of renewable energy and alleviate the influence of intermittence.Demand-response service entities from the load side are used to reduce and move the demand during peak load periods.The uncertainties in wind,solar energy,and loads were simulated using stochastic programming.Finally,the effectiveness of the proposed model is verified through numerical simulations.

Correlates of Mistimed Pregnancy and Unmet Need for Family Planning among Women of Reproductive Age in Sandema, Ghana

Background: Globally, an estimated 80 million unintended pregnancies comprising both mistimed and unwanted pregnancies are recorded yearly. Yet only half of the women at risk of mistimed pregnancy use contraceptives. In developing countries, over 100 million females have unmet need, and national surveys in Ghana indicate 23% unmet need rate. Methods: Using a cross-sectional community-based approach, a sample size of 300 women of reproductive age were selected using multi-step cluster sampling techniques. The study was quantitative, using structured interviewer-administered questionnaires. Results: Two-third (66%) of the women in reproductive age still had unmet need, 71% were currently pregnant, and more than a third (36%) confirmed ever having a mistimed pregnancy. Fifty-three percent (53%) of the women confirmed never communicating with their partners on family planning issues, a little below half (45%) took their own health care decisions. Seventy nine percent (79%) ever received family planning services from a health professional. Factors related to unmet needs included mistimed pregnancy, level of education, preferred birth/pregnancy interval, communication between partners and the autonomy to spend self-earnings. Conclusion: Considering that high rates of unmet need results in mistimed pregnancy, improved policies around the influence of unmet need on mistimed pregnancies are needed.

A Planning Method for Operational Test of UAV Swarm Based on Mission Reliability

The unmanned aerial vehicle(UAV)swarm plays an increasingly important role in the modern battlefield,and the UAV swarm operational test is a vital means to validate the combat effectiveness of the UAV swarm.Due to the high cost and long duration of operational tests,it is essential to plan the test in advance.To solve the problem of planning UAV swarm operational test,this study considers the multi-stage feature of a UAV swarm mission,composed of launch,flight and combat stages,and proposes a method to find test plans that can maximize mission reliability.Therefore,a multi-stage mission reliability model for a UAV swarm is proposed to ensure successful implementation of the mission.A multi-objective integer optimization method that considers both mission reliability and cost is then formulated to obtain the optimal test plans.This study first constructs a mission reliability model for the UAV swarm in the combat stage.Then,the launch stage and flight stage are integrated to develop a complete PMS(Phased Mission Systems)reliability model.Finally,the Binary Decision Diagrams(BDD)and Multi Objective Quantum Particle Swarm Optimization(MOQPSO)methods are proposed to solve the model.The optimal plans considering both reliability and cost are obtained.The proposed model supports the planning of UAV swarm operational tests and represents a meaningful exploration of UAV swarm test planning.