Background:The impact of sleep disorders on active-duty soldiers’medical readiness is not currently quantified.Patient data generated at military treatment facilities can be accessed to create research reports and th...Background:The impact of sleep disorders on active-duty soldiers’medical readiness is not currently quantified.Patient data generated at military treatment facilities can be accessed to create research reports and thus can be used to estimate the prevalence of sleep disturbances and the role of sleep on overall health in service members.The current study aimed to quantify sleep-related health issues and their impact on health and nondeployability through the analysis of U.S.military healthcare records from fiscal year 2018(FY2018).Methods:Medical diagnosis information and deployability profiles(e-Profiles)were queried for all active-duty U.S.Army patients with a concurrent sleep disorder diagnosis receiving medical care within FY2018.Nondeployability was predicted from medical reasons for having an e-Profile(categorized as sleep,behavioral health,musculoskeletal,cardiometabolic,injury,or accident)using binomial logistic regression.Sleep e-Profiles were investigated as a moderator between other e-Profile categories and nondeployability.Results:Out of 582,031 soldiers,48.4%(n=281,738)had a sleep-related diagnosis in their healthcare records,9.7%(n=56,247)of soldiers had e-Profiles,and 1.9%(n=10,885)had a sleep e-Profile.Soldiers with sleep e-Profiles were more likely to have had a motor vehicle accident(p OR(prevalence odds ratio)=4.7,95%CI 2.63–8.39,P≤0.001)or work/duty-related injury(p OR=1.6,95%CI 1.32–1.94,P≤0.001).The likelihood of nondeployability was greater in soldiers with a sleep e-Profile and a musculoskeletal e-Profile(p OR=4.25,95%CI 3.75–4.81,P≤0.001)or work/dutyrelated injury(p OR=2.62,95%CI 1.63–4.21,P≤0.001).Conclusion:Nearly half of soldiers had a sleep disorder or sleep-related medical diagnosis in 2018,but their sleep problems are largely not profiled as limitations to medical readiness.Musculoskeletal issues and physical injury predict nondeployability,and nondeployability is more likely to occur in soldiers who have sleep e-Profiles in addition to these issues.Addressing sleep problems may prevent accidents and injuries that could render a soldier nondeployable.展开更多
Microservices have revolutionized traditional software architecture. While monolithic designs continue to be common, particularly in legacy applications, there is a growing trend towards the modularity, independent de...Microservices have revolutionized traditional software architecture. While monolithic designs continue to be common, particularly in legacy applications, there is a growing trend towards the modularity, independent deployability, and flexibility offered by microservices, which is further enhanced by developments in cloud technology. This shift towards microservice architecture meets the modern business need for agility, facilitating rapid adaptability in a competitive landscape. Microservices offer an agile framework and, in many cases, can simplify the development process, though the implementation can vary and sometimes introduce complexities. Unlike monolithic systems, which can be cumbersome to modify, microservices enable quicker adjustments and faster deployment times, essential in today’s dynamic environment. This article delves into the essence of microservices and explores their growing prominence in the software industry.展开更多
1.Introduction Climate change mitigation pathways aimed at limiting global anthropogenic carbon dioxide(CO_(2))emissions while striving to constrain the global temperature increase to below 2℃—as outlined by the Int...1.Introduction Climate change mitigation pathways aimed at limiting global anthropogenic carbon dioxide(CO_(2))emissions while striving to constrain the global temperature increase to below 2℃—as outlined by the Intergovernmental Panel on Climate Change(IPCC)—consistently predict the widespread implementation of CO_(2)geological storage on a global scale.展开更多
The wireless signals emitted by base stations serve as a vital link connecting people in today’s society and have been occupying an increasingly important role in real life.The development of the Internet of Things(I...The wireless signals emitted by base stations serve as a vital link connecting people in today’s society and have been occupying an increasingly important role in real life.The development of the Internet of Things(IoT)relies on the support of base stations,which provide a solid foundation for achieving a more intelligent way of living.In a specific area,achieving higher signal coverage with fewer base stations has become an urgent problem.Therefore,this article focuses on the effective coverage area of base station signals and proposes a novel Evolutionary Particle Swarm Optimization(EPSO)algorithm based on collective prediction,referred to herein as ECPPSO.Introducing a new strategy called neighbor-based evolution prediction(NEP)addresses the issue of premature convergence often encountered by PSO.ECPPSO also employs a strengthening evolution(SE)strategy to enhance the algorithm’s global search capability and efficiency,ensuring enhanced robustness and a faster convergence speed when solving complex optimization problems.To better adapt to the actual communication needs of base stations,this article conducts simulation experiments by changing the number of base stations.The experimental results demonstrate thatunder the conditionof 50 ormore base stations,ECPPSOconsistently achieves the best coverage rate exceeding 95%,peaking at 99.4400%when the number of base stations reaches 80.These results validate the optimization capability of the ECPPSO algorithm,proving its feasibility and effectiveness.Further ablative experiments and comparisons with other algorithms highlight the advantages of ECPPSO.展开更多
As the mining industry continues to expand and international oil prices increase,more rigorous demands are being placed on the design of mining equipment.Given this,there is an urgent need to develop new power-driven ...As the mining industry continues to expand and international oil prices increase,more rigorous demands are being placed on the design of mining equipment.Given this,there is an urgent need to develop new power-driven mining equipment to solve the problems of high energy consumption and insufficient power coupling of current equipment.This study proposed a design of a hybrid power system for underground Load Haul Dump(LHD).The proposed design integrated Quality Function Deployment(QFD)and Theory of Inventive Problem Solving(TRIZ).It identified 7 user requirements and 10 related technical features,formulated 11 innovative design solutions,and ultimately adopting an electric drive hybrid power scheme.This scheme effectively addressesd power transmission coupling problems and improve the efficiency of loaders.A 6 m³hybrid power loader prototype has been developed,which reduces operational energy consumption and advances the electrification and green,low-carbon evolution of mining equipment.展开更多
The quantity of space debris on Earth orbit has escalated tremendously in recent years, presenting a significant hazard to human space operations. It is urgent to develop effective measures to capture and remove vario...The quantity of space debris on Earth orbit has escalated tremendously in recent years, presenting a significant hazard to human space operations. It is urgent to develop effective measures to capture and remove various space debris. For this purpose, this paper presents a tendon-actuated flexible deployable manipulator. The flexible manipulator consists of several deployable units connected by Cardan joints and actuated by tendons. Compared with the present technologies for capturing space debris such as rigid robotic arm or flying net, this flexible manipulator is deployable, reusable, lightweight and applicable to the capture of large space debris. In order to investigate its deployment dynamics, an accurate dynamic model of the flexible manipulator is established based on the natural coordinate formulation (NCF) and the absolute nodal coordinate formulation (ANCF). Subsequently, numerical simulations are carried out to study the effects of system parameters and the base satellite on its deployment dynamics. Finally, ground experiments for both deployment and bending of the flexible manipulator are conducted to verify its effectiveness and feasibility.展开更多
The rapid evolution of Fifth-Generation(5G)networks and the strategic development of Sixth-Generation(6G)technologies have significantly advanced the implementation of air-ground integrated networks with seamless cove...The rapid evolution of Fifth-Generation(5G)networks and the strategic development of Sixth-Generation(6G)technologies have significantly advanced the implementation of air-ground integrated networks with seamless coverage.Unmanned Aerial Vehicles(UAVs),serving as high-mobility aerial platforms,are extensively utilized to enhance coverage in long-distance emergency communication scenarios.The resource-constrained communication environments in emergencies by classifying UAVs into swarm UAVs and relay UAVs as aerial communication nodes is inversitgated.A horizontal deployment strategy for swarm UAVs is formulated through K-means clustering algorithm optimization,while a vertical deployment scheme is established using convex optimization methods.The minimum-path trajectory planning for relay UAVs is optimized via the Particle Swarm Optimization(PSO)algorithm,enhancing communication reliability between UAV swarms and terrestrial base stations.A three-dimensional heterogeneous network architecture is realized by modeling spatial multi-hop relay links.Experimental results demonstrate that the proposed joint UAV relay optimization framework outperforms conventional algorithms in both coverage performance and relay capability during video stream transmission,achieving significant improvements in coverage enhancement and relay efficiency.This work provides technical foundations for constructing high-reliability air-ground cooperative systems in emergency communications.展开更多
Machine learning(ML)has been increasingly adopted to solve engineering problems with performance gauged by accuracy,efficiency,and security.Notably,blockchain technology(BT)has been added to ML when security is a part...Machine learning(ML)has been increasingly adopted to solve engineering problems with performance gauged by accuracy,efficiency,and security.Notably,blockchain technology(BT)has been added to ML when security is a particular concern.Nevertheless,there is a research gap that prevailing solutions focus primarily on data security using blockchain but ignore computational security,making the traditional ML process vulnerable to off-chain risks.Therefore,the research objective is to develop a novel ML on blockchain(MLOB)framework to ensure both the data and computational process security.The central tenet is to place them both on the blockchain,execute them as blockchain smart contracts,and protect the execution records on-chain.The framework is established by developing a prototype and further calibrated using a case study of industrial inspection.It is shown that the MLOB framework,compared with existing ML and BT isolated solutions,is superior in terms of security(successfully defending against corruption on six designed attack scenario),maintaining accuracy(0.01%difference with baseline),albeit with a slightly compromised efficiency(0.231 second latency increased).The key finding is MLOB can significantly enhances the computational security of engineering computing without increasing computing power demands.This finding can alleviate concerns regarding the computational resource requirements of ML-BT integration.With proper adaption,the MLOB framework can inform various novel solutions to achieve computational security in broader engineering challenges.展开更多
Mesh reflector antennas are the mainstream of large space-borne antennas,and the stretching of the truss achieves their deployment.Currently,the truss is commonly designed to be a single degree of freedom(DOF)deployab...Mesh reflector antennas are the mainstream of large space-borne antennas,and the stretching of the truss achieves their deployment.Currently,the truss is commonly designed to be a single degree of freedom(DOF)deployable mechanism with synchronization constraints.However,each deployable unit’s drive distribution and resistance load are uneven,and the forced synchronization constraints lead to the flexible deformation of rods and difficulties in the deployment scheme design.This paper introduces an asynchronous deployment scheme with a multi-DOF closed-chain deployable truss.The DOF of the truss is calculated,and the kinematic and dynamic models are established,considering the truss’s and cable net’s real-time coupling.An integrated solving algorithm for implicit differential-algebraic equations is proposed to solve the dynamic models.A prototype of a six-unit antenna was fabricated,and the experiment was carried out.The dynamic performances in synchronous and asynchronous deployment schemes are analyzed,and the results show that the cable resistance and truss kinetic energy impact under the asynchronous deployment scheme are minor,and the antenna is more straightforward to deploy.The work provides a new asynchronous deployment scheme and a universal antenna modeling method for dynamic design and performance improvement.展开更多
The research on optimization methods for constellation launch deployment strategies focused on the consideration of mission interval time constraints at the launch site.Firstly,a dynamic modeling of the constellation ...The research on optimization methods for constellation launch deployment strategies focused on the consideration of mission interval time constraints at the launch site.Firstly,a dynamic modeling of the constellation deployment process was established,and the relationship between the deployment window and the phase difference of the orbit insertion point,as well as the cost of phase adjustment after orbit insertion,was derived.Then,the combination of the constellation deployment position sequence was treated as a parameter,together with the sequence of satellite deployment intervals,as optimization variables,simplifying a highdimensional search problem within a wide range of dates to a finite-dimensional integer programming problem.An improved genetic algorithm with local search on deployment dates was introduced to optimize the launch deployment strategy.With the new description of the optimization variables,the total number of elements in the solution space was reduced by N orders of magnitude.Numerical simulation confirms that the proposed optimization method accelerates the convergence speed from hours to minutes.展开更多
The aerial deployment method enables Unmanned Aerial Vehicles(UAVs)to be directly positioned at the required altitude for their mission.This method typically employs folding technology to improve loading efficiency,wi...The aerial deployment method enables Unmanned Aerial Vehicles(UAVs)to be directly positioned at the required altitude for their mission.This method typically employs folding technology to improve loading efficiency,with applications such as the gravity-only aerial deployment of high-aspect-ratio solar-powered UAVs,and aerial takeoff of fixed-wing drones in Mars research.However,the significant morphological changes during deployment are accompanied by strong nonlinear dynamic aerodynamic forces,which result in multiple degrees of freedom and an unstable character.This hinders the description and analysis of unknown dynamic behaviors,further leading to difficulties in the design of deployment strategies and flight control.To address this issue,this paper proposes an analysis method for dynamic behaviors during aerial deployment based on the Variational Autoencoder(VAE).Focusing on the gravity-only deployment problem of highaspect-ratio foldable-wing UAVs,the method encodes the multi-degree-of-freedom unstable motion signals into a low-dimensional feature space through a data-driven approach.By clustering in the feature space,this paper identifies and studies several dynamic behaviors during aerial deployment.The research presented in this paper offers a new method and perspective for feature extraction and analysis of complex and difficult-to-describe extreme flight dynamics,guiding the research on aerial deployment drones design and control strategies.展开更多
As Internet ofThings(IoT)technologies continue to evolve at an unprecedented pace,intelligent big data control and information systems have become critical enablers for organizational digital transformation,facilitati...As Internet ofThings(IoT)technologies continue to evolve at an unprecedented pace,intelligent big data control and information systems have become critical enablers for organizational digital transformation,facilitating data-driven decision making,fostering innovation ecosystems,and maintaining operational stability.In this study,we propose an advanced deployment algorithm for Service Function Chaining(SFC)that leverages an enhanced Practical Byzantine Fault Tolerance(PBFT)mechanism.The main goal is to tackle the issues of security and resource efficiency in SFC implementation across diverse network settings.By integrating blockchain technology and Deep Reinforcement Learning(DRL),our algorithm not only optimizes resource utilization and quality of service but also ensures robust security during SFC deployment.Specifically,the enhanced PBFT consensus mechanism(VRPBFT)significantly reduces consensus latency and improves Byzantine node detection through the introduction of a Verifiable Random Function(VRF)and a node reputation grading model.Experimental results demonstrate that compared to traditional PBFT,the proposed VRPBFT algorithm reduces consensus latency by approximately 30%and decreases the proportion of Byzantine nodes by 40%after 100 rounds of consensus.Furthermore,the DRL-based SFC deployment algorithm(SDRL)exhibits rapid convergence during training,with improvements in long-term average revenue,request acceptance rate,and revenue/cost ratio of 17%,14.49%,and 20.35%,respectively,over existing algorithms.Additionally,the CPU resource utilization of the SDRL algorithmreaches up to 42%,which is 27.96%higher than other algorithms.These findings indicate that the proposed algorithm substantially enhances resource utilization efficiency,service quality,and security in SFC deployment.展开更多
Deep learning-based intelligent recognition algorithms are increasingly recognized for their potential to address the labor-intensive challenge of manual pest detection.However,their deployment on mobile devices has b...Deep learning-based intelligent recognition algorithms are increasingly recognized for their potential to address the labor-intensive challenge of manual pest detection.However,their deployment on mobile devices has been constrained by high computational demands.Here,we developed GBiDC-PEST,a mobile application that incorporates an improved,lightweight detection algorithm based on the You Only Look Once(YOLO)series singlestage architecture,for real-time detection of four tiny pests(wheat mites,sugarcane aphids,wheat aphids,and rice planthoppers).GBiDC-PEST incorporates several innovative modules,including GhostNet for lightweight feature extraction and architecture optimization by reconstructing the backbone,the bi-directional feature pyramid network(BiFPN)for enhanced multiscale feature fusion,depthwise convolution(DWConv)layers to reduce computational load,and the convolutional block attention module(CBAM)to enable precise feature focus.The newly developed GBiDC-PEST was trained and validated using a multitarget agricultural tiny pest dataset(Tpest-3960)that covered various field environments.GBiDC-PEST(2.8 MB)significantly reduced the model size to only 20%of the original model size,offering a smaller size than the YOLO series(v5-v10),higher detection accuracy than YOLOv10n and v10s,and faster detection speed than v8s,v9c,v10m and v10b.In Android deployment experiments,GBiDCPEST demonstrated enhanced performance in detecting pests against complex backgrounds,and the accuracy for wheat mites and rice planthoppers was improved by 4.5-7.5%compared with the original model.The GBiDC-PEST optimization algorithm and its mobile deployment proposed in this study offer a robust technical framework for the rapid,onsite identification and localization of tiny pests.This advancement provides valuable insights for effective pest monitoring,counting,and control in various agricultural settings.展开更多
Inflatable deployable structures inspired by origami have significant applications in space missions such as solar arrays and antennas.In this paper,a generalized Miura-ori tubular cell(GMTC)is presented as the basic ...Inflatable deployable structures inspired by origami have significant applications in space missions such as solar arrays and antennas.In this paper,a generalized Miura-ori tubular cell(GMTC)is presented as the basic cell to design a family of inflatable origami tubular structures with the targeted configuration.First,the classification of rigid foldable degree-4 vertices is studied thoroughly.Since the proposed GMTC is comprised of forming units(FU)and linking units(LU),types of FUs and LUs are investigated based on the classification of degree-4 vertices,respectively.The rigid foldability of the GMTC is presented by studying the kinematics of the FUs and LUs.Volume of the GMTC is analyzed to investigate multistable configurations of the basic cell.The variations in volume of the GMTC offer great potential for developing the inflatable tubular structure.Design method and parametric optimization of the tubular structure with targeted configuration are proposed.The feasibility of the approach is validated by the approximation of four different cases,namely parabolic,semicircular,trapezoidal,and straight-arc hybrid tubular structures.展开更多
Supplier selection in a mass customization environment is a systematic engineering,and Quality Function Deployment(QFD)based on customer demand is a systematic product development method.This paper studies the adaptab...Supplier selection in a mass customization environment is a systematic engineering,and Quality Function Deployment(QFD)based on customer demand is a systematic product development method.This paper studies the adaptability of the QFD method and supplier selection process in a mass customization environment and puts forward a supplier selection framework based on the QFD idea.Furthermore,both the objective environment of demand factor analysis and the thinking of the customer representatives participating in the analysis have great uncertainty and fuzziness.Therefore,a demand factor analysis method for supplier selection in the mass customization environment based on language phrases of different granularity is proposed.The proposed method allows the customer representatives participating in the selection to use their preferred language phrase set to represent the importance of demand factors.Finally,the effectiveness and feasibility of the proposed method are verified by an example of a vehicle manufacturer.展开更多
This study investigates the disparities in the deployment of photovoltaic(PV)technology for carbon emissions reduction across different nations,highlighting the mismatch between countries with high economic capacity a...This study investigates the disparities in the deployment of photovoltaic(PV)technology for carbon emissions reduction across different nations,highlighting the mismatch between countries with high economic capacity and those where PV installation would maximize global decarbonization benefits.This mismatch is discussed based on three key factors influencing decarbonization via PV technology:per capita gross domestic product;carbon intensity of the energy system;and solar resource availability.Current PV deployment is predominantly concentrated in economically advanced countries,and does not coincide with regions where the environmental and economic impact of such installations would be most significant.Through a series of thought experiments,it is demonstrated how alternative prioritization strategies could significantly reduce global carbon emissions.Argument is put forward for a globally coordinated approach to PV deployment,particularly targeting high-impact sunbelt regions,to enhance the efficacy of decarbonization efforts and promote equitable energy access.The study underscores the need for international policies that support sustainable energy transitions in economically less developed regions through workforce development and assistance with the activation of capital.展开更多
In the context of security systems,adequate signal coverage is paramount for the communication between security personnel and the accurate positioning of personnel.Most studies focus on optimizing base station deploym...In the context of security systems,adequate signal coverage is paramount for the communication between security personnel and the accurate positioning of personnel.Most studies focus on optimizing base station deployment under the assumption of static obstacles,aiming to maximize the perception coverage of wireless RF(Radio Frequency)signals and reduce positioning blind spots.However,in practical security systems,obstacles are subject to change,necessitating the consideration of base station deployment in dynamic environments.Nevertheless,research in this area still needs to be conducted.This paper proposes a Dynamic Indoor Environment Beacon Deployment Algorithm(DIE-BDA)to address this problem.This algorithm considers the dynamic alterations in obstacle locations within the designated area.It determines the requisite number of base stations,the requisite time,and the area’s practical and overall signal coverage rates.The experimental results demonstrate that the algorithm can calculate the deployment strategy in 0.12 s following a change in obstacle positions.Experimental results show that the algorithm in this paper requires 0.12 s to compute the deployment strategy after the positions of obstacles change.With 13 base stations,it achieves an effective coverage rate of 93.5%and an overall coverage rate of 97.75%.The algorithm can rapidly compute a revised deployment strategy in response to changes in obstacle positions within security systems,thereby ensuring the efficacy of signal coverage.展开更多
In the deployment of wireless networks in two-dimensional outdoor campus spaces,aiming at the problem of efficient coverage of the monitoring area by limited number of access points(APs),this paper proposes a deployme...In the deployment of wireless networks in two-dimensional outdoor campus spaces,aiming at the problem of efficient coverage of the monitoring area by limited number of access points(APs),this paper proposes a deployment method of multi-objective optimization with virtual force fusion bat algorithm(VFBA)using the classical four-node regular distribution as an entry point.The introduction of Lévy flight strategy for bat position updating helps to maintain the population diversity,reduce the premature maturity problem caused by population convergence,avoid the over aggregation of individuals in the local optimal region,and enhance the superiority in global search;the virtual force algorithm simulates the attraction and repulsion between individuals,which enables individual bats to precisely locate the optimal solution within the search space.At the same time,the fusion effect of virtual force prompts the bat individuals to move faster to the potential optimal solution.To validate the effectiveness of the fusion algorithm,the benchmark test function is selected for simulation testing.Finally,the simulation result verifies that the VFBA achieves superior coverage and effectively reduces node redundancy compared to the other three regular layout methods.The VFBA also shows better coverage results when compared to other optimization algorithms.展开更多
For large-scale heterogeneous multi-agent systems(MASs)with characteristics of dense-sparse mixed distribution,this paper investigates the practical finite-time deployment problem by establishing a novel crossspecies ...For large-scale heterogeneous multi-agent systems(MASs)with characteristics of dense-sparse mixed distribution,this paper investigates the practical finite-time deployment problem by establishing a novel crossspecies bionic analytical framework based on the partial differential equation-ordinary differential equation(PDE-ODE)approach.Specifically,by designing a specialized network communication protocol and employing the spatial continuum method for densely distributed agents,this paper models the tracking errors of densely distributed agents as a PDE equivalent to a human disease transmission model,and that of sparsely distributed agents as several ODEs equivalent to the predator population models.The coupling relationship between the PDE and ODE models is established through boundary conditions of the PDE,thereby forming a PDE-ODE-based tracking error model for the considered MASs.Furthermore,by integrating adaptive neural control scheme with the aforementioned biological models,a“Flexible Neural Network”endowed with adaptive and self-stabilized capabilities is constructed,which acts upon the considered MASs,enabling their practical finite-time deployment.Finally,effectiveness of the developed approach is illustrated through a numerical example.展开更多
Aiming at node deployment in the monitoring area of the field observation instrument network in the cold and arid regions,we propose a virtual force algorithm based on Voronoi diagram(VFAVD),which adopts probabilistic...Aiming at node deployment in the monitoring area of the field observation instrument network in the cold and arid regions,we propose a virtual force algorithm based on Voronoi diagram(VFAVD),which adopts probabilistic sensing model that is more in line with the actual situation.First,the Voronoi diagram is constructed in the monitoring area to determine the Thiessen polygon of each node.Then,the virtual force on each node is calculated,and the node update its position according to the direction and size of the total force,so as to achieve the purpose of improving the network coverage rate.The simulation results show that the proposed algorithm can effectively improve the coverage rate of the network,and also has a good effect on the coverage uniformity.展开更多
基金The Department of Defense Military Operational Medicine Research Program(MOMRP)supported this study。
文摘Background:The impact of sleep disorders on active-duty soldiers’medical readiness is not currently quantified.Patient data generated at military treatment facilities can be accessed to create research reports and thus can be used to estimate the prevalence of sleep disturbances and the role of sleep on overall health in service members.The current study aimed to quantify sleep-related health issues and their impact on health and nondeployability through the analysis of U.S.military healthcare records from fiscal year 2018(FY2018).Methods:Medical diagnosis information and deployability profiles(e-Profiles)were queried for all active-duty U.S.Army patients with a concurrent sleep disorder diagnosis receiving medical care within FY2018.Nondeployability was predicted from medical reasons for having an e-Profile(categorized as sleep,behavioral health,musculoskeletal,cardiometabolic,injury,or accident)using binomial logistic regression.Sleep e-Profiles were investigated as a moderator between other e-Profile categories and nondeployability.Results:Out of 582,031 soldiers,48.4%(n=281,738)had a sleep-related diagnosis in their healthcare records,9.7%(n=56,247)of soldiers had e-Profiles,and 1.9%(n=10,885)had a sleep e-Profile.Soldiers with sleep e-Profiles were more likely to have had a motor vehicle accident(p OR(prevalence odds ratio)=4.7,95%CI 2.63–8.39,P≤0.001)or work/duty-related injury(p OR=1.6,95%CI 1.32–1.94,P≤0.001).The likelihood of nondeployability was greater in soldiers with a sleep e-Profile and a musculoskeletal e-Profile(p OR=4.25,95%CI 3.75–4.81,P≤0.001)or work/dutyrelated injury(p OR=2.62,95%CI 1.63–4.21,P≤0.001).Conclusion:Nearly half of soldiers had a sleep disorder or sleep-related medical diagnosis in 2018,but their sleep problems are largely not profiled as limitations to medical readiness.Musculoskeletal issues and physical injury predict nondeployability,and nondeployability is more likely to occur in soldiers who have sleep e-Profiles in addition to these issues.Addressing sleep problems may prevent accidents and injuries that could render a soldier nondeployable.
文摘Microservices have revolutionized traditional software architecture. While monolithic designs continue to be common, particularly in legacy applications, there is a growing trend towards the modularity, independent deployability, and flexibility offered by microservices, which is further enhanced by developments in cloud technology. This shift towards microservice architecture meets the modern business need for agility, facilitating rapid adaptability in a competitive landscape. Microservices offer an agile framework and, in many cases, can simplify the development process, though the implementation can vary and sometimes introduce complexities. Unlike monolithic systems, which can be cumbersome to modify, microservices enable quicker adjustments and faster deployment times, essential in today’s dynamic environment. This article delves into the essence of microservices and explores their growing prominence in the software industry.
基金supported by the National Key Research and Development Program of China(2022YFE0206700)。
文摘1.Introduction Climate change mitigation pathways aimed at limiting global anthropogenic carbon dioxide(CO_(2))emissions while striving to constrain the global temperature increase to below 2℃—as outlined by the Intergovernmental Panel on Climate Change(IPCC)—consistently predict the widespread implementation of CO_(2)geological storage on a global scale.
基金supported by the National Natural Science Foundation of China(Nos.62272418,62102058)Basic Public Welfare Research Program of Zhejiang Province(No.LGG18E050011)the Major Open Project of Key Laboratory for Advanced Design and Intelligent Computing of the Ministry of Education under Grant ADIC2023ZD001,National Undergraduate Training Program on Innovation and Entrepreneurship(No.202410345054).
文摘The wireless signals emitted by base stations serve as a vital link connecting people in today’s society and have been occupying an increasingly important role in real life.The development of the Internet of Things(IoT)relies on the support of base stations,which provide a solid foundation for achieving a more intelligent way of living.In a specific area,achieving higher signal coverage with fewer base stations has become an urgent problem.Therefore,this article focuses on the effective coverage area of base station signals and proposes a novel Evolutionary Particle Swarm Optimization(EPSO)algorithm based on collective prediction,referred to herein as ECPPSO.Introducing a new strategy called neighbor-based evolution prediction(NEP)addresses the issue of premature convergence often encountered by PSO.ECPPSO also employs a strengthening evolution(SE)strategy to enhance the algorithm’s global search capability and efficiency,ensuring enhanced robustness and a faster convergence speed when solving complex optimization problems.To better adapt to the actual communication needs of base stations,this article conducts simulation experiments by changing the number of base stations.The experimental results demonstrate thatunder the conditionof 50 ormore base stations,ECPPSOconsistently achieves the best coverage rate exceeding 95%,peaking at 99.4400%when the number of base stations reaches 80.These results validate the optimization capability of the ECPPSO algorithm,proving its feasibility and effectiveness.Further ablative experiments and comparisons with other algorithms highlight the advantages of ECPPSO.
文摘As the mining industry continues to expand and international oil prices increase,more rigorous demands are being placed on the design of mining equipment.Given this,there is an urgent need to develop new power-driven mining equipment to solve the problems of high energy consumption and insufficient power coupling of current equipment.This study proposed a design of a hybrid power system for underground Load Haul Dump(LHD).The proposed design integrated Quality Function Deployment(QFD)and Theory of Inventive Problem Solving(TRIZ).It identified 7 user requirements and 10 related technical features,formulated 11 innovative design solutions,and ultimately adopting an electric drive hybrid power scheme.This scheme effectively addressesd power transmission coupling problems and improve the efficiency of loaders.A 6 m³hybrid power loader prototype has been developed,which reduces operational energy consumption and advances the electrification and green,low-carbon evolution of mining equipment.
基金the National Natural Science Foundation of China(Nos.11832005,12372042,12232011)Young Elite Scientists Sponsorship Program by CAST(No.2022QNRC001)+1 种基金the Fundamental Research Funds for the Central Universities(No.NS2023002)State Key Laboratory of Mechanics and Control for Aerospace Structures(Nanjing University of Aeronautics and Astronautics)(No.MCAS-S-0223K04).
文摘The quantity of space debris on Earth orbit has escalated tremendously in recent years, presenting a significant hazard to human space operations. It is urgent to develop effective measures to capture and remove various space debris. For this purpose, this paper presents a tendon-actuated flexible deployable manipulator. The flexible manipulator consists of several deployable units connected by Cardan joints and actuated by tendons. Compared with the present technologies for capturing space debris such as rigid robotic arm or flying net, this flexible manipulator is deployable, reusable, lightweight and applicable to the capture of large space debris. In order to investigate its deployment dynamics, an accurate dynamic model of the flexible manipulator is established based on the natural coordinate formulation (NCF) and the absolute nodal coordinate formulation (ANCF). Subsequently, numerical simulations are carried out to study the effects of system parameters and the base satellite on its deployment dynamics. Finally, ground experiments for both deployment and bending of the flexible manipulator are conducted to verify its effectiveness and feasibility.
文摘The rapid evolution of Fifth-Generation(5G)networks and the strategic development of Sixth-Generation(6G)technologies have significantly advanced the implementation of air-ground integrated networks with seamless coverage.Unmanned Aerial Vehicles(UAVs),serving as high-mobility aerial platforms,are extensively utilized to enhance coverage in long-distance emergency communication scenarios.The resource-constrained communication environments in emergencies by classifying UAVs into swarm UAVs and relay UAVs as aerial communication nodes is inversitgated.A horizontal deployment strategy for swarm UAVs is formulated through K-means clustering algorithm optimization,while a vertical deployment scheme is established using convex optimization methods.The minimum-path trajectory planning for relay UAVs is optimized via the Particle Swarm Optimization(PSO)algorithm,enhancing communication reliability between UAV swarms and terrestrial base stations.A three-dimensional heterogeneous network architecture is realized by modeling spatial multi-hop relay links.Experimental results demonstrate that the proposed joint UAV relay optimization framework outperforms conventional algorithms in both coverage performance and relay capability during video stream transmission,achieving significant improvements in coverage enhancement and relay efficiency.This work provides technical foundations for constructing high-reliability air-ground cooperative systems in emergency communications.
文摘Machine learning(ML)has been increasingly adopted to solve engineering problems with performance gauged by accuracy,efficiency,and security.Notably,blockchain technology(BT)has been added to ML when security is a particular concern.Nevertheless,there is a research gap that prevailing solutions focus primarily on data security using blockchain but ignore computational security,making the traditional ML process vulnerable to off-chain risks.Therefore,the research objective is to develop a novel ML on blockchain(MLOB)framework to ensure both the data and computational process security.The central tenet is to place them both on the blockchain,execute them as blockchain smart contracts,and protect the execution records on-chain.The framework is established by developing a prototype and further calibrated using a case study of industrial inspection.It is shown that the MLOB framework,compared with existing ML and BT isolated solutions,is superior in terms of security(successfully defending against corruption on six designed attack scenario),maintaining accuracy(0.01%difference with baseline),albeit with a slightly compromised efficiency(0.231 second latency increased).The key finding is MLOB can significantly enhances the computational security of engineering computing without increasing computing power demands.This finding can alleviate concerns regarding the computational resource requirements of ML-BT integration.With proper adaption,the MLOB framework can inform various novel solutions to achieve computational security in broader engineering challenges.
基金supported by the National Key R&D Program of China(Grant No.2023YFB3407103)the National Natural Science Foundation of China(Grant Nos.52175242 and 52175027)Young Elite Scientists Sponsorship Program by China Association for Science and Technology(Grant No.2022QNRC001).
文摘Mesh reflector antennas are the mainstream of large space-borne antennas,and the stretching of the truss achieves their deployment.Currently,the truss is commonly designed to be a single degree of freedom(DOF)deployable mechanism with synchronization constraints.However,each deployable unit’s drive distribution and resistance load are uneven,and the forced synchronization constraints lead to the flexible deformation of rods and difficulties in the deployment scheme design.This paper introduces an asynchronous deployment scheme with a multi-DOF closed-chain deployable truss.The DOF of the truss is calculated,and the kinematic and dynamic models are established,considering the truss’s and cable net’s real-time coupling.An integrated solving algorithm for implicit differential-algebraic equations is proposed to solve the dynamic models.A prototype of a six-unit antenna was fabricated,and the experiment was carried out.The dynamic performances in synchronous and asynchronous deployment schemes are analyzed,and the results show that the cable resistance and truss kinetic energy impact under the asynchronous deployment scheme are minor,and the antenna is more straightforward to deploy.The work provides a new asynchronous deployment scheme and a universal antenna modeling method for dynamic design and performance improvement.
文摘The research on optimization methods for constellation launch deployment strategies focused on the consideration of mission interval time constraints at the launch site.Firstly,a dynamic modeling of the constellation deployment process was established,and the relationship between the deployment window and the phase difference of the orbit insertion point,as well as the cost of phase adjustment after orbit insertion,was derived.Then,the combination of the constellation deployment position sequence was treated as a parameter,together with the sequence of satellite deployment intervals,as optimization variables,simplifying a highdimensional search problem within a wide range of dates to a finite-dimensional integer programming problem.An improved genetic algorithm with local search on deployment dates was introduced to optimize the launch deployment strategy.With the new description of the optimization variables,the total number of elements in the solution space was reduced by N orders of magnitude.Numerical simulation confirms that the proposed optimization method accelerates the convergence speed from hours to minutes.
基金co-supported by the Natural Science Basic Research Program of Shaanxi,China(No.2023-JC-QN-0043)the ND Basic Research Funds,China(No.G2022WD).
文摘The aerial deployment method enables Unmanned Aerial Vehicles(UAVs)to be directly positioned at the required altitude for their mission.This method typically employs folding technology to improve loading efficiency,with applications such as the gravity-only aerial deployment of high-aspect-ratio solar-powered UAVs,and aerial takeoff of fixed-wing drones in Mars research.However,the significant morphological changes during deployment are accompanied by strong nonlinear dynamic aerodynamic forces,which result in multiple degrees of freedom and an unstable character.This hinders the description and analysis of unknown dynamic behaviors,further leading to difficulties in the design of deployment strategies and flight control.To address this issue,this paper proposes an analysis method for dynamic behaviors during aerial deployment based on the Variational Autoencoder(VAE).Focusing on the gravity-only deployment problem of highaspect-ratio foldable-wing UAVs,the method encodes the multi-degree-of-freedom unstable motion signals into a low-dimensional feature space through a data-driven approach.By clustering in the feature space,this paper identifies and studies several dynamic behaviors during aerial deployment.The research presented in this paper offers a new method and perspective for feature extraction and analysis of complex and difficult-to-describe extreme flight dynamics,guiding the research on aerial deployment drones design and control strategies.
基金supported by the National Natural Science Foundation of China under Grant 62471493 and 62402257partially supported by the Natural Science Foundation of Shandong Province under Grant ZR2023LZH017,ZR2024MF066 and 2023QF025+2 种基金partially supported by the Open Research Subject of State Key Laboratory of Intelligent Game(No.ZBKF-24-12)partially supported by the Foundation of Key Laboratory of Education Informatization for Nationalities(Yunnan Normal University),the Ministry of Education(No.EIN2024C006)partially supported by the Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE(No.202306).
文摘As Internet ofThings(IoT)technologies continue to evolve at an unprecedented pace,intelligent big data control and information systems have become critical enablers for organizational digital transformation,facilitating data-driven decision making,fostering innovation ecosystems,and maintaining operational stability.In this study,we propose an advanced deployment algorithm for Service Function Chaining(SFC)that leverages an enhanced Practical Byzantine Fault Tolerance(PBFT)mechanism.The main goal is to tackle the issues of security and resource efficiency in SFC implementation across diverse network settings.By integrating blockchain technology and Deep Reinforcement Learning(DRL),our algorithm not only optimizes resource utilization and quality of service but also ensures robust security during SFC deployment.Specifically,the enhanced PBFT consensus mechanism(VRPBFT)significantly reduces consensus latency and improves Byzantine node detection through the introduction of a Verifiable Random Function(VRF)and a node reputation grading model.Experimental results demonstrate that compared to traditional PBFT,the proposed VRPBFT algorithm reduces consensus latency by approximately 30%and decreases the proportion of Byzantine nodes by 40%after 100 rounds of consensus.Furthermore,the DRL-based SFC deployment algorithm(SDRL)exhibits rapid convergence during training,with improvements in long-term average revenue,request acceptance rate,and revenue/cost ratio of 17%,14.49%,and 20.35%,respectively,over existing algorithms.Additionally,the CPU resource utilization of the SDRL algorithmreaches up to 42%,which is 27.96%higher than other algorithms.These findings indicate that the proposed algorithm substantially enhances resource utilization efficiency,service quality,and security in SFC deployment.
基金support of the Natural Science Foundation of Jiangsu Province,China(BK20240977)the China Scholarship Council(201606850024)+1 种基金the National High Technology Research and Development Program of China(2016YFD0701003)the Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(SJCX23_1488)。
文摘Deep learning-based intelligent recognition algorithms are increasingly recognized for their potential to address the labor-intensive challenge of manual pest detection.However,their deployment on mobile devices has been constrained by high computational demands.Here,we developed GBiDC-PEST,a mobile application that incorporates an improved,lightweight detection algorithm based on the You Only Look Once(YOLO)series singlestage architecture,for real-time detection of four tiny pests(wheat mites,sugarcane aphids,wheat aphids,and rice planthoppers).GBiDC-PEST incorporates several innovative modules,including GhostNet for lightweight feature extraction and architecture optimization by reconstructing the backbone,the bi-directional feature pyramid network(BiFPN)for enhanced multiscale feature fusion,depthwise convolution(DWConv)layers to reduce computational load,and the convolutional block attention module(CBAM)to enable precise feature focus.The newly developed GBiDC-PEST was trained and validated using a multitarget agricultural tiny pest dataset(Tpest-3960)that covered various field environments.GBiDC-PEST(2.8 MB)significantly reduced the model size to only 20%of the original model size,offering a smaller size than the YOLO series(v5-v10),higher detection accuracy than YOLOv10n and v10s,and faster detection speed than v8s,v9c,v10m and v10b.In Android deployment experiments,GBiDCPEST demonstrated enhanced performance in detecting pests against complex backgrounds,and the accuracy for wheat mites and rice planthoppers was improved by 4.5-7.5%compared with the original model.The GBiDC-PEST optimization algorithm and its mobile deployment proposed in this study offer a robust technical framework for the rapid,onsite identification and localization of tiny pests.This advancement provides valuable insights for effective pest monitoring,counting,and control in various agricultural settings.
基金supported by the National Natural Science Foundation of China(No.52222501,52075016,52192632)the Fundamental Research Funds for the Central Universities(Grant No.YWF-23-L-904).
文摘Inflatable deployable structures inspired by origami have significant applications in space missions such as solar arrays and antennas.In this paper,a generalized Miura-ori tubular cell(GMTC)is presented as the basic cell to design a family of inflatable origami tubular structures with the targeted configuration.First,the classification of rigid foldable degree-4 vertices is studied thoroughly.Since the proposed GMTC is comprised of forming units(FU)and linking units(LU),types of FUs and LUs are investigated based on the classification of degree-4 vertices,respectively.The rigid foldability of the GMTC is presented by studying the kinematics of the FUs and LUs.Volume of the GMTC is analyzed to investigate multistable configurations of the basic cell.The variations in volume of the GMTC offer great potential for developing the inflatable tubular structure.Design method and parametric optimization of the tubular structure with targeted configuration are proposed.The feasibility of the approach is validated by the approximation of four different cases,namely parabolic,semicircular,trapezoidal,and straight-arc hybrid tubular structures.
文摘Supplier selection in a mass customization environment is a systematic engineering,and Quality Function Deployment(QFD)based on customer demand is a systematic product development method.This paper studies the adaptability of the QFD method and supplier selection process in a mass customization environment and puts forward a supplier selection framework based on the QFD idea.Furthermore,both the objective environment of demand factor analysis and the thinking of the customer representatives participating in the analysis have great uncertainty and fuzziness.Therefore,a demand factor analysis method for supplier selection in the mass customization environment based on language phrases of different granularity is proposed.The proposed method allows the customer representatives participating in the selection to use their preferred language phrase set to represent the importance of demand factors.Finally,the effectiveness and feasibility of the proposed method are verified by an example of a vehicle manufacturer.
基金supported by the Helmholtz Association within the framework of the innovation platform“Solar TAP”[Az:714-62150-3/1(2023)]co-funded by the European Union(ERC,C2C-PV,project number 101088359)。
文摘This study investigates the disparities in the deployment of photovoltaic(PV)technology for carbon emissions reduction across different nations,highlighting the mismatch between countries with high economic capacity and those where PV installation would maximize global decarbonization benefits.This mismatch is discussed based on three key factors influencing decarbonization via PV technology:per capita gross domestic product;carbon intensity of the energy system;and solar resource availability.Current PV deployment is predominantly concentrated in economically advanced countries,and does not coincide with regions where the environmental and economic impact of such installations would be most significant.Through a series of thought experiments,it is demonstrated how alternative prioritization strategies could significantly reduce global carbon emissions.Argument is put forward for a globally coordinated approach to PV deployment,particularly targeting high-impact sunbelt regions,to enhance the efficacy of decarbonization efforts and promote equitable energy access.The study underscores the need for international policies that support sustainable energy transitions in economically less developed regions through workforce development and assistance with the activation of capital.
文摘In the context of security systems,adequate signal coverage is paramount for the communication between security personnel and the accurate positioning of personnel.Most studies focus on optimizing base station deployment under the assumption of static obstacles,aiming to maximize the perception coverage of wireless RF(Radio Frequency)signals and reduce positioning blind spots.However,in practical security systems,obstacles are subject to change,necessitating the consideration of base station deployment in dynamic environments.Nevertheless,research in this area still needs to be conducted.This paper proposes a Dynamic Indoor Environment Beacon Deployment Algorithm(DIE-BDA)to address this problem.This algorithm considers the dynamic alterations in obstacle locations within the designated area.It determines the requisite number of base stations,the requisite time,and the area’s practical and overall signal coverage rates.The experimental results demonstrate that the algorithm can calculate the deployment strategy in 0.12 s following a change in obstacle positions.Experimental results show that the algorithm in this paper requires 0.12 s to compute the deployment strategy after the positions of obstacles change.With 13 base stations,it achieves an effective coverage rate of 93.5%and an overall coverage rate of 97.75%.The algorithm can rapidly compute a revised deployment strategy in response to changes in obstacle positions within security systems,thereby ensuring the efficacy of signal coverage.
基金supported in part by the National Natural Science Foundation of China under Grant No.62271453in part by the National Natural Science Foundation of China No.62101512+2 种基金in part by the Central Support for Local Projects under Grant No.YDZJSX2024D031in part by Project supported by the Shanxi Provincial Foundation for Leaders of Disciplines in Science,China under Grant No.2024Q022in part by Shanxi Province Patent Conversion Special Plan Funding Projects under Grant No.202405004。
文摘In the deployment of wireless networks in two-dimensional outdoor campus spaces,aiming at the problem of efficient coverage of the monitoring area by limited number of access points(APs),this paper proposes a deployment method of multi-objective optimization with virtual force fusion bat algorithm(VFBA)using the classical four-node regular distribution as an entry point.The introduction of Lévy flight strategy for bat position updating helps to maintain the population diversity,reduce the premature maturity problem caused by population convergence,avoid the over aggregation of individuals in the local optimal region,and enhance the superiority in global search;the virtual force algorithm simulates the attraction and repulsion between individuals,which enables individual bats to precisely locate the optimal solution within the search space.At the same time,the fusion effect of virtual force prompts the bat individuals to move faster to the potential optimal solution.To validate the effectiveness of the fusion algorithm,the benchmark test function is selected for simulation testing.Finally,the simulation result verifies that the VFBA achieves superior coverage and effectively reduces node redundancy compared to the other three regular layout methods.The VFBA also shows better coverage results when compared to other optimization algorithms.
基金The National Key R&D Program of China(2021ZD0201300)the National Natural Science Foundation of China(624B2058,U1913602 and 61936004)+1 种基金the Innovation Group Project of the National Natural Science Foundation of China(61821003)the 111 Project on Computational Intelligence and Intelligent Control(B18024).
文摘For large-scale heterogeneous multi-agent systems(MASs)with characteristics of dense-sparse mixed distribution,this paper investigates the practical finite-time deployment problem by establishing a novel crossspecies bionic analytical framework based on the partial differential equation-ordinary differential equation(PDE-ODE)approach.Specifically,by designing a specialized network communication protocol and employing the spatial continuum method for densely distributed agents,this paper models the tracking errors of densely distributed agents as a PDE equivalent to a human disease transmission model,and that of sparsely distributed agents as several ODEs equivalent to the predator population models.The coupling relationship between the PDE and ODE models is established through boundary conditions of the PDE,thereby forming a PDE-ODE-based tracking error model for the considered MASs.Furthermore,by integrating adaptive neural control scheme with the aforementioned biological models,a“Flexible Neural Network”endowed with adaptive and self-stabilized capabilities is constructed,which acts upon the considered MASs,enabling their practical finite-time deployment.Finally,effectiveness of the developed approach is illustrated through a numerical example.
基金supported by National Natural Science Foundation of China(No.61862038)Lanzhou Talent Innovation and Entrepreneurship Technology Plan Project(No.2019-RC-14).
文摘Aiming at node deployment in the monitoring area of the field observation instrument network in the cold and arid regions,we propose a virtual force algorithm based on Voronoi diagram(VFAVD),which adopts probabilistic sensing model that is more in line with the actual situation.First,the Voronoi diagram is constructed in the monitoring area to determine the Thiessen polygon of each node.Then,the virtual force on each node is calculated,and the node update its position according to the direction and size of the total force,so as to achieve the purpose of improving the network coverage rate.The simulation results show that the proposed algorithm can effectively improve the coverage rate of the network,and also has a good effect on the coverage uniformity.
