Safe and efficient sortie scheduling on the confined flight deck is crucial for maintaining high combat effectiveness of the aircraft carrier.The primary difficulty exactly lies in the spatiotemporal coordination,i.e....Safe and efficient sortie scheduling on the confined flight deck is crucial for maintaining high combat effectiveness of the aircraft carrier.The primary difficulty exactly lies in the spatiotemporal coordination,i.e.,allocation of limited supporting resources and collision-avoidance between heterogeneous dispatch entities.In this paper,the problem is investigated in the perspective of hybrid flow-shop scheduling problem by synthesizing the precedence,space and resource constraints.Specifically,eight processing procedures are abstracted,where tractors,preparing spots,catapults,and launching are virtualized as machines.By analyzing the constraints in sortie scheduling,a mixed-integer planning model is constructed.In particular,the constraint on preparing spot occupancy is improved to further enhance the sortie efficiency.The basic trajectory library for each dispatch entity is generated and a delayed strategy is integrated to address the collision-avoidance issue.To efficiently solve the formulated HFSP,which is essentially a combinatorial problem with tightly coupled constraints,a chaos-initialized genetic algorithm is developed.The solution framework is validated by the simulation environment referring to the Fort-class carrier,exhibiting higher sortie efficiency when compared to existing strategies.And animation of the simulation results is available at www.bilibili.com/video/BV14t421A7Tt/.The study presents a promising supporting technique for autonomous flight deck operation in the foreseeable future,and can be easily extended to other supporting scenarios,e.g.,ammunition delivery and aircraft maintenance.展开更多
In this manuscript, Local dynamic behaviors including stability and Hopf bifurcation of a new four-dimensional quadratic autonomous system are studied both analytically and numerically. Determining conditions of equil...In this manuscript, Local dynamic behaviors including stability and Hopf bifurcation of a new four-dimensional quadratic autonomous system are studied both analytically and numerically. Determining conditions of equilibrium points on different parameters are derived. Next, the stability conditions are investigated by using Routh-Hurwitz criterion and bifurcation conditions are investigated by using Hopf bifurcation theory, respectively. It is found that Hopf bifurcation on the initial point is supercritical in this four-dimensional autonomous system. The theoretical results are verified by numerical simulation. Besides, the new four-dimensional autonomous system under the parametric conditions of hyperchaos is studied in detail. It is also found that the system can enter hyperchaos, first through Hopf bifurcation and then through periodic bifurcation.展开更多
Integrating autonomous vehicles (AVs) and autonomous parking spaces (APS) marks a transformative development in urban mobility and sustainability. This paper reflects on these technologies’ historical evolution, curr...Integrating autonomous vehicles (AVs) and autonomous parking spaces (APS) marks a transformative development in urban mobility and sustainability. This paper reflects on these technologies’ historical evolution, current interdependence, and future potential through the lens of environmental, social, and economic sustainability. Historically, parking systems evolved from manual designs to automated processes yet remained focused on convenience rather than sustainability. Presently, advancements in smart infrastructure and vehicle-to-infrastructure (V2I) communication have enabled AVs and APS to operate as a cohesive system, optimizing space, energy, and transportation efficiency. Looking ahead, the seamless integration of AVs and APS into broader smart city ecosystems promises to redefine urban landscapes by repurposing traditional parking infrastructure into multifunctional spaces and supporting renewable energy initiatives. These technologies align with global sustainability goals by mitigating emissions, reducing urban sprawl, and fostering adaptive land uses. This reflection highlights the need for collaborative efforts among stakeholders to address regulatory and technological challenges, ensuring the equitable and efficient deployment of AVs and APS for smarter, greener cities.展开更多
Autonomous Transporta tion Research(中文刊名《自主交通研究》,简称ATRes期刊)是由武汉理工大学主办,水路交通控制全国重点实验室、国家水运安全工程技术研究中心、交通信息与安全教育部工程研究中心等协办,科爱出版社出版发行的英...Autonomous Transporta tion Research(中文刊名《自主交通研究》,简称ATRes期刊)是由武汉理工大学主办,水路交通控制全国重点实验室、国家水运安全工程技术研究中心、交通信息与安全教育部工程研究中心等协办,科爱出版社出版发行的英文开放获取式高水平学术期刊,国际标准连续出版物号:ISSN 3050-8622。展开更多
With the rapid development of Internet of things technology,the efficiency of data transmission between devices has been significantly improved.However,the open network environment also poses serious security risks.Th...With the rapid development of Internet of things technology,the efficiency of data transmission between devices has been significantly improved.However,the open network environment also poses serious security risks.This paper proposes an innovative fingerprint template protection scheme,which generates key streams through an improved fourdimensional superchaotic system(4CSCS),uses the space-filling property of Hilbert curves to achieve pixel scrambling,and introduces dynamic DNA encoding to improve encryption.Experimental results show that this scheme has a large key space 2^(528),encrypts image information entropy of more than 7.9970,and shows excellent performance in defending against statistical attacks and differential attacks.Compared with existing methods,this scheme has significant advantages in terms of encryption performance and security,and provides a reliable protection mechanism for fingerprint authentication systems in the Internet of things environment.展开更多
1. Introduction Celestial navigation is a kind of navigation with a long history.With the increasing demand for intelligent autonomy and antielectromagnetic interference in spacecraft, celestial navigation has become ...1. Introduction Celestial navigation is a kind of navigation with a long history.With the increasing demand for intelligent autonomy and antielectromagnetic interference in spacecraft, celestial navigation has become one of the current research hotspots in spacecraft autonomous navigation. Spacecraft face complex electromagnetic interference in orbit. The time-varying, non-Gaussian interference from internal devices and external environment can lead to measurement distortion.展开更多
Enhancing Autonomous Decision-Making (ADM) for unmanned combat aerial vehicle formations in beyond-visual-range air combat is pivotal for future battlefields, whereas the predominant reinforcement learning technique f...Enhancing Autonomous Decision-Making (ADM) for unmanned combat aerial vehicle formations in beyond-visual-range air combat is pivotal for future battlefields, whereas the predominant reinforcement learning technique for ADM has been proven to be inadequately fitting complex tactical Unit Coordination (UC), limiting the integrity of decision-making for formations. This study proposes a knowledge-enhanced ADM method, with a focus on UC, to elevate formation combat effectiveness. The main innovation is integrating data mining technique with tactical knowledge mining and integration. Foremost, based on Frequent Event Arrangement Mining (FEAM) theory, a cross-channel UC knowledge mining method is designed by introducing data flow, which is capable of capturing dynamic coordinative action sequences. Then, a dual-mode knowledge integration method is proposed by employing the Graph Attention Network (GAT) and attenuated structural similarity, bolstering the interplay between autonomous UC tactics fitting and knowledge injection. The experimental results demonstrate that the algorithm surpasses the existing methods, providing more strategic maneuver trajectories and a win rate of more than 90% in different scenarios. The method is promising to augment the autonomous operational capabilities of unmanned formations and drive the evolution of combat effectiveness.展开更多
Leesmidt et al present a comprehensive analysis of abdominal vascular flow in children using four-dimensional(4D)flow magnetic resonance imaging(MRI),aim to establish normal hemodynamic values for the abdominal viscer...Leesmidt et al present a comprehensive analysis of abdominal vascular flow in children using four-dimensional(4D)flow magnetic resonance imaging(MRI),aim to establish normal hemodynamic values for the abdominal visceral organs and to assess the feasibility of 4D flow MRI(4D-f-MRI)in this population.The researchers performed 4D-f-MRI on 9 pediatric patients with a history or suspi-cion of bowel pathology.Flow velocities were measured in the abdominal aorta and superior and inferior mesenteric arteries.The quality of the 4D-f-MRI images was evaluated,and the agreement between the measured flow velocities and those obtained from Duplex ultrasound was established.However,due to the specific limitations of this work,future studies should address the issues of small sample size and the specific age group design.展开更多
For decades,antigen presentation on major histocompatibility complex class I for T cell-mediated immunity has been considered the primary function of proteasome-derived peptides1,2.However,whether the products of prot...For decades,antigen presentation on major histocompatibility complex class I for T cell-mediated immunity has been considered the primary function of proteasome-derived peptides1,2.However,whether the products of proteasomal degradation play additional parts in mounting immune responses remains unknown.Antimicrobial peptides serve as a first line of defence against invading pathogens before the adaptive immune system responds.Although the protective function of antimicrobial peptides across numerous tissues is well established,the cellular mechanisms underlying their generation are not fully understood.Here we uncover a role for proteasomes in the constitutive and bacterial-induced generation of defence peptides that impede bacterial growth both in vitro and in vivo by disrupting bacterial membranes.In silico prediction of proteome-wide proteasomal cleavage identified hundreds of thousands of potential proteasome-derived defence peptides with cationic properties that may be generated en route to degradation to act as a first line of defence.展开更多
Addressing the core weaknesses in the innovation and entrepreneurship capabilities of vocational college graduates,such as market insight and risk tolerance,as well as issues with the existing training model,including...Addressing the core weaknesses in the innovation and entrepreneurship capabilities of vocational college graduates,such as market insight and risk tolerance,as well as issues with the existing training model,including courses that are disconnected from industry,a lack of systematic practical training,and superficial school-enterprise cooperation,this paper constructs a“three-dimensional,four-dimensional”training system.The“three-dimensional”foundational framework encompasses three pillars:curriculum,general education layer,professional integration layer,practical application layer,practice as in three stages:introductory,simulated,and practical,and support including dual mentors,policies,and platforms.The“four-dimensional”differentiated strategies include four implementation pathways:professional differentiation,stage differentiation,addressing capability shortcomings,and school-government-industry collaboration.This system is grounded in theories such as multiple intelligences theory and systems theory,forming a closed-loop process of“theoretical input—practical application—support mechanisms”.Based on the practices of Guangdong Vocational Institute of Public Administration,the paper proposes a competency development pathway tailored by major and stage,which can effectively enhance the innovative and entrepreneurial core competencies of vocational college graduates.This provides a replicable systematic solution for vocational college innovative and entrepreneurial education,supporting vocational education reform and regional economic development.展开更多
Complex road conditions without signalized intersections when the traffic flow is nearly saturated result in high traffic congestion and accidents,reducing the traffic efficiency of intelligent vehicles.The complex ro...Complex road conditions without signalized intersections when the traffic flow is nearly saturated result in high traffic congestion and accidents,reducing the traffic efficiency of intelligent vehicles.The complex road traffic environment of smart vehicles and other vehicles frequently experiences conflicting start and stop motion.The fine-grained scheduling of autonomous vehicles(AVs)at non-signalized intersections,which is a promising technique for exploring optimal driving paths for both assisted driving nowadays and driverless cars in the near future,has attracted significant attention owing to its high potential for improving road safety and traffic efficiency.Fine-grained scheduling primarily focuses on signalized intersection scenarios,as applying it directly to non-signalized intersections is challenging because each AV can move freely without traffic signal control.This may cause frequent driving collisions and low road traffic efficiency.Therefore,this study proposes a novel algorithm to address this issue.Our work focuses on the fine-grained scheduling of automated vehicles at non-signal intersections via dual reinforced training(FS-DRL).For FS-DRL,we first use a grid to describe the non-signalized intersection and propose a convolutional neural network(CNN)-based fast decision model that can rapidly yield a coarse-grained scheduling decision for each AV in a distributed manner.We then load these coarse-grained scheduling decisions onto a deep Q-learning network(DQN)for further evaluation.We use an adaptive learning rate to maximize the reward function and employ parameterεto tradeoff the fast speed of coarse-grained scheduling in the CNN and optimal fine-grained scheduling in the DQN.In addition,we prove that using this adaptive learning rate leads to a converged loss rate with an extremely small number of training loops.The simulation results show that compared with Dijkstra,RNN,and ant colony-based scheduling,FS-DRL yields a high accuracy of 96.5%on the sample,with improved performance of approximately 61.54%-85.37%in terms of the average conflict and traffic efficiency.展开更多
This paper introduces autonomous driving image perception technology,including deep learning models(such as CNN and RNN)and their applications,analyzing the limitations of traditional algorithms.It elaborates on the s...This paper introduces autonomous driving image perception technology,including deep learning models(such as CNN and RNN)and their applications,analyzing the limitations of traditional algorithms.It elaborates on the shortcomings of Faster R-CNN and YOLO series models,proposes various improvement techniques such as data fusion,attention mechanisms,and model compression,and introduces relevant datasets,evaluation metrics,and testing frameworks to demonstrate the advantages of the improved models.展开更多
This study aims to develop a quantum computing-based neurocognitive architecture that allows an agent to perform autonomous behaviors.Therefore,we present a brain-inspired cognitive architecture for autonomous agents ...This study aims to develop a quantum computing-based neurocognitive architecture that allows an agent to perform autonomous behaviors.Therefore,we present a brain-inspired cognitive architecture for autonomous agents that integrates a prefrontal cortex-inspired model with modern deep learning(a transformer-based reinforcement learning module)and quantum algorithms.In particular,our framework incorporates quantum computational routines(Deutsch-Jozsa,Bernstein-Vazirani,and Grover’s search)to enhance decision-making efficiency.As a novelty of this research,this comprehensive computational structure is empowered by quantum computing operations so that superiority in speed and robustness of learning compared to classical methods can be demonstrated.Another main contribution is that the proposed architecture offers some features,such as meta-cognition and situation awareness.The meta-cognition aspect is responsible for hierarchically learning sub-tasks,enabling the agent to achieve the master goal.The situation-awareness property identifies how spatial-temporal reasoning activities related to the world model of the agent can be extracted in a dynamic simulation environment with unstructured uncertainties by quantum computation-based machine learning algorithms with the explainable artificial intelligence paradigm.In this research,the Minecraft game-based simulation environment is utilized for the experimental evaluation of performance and verification tests within complex,multi-objective tasks related to the autonomous behaviors of a smart agent.By implementing several interaction scenarios,the results of the system performance and comparative superiority over alternative solutions are presented,and it is discussed how these autonomous behaviors and cognitive skills of a smart agent can be improved in further studies.Results show that the quantum-enhanced agent achieves faster convergence to an 80%task 2×success rate in exploration tasks and approximately 15%higher cumulative rewards compared to a classical deep RL baseline.These findings demonstrate the potential of quantum algorithms to significantly improve learning and performance in cognitive agent architectures.However,advantages are task-specific and less pronounced under high-uncertainty,reactive scenarios.Limitations of the simulation environment are acknowledged,and a structured future research roadmap is proposed involving highfidelity simulation validation,hardware-in-the-loop robotic testing,and integration of advanced hybrid quantum-classical architectures.展开更多
Autonomous,adaptable,and multimodal locomotion capabilities,which are crucial for the advanced intelligence of biological systems.A prominent focus of investigations in the domain of bionic soft robotics pertains to t...Autonomous,adaptable,and multimodal locomotion capabilities,which are crucial for the advanced intelligence of biological systems.A prominent focus of investigations in the domain of bionic soft robotics pertains to the emulation of autonomous motion,as observed in natural organisms.This research endeavor faces the challenge of enabling spontaneous and sustained motion in soft robots without relying on external stimuli.Considerable progress has been made in the development of autonomous bionic soft robots that utilize smart polymer materials,particularly in the realms of material design,microfabrication technology,and operational mechanisms.Nonetheless,there remains a conspicuous deficiency in the literature concerning a thorough review of this subject matter.This study aims to provide a comprehensive review of autonomous soft robots that have been developed based on self-regulation strategies that encompass self-propulsion,self-oscillation,multistimulus response,and topological constraint structures.Furthermore,this review engages in an in-depth discussion regarding their tunable selfsustaining motion and recovery capabilities,while also contemplating the future development of autonomous soft robotic systems and their potential applications in fields such as biomechanics.展开更多
Lethal autonomous weapon systems(LAWS)have become a prominent issue in global security governance.However,significant divergences remain among the world's major countries regarding their regulation:some support re...Lethal autonomous weapon systems(LAWS)have become a prominent issue in global security governance.However,significant divergences remain among the world's major countries regarding their regulation:some support restrictions,while others oppose any binding rules,and there are also countries adopting a neutral stance,though their choices are subject to external pressure.In the foreseeable future,the deployment of LAWS will increase further,raising the risk of arms races,with international support for regulating these weapons gaining strength.Yet,establishing legally binding international rules within the United Nations(UN)framework remains a distant prospect.Given this backdrop,China should continue to actively participate in international norms setting concerning LAWS under UN auspices and deepen coordination and cooperation with other countries towards more applicable norms and global governance frameworks.展开更多
Autonomous trucks have the potential to enhance both safety and convenience in intelligent transportation.However,their maximum speed and ability to navigate a variety of driving conditions,particularly uneven roads,a...Autonomous trucks have the potential to enhance both safety and convenience in intelligent transportation.However,their maximum speed and ability to navigate a variety of driving conditions,particularly uneven roads,are limited by a high center of gravity,which increases the risk of rollover.Road bulges,sinkholes,and unexpected debris all present additional challenges for autonomous trucks’operational design,which current perception and decisionmaking algorithms often overlook.To mitigate rollover risks and improve adaptability to damaged roads,this paper presents a novel Road Obstacle-Involved Trajectory Planner(ROITP).The planner categorizes road obstacles using a learning-based algorithm.A discrete optimization algorithm selects a multi-objective optimal trajectory while taking into account constraints and objective functions derived from truck dynamics.Validation across various scenarios on a hardware-in-loop platform demonstrates that the proposed planner is effective and feasible for real-time implementation.展开更多
BACKGROUND Four-dimensional(4D)flow magnetic resonance imaging(MRI)is used as a noninvasive modality for assessing hemodynamic information with neurovascular and body applications.The application of 4D flow MRI for as...BACKGROUND Four-dimensional(4D)flow magnetic resonance imaging(MRI)is used as a noninvasive modality for assessing hemodynamic information with neurovascular and body applications.The application of 4D flow MRI for assessment of bowel disease in children has not been previously described.AIM To determine feasibility of superior mesenteric venous and arterial flow quantitation in pediatric patients using 4D flow MRI.METHODS Nine pediatric patients(7-14 years old,5 male and 4 female)with history or suspicion of bowel pathology,who underwent magnetic resonance(MR)enterography with 4D flow MR protocol from November 2022 to October 2023.Field strength/sequence:3T MRI using 4D flow MR protocol.Flow velocity and peak speed measurements were performed by two diagnostic radiologists placing the region of interest in perpendicular plane to blood flow on each cross section of superior mesenteric artery(SMA)and superior mesenteric vein(SMV)at three predetermined levels.Bland-Altman analysis,showed good agreement of flow velocity and peak speed measurements of SMV and SMA between two readers.RESULTS Mean SMV flow velocity increased from proximal to mid to distal(0.14 L/minute,0.17 L/minute,0.22 L/minute respectively).Mean SMA flow velocity decreased from proximal to mid to distal(0.35 L/minute,0.27 L/minute,0.21 L/minute respectively).Observed agreement was good for flow velocity measurements of SMV(mean bias-0.01 L/minute and 95%limits of agreement,-0.09 to 0.08 L/minute)and SMA(mean bias-0.03 L/minute and 95%limits of agreement,-0.23 to 0.17 L/minute)between two readers.Good agreement for peak speed measurements of SMV(mean bias-1.2 cm/second and 95%limits of agreement,-9.4 to 7.0 cm/second)and SMA(mean bias-3.2 cm/second and 95%limits of agreement,-31.4 to 24.9 cm/second).CONCLUSION Flow quantitation using 4D Flow is feasible to provide hemodynamic information for SMV and SMA in children.展开更多
基金the financial support of the National Key Research and Development Plan(2021YFB3302501)the financial support of the National Natural Science Foundation of China(12102077)。
文摘Safe and efficient sortie scheduling on the confined flight deck is crucial for maintaining high combat effectiveness of the aircraft carrier.The primary difficulty exactly lies in the spatiotemporal coordination,i.e.,allocation of limited supporting resources and collision-avoidance between heterogeneous dispatch entities.In this paper,the problem is investigated in the perspective of hybrid flow-shop scheduling problem by synthesizing the precedence,space and resource constraints.Specifically,eight processing procedures are abstracted,where tractors,preparing spots,catapults,and launching are virtualized as machines.By analyzing the constraints in sortie scheduling,a mixed-integer planning model is constructed.In particular,the constraint on preparing spot occupancy is improved to further enhance the sortie efficiency.The basic trajectory library for each dispatch entity is generated and a delayed strategy is integrated to address the collision-avoidance issue.To efficiently solve the formulated HFSP,which is essentially a combinatorial problem with tightly coupled constraints,a chaos-initialized genetic algorithm is developed.The solution framework is validated by the simulation environment referring to the Fort-class carrier,exhibiting higher sortie efficiency when compared to existing strategies.And animation of the simulation results is available at www.bilibili.com/video/BV14t421A7Tt/.The study presents a promising supporting technique for autonomous flight deck operation in the foreseeable future,and can be easily extended to other supporting scenarios,e.g.,ammunition delivery and aircraft maintenance.
文摘In this manuscript, Local dynamic behaviors including stability and Hopf bifurcation of a new four-dimensional quadratic autonomous system are studied both analytically and numerically. Determining conditions of equilibrium points on different parameters are derived. Next, the stability conditions are investigated by using Routh-Hurwitz criterion and bifurcation conditions are investigated by using Hopf bifurcation theory, respectively. It is found that Hopf bifurcation on the initial point is supercritical in this four-dimensional autonomous system. The theoretical results are verified by numerical simulation. Besides, the new four-dimensional autonomous system under the parametric conditions of hyperchaos is studied in detail. It is also found that the system can enter hyperchaos, first through Hopf bifurcation and then through periodic bifurcation.
文摘Integrating autonomous vehicles (AVs) and autonomous parking spaces (APS) marks a transformative development in urban mobility and sustainability. This paper reflects on these technologies’ historical evolution, current interdependence, and future potential through the lens of environmental, social, and economic sustainability. Historically, parking systems evolved from manual designs to automated processes yet remained focused on convenience rather than sustainability. Presently, advancements in smart infrastructure and vehicle-to-infrastructure (V2I) communication have enabled AVs and APS to operate as a cohesive system, optimizing space, energy, and transportation efficiency. Looking ahead, the seamless integration of AVs and APS into broader smart city ecosystems promises to redefine urban landscapes by repurposing traditional parking infrastructure into multifunctional spaces and supporting renewable energy initiatives. These technologies align with global sustainability goals by mitigating emissions, reducing urban sprawl, and fostering adaptive land uses. This reflection highlights the need for collaborative efforts among stakeholders to address regulatory and technological challenges, ensuring the equitable and efficient deployment of AVs and APS for smarter, greener cities.
文摘Autonomous Transporta tion Research(中文刊名《自主交通研究》,简称ATRes期刊)是由武汉理工大学主办,水路交通控制全国重点实验室、国家水运安全工程技术研究中心、交通信息与安全教育部工程研究中心等协办,科爱出版社出版发行的英文开放获取式高水平学术期刊,国际标准连续出版物号:ISSN 3050-8622。
文摘With the rapid development of Internet of things technology,the efficiency of data transmission between devices has been significantly improved.However,the open network environment also poses serious security risks.This paper proposes an innovative fingerprint template protection scheme,which generates key streams through an improved fourdimensional superchaotic system(4CSCS),uses the space-filling property of Hilbert curves to achieve pixel scrambling,and introduces dynamic DNA encoding to improve encryption.Experimental results show that this scheme has a large key space 2^(528),encrypts image information entropy of more than 7.9970,and shows excellent performance in defending against statistical attacks and differential attacks.Compared with existing methods,this scheme has significant advantages in terms of encryption performance and security,and provides a reliable protection mechanism for fingerprint authentication systems in the Internet of things environment.
基金supported by the National Level Project of China (No. 2020-JCJQ-ZQ-059)。
文摘1. Introduction Celestial navigation is a kind of navigation with a long history.With the increasing demand for intelligent autonomy and antielectromagnetic interference in spacecraft, celestial navigation has become one of the current research hotspots in spacecraft autonomous navigation. Spacecraft face complex electromagnetic interference in orbit. The time-varying, non-Gaussian interference from internal devices and external environment can lead to measurement distortion.
文摘Enhancing Autonomous Decision-Making (ADM) for unmanned combat aerial vehicle formations in beyond-visual-range air combat is pivotal for future battlefields, whereas the predominant reinforcement learning technique for ADM has been proven to be inadequately fitting complex tactical Unit Coordination (UC), limiting the integrity of decision-making for formations. This study proposes a knowledge-enhanced ADM method, with a focus on UC, to elevate formation combat effectiveness. The main innovation is integrating data mining technique with tactical knowledge mining and integration. Foremost, based on Frequent Event Arrangement Mining (FEAM) theory, a cross-channel UC knowledge mining method is designed by introducing data flow, which is capable of capturing dynamic coordinative action sequences. Then, a dual-mode knowledge integration method is proposed by employing the Graph Attention Network (GAT) and attenuated structural similarity, bolstering the interplay between autonomous UC tactics fitting and knowledge injection. The experimental results demonstrate that the algorithm surpasses the existing methods, providing more strategic maneuver trajectories and a win rate of more than 90% in different scenarios. The method is promising to augment the autonomous operational capabilities of unmanned formations and drive the evolution of combat effectiveness.
文摘Leesmidt et al present a comprehensive analysis of abdominal vascular flow in children using four-dimensional(4D)flow magnetic resonance imaging(MRI),aim to establish normal hemodynamic values for the abdominal visceral organs and to assess the feasibility of 4D flow MRI(4D-f-MRI)in this population.The researchers performed 4D-f-MRI on 9 pediatric patients with a history or suspi-cion of bowel pathology.Flow velocities were measured in the abdominal aorta and superior and inferior mesenteric arteries.The quality of the 4D-f-MRI images was evaluated,and the agreement between the measured flow velocities and those obtained from Duplex ultrasound was established.However,due to the specific limitations of this work,future studies should address the issues of small sample size and the specific age group design.
文摘For decades,antigen presentation on major histocompatibility complex class I for T cell-mediated immunity has been considered the primary function of proteasome-derived peptides1,2.However,whether the products of proteasomal degradation play additional parts in mounting immune responses remains unknown.Antimicrobial peptides serve as a first line of defence against invading pathogens before the adaptive immune system responds.Although the protective function of antimicrobial peptides across numerous tissues is well established,the cellular mechanisms underlying their generation are not fully understood.Here we uncover a role for proteasomes in the constitutive and bacterial-induced generation of defence peptides that impede bacterial growth both in vitro and in vivo by disrupting bacterial membranes.In silico prediction of proteome-wide proteasomal cleavage identified hundreds of thousands of potential proteasome-derived defence peptides with cationic properties that may be generated en route to degradation to act as a first line of defence.
基金2024 University-level Innovation and Entrepreneurship Educational Reform Project,“Research on the Innovation and Entrepreneurship Education Model of Higher Vocational Colleges Based on the Theory of Technological Innovation Diffusion”(Project No.:CYJG202414)Academic Year Higher Education Institution Graduate Employment and Entrepreneurship Research Project,“Research on Strategies for Cultivating Innovation and Entrepreneurship Abilities Among Graduates of Higher Vocational Colleges”(Project No.:GJXY2024N083)2024 Guangdong Province General Higher Education Institution Specialized Innovation Project,“Research on a Specialized-Entrepreneurial Integration Talent Development System Guided by Core Competencies in the Era of Artificial Intelligence”(Project No.:2024WTSCX339)。
文摘Addressing the core weaknesses in the innovation and entrepreneurship capabilities of vocational college graduates,such as market insight and risk tolerance,as well as issues with the existing training model,including courses that are disconnected from industry,a lack of systematic practical training,and superficial school-enterprise cooperation,this paper constructs a“three-dimensional,four-dimensional”training system.The“three-dimensional”foundational framework encompasses three pillars:curriculum,general education layer,professional integration layer,practical application layer,practice as in three stages:introductory,simulated,and practical,and support including dual mentors,policies,and platforms.The“four-dimensional”differentiated strategies include four implementation pathways:professional differentiation,stage differentiation,addressing capability shortcomings,and school-government-industry collaboration.This system is grounded in theories such as multiple intelligences theory and systems theory,forming a closed-loop process of“theoretical input—practical application—support mechanisms”.Based on the practices of Guangdong Vocational Institute of Public Administration,the paper proposes a competency development pathway tailored by major and stage,which can effectively enhance the innovative and entrepreneurial core competencies of vocational college graduates.This provides a replicable systematic solution for vocational college innovative and entrepreneurial education,supporting vocational education reform and regional economic development.
基金Supported by National Natural Science Foundation of China(Grant No.61803206)Jiangsu Provincial Natural Science Foundation(Grant No.222300420468)Jiangsu Provincial key R&D Program(Grant No.BE2017008-2).
文摘Complex road conditions without signalized intersections when the traffic flow is nearly saturated result in high traffic congestion and accidents,reducing the traffic efficiency of intelligent vehicles.The complex road traffic environment of smart vehicles and other vehicles frequently experiences conflicting start and stop motion.The fine-grained scheduling of autonomous vehicles(AVs)at non-signalized intersections,which is a promising technique for exploring optimal driving paths for both assisted driving nowadays and driverless cars in the near future,has attracted significant attention owing to its high potential for improving road safety and traffic efficiency.Fine-grained scheduling primarily focuses on signalized intersection scenarios,as applying it directly to non-signalized intersections is challenging because each AV can move freely without traffic signal control.This may cause frequent driving collisions and low road traffic efficiency.Therefore,this study proposes a novel algorithm to address this issue.Our work focuses on the fine-grained scheduling of automated vehicles at non-signal intersections via dual reinforced training(FS-DRL).For FS-DRL,we first use a grid to describe the non-signalized intersection and propose a convolutional neural network(CNN)-based fast decision model that can rapidly yield a coarse-grained scheduling decision for each AV in a distributed manner.We then load these coarse-grained scheduling decisions onto a deep Q-learning network(DQN)for further evaluation.We use an adaptive learning rate to maximize the reward function and employ parameterεto tradeoff the fast speed of coarse-grained scheduling in the CNN and optimal fine-grained scheduling in the DQN.In addition,we prove that using this adaptive learning rate leads to a converged loss rate with an extremely small number of training loops.The simulation results show that compared with Dijkstra,RNN,and ant colony-based scheduling,FS-DRL yields a high accuracy of 96.5%on the sample,with improved performance of approximately 61.54%-85.37%in terms of the average conflict and traffic efficiency.
文摘This paper introduces autonomous driving image perception technology,including deep learning models(such as CNN and RNN)and their applications,analyzing the limitations of traditional algorithms.It elaborates on the shortcomings of Faster R-CNN and YOLO series models,proposes various improvement techniques such as data fusion,attention mechanisms,and model compression,and introduces relevant datasets,evaluation metrics,and testing frameworks to demonstrate the advantages of the improved models.
文摘This study aims to develop a quantum computing-based neurocognitive architecture that allows an agent to perform autonomous behaviors.Therefore,we present a brain-inspired cognitive architecture for autonomous agents that integrates a prefrontal cortex-inspired model with modern deep learning(a transformer-based reinforcement learning module)and quantum algorithms.In particular,our framework incorporates quantum computational routines(Deutsch-Jozsa,Bernstein-Vazirani,and Grover’s search)to enhance decision-making efficiency.As a novelty of this research,this comprehensive computational structure is empowered by quantum computing operations so that superiority in speed and robustness of learning compared to classical methods can be demonstrated.Another main contribution is that the proposed architecture offers some features,such as meta-cognition and situation awareness.The meta-cognition aspect is responsible for hierarchically learning sub-tasks,enabling the agent to achieve the master goal.The situation-awareness property identifies how spatial-temporal reasoning activities related to the world model of the agent can be extracted in a dynamic simulation environment with unstructured uncertainties by quantum computation-based machine learning algorithms with the explainable artificial intelligence paradigm.In this research,the Minecraft game-based simulation environment is utilized for the experimental evaluation of performance and verification tests within complex,multi-objective tasks related to the autonomous behaviors of a smart agent.By implementing several interaction scenarios,the results of the system performance and comparative superiority over alternative solutions are presented,and it is discussed how these autonomous behaviors and cognitive skills of a smart agent can be improved in further studies.Results show that the quantum-enhanced agent achieves faster convergence to an 80%task 2×success rate in exploration tasks and approximately 15%higher cumulative rewards compared to a classical deep RL baseline.These findings demonstrate the potential of quantum algorithms to significantly improve learning and performance in cognitive agent architectures.However,advantages are task-specific and less pronounced under high-uncertainty,reactive scenarios.Limitations of the simulation environment are acknowledged,and a structured future research roadmap is proposed involving highfidelity simulation validation,hardware-in-the-loop robotic testing,and integration of advanced hybrid quantum-classical architectures.
基金supported by the National Natural Science Foundation of China(Nos.52275290 and 51905222)the Research Project of State Key Laboratory of Mechanical System and Oscillation(No.MSV202419)+2 种基金Major Program of the National Natural Science Foundation of China(NSFC)for Basic Theory and Key Technology of Tri-Co Robots(No.92248301)Opening Project of the Key Laboratory of Bionic Engineering(Ministry of Education),Jilin University(No.KF2023006)Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.SJCX23_2091)。
文摘Autonomous,adaptable,and multimodal locomotion capabilities,which are crucial for the advanced intelligence of biological systems.A prominent focus of investigations in the domain of bionic soft robotics pertains to the emulation of autonomous motion,as observed in natural organisms.This research endeavor faces the challenge of enabling spontaneous and sustained motion in soft robots without relying on external stimuli.Considerable progress has been made in the development of autonomous bionic soft robots that utilize smart polymer materials,particularly in the realms of material design,microfabrication technology,and operational mechanisms.Nonetheless,there remains a conspicuous deficiency in the literature concerning a thorough review of this subject matter.This study aims to provide a comprehensive review of autonomous soft robots that have been developed based on self-regulation strategies that encompass self-propulsion,self-oscillation,multistimulus response,and topological constraint structures.Furthermore,this review engages in an in-depth discussion regarding their tunable selfsustaining motion and recovery capabilities,while also contemplating the future development of autonomous soft robotic systems and their potential applications in fields such as biomechanics.
文摘Lethal autonomous weapon systems(LAWS)have become a prominent issue in global security governance.However,significant divergences remain among the world's major countries regarding their regulation:some support restrictions,while others oppose any binding rules,and there are also countries adopting a neutral stance,though their choices are subject to external pressure.In the foreseeable future,the deployment of LAWS will increase further,raising the risk of arms races,with international support for regulating these weapons gaining strength.Yet,establishing legally binding international rules within the United Nations(UN)framework remains a distant prospect.Given this backdrop,China should continue to actively participate in international norms setting concerning LAWS under UN auspices and deepen coordination and cooperation with other countries towards more applicable norms and global governance frameworks.
基金Supported by National Natural Science Foundation of China (Grant Nos. 52072215, 52221005, 52272386)Beijing Municipal Natrual Science Foundation (Grant No. L243025)+2 种基金National Key R&D Program of China (Grant No. 2022YFB2503003)State Key Laboratory of Intelligent Green Vehicle and Mobilityfundamental Research Funds for the Central Universities
文摘Autonomous trucks have the potential to enhance both safety and convenience in intelligent transportation.However,their maximum speed and ability to navigate a variety of driving conditions,particularly uneven roads,are limited by a high center of gravity,which increases the risk of rollover.Road bulges,sinkholes,and unexpected debris all present additional challenges for autonomous trucks’operational design,which current perception and decisionmaking algorithms often overlook.To mitigate rollover risks and improve adaptability to damaged roads,this paper presents a novel Road Obstacle-Involved Trajectory Planner(ROITP).The planner categorizes road obstacles using a learning-based algorithm.A discrete optimization algorithm selects a multi-objective optimal trajectory while taking into account constraints and objective functions derived from truck dynamics.Validation across various scenarios on a hardware-in-loop platform demonstrates that the proposed planner is effective and feasible for real-time implementation.
文摘BACKGROUND Four-dimensional(4D)flow magnetic resonance imaging(MRI)is used as a noninvasive modality for assessing hemodynamic information with neurovascular and body applications.The application of 4D flow MRI for assessment of bowel disease in children has not been previously described.AIM To determine feasibility of superior mesenteric venous and arterial flow quantitation in pediatric patients using 4D flow MRI.METHODS Nine pediatric patients(7-14 years old,5 male and 4 female)with history or suspicion of bowel pathology,who underwent magnetic resonance(MR)enterography with 4D flow MR protocol from November 2022 to October 2023.Field strength/sequence:3T MRI using 4D flow MR protocol.Flow velocity and peak speed measurements were performed by two diagnostic radiologists placing the region of interest in perpendicular plane to blood flow on each cross section of superior mesenteric artery(SMA)and superior mesenteric vein(SMV)at three predetermined levels.Bland-Altman analysis,showed good agreement of flow velocity and peak speed measurements of SMV and SMA between two readers.RESULTS Mean SMV flow velocity increased from proximal to mid to distal(0.14 L/minute,0.17 L/minute,0.22 L/minute respectively).Mean SMA flow velocity decreased from proximal to mid to distal(0.35 L/minute,0.27 L/minute,0.21 L/minute respectively).Observed agreement was good for flow velocity measurements of SMV(mean bias-0.01 L/minute and 95%limits of agreement,-0.09 to 0.08 L/minute)and SMA(mean bias-0.03 L/minute and 95%limits of agreement,-0.23 to 0.17 L/minute)between two readers.Good agreement for peak speed measurements of SMV(mean bias-1.2 cm/second and 95%limits of agreement,-9.4 to 7.0 cm/second)and SMA(mean bias-3.2 cm/second and 95%limits of agreement,-31.4 to 24.9 cm/second).CONCLUSION Flow quantitation using 4D Flow is feasible to provide hemodynamic information for SMV and SMA in children.
