This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obsta...This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obstacle avoidance.The system employs the YOLOv7 deep-learning framework for semantic detection and SLAM for localization and mapping,fusing geometric and visual data to build a high-fidelity 2D semantic map.This map enables the robot to identify and project object information for improved situational awareness.Experimental results show that object recognition reached 95.4%mAP@0.5.Semantic completeness increased from 68.7%(single view)to 94.1%(multi-view)with an average position error of 3.1 cm.During navigation,the robot achieved 98.0%reliability,avoided moving obstacles in 90.0%of encounters,and replanned paths in 0.42 s on average.The integration of LiDAR-based SLAMwith deep-learning–driven semantic perception establishes a robust foundation for intelligent,adaptive,and safe robotic navigation in dynamic environments.展开更多
Researchers are increasingly focused on enabling groups of multiple unmanned vehicles to operate cohesively in complex,real-world environments,where coordinated formation control and obstacle avoidance are essential f...Researchers are increasingly focused on enabling groups of multiple unmanned vehicles to operate cohesively in complex,real-world environments,where coordinated formation control and obstacle avoidance are essential for executing sophisticated collective tasks.This paper presents a Distributed Formation Control and Obstacle Avoidance(DFCOA)framework for multi-unmanned ground vehicles(UGV).DFCOA integrates a virtual leader structure for global guidance,an improved A^(*)path planning algorithm with an advanced cost function for efficient path planning,and a repulsive-force-based improved vector field histogram star(VFH^(*))technique for collision avoidance.The virtual leader generates a reference trajectory while enabling distributed execution;the improved A^(*)algorithm reduces planning time and number of nodes to determine the shortest path from the starting position to the goal;and the improved VFH^(*)uses 2D LiDAR data with inter-agent repulsive force to simultaneously avoid collision with obstacles and maintain safe inter-vehicle distances.The formation stability of the proposed DFCOA reaches 95.8%and 94.6%in two scenarios,with root mean square(RMS)centroid errors of 0.9516 and 1.0008 m,respectively.Velocity tracking is precise(velocity centroid error RMS of 0.2699 and 0.1700 m/s),and linear velocities closely match the desired 0.3 m/s.Safety metrics showed average collision risks of 0.7773 and 0.5143,with minimum inter-vehicle distances of 0.4702 and 0.8763 m,confirming collision-free navigation of four UGVs.DFCOA outperforms conventional methods in formation stability,path efficiency,and scalability,proving its suitability for decentralized multi-UGV applications.展开更多
In response to the challenges faced by unmanned swarms in mountain obstacle-breaching missions within complex terrains,such as poor task-resource coupling,lengthy solution generation times,and poor inter-platform coll...In response to the challenges faced by unmanned swarms in mountain obstacle-breaching missions within complex terrains,such as poor task-resource coupling,lengthy solution generation times,and poor inter-platform collaboration,an unmanned swarm scheduling strategy tailored is proposed for mountain obstacle-breaching missions.Initially,by formalizing the descriptions of obstacle breaching operations,the swarm,and obstacle targets,an optimization model is constructed with the objectives of expected global benefit,timeliness,and task completion degree.A meta-task decomposition and reassembly strategy is then introduced to more precisely match the capabilities of unmanned platforms with task requirements.Additionally,a meta-task decomposition optimization model and a meta-task allocation operator are incorporated to achieve efficient allocation of swarm resources and collaborative scheduling.Simulation results demonstrate that the model can accurately generate reasonable and feasible obstacle breaching execution plans for unmanned swarms based on specific task requirements and environmental conditions.Moreover,compared to conventional strategies,the proposed strategy enhances task completion degree and expected returns while reducing the execution time of the plans.展开更多
Legged robots have considerable potential for traversing unstructured situations;nonetheless,their inflexible frameworks often constrain adaptability and obstacle negotiation.The study article presents a revolutionary...Legged robots have considerable potential for traversing unstructured situations;nonetheless,their inflexible frameworks often constrain adaptability and obstacle negotiation.The study article presents a revolutionary Soft Tri-Legged Robot(STLR)that improves movement and obstacle-avoidance skills by using a bio-inspired pneumatic artificial muscle(Bubble Artificial Muscles)and a bio-inspired tactile sensor(TacTip).The STLR is activated by BAMs,which are flexible,pneu-matic-driven actuators that provide fine control over forward,backward,and steering movements.Obstacle identification and avoidance are facilitated by the TacTip sensor,which delivers tactile input for traversing unstructured terrains.We delineate the mechanical features of the BAMs,assess the functionality of the robot's legs,and elaborate on the incorpora-tion of the tactile sensing system.Experimental results demonstrate that the STLR can effectively achieve multi-directional flexible movement and obstacle avoidance through a cross-modal perception-actuation mechanism.This study highlights the promise of soft robotics for search and rescue,medical aid,and autonomous exploration,while delineating difficulties and opportunities for future improvements in functionality and efficiency.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
The flow field architecture of the proton exchange membrane fuel cell(PEMFC)cathode critically determines its performance.To enhance PEMFC operation through structural optimization,trapezoidal obstacles were implement...The flow field architecture of the proton exchange membrane fuel cell(PEMFC)cathode critically determines its performance.To enhance PEMFC operation through structural optimization,trapezoidal obstacles were implemented in the cathode flow channels.The height dependence of these obstacles was systematically investigated,revealing that a 0.7 mm obstacle height enhanced mass transfer from channels to the gas diffusion layer(GDL)compared to conventional triple-serpentine designs.This configuration achieved a 12.08%increase in limiting current density alongside improved water management.Subsequent studies on obstacle distribution density identified 75%density as optimal,delivering maximum net power density with 10.6%lower pressure drop than full-density arrangements.展开更多
Every day walking consists of frequent voluntary modifications in the gait pattern to negotiate obstacles.After spinal cord injury,stepping over an obstacle becomes challenging.Stepping over an obstacle requires senso...Every day walking consists of frequent voluntary modifications in the gait pattern to negotiate obstacles.After spinal cord injury,stepping over an obstacle becomes challenging.Stepping over an obstacle requires sensorimotor transformations in several structures of the brain,including the parietal cortex,premotor cortex,and motor cortex.Sensory information and planning are transformed into motor commands,which are sent from the motor cortex to spinal neuronal circuits to alter limb trajectory,coordinate the limbs,and maintain balance.After spinal cord injury,bidirectional communication between the brain and spinal cord is disrupted and animals,including humans,fail to voluntarily modify limb trajectory to step over an obstacle.Therefore,in this review,we discuss the neuromechanical control of stepping over an obstacle,why it fails after spinal cord injury,and how it recovers to a certain extent.展开更多
In recent years,the rapid evolution of unmanned aerial vehicles(UAVs)has brought about transformative changes across various industries.However,addressing fundamental challenges in UAV technology,particularly target t...In recent years,the rapid evolution of unmanned aerial vehicles(UAVs)has brought about transformative changes across various industries.However,addressing fundamental challenges in UAV technology,particularly target tracking and obstacle avoidance,remains crucial for wildlife protection,military industry security,etc.Many existing methods based on reinforcement learning to solve UAV multi-tasks need to be redesigned and retrained,and cannot be quickly and effectively extended to other scenarios.To this end,we propose a novel solution based on a hazard-aware weighted advantage combination for UAV target tracking and obstacle avoidance.First,we independently trained the UAV target tracking and obstacle avoidance using the dueling double deep Q-network reinforcement learning algorithm.Subsequently,in a multitasking scenario,we introduce the two pre-trained networks.Meanwhile,we design a weight determined by the present risk level encountered by the UAV.This weight is utilized to perform a weighted summation of the advantage values from both networks,eliminating the need for retraining to obtain the final action.We validate our approach through extensive simulation experiments in the robotics simulator known as CoppeliaSim.The results demonstrate that our method outper-forms current state-of-the-art techniques,achieving superior performance in both tracking accuracy and avoidance of collisions.展开更多
Intelligent robots are increasingly being deployed across industries ranging from manufacturing to household applications and outdoor exploration.Their autonomous obstacle avoidance capabilities in complex environment...Intelligent robots are increasingly being deployed across industries ranging from manufacturing to household applications and outdoor exploration.Their autonomous obstacle avoidance capabilities in complex environments have become a critical factor determining operational stability.Multimodal perception technology,which integrates visual,auditory,tactile,and LiDAR data,provides robots with comprehensive environmental awareness.By establishing efficient autonomous obstacle avoidance decision-making mechanisms based on this information,the system’s adaptability to challenging scenarios can be significantly enhanced.This study investigates the integration of multimodal perception with autonomous obstacle avoidance decision-making,analyzing the acquisition and processing of perceptual information,core modules and logic of decision-making mechanisms,and proposing optimization strategies for specific scenarios.The research aims to provide theoretical references for advancing autonomous obstacle avoidance technology in intelligent robots,enabling safer and more flexible movement in diverse environments.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
[Objectives]This study was conducted to comprehensively understand the changes in gene expression of plants under environmental stress during different growth and development stages.[Methods]The effects of continuous ...[Objectives]This study was conducted to comprehensively understand the changes in gene expression of plants under environmental stress during different growth and development stages.[Methods]The effects of continuous cropping on the roots and leaves of Polygonatum sibiricum were investigated using transcriptome sequencing.Normally-grown first crop P.sibiricum was used as the control group,while continuous cropping plants served as the treatment group.Transcriptomic differences in roots and leaves under different conditions were compared.[Results]The leaf materials of first crop and continuous cropping P.sibiricum(CCLZ vs FCLZ)showed 21916 differentially expressed genes(DEGs),while the root materials of first crop and continuous cropping P.sibiricum(CCRZ vs FCRZ)exhibited 12726 DEGs(the lowest DEG count)(12726).Among them,1896 DEGs were common.GO enrichment analysis revealed that DEGs were mainly enriched in metabolism,cell wall degradation,and pathogen defense.KEGG enrichment analysis indicated that DEGs in CCLZ vs FCLZ and CCRZ vs FCRZ primarily affected hormone signal transduction and pathogen interaction pathways.[Conclusions]This study preliminarily elucidate the regulatory mechanisms in the roots and leaves of continuous cropping P.sibiricum at the molecular level,providing reference for research on its adaptation to continuous cropping.展开更多
In recent years, the uncontrollable risks of urban production-living-ecological(PLE)space have increased sharply, making resilience enhancement essential for sustainable urban development. Based on the social-ecologic...In recent years, the uncontrollable risks of urban production-living-ecological(PLE)space have increased sharply, making resilience enhancement essential for sustainable urban development. Based on the social-ecological system(SES) theory, this study constructs an assessment framework for urban PLE space resilience by analyzing its inherent characteristics. The central urban area of Ganzhou city is taken as a case study to evaluate urban PLE space resilience and diagnose its obstacles. The results are as follows: The PLE space resilience in the central urban area of Ganzhou exhibits gradations and substantial spatial differentiation. The ecological space resilience in the study area was the highest, followed by that of production space, while living space resilience was the lowest. The primary factors influencing PLE space resilience are concentrated in the dimensions of robustness and adaptability. In particular, the robustness of the PLE space is relatively low. Based on these results, targeted spatial resilience governance strategies for the PLE space have been proposed. These strategies serve as theoretical and technical references for the study area. By adopting the PLE space perspective, this paper enriches resilience research and provide theoretical support for sustainable urban development.展开更多
A three-dimensional path-planning approach has been developed to coordinate multiple fixed-wing unmanned aerial vehicles(UAVs)while avoiding collisions.The hierarchical path-planning architecture that divides the path...A three-dimensional path-planning approach has been developed to coordinate multiple fixed-wing unmanned aerial vehicles(UAVs)while avoiding collisions.The hierarchical path-planning architecture that divides the path-planning process into two layers is proposed by designing the velocityobstacle strategy for satisfying timeliness and effectiveness.The upper-level layer focuses on creating an efficient Dubins initial path considering the dynamic constraints of the fixed wing.Subsequently,the lower-level layer detects potential collisions and adjusts its flight paths to avoid collisions by using the threedimensional velocity obstacle method,which describes the maneuvering space of collision avoidance as the intersection space of half space.To further handle the dynamic and collisionavoidance constraints,a priority mechanism is designed to ensure that the adjusted path is still feasible for fixed-wing UAVs.Simulation experiments demonstrate the effectiveness of the proposed method.展开更多
Despite its immense potential,the application of digital twin technology in real industrial scenarios still faces numerous challenges.This study focuses on industrial assembly lines in sectors such as microelectronics...Despite its immense potential,the application of digital twin technology in real industrial scenarios still faces numerous challenges.This study focuses on industrial assembly lines in sectors such as microelectronics,pharmaceuticals,and food packaging,where precision and speed are paramount,applying digital twin technology to the robotic assembly process.The innovation of this research lies in the development of a digital twin architecture and system for Delta robots that is suitable for real industrial environments.Based on this system,a deep reinforcement learning algorithm for obstacle avoidance path planning in Delta robots has been developed,significantly enhancing learning efficiency through an improved intermediate reward mechanism.Experiments on communication and interaction between the digital twin system and the physical robot validate the effectiveness of this method.The system not only enhances the integration of digital twin technology,deep reinforcement learning and robotics,offering an efficient solution for path planning and target grasping inDelta robots,but also underscores the transformative potential of digital twin technology in intelligent manufacturing,with extensive applicability across diverse industrial domains.展开更多
Under the new development pattern,promoting the balanced development of the regional life service level is the key to attaining the goal of meeting people’s need for an improved life.This study constructed an index s...Under the new development pattern,promoting the balanced development of the regional life service level is the key to attaining the goal of meeting people’s need for an improved life.This study constructed an index system with six dimensions,namely,economic base,innovation drive,living environment,transportation,public services and life security,and explored the balanced characteristics and obstacles of the life service level in the Yangtze River Delta,China in 2020 using the Gini coefficient,the standard deviation ellipse,the global spatial autocorrelation,the equilibrium entropy and the obstacle degree model.Results show that:1)the current overall level of life services in the Yangtze River Delta is in relative equilibrium,and the Gini coefficient of three dimensions,namely,economic base,innovation drive and life security,is relatively low and on the verge of imbalance.2)The spatial pattern of the six dimensions of the overall level of the life service is oriented toward the‘southeast-northwest’direction with significant spatial differentiation and spatial agglomeration.3)At present,most of the cities have a comparative advantage in the coordination of their life services,and the potential of their life service system shows room for further improvement in the future.4)Currently,the quality of economic development,talent concentration,innovation inputs,innovation outputs,basic education,health care,cultural sharing,the living standards of the urban and rural residents and the construction of a transportation system are the main factors constraining the improvement of the level of life services in the Yangtze River Delta.This study attempts to research on the evaluation and measurement of regional life service level in the new era,and provides a reference for the promotion of regional coordination and sustainable development.展开更多
Current developments in magnetohydrodynamic(MHD)convection and nanofluid engineering technology have have greatly enhanced heat transfer performance in process systems,particularly through the use of carbon nanotube(C...Current developments in magnetohydrodynamic(MHD)convection and nanofluid engineering technology have have greatly enhanced heat transfer performance in process systems,particularly through the use of carbon nanotube(CNT)–based fluids that offer exceptional thermal conductivity.Despite extensive research on MHD natural convection in enclosures,the combined effects of complex obstacle geometries,magnetic fields,and CNT nanofluids in three-dimensional configurations remain insufficiently explored.This research investigates MHD natural convection of carbon nanotube(CNT)-water nanofluid within a three-dimensional cavity.The study considers an inclined cross-shaped hot obstacle,a configuration not extensively explored in previous works.The work aims to elucidate the combined effects of CNT nanofluid concentration,magnetic field strength,and obstacle inclination on fluid flow patterns and heat transfer characteristics.Numerical simulations are performed using the finite element method(FEM)based on the Galerkin Weighted Residual approach.The analysis systematically considers variations in Rayleigh number(Ra),Hartmann number(Ha),nanoparticle volume fraction(Φ),and obstacle inclination angle(θ).Results show that increasing Ra from 103 to 106 enhances convective heat transfer by up to 228%,while raising the CNT volume fraction to 4.5%improves heat transfer by about 64%.In contrast,strengthening the magnetic field from Ha=0 to Ha=100 suppresses fluid motion and reduces heat transfer by nearly 67%,whereas varying the obstacle inclination from 0○to 45○leads to a 4.6%decrease in efficiency.The addition of nanoparticles slightly increases viscosity,reducing flow intensity by 8.3%when Ha=0.Furthermore,a novel multiparametric correlation is proposed,accurately predicting the average Nusselt number as a function of Ra,Ha,ϕ,andθ,with an R2 of 0.98.These findings provide new insights into the role of geometry,magnetic effects,and nanofluids in heat transfer enhancement,offering practical guidance for the design and optimization of advanced thermal systems.展开更多
To avoid collisions between a suspended object,cables,towing robots,and obstacles in the environment in a multi-robot suspension system,obstacle avoidance planning was studied based on a collaborative optimization met...To avoid collisions between a suspended object,cables,towing robots,and obstacles in the environment in a multi-robot suspension system,obstacle avoidance planning was studied based on a collaborative optimization method for force and position.Based on the analysis of the kinematics and dynamics of the system,the inverse kinematics and inverse dynamics of the system are solved using the least variance method.The obstacle avoidance planning is performed in the solved collisionfree feasible space using the stable dung beetle optimization(SDBO)algorithm,which ensures that the suspended object can move stably to the target point in the workspace.The optimal obstacle avoidance trajectory of the multi-robot suspension system can be accurately determined by using the collaborative optimization method for force and position to plan the towing robot and the cable.Finally,the correctness of the obstacle avoidance planning method is verified by simulations.By taking a special scenario,the remarkable findings reveal that the SDBO algorithm outperforms the dung beetle optimization algorithm by reducing the length of the planned trajectory of the suspended object by 14.51%and the height by 79.88%,and reducing the minimum fitness by 95.84%and the average fitness by 94.77%.The results can help the multi-robot suspension system to perform various towing tasks safely and stably,and extend the related planning and control theory.展开更多
Rural resilience,a core capability for addressing systemic risks and enabling sustainable development,is increasingly vital to promoting urban-rural integrated development and rural revitalization strategies.However,c...Rural resilience,a core capability for addressing systemic risks and enabling sustainable development,is increasingly vital to promoting urban-rural integrated development and rural revitalization strategies.However,current research lacks exploration of the collaborative mechanisms between rural economic resilience(RER)and rural social resilience(RSR)in ecologically vulnerable areas.Based on the practical context of rural sustainable development in such regions,this study investigates the interaction between RER and RSR from a resilience coordination perspective.In this paper,a rural resilience evaluation framework for collaborative development of economic and social resilience was established.By employing the coupling coordination degree model,obstacle degree model,and equilibrium entropy model,this paper examines the synergies,constraints,and potential of rural resilience subsystems in Jinchang City,Gansu Province,China,in 2020.The results reveal that:1)RER contributes to RSR by stabilizing the economy,enhancing community adaptability,and driving modernization.In turn,RSR strengthens RER by mitigating instability,building social capital,and fostering confidence—together forming a mutually reinforcing coupling mechanism.2)The rural economic and social resilience level in Jinchang City remains generally low with spatially clustered patterns,while the coupling coordination degree is at an intermediate level overall,with 62.59%of villages exhibiting unbalanced development between rural economic and social resilience.3)RER and RSR demonstrate synergistic degradation in ecologically vulnerable areas,where low-level rural economic and social resilience induce integrated systemic deterioration.4)Considering the unbalanced development of rural economic and social resilience in ecologically fragile areas,differentiated coordination pathways are proposed for three village typologies:RER-lagging villages,RSR-lagging villages,and villages where RER and RSR develop synchronously but lack effective coordination.These findings offer spatial governance strategies and practical guidance for enhancing rural resilience and advancing sustainable development in ecologically vulnerable regions.展开更多
Introduction: Vaccination faces several obstacles in the fight against COVID-19, yet it has been identified as one of the most effective means of preventing new epidemics of COVID-19. The aim was to contribute to impr...Introduction: Vaccination faces several obstacles in the fight against COVID-19, yet it has been identified as one of the most effective means of preventing new epidemics of COVID-19. The aim was to contribute to improving vaccination coverage against COVID-19 in the Kindu health zone. Method: We conducted a cross-sectional descriptive study with an analytical focus, using a questionnaire that enabled us to carry out a survey from October 03 to 30, 2022. Our target study population was residents of the Kindu health zone. A total of 420 subjects participated in our study, including 42 per site. Results: The study revealed a low proportion of vaccinated subjects (38.3%) and a high proportion of non-vaccinated subjects (61.70%). Non-belief in the efficacy of vaccines (p = 0.001), infodemia (p = 0.001) and respect for ethnic norms (p = 0.001) were identified as perceived barriers to vaccination. Fear of being branded with the “666” beast badge (p = 0.004) as the perceived severity. Respondents’ perceptions of mass vaccination against COVID-19 are mixed, and their opinions and expectations of COVID-19 vaccination in the town of Kindu are divided. Conclusion: In order to increase the proportion of people vaccinated against COVID-19, it is suggested here to increase the population’s ability to detect false information through a well-structured communication and health education program.展开更多
In the ever-evolving landscape of cancer therapy,while cancer treatments such as chemotherapy,radiotherapy,and targeted therapy aim to eradicate malignant cells,they also inadvertently trigger cellular senescence in b...In the ever-evolving landscape of cancer therapy,while cancer treatments such as chemotherapy,radiotherapy,and targeted therapy aim to eradicate malignant cells,they also inadvertently trigger cellular senescence in both cancerous and microenvironmental tissues.Therapy-induced senescence(TIS)can act as a barrier against tumor growth by halting cell proliferation in the short term,but the long-term persistence of therapy-induced senescent(TISnt)cells may pose a significant challenge in cancer management.Their distinct characteristics,like senescence-associated secretory phenotype(SASP),metabolic dysregulation,and immune evasion,make them exhibit remarkable heterogeneity to orchestrate the tumor microenvironment(TME),resulting in therapy resistance.However,how these TISnt cells functioning differently in cancer progression,and the intricate mechanisms by which they remodel the senescence-associated immunosuppressive microenvironment present challenges for improving anticancer therapy.Therefore,this review summarizes the heterogeneous TISnt cell phenotypes contributing to an accumulated senescent state,outlines their multidimensional interactions in the senescent microenvironment,and discusses current senescence-targeting strategies.Building on the current understanding of TIS,we propose potential avenues for improving TIS-targeting methodologies in the context of head and neck cancer,a representative heterogeneous malignancy,which can substantially enhance the efficacy of the“one-two punch”sequential treatment approach for head and neck cancer.展开更多
基金supported by the National Science and Technology Council of under Grant NSTC 114-2221-E-130-007.
文摘This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obstacle avoidance.The system employs the YOLOv7 deep-learning framework for semantic detection and SLAM for localization and mapping,fusing geometric and visual data to build a high-fidelity 2D semantic map.This map enables the robot to identify and project object information for improved situational awareness.Experimental results show that object recognition reached 95.4%mAP@0.5.Semantic completeness increased from 68.7%(single view)to 94.1%(multi-view)with an average position error of 3.1 cm.During navigation,the robot achieved 98.0%reliability,avoided moving obstacles in 90.0%of encounters,and replanned paths in 0.42 s on average.The integration of LiDAR-based SLAMwith deep-learning–driven semantic perception establishes a robust foundation for intelligent,adaptive,and safe robotic navigation in dynamic environments.
文摘Researchers are increasingly focused on enabling groups of multiple unmanned vehicles to operate cohesively in complex,real-world environments,where coordinated formation control and obstacle avoidance are essential for executing sophisticated collective tasks.This paper presents a Distributed Formation Control and Obstacle Avoidance(DFCOA)framework for multi-unmanned ground vehicles(UGV).DFCOA integrates a virtual leader structure for global guidance,an improved A^(*)path planning algorithm with an advanced cost function for efficient path planning,and a repulsive-force-based improved vector field histogram star(VFH^(*))technique for collision avoidance.The virtual leader generates a reference trajectory while enabling distributed execution;the improved A^(*)algorithm reduces planning time and number of nodes to determine the shortest path from the starting position to the goal;and the improved VFH^(*)uses 2D LiDAR data with inter-agent repulsive force to simultaneously avoid collision with obstacles and maintain safe inter-vehicle distances.The formation stability of the proposed DFCOA reaches 95.8%and 94.6%in two scenarios,with root mean square(RMS)centroid errors of 0.9516 and 1.0008 m,respectively.Velocity tracking is precise(velocity centroid error RMS of 0.2699 and 0.1700 m/s),and linear velocities closely match the desired 0.3 m/s.Safety metrics showed average collision risks of 0.7773 and 0.5143,with minimum inter-vehicle distances of 0.4702 and 0.8763 m,confirming collision-free navigation of four UGVs.DFCOA outperforms conventional methods in formation stability,path efficiency,and scalability,proving its suitability for decentralized multi-UGV applications.
基金supported by the National Natural Science Foundation of China(61374186)。
文摘In response to the challenges faced by unmanned swarms in mountain obstacle-breaching missions within complex terrains,such as poor task-resource coupling,lengthy solution generation times,and poor inter-platform collaboration,an unmanned swarm scheduling strategy tailored is proposed for mountain obstacle-breaching missions.Initially,by formalizing the descriptions of obstacle breaching operations,the swarm,and obstacle targets,an optimization model is constructed with the objectives of expected global benefit,timeliness,and task completion degree.A meta-task decomposition and reassembly strategy is then introduced to more precisely match the capabilities of unmanned platforms with task requirements.Additionally,a meta-task decomposition optimization model and a meta-task allocation operator are incorporated to achieve efficient allocation of swarm resources and collaborative scheduling.Simulation results demonstrate that the model can accurately generate reasonable and feasible obstacle breaching execution plans for unmanned swarms based on specific task requirements and environmental conditions.Moreover,compared to conventional strategies,the proposed strategy enhances task completion degree and expected returns while reducing the execution time of the plans.
基金the Natural Science Foundation of China(Project for Young Scientists:Grant No.52105010,Regular Project:Grant No.62173096)Natural Science Foundationof Guangdong Province(Regular Project:Grant No.2025A1515012124,Grant No.2022A1515010327)Guangdong-Hong Kong-Macao Key Laboratory of Multi-scaleInformation Fusion and Collaborative Optimization Control Manufacturing Process.
文摘Legged robots have considerable potential for traversing unstructured situations;nonetheless,their inflexible frameworks often constrain adaptability and obstacle negotiation.The study article presents a revolutionary Soft Tri-Legged Robot(STLR)that improves movement and obstacle-avoidance skills by using a bio-inspired pneumatic artificial muscle(Bubble Artificial Muscles)and a bio-inspired tactile sensor(TacTip).The STLR is activated by BAMs,which are flexible,pneu-matic-driven actuators that provide fine control over forward,backward,and steering movements.Obstacle identification and avoidance are facilitated by the TacTip sensor,which delivers tactile input for traversing unstructured terrains.We delineate the mechanical features of the BAMs,assess the functionality of the robot's legs,and elaborate on the incorpora-tion of the tactile sensing system.Experimental results demonstrate that the STLR can effectively achieve multi-directional flexible movement and obstacle avoidance through a cross-modal perception-actuation mechanism.This study highlights the promise of soft robotics for search and rescue,medical aid,and autonomous exploration,while delineating difficulties and opportunities for future improvements in functionality and efficiency.
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.
文摘The flow field architecture of the proton exchange membrane fuel cell(PEMFC)cathode critically determines its performance.To enhance PEMFC operation through structural optimization,trapezoidal obstacles were implemented in the cathode flow channels.The height dependence of these obstacles was systematically investigated,revealing that a 0.7 mm obstacle height enhanced mass transfer from channels to the gas diffusion layer(GDL)compared to conventional triple-serpentine designs.This configuration achieved a 12.08%increase in limiting current density alongside improved water management.Subsequent studies on obstacle distribution density identified 75%density as optimal,delivering maximum net power density with 10.6%lower pressure drop than full-density arrangements.
文摘Every day walking consists of frequent voluntary modifications in the gait pattern to negotiate obstacles.After spinal cord injury,stepping over an obstacle becomes challenging.Stepping over an obstacle requires sensorimotor transformations in several structures of the brain,including the parietal cortex,premotor cortex,and motor cortex.Sensory information and planning are transformed into motor commands,which are sent from the motor cortex to spinal neuronal circuits to alter limb trajectory,coordinate the limbs,and maintain balance.After spinal cord injury,bidirectional communication between the brain and spinal cord is disrupted and animals,including humans,fail to voluntarily modify limb trajectory to step over an obstacle.Therefore,in this review,we discuss the neuromechanical control of stepping over an obstacle,why it fails after spinal cord injury,and how it recovers to a certain extent.
基金supported by the National Natural Science Foundation of China(62236002,61921004).
文摘In recent years,the rapid evolution of unmanned aerial vehicles(UAVs)has brought about transformative changes across various industries.However,addressing fundamental challenges in UAV technology,particularly target tracking and obstacle avoidance,remains crucial for wildlife protection,military industry security,etc.Many existing methods based on reinforcement learning to solve UAV multi-tasks need to be redesigned and retrained,and cannot be quickly and effectively extended to other scenarios.To this end,we propose a novel solution based on a hazard-aware weighted advantage combination for UAV target tracking and obstacle avoidance.First,we independently trained the UAV target tracking and obstacle avoidance using the dueling double deep Q-network reinforcement learning algorithm.Subsequently,in a multitasking scenario,we introduce the two pre-trained networks.Meanwhile,we design a weight determined by the present risk level encountered by the UAV.This weight is utilized to perform a weighted summation of the advantage values from both networks,eliminating the need for retraining to obtain the final action.We validate our approach through extensive simulation experiments in the robotics simulator known as CoppeliaSim.The results demonstrate that our method outper-forms current state-of-the-art techniques,achieving superior performance in both tracking accuracy and avoidance of collisions.
文摘Intelligent robots are increasingly being deployed across industries ranging from manufacturing to household applications and outdoor exploration.Their autonomous obstacle avoidance capabilities in complex environments have become a critical factor determining operational stability.Multimodal perception technology,which integrates visual,auditory,tactile,and LiDAR data,provides robots with comprehensive environmental awareness.By establishing efficient autonomous obstacle avoidance decision-making mechanisms based on this information,the system’s adaptability to challenging scenarios can be significantly enhanced.This study investigates the integration of multimodal perception with autonomous obstacle avoidance decision-making,analyzing the acquisition and processing of perceptual information,core modules and logic of decision-making mechanisms,and proposing optimization strategies for specific scenarios.The research aims to provide theoretical references for advancing autonomous obstacle avoidance technology in intelligent robots,enabling safer and more flexible movement in diverse environments.
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.
基金Supported by Agricultural Science and Technology Innovation Fund of Hunan Province(XCNZ[2021]No.15)Loudi Science and Technology Innovation Program(LKF[2022]29)+1 种基金Applied Characteristic Discipline Construction Project of Hunan Province:Plant ProtectionPostgraduate Research and Innovation Project of Hunan University of Humanities,Science and Technology(ZSCX2022Y12).
文摘[Objectives]This study was conducted to comprehensively understand the changes in gene expression of plants under environmental stress during different growth and development stages.[Methods]The effects of continuous cropping on the roots and leaves of Polygonatum sibiricum were investigated using transcriptome sequencing.Normally-grown first crop P.sibiricum was used as the control group,while continuous cropping plants served as the treatment group.Transcriptomic differences in roots and leaves under different conditions were compared.[Results]The leaf materials of first crop and continuous cropping P.sibiricum(CCLZ vs FCLZ)showed 21916 differentially expressed genes(DEGs),while the root materials of first crop and continuous cropping P.sibiricum(CCRZ vs FCRZ)exhibited 12726 DEGs(the lowest DEG count)(12726).Among them,1896 DEGs were common.GO enrichment analysis revealed that DEGs were mainly enriched in metabolism,cell wall degradation,and pathogen defense.KEGG enrichment analysis indicated that DEGs in CCLZ vs FCLZ and CCRZ vs FCRZ primarily affected hormone signal transduction and pathogen interaction pathways.[Conclusions]This study preliminarily elucidate the regulatory mechanisms in the roots and leaves of continuous cropping P.sibiricum at the molecular level,providing reference for research on its adaptation to continuous cropping.
基金Social Science Foundation Project of Jiangxi Province,No.24GL61D。
文摘In recent years, the uncontrollable risks of urban production-living-ecological(PLE)space have increased sharply, making resilience enhancement essential for sustainable urban development. Based on the social-ecological system(SES) theory, this study constructs an assessment framework for urban PLE space resilience by analyzing its inherent characteristics. The central urban area of Ganzhou city is taken as a case study to evaluate urban PLE space resilience and diagnose its obstacles. The results are as follows: The PLE space resilience in the central urban area of Ganzhou exhibits gradations and substantial spatial differentiation. The ecological space resilience in the study area was the highest, followed by that of production space, while living space resilience was the lowest. The primary factors influencing PLE space resilience are concentrated in the dimensions of robustness and adaptability. In particular, the robustness of the PLE space is relatively low. Based on these results, targeted spatial resilience governance strategies for the PLE space have been proposed. These strategies serve as theoretical and technical references for the study area. By adopting the PLE space perspective, this paper enriches resilience research and provide theoretical support for sustainable urban development.
基金supported by the National Science Fund for Distinguished Young Scholars(52425211)BIT Research Fund Program for Young Scholars(XSQD-202201005).
文摘A three-dimensional path-planning approach has been developed to coordinate multiple fixed-wing unmanned aerial vehicles(UAVs)while avoiding collisions.The hierarchical path-planning architecture that divides the path-planning process into two layers is proposed by designing the velocityobstacle strategy for satisfying timeliness and effectiveness.The upper-level layer focuses on creating an efficient Dubins initial path considering the dynamic constraints of the fixed wing.Subsequently,the lower-level layer detects potential collisions and adjusts its flight paths to avoid collisions by using the threedimensional velocity obstacle method,which describes the maneuvering space of collision avoidance as the intersection space of half space.To further handle the dynamic and collisionavoidance constraints,a priority mechanism is designed to ensure that the adjusted path is still feasible for fixed-wing UAVs.Simulation experiments demonstrate the effectiveness of the proposed method.
基金supported in part by the National Natural Science Foundation of China under Grants 62303098 and 62173073in part by China Postdoctoral Science Foundation under Grant 2022M720679+1 种基金in part by the Central University Basic Research Fund of China under Grant N2304021in part by the Liaoning Provincial Science and Technology Plan Project-Technology Innovation Guidance of the Science and Technology Department under Grant 2023JH1/10400011.
文摘Despite its immense potential,the application of digital twin technology in real industrial scenarios still faces numerous challenges.This study focuses on industrial assembly lines in sectors such as microelectronics,pharmaceuticals,and food packaging,where precision and speed are paramount,applying digital twin technology to the robotic assembly process.The innovation of this research lies in the development of a digital twin architecture and system for Delta robots that is suitable for real industrial environments.Based on this system,a deep reinforcement learning algorithm for obstacle avoidance path planning in Delta robots has been developed,significantly enhancing learning efficiency through an improved intermediate reward mechanism.Experiments on communication and interaction between the digital twin system and the physical robot validate the effectiveness of this method.The system not only enhances the integration of digital twin technology,deep reinforcement learning and robotics,offering an efficient solution for path planning and target grasping inDelta robots,but also underscores the transformative potential of digital twin technology in intelligent manufacturing,with extensive applicability across diverse industrial domains.
基金Under the auspices of National Natural Science Foundation of China(No.42371185)。
文摘Under the new development pattern,promoting the balanced development of the regional life service level is the key to attaining the goal of meeting people’s need for an improved life.This study constructed an index system with six dimensions,namely,economic base,innovation drive,living environment,transportation,public services and life security,and explored the balanced characteristics and obstacles of the life service level in the Yangtze River Delta,China in 2020 using the Gini coefficient,the standard deviation ellipse,the global spatial autocorrelation,the equilibrium entropy and the obstacle degree model.Results show that:1)the current overall level of life services in the Yangtze River Delta is in relative equilibrium,and the Gini coefficient of three dimensions,namely,economic base,innovation drive and life security,is relatively low and on the verge of imbalance.2)The spatial pattern of the six dimensions of the overall level of the life service is oriented toward the‘southeast-northwest’direction with significant spatial differentiation and spatial agglomeration.3)At present,most of the cities have a comparative advantage in the coordination of their life services,and the potential of their life service system shows room for further improvement in the future.4)Currently,the quality of economic development,talent concentration,innovation inputs,innovation outputs,basic education,health care,cultural sharing,the living standards of the urban and rural residents and the construction of a transportation system are the main factors constraining the improvement of the level of life services in the Yangtze River Delta.This study attempts to research on the evaluation and measurement of regional life service level in the new era,and provides a reference for the promotion of regional coordination and sustainable development.
基金Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the project number RI-44-0451.
文摘Current developments in magnetohydrodynamic(MHD)convection and nanofluid engineering technology have have greatly enhanced heat transfer performance in process systems,particularly through the use of carbon nanotube(CNT)–based fluids that offer exceptional thermal conductivity.Despite extensive research on MHD natural convection in enclosures,the combined effects of complex obstacle geometries,magnetic fields,and CNT nanofluids in three-dimensional configurations remain insufficiently explored.This research investigates MHD natural convection of carbon nanotube(CNT)-water nanofluid within a three-dimensional cavity.The study considers an inclined cross-shaped hot obstacle,a configuration not extensively explored in previous works.The work aims to elucidate the combined effects of CNT nanofluid concentration,magnetic field strength,and obstacle inclination on fluid flow patterns and heat transfer characteristics.Numerical simulations are performed using the finite element method(FEM)based on the Galerkin Weighted Residual approach.The analysis systematically considers variations in Rayleigh number(Ra),Hartmann number(Ha),nanoparticle volume fraction(Φ),and obstacle inclination angle(θ).Results show that increasing Ra from 103 to 106 enhances convective heat transfer by up to 228%,while raising the CNT volume fraction to 4.5%improves heat transfer by about 64%.In contrast,strengthening the magnetic field from Ha=0 to Ha=100 suppresses fluid motion and reduces heat transfer by nearly 67%,whereas varying the obstacle inclination from 0○to 45○leads to a 4.6%decrease in efficiency.The addition of nanoparticles slightly increases viscosity,reducing flow intensity by 8.3%when Ha=0.Furthermore,a novel multiparametric correlation is proposed,accurately predicting the average Nusselt number as a function of Ra,Ha,ϕ,andθ,with an R2 of 0.98.These findings provide new insights into the role of geometry,magnetic effects,and nanofluids in heat transfer enhancement,offering practical guidance for the design and optimization of advanced thermal systems.
基金supported by the Excellent Graduate Student“Innovation Star”project of Education Department of Gansu Province(Grant No.2025CXZX-675)the National Natural Science Foundation of China(Grant No.51965032)+3 种基金the National Natural Science Foundation of Gansu Province of China(Grant No.22JR5RA319)the Excellent Doctoral Student Foundation of Gansu Province of China(Grant No.23JRRA842)the Open Project of State Key Laboratory of Rail Transit Vehicle System,Southwest Jiaotong University(Grant No.RVL2411)the Key Research and Development Project of Lanzhou Jiaotong University(Grant No.LZJTU-ZDYF2302).
文摘To avoid collisions between a suspended object,cables,towing robots,and obstacles in the environment in a multi-robot suspension system,obstacle avoidance planning was studied based on a collaborative optimization method for force and position.Based on the analysis of the kinematics and dynamics of the system,the inverse kinematics and inverse dynamics of the system are solved using the least variance method.The obstacle avoidance planning is performed in the solved collisionfree feasible space using the stable dung beetle optimization(SDBO)algorithm,which ensures that the suspended object can move stably to the target point in the workspace.The optimal obstacle avoidance trajectory of the multi-robot suspension system can be accurately determined by using the collaborative optimization method for force and position to plan the towing robot and the cable.Finally,the correctness of the obstacle avoidance planning method is verified by simulations.By taking a special scenario,the remarkable findings reveal that the SDBO algorithm outperforms the dung beetle optimization algorithm by reducing the length of the planned trajectory of the suspended object by 14.51%and the height by 79.88%,and reducing the minimum fitness by 95.84%and the average fitness by 94.77%.The results can help the multi-robot suspension system to perform various towing tasks safely and stably,and extend the related planning and control theory.
基金Under the auspices of National Natural Science Foundation of China(No.42271222)Natural Science Foundation of Gansu Province(No.22JR5RA130)。
文摘Rural resilience,a core capability for addressing systemic risks and enabling sustainable development,is increasingly vital to promoting urban-rural integrated development and rural revitalization strategies.However,current research lacks exploration of the collaborative mechanisms between rural economic resilience(RER)and rural social resilience(RSR)in ecologically vulnerable areas.Based on the practical context of rural sustainable development in such regions,this study investigates the interaction between RER and RSR from a resilience coordination perspective.In this paper,a rural resilience evaluation framework for collaborative development of economic and social resilience was established.By employing the coupling coordination degree model,obstacle degree model,and equilibrium entropy model,this paper examines the synergies,constraints,and potential of rural resilience subsystems in Jinchang City,Gansu Province,China,in 2020.The results reveal that:1)RER contributes to RSR by stabilizing the economy,enhancing community adaptability,and driving modernization.In turn,RSR strengthens RER by mitigating instability,building social capital,and fostering confidence—together forming a mutually reinforcing coupling mechanism.2)The rural economic and social resilience level in Jinchang City remains generally low with spatially clustered patterns,while the coupling coordination degree is at an intermediate level overall,with 62.59%of villages exhibiting unbalanced development between rural economic and social resilience.3)RER and RSR demonstrate synergistic degradation in ecologically vulnerable areas,where low-level rural economic and social resilience induce integrated systemic deterioration.4)Considering the unbalanced development of rural economic and social resilience in ecologically fragile areas,differentiated coordination pathways are proposed for three village typologies:RER-lagging villages,RSR-lagging villages,and villages where RER and RSR develop synchronously but lack effective coordination.These findings offer spatial governance strategies and practical guidance for enhancing rural resilience and advancing sustainable development in ecologically vulnerable regions.
文摘Introduction: Vaccination faces several obstacles in the fight against COVID-19, yet it has been identified as one of the most effective means of preventing new epidemics of COVID-19. The aim was to contribute to improving vaccination coverage against COVID-19 in the Kindu health zone. Method: We conducted a cross-sectional descriptive study with an analytical focus, using a questionnaire that enabled us to carry out a survey from October 03 to 30, 2022. Our target study population was residents of the Kindu health zone. A total of 420 subjects participated in our study, including 42 per site. Results: The study revealed a low proportion of vaccinated subjects (38.3%) and a high proportion of non-vaccinated subjects (61.70%). Non-belief in the efficacy of vaccines (p = 0.001), infodemia (p = 0.001) and respect for ethnic norms (p = 0.001) were identified as perceived barriers to vaccination. Fear of being branded with the “666” beast badge (p = 0.004) as the perceived severity. Respondents’ perceptions of mass vaccination against COVID-19 are mixed, and their opinions and expectations of COVID-19 vaccination in the town of Kindu are divided. Conclusion: In order to increase the proportion of people vaccinated against COVID-19, it is suggested here to increase the population’s ability to detect false information through a well-structured communication and health education program.
基金supported by Noncommunicable Chronic Diseases-National Scienceand Technology Major Project(No.2023ZD0503000)National Natural ScienceFoundation of China(No.82301095,82301094)+1 种基金Sichuan Science and Technology Program(No.2025ZNSFSC0548,2024YFFK0373)the Research Funding fromWest China Hospital of Stomatology,Sichuan University(No.RD-03-202410)。
文摘In the ever-evolving landscape of cancer therapy,while cancer treatments such as chemotherapy,radiotherapy,and targeted therapy aim to eradicate malignant cells,they also inadvertently trigger cellular senescence in both cancerous and microenvironmental tissues.Therapy-induced senescence(TIS)can act as a barrier against tumor growth by halting cell proliferation in the short term,but the long-term persistence of therapy-induced senescent(TISnt)cells may pose a significant challenge in cancer management.Their distinct characteristics,like senescence-associated secretory phenotype(SASP),metabolic dysregulation,and immune evasion,make them exhibit remarkable heterogeneity to orchestrate the tumor microenvironment(TME),resulting in therapy resistance.However,how these TISnt cells functioning differently in cancer progression,and the intricate mechanisms by which they remodel the senescence-associated immunosuppressive microenvironment present challenges for improving anticancer therapy.Therefore,this review summarizes the heterogeneous TISnt cell phenotypes contributing to an accumulated senescent state,outlines their multidimensional interactions in the senescent microenvironment,and discusses current senescence-targeting strategies.Building on the current understanding of TIS,we propose potential avenues for improving TIS-targeting methodologies in the context of head and neck cancer,a representative heterogeneous malignancy,which can substantially enhance the efficacy of the“one-two punch”sequential treatment approach for head and neck cancer.
