UAV-based object detection is rapidly expanding in both civilian and military applications,including security surveillance,disaster assessment,and border patrol.However,challenges such as small objects,occlusions,comp...UAV-based object detection is rapidly expanding in both civilian and military applications,including security surveillance,disaster assessment,and border patrol.However,challenges such as small objects,occlusions,complex backgrounds,and variable lighting persist due to the unique perspective of UAV imagery.To address these issues,this paper introduces DAFPN-YOLO,an innovative model based on YOLOv8s(You Only Look Once version 8s).Themodel strikes a balance between detection accuracy and speed while reducing parameters,making itwell-suited for multi-object detection tasks from drone perspectives.A key feature of DAFPN-YOLO is the enhanced Drone-AFPN(Adaptive Feature Pyramid Network),which adaptively fuses multi-scale features to optimize feature extraction and enhance spatial and small-object information.To leverage Drone-AFPN’smulti-scale capabilities fully,a dedicated 160×160 small-object detection head was added,significantly boosting detection accuracy for small targets.In the backbone,the C2f_Dual(Cross Stage Partial with Cross-Stage Feature Fusion Dual)module and SPPELAN(Spatial Pyramid Pooling with Enhanced LocalAttentionNetwork)modulewere integrated.These components improve feature extraction and information aggregationwhile reducing parameters and computational complexity,enhancing inference efficiency.Additionally,Shape-IoU(Shape Intersection over Union)is used as the loss function for bounding box regression,enabling more precise shape-based object matching.Experimental results on the VisDrone 2019 dataset demonstrate the effectiveness ofDAFPN-YOLO.Compared to YOLOv8s,the proposedmodel achieves a 5.4 percentage point increase inmAP@0.5,a 3.8 percentage point improvement in mAP@0.5:0.95,and a 17.2%reduction in parameter count.These results highlight DAFPN-YOLO’s advantages in UAV-based object detection,offering valuable insights for applying deep learning to UAV-specific multi-object detection tasks.展开更多
In foggy traffic scenarios, existing object detection algorithms face challenges such as low detection accuracy, poor robustness, occlusion, missed detections, and false detections. To address this issue, a multi-scal...In foggy traffic scenarios, existing object detection algorithms face challenges such as low detection accuracy, poor robustness, occlusion, missed detections, and false detections. To address this issue, a multi-scale object detection algorithm based on an improved YOLOv8 has been proposed. Firstly, a lightweight attention mechanism, Triplet Attention, is introduced to enhance the algorithm’s ability to extract multi-dimensional and multi-scale features, thereby improving the receptive capability of the feature maps. Secondly, the Diverse Branch Block (DBB) is integrated into the CSP Bottleneck with two Convolutions (C2F) module to strengthen the fusion of semantic information across different layers. Thirdly, a new decoupled detection head is proposed by redesigning the original network head based on the Diverse Branch Block module to improve detection accuracy and reduce missed and false detections. Finally, the Minimum Point Distance based Intersection-over-Union (MPDIoU) is used to replace the original YOLOv8 Complete Intersection-over-Union (CIoU) to accelerate the network’s training convergence. Comparative experiments and dehazing pre-processing tests were conducted on the RTTS and VOC-Fog datasets. Compared to the baseline YOLOv8 model, the improved algorithm achieved mean Average Precision (mAP) improvements of 4.6% and 3.8%, respectively. After defogging pre-processing, the mAP increased by 5.3% and 4.4%, respectively. The experimental results demonstrate that the improved algorithm exhibits high practicality and effectiveness in foggy traffic scenarios.展开更多
Forest fires pose a serious threat to ecological balance, air quality, and the safety of both humans and wildlife. This paper presents an improved model based on You Only Look Once version 5 (YOLOv5), named YOLO Light...Forest fires pose a serious threat to ecological balance, air quality, and the safety of both humans and wildlife. This paper presents an improved model based on You Only Look Once version 5 (YOLOv5), named YOLO Lightweight Fire Detector (YOLO-LFD), to address the limitations of traditional sensor-based fire detection methods in terms of real-time performance and accuracy. The proposed model is designed to enhance inference speed while maintaining high detection accuracy on resource-constrained devices such as drones and embedded systems. Firstly, we introduce Depthwise Separable Convolutions (DSConv) to reduce the complexity of the feature extraction network. Secondly, we design and implement the Lightweight Faster Implementation of Cross Stage Partial (CSP) Bottleneck with 2 Convolutions (C2f-Light) and the CSP Structure with 3 Compact Inverted Blocks (C3CIB) modules to replace the traditional C3 modules. This optimization enhances deep feature extraction and semantic information processing, thereby significantly increasing inference speed. To enhance the detection capability for small fires, the model employs a Normalized Wasserstein Distance (NWD) loss function, which effectively reduces the missed detection rate and improves the accuracy of detecting small fire sources. Experimental results demonstrate that compared to the baseline YOLOv5s model, the YOLO-LFD model not only increases inference speed by 19.3% but also significantly improves the detection accuracy for small fire targets, with only a 1.6% reduction in overall mean average precision (mAP)@0.5. Through these innovative improvements to YOLOv5s, the YOLO-LFD model achieves a balance between speed and accuracy, making it particularly suitable for real-time detection tasks on mobile and embedded devices.展开更多
Smart farming with outdoor monitoring systems is critical to address food shortages and sustainability challenges.These systems facilitate informed decisions that enhance efficiency in broader environmental management...Smart farming with outdoor monitoring systems is critical to address food shortages and sustainability challenges.These systems facilitate informed decisions that enhance efficiency in broader environmental management.Existing outdoor systems equipped with energy harvesters and self-powered sensors often struggle with fluctuating energy sources,low durability under harsh conditions,non-transparent or non-biocompatible materials,and complex structures.Herein,a multifunctional hydrogel is developed,which can fulfill all the above requirements and build selfsustainable outdoor monitoring systems solely by it.It can serve as a stable energy harvester that continuously generates direct current output with an average power density of 1.9 W m^(-3)for nearly 60 days of operation in normal environments(24℃,60%RH),with an energy density of around 1.36×10^(7)J m^(-3).It also shows good self-recoverability in severe environments(45℃,30%RH)in nearly 40 days of continuous operation.Moreover,this hydrogel enables noninvasive and self-powered monitoring of leaf relative water content,providing critical data on evaluating plant health,previously obtainable only through invasive or high-power consumption methods.Its potential extends to acting as other self-powered environmental sensors.This multifunctional hydrogel enables self-sustainable outdoor systems with scalable and low-cost production,paving the way for future agriculture.展开更多
Following global catastrophic infrastructure loss(GCIL),traditional electricity networks would be damaged and unavailable for energy supply,necessitating alternative solutions to sustain critical services.These altern...Following global catastrophic infrastructure loss(GCIL),traditional electricity networks would be damaged and unavailable for energy supply,necessitating alternative solutions to sustain critical services.These alternative solutions would need to run without damaged infrastructure and would likely need to be located at the point of use,such as decentralized electricity generation from wood gas.This study explores the feasibility of using modified light duty vehicles to self-sustain electricity generation by producing wood chips for wood gasification.A 2004 Ford Falcon Fairmont was modified to power a woodchipper and an electrical generator.The vehicle successfully produced wood chips suitable for gasification with an energy return on investment(EROI)of 3.7 and sustained a stable output of 20 kW electrical power.Scalability analyses suggest such solutions could provide electricity to the critical water sanitation sector,equivalent to 4%of global electricity demand,if production of woodchippers was increased postcatastrophe.Future research could investigate the long-term durability of modified vehicles and alternative electricity generation,and quantify the scalability of wood gasification in GCIL scenarios.This work provides a foundation for developing resilient,decentralized energy systems to ensure the continuity of critical services during catastrophic events,leveraging existing vehicle infrastructure to enhance disaster preparedness.展开更多
Hydrogen is emerging as a promising alternative to fossil fuels in the transportation sector.This study evaluated the feasibility of estab-lishing hydrogen refueling stations in five cities in Oman,Duqm,Haima,Sur,Al B...Hydrogen is emerging as a promising alternative to fossil fuels in the transportation sector.This study evaluated the feasibility of estab-lishing hydrogen refueling stations in five cities in Oman,Duqm,Haima,Sur,Al Buraymi,and Salalah,using Hybrid Optimization of Multiple Electric Renewables(HOMER)software.Three hybrid energy systems,photovoltaic-wind turbine-battery,photovoltaic-battery,and wind turbine-battery were analyzed for each city.Results indicated that Duqm offers the lowest net present cost(NPC),levelized cost of energy,and levelized cost of hydrogen,making it the most cost-effective location.Additionally,Sensitivity analysis showed that as the life of electrolyzer increases during operation,the initial capital expenditure is distributed over a longer operational period,leading to a reduction in the NPC.More so,renewable energy systems produced no emissions which supports Oman’s mission target.This comprehensive analysis confirms the feasibility of establishing a hydrogen refueling station in Duqm,Oman,and highlights advanced optimization techniques’superior capability in designing cost-effective,sustainable energy systems.展开更多
This study investigates the application of deep learning,ensemble learning,metaheuristic optimization,and image processing techniques for detecting lung and colon cancers,aiming to enhance treatment efficacy and impro...This study investigates the application of deep learning,ensemble learning,metaheuristic optimization,and image processing techniques for detecting lung and colon cancers,aiming to enhance treatment efficacy and improve survival rates.We introduce a metaheuristic-driven two-stage ensemble deep learning model for efficient lung/colon cancer classification.The diagnosis of lung and colon cancers is attempted using several unique indicators by different versions of deep Convolutional Neural Networks(CNNs)in feature extraction and model constructions,and utilizing the power of various Machine Learning(ML)algorithms for final classification.Specifically,we consider different scenarios consisting of two-class colon cancer,three-class lung cancer,and fiveclass combined lung/colon cancer to conduct feature extraction using four CNNs.These extracted features are then integrated to create a comprehensive feature set.In the next step,the optimization of the feature selection is conducted using a metaheuristic algorithm based on the Electric Eel Foraging Optimization(EEFO).This optimized feature subset is subsequently employed in various ML algorithms to determine the most effective ones through a rigorous evaluation process.The top-performing algorithms are refined using the High-Performance Filter(HPF)and integrated into an ensemble learning framework employing weighted averaging.Our findings indicate that the proposed ensemble learning model significantly surpasses existing methods in classification accuracy across all datasets,achieving accuracies of 99.85%for the two-class,98.70%for the three-class,and 98.96%for the five-class datasets.展开更多
Tremor occurs in about half of multiple sclerosis(MS)patients.MS tremor has a broad frequency range of 2.5-7 Hz,with a higher prevalence of postural tremor(44%)compared to intentional tremor(6%)(Alusi et al.,2001).Tre...Tremor occurs in about half of multiple sclerosis(MS)patients.MS tremor has a broad frequency range of 2.5-7 Hz,with a higher prevalence of postural tremor(44%)compared to intentional tremor(6%)(Alusi et al.,2001).Tremor may affect the upper and lower extremities,head,and trunk,and may even affect the vocal cords in isolated cases of palatal tremor.MS tremor is classically attributed to lesions of the brain stem,cerebellum,or cerebellar peduncles,and tremor intensity has been shown to correlate with the number of lesions or their functional connections.However,recent work has demonstrated that inflammatory damage to the cerebello-thalamic and cortico-thalamic pathways might also play an important role in causing tremor,as it co-occurs with other signs and symptoms of MS such as dysarthria,dysmetria,dysdiadochokinesia,and dystonia(Alusi et al.,2001).展开更多
Multi-Agent Reinforcement Learning(MARL)has proven to be successful in cooperative assignments.MARL is used to investigate how autonomous agents with the same interests can connect and act in one team.MARL cooperation...Multi-Agent Reinforcement Learning(MARL)has proven to be successful in cooperative assignments.MARL is used to investigate how autonomous agents with the same interests can connect and act in one team.MARL cooperation scenarios are explored in recreational cooperative augmented reality environments,as well as realworld scenarios in robotics.In this paper,we explore the realm of MARL and its potential applications in cooperative assignments.Our focus is on developing a multi-agent system that can collaborate to attack or defend against enemies and achieve victory withminimal damage.To accomplish this,we utilize the StarCraftMulti-Agent Challenge(SMAC)environment and train four MARL algorithms:Q-learning with Mixtures of Experts(QMIX),Value-DecompositionNetwork(VDN),Multi-agent Proximal PolicyOptimizer(MAPPO),andMulti-Agent Actor Attention Critic(MAA2C).These algorithms allow multiple agents to cooperate in a specific scenario to achieve the targeted mission.Our results show that the QMIX algorithm outperforms the other three algorithms in the attacking scenario,while the VDN algorithm achieves the best results in the defending scenario.Specifically,the VDNalgorithmreaches the highest value of battle wonmean and the lowest value of dead alliesmean.Our research demonstrates the potential forMARL algorithms to be used in real-world applications,such as controllingmultiple robots to provide helpful services or coordinating teams of agents to accomplish tasks that would be impossible for a human to do.The SMAC environment provides a unique opportunity to test and evaluate MARL algorithms in a challenging and dynamic environment,and our results show that these algorithms can be used to achieve victory with minimal damage.展开更多
Piezoelectric ultrasonic transducers have shown great potential in biomedical applications due to their high acoustic-to-electric conversion efficiency and large power capacity.The focusing technique enables the trans...Piezoelectric ultrasonic transducers have shown great potential in biomedical applications due to their high acoustic-to-electric conversion efficiency and large power capacity.The focusing technique enables the transducer to produce an extremely narrow beam,greatly improving the resolution and sensitivity.In this work,we summarize the fundamental properties and biological effects of the ultrasound field,aiming to establish a correlation between device design and application.Focusing techniques for piezoelectric transducers are highlighted,including material selection and fabrication methods,which determine the final performance of piezoelectric transducers.Numerous examples,from ultrasound imaging,neuromodulation,tumor ablation to ultrasonic wireless energy transfer,are summarized to highlight the great promise of biomedical applications.Finally,the challenges and opportunities of focused ultrasound transducers are presented.The aim of this review is to bridge the gap between focused ultrasound systems and biomedical applications.展开更多
In the plethora of conceptual and algorithmic developments supporting data analytics and system modeling,humancentric pursuits assume a particular position owing to ways they emphasize and realize interaction between ...In the plethora of conceptual and algorithmic developments supporting data analytics and system modeling,humancentric pursuits assume a particular position owing to ways they emphasize and realize interaction between users and the data.We advocate that the level of abstraction,which can be flexibly adjusted,is conveniently realized through Granular Computing.Granular Computing is concerned with the development and processing information granules–formal entities which facilitate a way of organizing knowledge about the available data and relationships existing there.This study identifies the principles of Granular Computing,shows how information granules are constructed and subsequently used in describing relationships present among the data.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.62101275 and 62101274).
文摘UAV-based object detection is rapidly expanding in both civilian and military applications,including security surveillance,disaster assessment,and border patrol.However,challenges such as small objects,occlusions,complex backgrounds,and variable lighting persist due to the unique perspective of UAV imagery.To address these issues,this paper introduces DAFPN-YOLO,an innovative model based on YOLOv8s(You Only Look Once version 8s).Themodel strikes a balance between detection accuracy and speed while reducing parameters,making itwell-suited for multi-object detection tasks from drone perspectives.A key feature of DAFPN-YOLO is the enhanced Drone-AFPN(Adaptive Feature Pyramid Network),which adaptively fuses multi-scale features to optimize feature extraction and enhance spatial and small-object information.To leverage Drone-AFPN’smulti-scale capabilities fully,a dedicated 160×160 small-object detection head was added,significantly boosting detection accuracy for small targets.In the backbone,the C2f_Dual(Cross Stage Partial with Cross-Stage Feature Fusion Dual)module and SPPELAN(Spatial Pyramid Pooling with Enhanced LocalAttentionNetwork)modulewere integrated.These components improve feature extraction and information aggregationwhile reducing parameters and computational complexity,enhancing inference efficiency.Additionally,Shape-IoU(Shape Intersection over Union)is used as the loss function for bounding box regression,enabling more precise shape-based object matching.Experimental results on the VisDrone 2019 dataset demonstrate the effectiveness ofDAFPN-YOLO.Compared to YOLOv8s,the proposedmodel achieves a 5.4 percentage point increase inmAP@0.5,a 3.8 percentage point improvement in mAP@0.5:0.95,and a 17.2%reduction in parameter count.These results highlight DAFPN-YOLO’s advantages in UAV-based object detection,offering valuable insights for applying deep learning to UAV-specific multi-object detection tasks.
基金supported by the National Natural Science Foundation of China(Grant Nos.62101275 and 62101274).
文摘In foggy traffic scenarios, existing object detection algorithms face challenges such as low detection accuracy, poor robustness, occlusion, missed detections, and false detections. To address this issue, a multi-scale object detection algorithm based on an improved YOLOv8 has been proposed. Firstly, a lightweight attention mechanism, Triplet Attention, is introduced to enhance the algorithm’s ability to extract multi-dimensional and multi-scale features, thereby improving the receptive capability of the feature maps. Secondly, the Diverse Branch Block (DBB) is integrated into the CSP Bottleneck with two Convolutions (C2F) module to strengthen the fusion of semantic information across different layers. Thirdly, a new decoupled detection head is proposed by redesigning the original network head based on the Diverse Branch Block module to improve detection accuracy and reduce missed and false detections. Finally, the Minimum Point Distance based Intersection-over-Union (MPDIoU) is used to replace the original YOLOv8 Complete Intersection-over-Union (CIoU) to accelerate the network’s training convergence. Comparative experiments and dehazing pre-processing tests were conducted on the RTTS and VOC-Fog datasets. Compared to the baseline YOLOv8 model, the improved algorithm achieved mean Average Precision (mAP) improvements of 4.6% and 3.8%, respectively. After defogging pre-processing, the mAP increased by 5.3% and 4.4%, respectively. The experimental results demonstrate that the improved algorithm exhibits high practicality and effectiveness in foggy traffic scenarios.
基金supported by the National Natural Science Foundation of China(Grant Nos.62101275 and 62101274).
文摘Forest fires pose a serious threat to ecological balance, air quality, and the safety of both humans and wildlife. This paper presents an improved model based on You Only Look Once version 5 (YOLOv5), named YOLO Lightweight Fire Detector (YOLO-LFD), to address the limitations of traditional sensor-based fire detection methods in terms of real-time performance and accuracy. The proposed model is designed to enhance inference speed while maintaining high detection accuracy on resource-constrained devices such as drones and embedded systems. Firstly, we introduce Depthwise Separable Convolutions (DSConv) to reduce the complexity of the feature extraction network. Secondly, we design and implement the Lightweight Faster Implementation of Cross Stage Partial (CSP) Bottleneck with 2 Convolutions (C2f-Light) and the CSP Structure with 3 Compact Inverted Blocks (C3CIB) modules to replace the traditional C3 modules. This optimization enhances deep feature extraction and semantic information processing, thereby significantly increasing inference speed. To enhance the detection capability for small fires, the model employs a Normalized Wasserstein Distance (NWD) loss function, which effectively reduces the missed detection rate and improves the accuracy of detecting small fire sources. Experimental results demonstrate that compared to the baseline YOLOv5s model, the YOLO-LFD model not only increases inference speed by 19.3% but also significantly improves the detection accuracy for small fire targets, with only a 1.6% reduction in overall mean average precision (mAP)@0.5. Through these innovative improvements to YOLOv5s, the YOLO-LFD model achieves a balance between speed and accuracy, making it particularly suitable for real-time detection tasks on mobile and embedded devices.
基金supported by the Research Platform for biomedical and Health Technology, NUS (Suzhou) Research Institute (RP-BHT-Prof. LEE Chengkuo)RIE Advanced Manufacturing and Engineering (AME) Programmatic Grant (Grant A18A4b0055)+1 种基金RIE 2025-Industry Alignment Fund – Industry Collaboration Projects (IAF-ICP) (Grant I2301E0027)Reimagine Research Scheme projects, National University of Singapore, A-0009037-03-00 and A-0009454-01-00 and Reimagine Research Scheme projects, National University of Singapore, A-0004772-00-00 and A-0004772-01-00。
文摘Smart farming with outdoor monitoring systems is critical to address food shortages and sustainability challenges.These systems facilitate informed decisions that enhance efficiency in broader environmental management.Existing outdoor systems equipped with energy harvesters and self-powered sensors often struggle with fluctuating energy sources,low durability under harsh conditions,non-transparent or non-biocompatible materials,and complex structures.Herein,a multifunctional hydrogel is developed,which can fulfill all the above requirements and build selfsustainable outdoor monitoring systems solely by it.It can serve as a stable energy harvester that continuously generates direct current output with an average power density of 1.9 W m^(-3)for nearly 60 days of operation in normal environments(24℃,60%RH),with an energy density of around 1.36×10^(7)J m^(-3).It also shows good self-recoverability in severe environments(45℃,30%RH)in nearly 40 days of continuous operation.Moreover,this hydrogel enables noninvasive and self-powered monitoring of leaf relative water content,providing critical data on evaluating plant health,previously obtainable only through invasive or high-power consumption methods.Its potential extends to acting as other self-powered environmental sensors.This multifunctional hydrogel enables self-sustainable outdoor systems with scalable and low-cost production,paving the way for future agriculture.
基金This work was funded in part by the Alliance to Feed the Earth in Disasters(ALLFED).
文摘Following global catastrophic infrastructure loss(GCIL),traditional electricity networks would be damaged and unavailable for energy supply,necessitating alternative solutions to sustain critical services.These alternative solutions would need to run without damaged infrastructure and would likely need to be located at the point of use,such as decentralized electricity generation from wood gas.This study explores the feasibility of using modified light duty vehicles to self-sustain electricity generation by producing wood chips for wood gasification.A 2004 Ford Falcon Fairmont was modified to power a woodchipper and an electrical generator.The vehicle successfully produced wood chips suitable for gasification with an energy return on investment(EROI)of 3.7 and sustained a stable output of 20 kW electrical power.Scalability analyses suggest such solutions could provide electricity to the critical water sanitation sector,equivalent to 4%of global electricity demand,if production of woodchippers was increased postcatastrophe.Future research could investigate the long-term durability of modified vehicles and alternative electricity generation,and quantify the scalability of wood gasification in GCIL scenarios.This work provides a foundation for developing resilient,decentralized energy systems to ensure the continuity of critical services during catastrophic events,leveraging existing vehicle infrastructure to enhance disaster preparedness.
文摘Hydrogen is emerging as a promising alternative to fossil fuels in the transportation sector.This study evaluated the feasibility of estab-lishing hydrogen refueling stations in five cities in Oman,Duqm,Haima,Sur,Al Buraymi,and Salalah,using Hybrid Optimization of Multiple Electric Renewables(HOMER)software.Three hybrid energy systems,photovoltaic-wind turbine-battery,photovoltaic-battery,and wind turbine-battery were analyzed for each city.Results indicated that Duqm offers the lowest net present cost(NPC),levelized cost of energy,and levelized cost of hydrogen,making it the most cost-effective location.Additionally,Sensitivity analysis showed that as the life of electrolyzer increases during operation,the initial capital expenditure is distributed over a longer operational period,leading to a reduction in the NPC.More so,renewable energy systems produced no emissions which supports Oman’s mission target.This comprehensive analysis confirms the feasibility of establishing a hydrogen refueling station in Duqm,Oman,and highlights advanced optimization techniques’superior capability in designing cost-effective,sustainable energy systems.
文摘This study investigates the application of deep learning,ensemble learning,metaheuristic optimization,and image processing techniques for detecting lung and colon cancers,aiming to enhance treatment efficacy and improve survival rates.We introduce a metaheuristic-driven two-stage ensemble deep learning model for efficient lung/colon cancer classification.The diagnosis of lung and colon cancers is attempted using several unique indicators by different versions of deep Convolutional Neural Networks(CNNs)in feature extraction and model constructions,and utilizing the power of various Machine Learning(ML)algorithms for final classification.Specifically,we consider different scenarios consisting of two-class colon cancer,three-class lung cancer,and fiveclass combined lung/colon cancer to conduct feature extraction using four CNNs.These extracted features are then integrated to create a comprehensive feature set.In the next step,the optimization of the feature selection is conducted using a metaheuristic algorithm based on the Electric Eel Foraging Optimization(EEFO).This optimized feature subset is subsequently employed in various ML algorithms to determine the most effective ones through a rigorous evaluation process.The top-performing algorithms are refined using the High-Performance Filter(HPF)and integrated into an ensemble learning framework employing weighted averaging.Our findings indicate that the proposed ensemble learning model significantly surpasses existing methods in classification accuracy across all datasets,achieving accuracies of 99.85%for the two-class,98.70%for the three-class,and 98.96%for the five-class datasets.
基金supported by the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation)Project-ID 424778381-TRR 295(to MM)the Fondazione Grigioni per il Morbo di Parkinson(to IUI).
文摘Tremor occurs in about half of multiple sclerosis(MS)patients.MS tremor has a broad frequency range of 2.5-7 Hz,with a higher prevalence of postural tremor(44%)compared to intentional tremor(6%)(Alusi et al.,2001).Tremor may affect the upper and lower extremities,head,and trunk,and may even affect the vocal cords in isolated cases of palatal tremor.MS tremor is classically attributed to lesions of the brain stem,cerebellum,or cerebellar peduncles,and tremor intensity has been shown to correlate with the number of lesions or their functional connections.However,recent work has demonstrated that inflammatory damage to the cerebello-thalamic and cortico-thalamic pathways might also play an important role in causing tremor,as it co-occurs with other signs and symptoms of MS such as dysarthria,dysmetria,dysdiadochokinesia,and dystonia(Alusi et al.,2001).
基金supported in part by United States Air Force Research Institute for Tactical Autonomy(RITA)University Affiliated Research Center(UARC)in part by the United States Air Force Office of Scientific Research(AFOSR)Contract FA9550-22-1-0268 awarded to KHA,https://www.afrl.af.mil/AFOSR/The contract is entitled:“Investigating Improving Safety of Autonomous Exploring Intelligent Agents with Human-in-the-Loop Reinforcement Learning,”and in part by Jackson State University.
文摘Multi-Agent Reinforcement Learning(MARL)has proven to be successful in cooperative assignments.MARL is used to investigate how autonomous agents with the same interests can connect and act in one team.MARL cooperation scenarios are explored in recreational cooperative augmented reality environments,as well as realworld scenarios in robotics.In this paper,we explore the realm of MARL and its potential applications in cooperative assignments.Our focus is on developing a multi-agent system that can collaborate to attack or defend against enemies and achieve victory withminimal damage.To accomplish this,we utilize the StarCraftMulti-Agent Challenge(SMAC)environment and train four MARL algorithms:Q-learning with Mixtures of Experts(QMIX),Value-DecompositionNetwork(VDN),Multi-agent Proximal PolicyOptimizer(MAPPO),andMulti-Agent Actor Attention Critic(MAA2C).These algorithms allow multiple agents to cooperate in a specific scenario to achieve the targeted mission.Our results show that the QMIX algorithm outperforms the other three algorithms in the attacking scenario,while the VDN algorithm achieves the best results in the defending scenario.Specifically,the VDNalgorithmreaches the highest value of battle wonmean and the lowest value of dead alliesmean.Our research demonstrates the potential forMARL algorithms to be used in real-world applications,such as controllingmultiple robots to provide helpful services or coordinating teams of agents to accomplish tasks that would be impossible for a human to do.The SMAC environment provides a unique opportunity to test and evaluate MARL algorithms in a challenging and dynamic environment,and our results show that these algorithms can be used to achieve victory with minimal damage.
基金National Natural Science Foundation of China(12072189,82171011)Shanghai Jiao Tong University‘Deep Blue Program’Fund(Grant No.SL2103)+1 种基金Project of Biobank(No.YBKB202117)from Shanghai Ninth People’s HospitalShanghai Jiao Tong University School of Medicine and Science Foundation of National Key Laboratory of Science and Technology on Advanced Composites in Special Environments(No.6142905223704)。
文摘Piezoelectric ultrasonic transducers have shown great potential in biomedical applications due to their high acoustic-to-electric conversion efficiency and large power capacity.The focusing technique enables the transducer to produce an extremely narrow beam,greatly improving the resolution and sensitivity.In this work,we summarize the fundamental properties and biological effects of the ultrasound field,aiming to establish a correlation between device design and application.Focusing techniques for piezoelectric transducers are highlighted,including material selection and fabrication methods,which determine the final performance of piezoelectric transducers.Numerous examples,from ultrasound imaging,neuromodulation,tumor ablation to ultrasonic wireless energy transfer,are summarized to highlight the great promise of biomedical applications.Finally,the challenges and opportunities of focused ultrasound transducers are presented.The aim of this review is to bridge the gap between focused ultrasound systems and biomedical applications.
文摘In the plethora of conceptual and algorithmic developments supporting data analytics and system modeling,humancentric pursuits assume a particular position owing to ways they emphasize and realize interaction between users and the data.We advocate that the level of abstraction,which can be flexibly adjusted,is conveniently realized through Granular Computing.Granular Computing is concerned with the development and processing information granules–formal entities which facilitate a way of organizing knowledge about the available data and relationships existing there.This study identifies the principles of Granular Computing,shows how information granules are constructed and subsequently used in describing relationships present among the data.
