Objective To determine the relationship between TSH receptor gene mutations and autonomously functioning thyroid adenomas (AFTAs). Methods The thyroid samples from 14 cases of diagnosed AFTAs were analyzed, with nor...Objective To determine the relationship between TSH receptor gene mutations and autonomously functioning thyroid adenomas (AFTAs). Methods The thyroid samples from 14 cases of diagnosed AFTAs were analyzed, with normal thyroid specimens adjacent to the tumors as controls. The 155 base pairs DNA fragments which encompassed the third cytoplasmic loop and the sixth transmembrane segments in the TSH receptor gene exon 10 were amplified by Polymerase chain reaction (PCR) and analyzed by the single-strand conformation polymorphism (SSCP). Direct sequencing of the PCR products was performed with Prism Dye Terminator Cycle Sequencing Core Kit. Results 6 of 14 AFTA specimens displayed abnormal migration in SSCP analysis. In sequence analysis of 3 abnormally migrated samples, one base substitution at nucleotide 1957 (A to C) and two same insertion mutations of one adenosine nucleotide between nucleotide 1972 and 1973 were identified. No mutations were found in controls. Conclusion This study confirmed the presence of TSH receptor gene mutations in AFTAs; both one-point substitution mutation and one-base insertion mutation were found to be responsible for the pathogenesis of AFTAs.展开更多
The autonomously trading agents described in this paper produce a decision to act such as: buy, sell or hold, based on the input data. In this work, we have simulated autonomously trading agents using the Echo State N...The autonomously trading agents described in this paper produce a decision to act such as: buy, sell or hold, based on the input data. In this work, we have simulated autonomously trading agents using the Echo State Network (ESNs) model. We generate a collection of trading agents that use different trading strategies using Evolutionary Programming (EP). The agents are tested on EUR/ USD real market data. The main goal of this study is to test the overall performance of this collection of agents when they are active simultaneously. Simulation results show that using different agents concurrently outperform a single agent acting alone.展开更多
Thermal cloaks offer the potential to conceal internal objects from detection or to prevent thermal shock by controlling external heat flow. However, most conventional natural materials lack the desired flexibility an...Thermal cloaks offer the potential to conceal internal objects from detection or to prevent thermal shock by controlling external heat flow. However, most conventional natural materials lack the desired flexibility and versatility required for on-demand thermal manipulation. We propose a solution in the form of homogeneous multilayer thermodynamic cloaks. Through an ingenious design, these cloaks achieve exceptional and extreme parameters, enabling the distribution of multiple materials in space. We first investigate the effects of important design parameters on the thermal shielding effectiveness of conventional thermal cloaks. Subsequently, we introduce an autonomous tuning function for the thermodynamic cloak, accomplished by leveraging two phase transition materials as thermal conductive layers. Remarkably, this tuning function does not require any energy input. Finite element analysis results demonstrate a significant reduction in the temperature gradient inside the thermal cloak compared to the surrounding background. This reduction indicates the cloak’s remarkable ability to manipulate the spatial thermal field. Furthermore, the utilization of materials undergoing phase transition leads to an increase in thermal conductivity, enabling the cloak to achieve the opposite variation of the temperature field between the object region and the background. This means that, while the temperature gradient within the cloak decreases, the temperature gradient in the background increases. This work addresses a compelling and crucial challenge in the realm of thermal metamaterials, i.e., autonomous tuning of the thermal field without energy input. Such an achievement is currently unattainable with existing natural materials. This study establishes the groundwork for the application of thermal metamaterials in thermodynamic cloaks, with potential extensions into thermal energy harvesting, thermal camouflage, and thermoelectric conversion devices.By harnessing phonons, our findings provide an unprecedented and practical approach to flexibly implementing thermal cloaks and manipulating heat flow.展开更多
Machine learning,a revolutionary and advanced technology,has been widely applied in the field of stock trading.However,training an autonomous trading strategy which can effectively balance risk and Return On Investmen...Machine learning,a revolutionary and advanced technology,has been widely applied in the field of stock trading.However,training an autonomous trading strategy which can effectively balance risk and Return On Investment without human supervision in the stock market with high uncertainty is still a bottleneck.This paper constructs a Bayesian-inferenced Gated Recurrent Unit architecture to support long-term stock price prediction based on characteristics of the stock information learned from historical data,augmented with memory of recent upand-down fluctuations occur in the data of short-term stock movement.The Gated Recurrent Unit architecture incorporates uncertainty estimation into the prediction process,which take care of decision-making in an ever-changing dynamic environment.Three trading strategies were implemented in this model;namely,a Price Model Strategy,a Probabilistic Model Strategy,and a Bayesian Gated Recurrent Unit Strategy,each leveraging the respective model’s outputs to optimize trading decisions.The experimental results show that,compared with the standard Gated Recurrent Unit models,the modified model exhibits a huge tremendous/dramatic advantage in managing volatility and improving return on investment Return On Investment.The results and findings underscore the significant potential of combining Bayesian inference with machine learning to operate effectively in chaotic decision-making environments.展开更多
As urban landscapes evolve and vehicular volumes soar,traditional traffic monitoring systems struggle to scale,often failing under the complexities of dense,dynamic,and occluded environments.This paper introduces a no...As urban landscapes evolve and vehicular volumes soar,traditional traffic monitoring systems struggle to scale,often failing under the complexities of dense,dynamic,and occluded environments.This paper introduces a novel,unified deep learning framework for vehicle detection,tracking,counting,and classification in aerial imagery designed explicitly for modern smart city infrastructure demands.Our approach begins with adaptive histogram equalization to optimize aerial image clarity,followed by a cutting-edge scene parsing technique using Mask2Former,enabling robust segmentation even in visually congested settings.Vehicle detection leverages the latest YOLOv11 architecture,delivering superior accuracy in aerial contexts by addressing occlusion,scale variance,and fine-grained object differentiation.We incorporate the highly efficient ByteTrack algorithm for tracking,enabling seamless identity preservation across frames.Vehicle counting is achieved through an unsupervised DBSCAN-based method,ensuring adaptability to varying traffic densities.We further introduce a hybrid feature extraction module combining Convolutional Neural Networks(CNNs)with Zernike Moments,capturing both deep semantic and geometric signatures of vehicles.The final classification is powered by NASNet,a neural architecture search-optimized model,ensuring high accuracy across diverse vehicle types and orientations.Extensive evaluations of the VAID benchmark dataset demonstrate the system’s outstanding performance,achieving 96%detection,94%tracking,and 96.4%classification accuracy.On the UAVDT dataset,the system attains 95%detection,93%tracking,and 95%classification accuracy,confirming its robustness across diverse aerial traffic scenarios.These results establish new benchmarks in aerial traffic analysis and validate the framework’s scalability,making it a powerful and adaptable solution for next-generation intelligent transportation systems and urban surveillance.展开更多
Spinal cord injury(SCI) often results in permanent dysfunction of locomotion,sensation,and autonomic regulation,imposing a substantial burden on both individuals and society(Anjum et al.,2020).SCI has a complex pathop...Spinal cord injury(SCI) often results in permanent dysfunction of locomotion,sensation,and autonomic regulation,imposing a substantial burden on both individuals and society(Anjum et al.,2020).SCI has a complex pathophysiology:an initial primary injury(mechanical trauma,axonal disruption,and hemorrhage) is followed by a progressive secondary injury cascade that involves ischemia,neuronal loss,and inflammation.Given the challenges in achieving regeneration of the injured spinal cord,neuroprotection has been at the forefront of clinical research.展开更多
Agrowing number of Chinese firms have begun to adopt driverless delivery vehicles in autonomous driving demonstration areas in big cities like Beijing,Shanghai and Shenzhen to enhance logistics efficiency,reduce logis...Agrowing number of Chinese firms have begun to adopt driverless delivery vehicles in autonomous driving demonstration areas in big cities like Beijing,Shanghai and Shenzhen to enhance logistics efficiency,reduce logistics costs and improve user experience.Violations by these vehicles.including obstructing traffic,running red lights and failing to yield in time.展开更多
PHOTO NEWS Unforgettable Winter Break Tourists take selfies in front of snow sculptures in the Fenghuang Mountain Scenic Area,Yakeshi City in Inner Mongolia Autonomous Region,on December 7,2025.
At present,energy consumption is one of the main bottlenecks in autonomous mobile robot development.To address the challenge of high energy consumption in path planning for autonomous mobile robots navigating unknown ...At present,energy consumption is one of the main bottlenecks in autonomous mobile robot development.To address the challenge of high energy consumption in path planning for autonomous mobile robots navigating unknown and complex environments,this paper proposes an Attention-Enhanced Dueling Deep Q-Network(ADDueling DQN),which integrates a multi-head attention mechanism and a prioritized experience replay strategy into a Dueling-DQN reinforcement learning framework.A multi-objective reward function,centered on energy efficiency,is designed to comprehensively consider path length,terrain slope,motion smoothness,and obstacle avoidance,enabling optimal low-energy trajectory generation in 3D space from the source.The incorporation of a multihead attention mechanism allows the model to dynamically focus on energy-critical state features—such as slope gradients and obstacle density—thereby significantly improving its ability to recognize and avoid energy-intensive paths.Additionally,the prioritized experience replay mechanism accelerates learning from key decision-making experiences,suppressing inefficient exploration and guiding the policy toward low-energy solutions more rapidly.The effectiveness of the proposed path planning algorithm is validated through simulation experiments conducted in multiple off-road scenarios.Results demonstrate that AD-Dueling DQN consistently achieves the lowest average energy consumption across all tested environments.Moreover,the proposed method exhibits faster convergence and greater training stability compared to baseline algorithms,highlighting its global optimization capability under energy-aware objectives in complex terrains.This study offers an efficient and scalable intelligent control strategy for the development of energy-conscious autonomous navigation systems.展开更多
When you hear about boundless grasslands,vast forests,stunning snowy landscapes,bubbling hot springs and cultural integration,do you ever consider they are descriptions-features actually-of one place?They are,believe ...When you hear about boundless grasslands,vast forests,stunning snowy landscapes,bubbling hot springs and cultural integration,do you ever consider they are descriptions-features actually-of one place?They are,believe it or not.Arxan,a tiny city in Hinggan League,North China's Inner Mongolia Autonomous Region,is that magical place.In recent years,Arxan,a hidden gem boasting breathtaking winter landscape,has captivated visitors seeking to enjoy a“pure-white,fairy-tale world.”展开更多
In this study, the discharge voltage behavior of electric double-layer capacitors （EDLCs） during high-g impact is studied both theoretically and experimentally. A micro-scale dynamic mechanism is proposed to describ...In this study, the discharge voltage behavior of electric double-layer capacitors （EDLCs） during high-g impact is studied both theoretically and experimentally. A micro-scale dynamic mechanism is proposed to describe the physical basis of the increase in the discharge voltage during a high-g impact. Based on this dynamic mechanism, a multi-field model is established, and the simulation and experimental studies of the discharge voltage increase phenomenon are conducted. From the simulation and experimental data, the relationship between the increased voltage and the high-g acceleration is revealed. An acceleration detection range of up to 10,000g is verified. The design of the device is optimized by studying the influences of the parameters, such as the electrode thickness and discharge current, on the outputs. This work opens up new avenues for the development of autonomous sensor systems based on energy storage devices and is significant for many practical applications such as in collision testing and automobile safety.展开更多
Self-healing hydrogels utilize inherent intermolecular forces to autonomously heal physical damage resulting from excessive strain,pressure,or tearing.Applying these materials in soft robotics and tissue engineering c...Self-healing hydrogels utilize inherent intermolecular forces to autonomously heal physical damage resulting from excessive strain,pressure,or tearing.Applying these materials in soft robotics and tissue engineering could be beneficial.On the other hand,their efficacy in stretchable and mechanically resistant circuits is hindered by their limited electrical conductivity.展开更多
Safe and efficient sortie scheduling on the confined flight deck is crucial for maintaining high combat effectiveness of the aircraft carrier.The primary difficulty exactly lies in the spatiotemporal coordination,i.e....Safe and efficient sortie scheduling on the confined flight deck is crucial for maintaining high combat effectiveness of the aircraft carrier.The primary difficulty exactly lies in the spatiotemporal coordination,i.e.,allocation of limited supporting resources and collision-avoidance between heterogeneous dispatch entities.In this paper,the problem is investigated in the perspective of hybrid flow-shop scheduling problem by synthesizing the precedence,space and resource constraints.Specifically,eight processing procedures are abstracted,where tractors,preparing spots,catapults,and launching are virtualized as machines.By analyzing the constraints in sortie scheduling,a mixed-integer planning model is constructed.In particular,the constraint on preparing spot occupancy is improved to further enhance the sortie efficiency.The basic trajectory library for each dispatch entity is generated and a delayed strategy is integrated to address the collision-avoidance issue.To efficiently solve the formulated HFSP,which is essentially a combinatorial problem with tightly coupled constraints,a chaos-initialized genetic algorithm is developed.The solution framework is validated by the simulation environment referring to the Fort-class carrier,exhibiting higher sortie efficiency when compared to existing strategies.And animation of the simulation results is available at www.bilibili.com/video/BV14t421A7Tt/.The study presents a promising supporting technique for autonomous flight deck operation in the foreseeable future,and can be easily extended to other supporting scenarios,e.g.,ammunition delivery and aircraft maintenance.展开更多
In recent years,advancements in autonomous vehicle technology have accelerated,promising safer and more efficient transportation systems.However,achieving fully autonomous driving in challenging weather conditions,par...In recent years,advancements in autonomous vehicle technology have accelerated,promising safer and more efficient transportation systems.However,achieving fully autonomous driving in challenging weather conditions,particularly in snowy environments,remains a challenge.Snow-covered roads introduce unpredictable surface conditions,occlusions,and reduced visibility,that require robust and adaptive path detection algorithms.This paper presents an enhanced road detection framework for snowy environments,leveraging Simple Framework forContrastive Learning of Visual Representations(SimCLR)for Self-Supervised pretraining,hyperparameter optimization,and uncertainty-aware object detection to improve the performance of YouOnly Look Once version 8(YOLOv8).Themodel is trained and evaluated on a custom-built dataset collected from snowy roads in Tromsø,Norway,which covers a range of snow textures,illumination conditions,and road geometries.The proposed framework achieves scores in terms of mAP@50 equal to 99%and mAP@50–95 equal to 97%,demonstrating the effectiveness of YOLOv8 for real-time road detection in extreme winter conditions.The findings contribute to the safe and reliable deployment of autonomous vehicles in Arctic environments,enabling robust decision-making in hazardous weather conditions.This research lays the groundwork for more resilient perceptionmodels in self-driving systems,paving the way for the future development of intelligent and adaptive transportation networks.展开更多
To solve problems of poor security guarantee and insufficient training efficiency in the conventional reinforcement learning methods for decision-making,this study proposes a hybrid framework to combine deep reinforce...To solve problems of poor security guarantee and insufficient training efficiency in the conventional reinforcement learning methods for decision-making,this study proposes a hybrid framework to combine deep reinforcement learning with rule-based decision-making methods.A risk assessment model for lane-change maneuvers considering uncertain predictions of surrounding vehicles is established as a safety filter to improve learning efficiency while correcting dangerous actions for safety enhancement.On this basis,a Risk-fused DDQN is constructed utilizing the model-based risk assessment and supervision mechanism.The proposed reinforcement learning algorithm sets up a separate experience buffer for dangerous trials and punishes such actions,which is shown to improve the sampling efficiency and training outcomes.Compared with conventional DDQN methods,the proposed algorithm improves the convergence value of cumulated reward by 7.6%and 2.2%in the two constructed scenarios in the simulation study and reduces the number of training episodes by 52.2%and 66.8%respectively.The success rate of lane change is improved by 57.3%while the time headway is increased at least by 16.5%in real vehicle tests,which confirms the higher training efficiency,scenario adaptability,and security of the proposed Risk-fused DDQN.展开更多
The fast growth of mobile autonomous machines from traditional equipment to unmanned autonomous vehicles has fueled the demand for accurate and reliable localization solutions in diverse application domains.Ultra Wide...The fast growth of mobile autonomous machines from traditional equipment to unmanned autonomous vehicles has fueled the demand for accurate and reliable localization solutions in diverse application domains.Ultra Wide Band(UWB)technology has emerged as a promising candidate for addressing this need,offering high precision,immunity to multipath interference,and robust performance in challenging environments.In this comprehensive survey,we systematically explore UWB-based localization for mobile autonomous machines,spanning from fundamental principles to future trends.To the best of our knowledge,this review paper stands as the pioneer in systematically dissecting the algorithms of UWB-based localization for mobile autonomous machines,covering a spectrum from bottom-ranging schemes to advanced sensor fusion,error mitigation,and optimization techniques.By synthesizing existing knowledge,evaluating current methodologies,and highlighting future trends,this review aims to catalyze progress and innovation in the field,unlocking new opportunities for mobile autonomous machine applications across diverse industries and domains.Thus,it serves as a valuable resource for researchers,practitioners,and stakeholders interested in advancing the state-of-the-art UWB-based localization for mobile autonomous machines.展开更多
The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment.The process of achievi...The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment.The process of achieving autonomic resource management is identified to be a herculean task due to its huge distributed and heterogeneous environment.Moreover,the cloud network needs to provide autonomic resource management and deliver potential services to the clients by complying with the requirements of Quality-of-Service(QoS)without impacting the Service Level Agreements(SLAs).However,the existing autonomic cloud resource managing frameworks are not capable in handling the resources of the cloud with its dynamic requirements.In this paper,Coot Bird Behavior Model-based Workload Aware Autonomic Resource Management Scheme(CBBM-WARMS)is proposed for handling the dynamic requirements of cloud resources through the estimation of workload that need to be policed by the cloud environment.This CBBM-WARMS initially adopted the algorithm of adaptive density peak clustering for workloads clustering of the cloud.Then,it utilized the fuzzy logic during the process of workload scheduling for achieving the determining the availability of cloud resources.It further used CBBM for potential Virtual Machine(VM)deployment that attributes towards the provision of optimal resources.It is proposed with the capability of achieving optimal QoS with minimized time,energy consumption,SLA cost and SLA violation.The experimental validation of the proposed CBBMWARMS confirms minimized SLA cost of 19.21%and reduced SLA violation rate of 18.74%,better than the compared autonomic cloud resource managing frameworks.展开更多
Acute mountain sickness(AMS) is an illness caused by hypoxia due to rapid ascent to altitudes above 2,500 m. Symptoms include headache,nausea, vomiting, and loss of appetite, all of which usually improve within 1 to 2...Acute mountain sickness(AMS) is an illness caused by hypoxia due to rapid ascent to altitudes above 2,500 m. Symptoms include headache,nausea, vomiting, and loss of appetite, all of which usually improve within 1 to 2 days. However,untreated AMS can progress to life-threatening conditions such as high-altitude cerebral and pulmonary edema(HACE and HAPE, respectively)^([1]).展开更多
Xi Jinping,general secretary of the Communist Party of China(CPC)Central Committee,stressed that we should adhere to the“two integrations”(namely,integrating the basic tenets of Marxism with China’s specific realit...Xi Jinping,general secretary of the Communist Party of China(CPC)Central Committee,stressed that we should adhere to the“two integrations”(namely,integrating the basic tenets of Marxism with China’s specific realities and fine traditional culture),root ourselves in Chinese soil,carry forward the Chinese cultural heritage,and strengthen the academic foundation.We should accelerate the building of an independent knowledge system for Chinese philosophy and social sciences,and formulate original concepts and develop systems of academic discipline,research and discourse,drawing on China’s rich experience of advancing human rights.In the face of changes of a magnitude not seen in a century,in the historic process of advancing the great rejuvenation of the Chinese nation on all fronts through Chinese modernization,we should and must strengthen our theoretical self-consciousness and confidence in the path of Chinese modernization.We need to enhance human rights research,develop the human rights theoretical system and paradigm that are based on Chinese realities and express Chinese voice,and an independent Chinese knowledge system for human rights.展开更多
Amyotrophic lateral sclerosis(ALS)is a neuromuscular condition resulting from the progressive degeneration of motor neurons in the cortex,brainstem,and spinal cord.While the typical clinical phenotype of ALS involves ...Amyotrophic lateral sclerosis(ALS)is a neuromuscular condition resulting from the progressive degeneration of motor neurons in the cortex,brainstem,and spinal cord.While the typical clinical phenotype of ALS involves both upper and lower motor neurons,human and animal studies over the years have highlighted the potential spread to other motor and non-motor regions,expanding the phenotype of ALS.Although superoxide dismutase 1(SOD1)mutations represent a minority of ALS cases,the SOD1 gene remains a milestone in ALS research as it represents the first genetic target for personalized therapies.Despite numerous single case reports or case series exhibiting extramotor symptoms in patients with ALS mutations in SOD1(SOD1-ALS),no studies have comprehensively explored the full spectrum of extramotor neurological manifestations in this subpopulation.In this narrative review,we analyze and discuss the available literature on extrapyramidal and non-motor features during SOD1-ALS.The multifaceted expression of SOD1 could deepen our understanding of the pathogenic mechanisms,pointing towards a multidisciplinary approach for affected patients in light of new therapeutic strategies for SOD1-ALS.展开更多
文摘Objective To determine the relationship between TSH receptor gene mutations and autonomously functioning thyroid adenomas (AFTAs). Methods The thyroid samples from 14 cases of diagnosed AFTAs were analyzed, with normal thyroid specimens adjacent to the tumors as controls. The 155 base pairs DNA fragments which encompassed the third cytoplasmic loop and the sixth transmembrane segments in the TSH receptor gene exon 10 were amplified by Polymerase chain reaction (PCR) and analyzed by the single-strand conformation polymorphism (SSCP). Direct sequencing of the PCR products was performed with Prism Dye Terminator Cycle Sequencing Core Kit. Results 6 of 14 AFTA specimens displayed abnormal migration in SSCP analysis. In sequence analysis of 3 abnormally migrated samples, one base substitution at nucleotide 1957 (A to C) and two same insertion mutations of one adenosine nucleotide between nucleotide 1972 and 1973 were identified. No mutations were found in controls. Conclusion This study confirmed the presence of TSH receptor gene mutations in AFTAs; both one-point substitution mutation and one-base insertion mutation were found to be responsible for the pathogenesis of AFTAs.
文摘The autonomously trading agents described in this paper produce a decision to act such as: buy, sell or hold, based on the input data. In this work, we have simulated autonomously trading agents using the Echo State Network (ESNs) model. We generate a collection of trading agents that use different trading strategies using Evolutionary Programming (EP). The agents are tested on EUR/ USD real market data. The main goal of this study is to test the overall performance of this collection of agents when they are active simultaneously. Simulation results show that using different agents concurrently outperform a single agent acting alone.
基金supported by the National Natural Science Foundation of China (Grant No. 11774278)the Fundamental Research Funds for Central Universities (Grant No. 2012jdgz04)。
文摘Thermal cloaks offer the potential to conceal internal objects from detection or to prevent thermal shock by controlling external heat flow. However, most conventional natural materials lack the desired flexibility and versatility required for on-demand thermal manipulation. We propose a solution in the form of homogeneous multilayer thermodynamic cloaks. Through an ingenious design, these cloaks achieve exceptional and extreme parameters, enabling the distribution of multiple materials in space. We first investigate the effects of important design parameters on the thermal shielding effectiveness of conventional thermal cloaks. Subsequently, we introduce an autonomous tuning function for the thermodynamic cloak, accomplished by leveraging two phase transition materials as thermal conductive layers. Remarkably, this tuning function does not require any energy input. Finite element analysis results demonstrate a significant reduction in the temperature gradient inside the thermal cloak compared to the surrounding background. This reduction indicates the cloak’s remarkable ability to manipulate the spatial thermal field. Furthermore, the utilization of materials undergoing phase transition leads to an increase in thermal conductivity, enabling the cloak to achieve the opposite variation of the temperature field between the object region and the background. This means that, while the temperature gradient within the cloak decreases, the temperature gradient in the background increases. This work addresses a compelling and crucial challenge in the realm of thermal metamaterials, i.e., autonomous tuning of the thermal field without energy input. Such an achievement is currently unattainable with existing natural materials. This study establishes the groundwork for the application of thermal metamaterials in thermodynamic cloaks, with potential extensions into thermal energy harvesting, thermal camouflage, and thermoelectric conversion devices.By harnessing phonons, our findings provide an unprecedented and practical approach to flexibly implementing thermal cloaks and manipulating heat flow.
文摘Machine learning,a revolutionary and advanced technology,has been widely applied in the field of stock trading.However,training an autonomous trading strategy which can effectively balance risk and Return On Investment without human supervision in the stock market with high uncertainty is still a bottleneck.This paper constructs a Bayesian-inferenced Gated Recurrent Unit architecture to support long-term stock price prediction based on characteristics of the stock information learned from historical data,augmented with memory of recent upand-down fluctuations occur in the data of short-term stock movement.The Gated Recurrent Unit architecture incorporates uncertainty estimation into the prediction process,which take care of decision-making in an ever-changing dynamic environment.Three trading strategies were implemented in this model;namely,a Price Model Strategy,a Probabilistic Model Strategy,and a Bayesian Gated Recurrent Unit Strategy,each leveraging the respective model’s outputs to optimize trading decisions.The experimental results show that,compared with the standard Gated Recurrent Unit models,the modified model exhibits a huge tremendous/dramatic advantage in managing volatility and improving return on investment Return On Investment.The results and findings underscore the significant potential of combining Bayesian inference with machine learning to operate effectively in chaotic decision-making environments.
基金funded by the Open Access Initiative of the University of Bremen and the DFG via SuUB BremenThe authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Group Project under grant number(RGP2/367/46)+1 种基金This research is supported and funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R410)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘As urban landscapes evolve and vehicular volumes soar,traditional traffic monitoring systems struggle to scale,often failing under the complexities of dense,dynamic,and occluded environments.This paper introduces a novel,unified deep learning framework for vehicle detection,tracking,counting,and classification in aerial imagery designed explicitly for modern smart city infrastructure demands.Our approach begins with adaptive histogram equalization to optimize aerial image clarity,followed by a cutting-edge scene parsing technique using Mask2Former,enabling robust segmentation even in visually congested settings.Vehicle detection leverages the latest YOLOv11 architecture,delivering superior accuracy in aerial contexts by addressing occlusion,scale variance,and fine-grained object differentiation.We incorporate the highly efficient ByteTrack algorithm for tracking,enabling seamless identity preservation across frames.Vehicle counting is achieved through an unsupervised DBSCAN-based method,ensuring adaptability to varying traffic densities.We further introduce a hybrid feature extraction module combining Convolutional Neural Networks(CNNs)with Zernike Moments,capturing both deep semantic and geometric signatures of vehicles.The final classification is powered by NASNet,a neural architecture search-optimized model,ensuring high accuracy across diverse vehicle types and orientations.Extensive evaluations of the VAID benchmark dataset demonstrate the system’s outstanding performance,achieving 96%detection,94%tracking,and 96.4%classification accuracy.On the UAVDT dataset,the system attains 95%detection,93%tracking,and 95%classification accuracy,confirming its robustness across diverse aerial traffic scenarios.These results establish new benchmarks in aerial traffic analysis and validate the framework’s scalability,making it a powerful and adaptable solution for next-generation intelligent transportation systems and urban surveillance.
文摘Spinal cord injury(SCI) often results in permanent dysfunction of locomotion,sensation,and autonomic regulation,imposing a substantial burden on both individuals and society(Anjum et al.,2020).SCI has a complex pathophysiology:an initial primary injury(mechanical trauma,axonal disruption,and hemorrhage) is followed by a progressive secondary injury cascade that involves ischemia,neuronal loss,and inflammation.Given the challenges in achieving regeneration of the injured spinal cord,neuroprotection has been at the forefront of clinical research.
文摘Agrowing number of Chinese firms have begun to adopt driverless delivery vehicles in autonomous driving demonstration areas in big cities like Beijing,Shanghai and Shenzhen to enhance logistics efficiency,reduce logistics costs and improve user experience.Violations by these vehicles.including obstructing traffic,running red lights and failing to yield in time.
文摘PHOTO NEWS Unforgettable Winter Break Tourists take selfies in front of snow sculptures in the Fenghuang Mountain Scenic Area,Yakeshi City in Inner Mongolia Autonomous Region,on December 7,2025.
文摘At present,energy consumption is one of the main bottlenecks in autonomous mobile robot development.To address the challenge of high energy consumption in path planning for autonomous mobile robots navigating unknown and complex environments,this paper proposes an Attention-Enhanced Dueling Deep Q-Network(ADDueling DQN),which integrates a multi-head attention mechanism and a prioritized experience replay strategy into a Dueling-DQN reinforcement learning framework.A multi-objective reward function,centered on energy efficiency,is designed to comprehensively consider path length,terrain slope,motion smoothness,and obstacle avoidance,enabling optimal low-energy trajectory generation in 3D space from the source.The incorporation of a multihead attention mechanism allows the model to dynamically focus on energy-critical state features—such as slope gradients and obstacle density—thereby significantly improving its ability to recognize and avoid energy-intensive paths.Additionally,the prioritized experience replay mechanism accelerates learning from key decision-making experiences,suppressing inefficient exploration and guiding the policy toward low-energy solutions more rapidly.The effectiveness of the proposed path planning algorithm is validated through simulation experiments conducted in multiple off-road scenarios.Results demonstrate that AD-Dueling DQN consistently achieves the lowest average energy consumption across all tested environments.Moreover,the proposed method exhibits faster convergence and greater training stability compared to baseline algorithms,highlighting its global optimization capability under energy-aware objectives in complex terrains.This study offers an efficient and scalable intelligent control strategy for the development of energy-conscious autonomous navigation systems.
文摘When you hear about boundless grasslands,vast forests,stunning snowy landscapes,bubbling hot springs and cultural integration,do you ever consider they are descriptions-features actually-of one place?They are,believe it or not.Arxan,a tiny city in Hinggan League,North China's Inner Mongolia Autonomous Region,is that magical place.In recent years,Arxan,a hidden gem boasting breathtaking winter landscape,has captivated visitors seeking to enjoy a“pure-white,fairy-tale world.”
文摘In this study, the discharge voltage behavior of electric double-layer capacitors （EDLCs） during high-g impact is studied both theoretically and experimentally. A micro-scale dynamic mechanism is proposed to describe the physical basis of the increase in the discharge voltage during a high-g impact. Based on this dynamic mechanism, a multi-field model is established, and the simulation and experimental studies of the discharge voltage increase phenomenon are conducted. From the simulation and experimental data, the relationship between the increased voltage and the high-g acceleration is revealed. An acceleration detection range of up to 10,000g is verified. The design of the device is optimized by studying the influences of the parameters, such as the electrode thickness and discharge current, on the outputs. This work opens up new avenues for the development of autonomous sensor systems based on energy storage devices and is significant for many practical applications such as in collision testing and automobile safety.
文摘Self-healing hydrogels utilize inherent intermolecular forces to autonomously heal physical damage resulting from excessive strain,pressure,or tearing.Applying these materials in soft robotics and tissue engineering could be beneficial.On the other hand,their efficacy in stretchable and mechanically resistant circuits is hindered by their limited electrical conductivity.
基金the financial support of the National Key Research and Development Plan(2021YFB3302501)the financial support of the National Natural Science Foundation of China(12102077)。
文摘Safe and efficient sortie scheduling on the confined flight deck is crucial for maintaining high combat effectiveness of the aircraft carrier.The primary difficulty exactly lies in the spatiotemporal coordination,i.e.,allocation of limited supporting resources and collision-avoidance between heterogeneous dispatch entities.In this paper,the problem is investigated in the perspective of hybrid flow-shop scheduling problem by synthesizing the precedence,space and resource constraints.Specifically,eight processing procedures are abstracted,where tractors,preparing spots,catapults,and launching are virtualized as machines.By analyzing the constraints in sortie scheduling,a mixed-integer planning model is constructed.In particular,the constraint on preparing spot occupancy is improved to further enhance the sortie efficiency.The basic trajectory library for each dispatch entity is generated and a delayed strategy is integrated to address the collision-avoidance issue.To efficiently solve the formulated HFSP,which is essentially a combinatorial problem with tightly coupled constraints,a chaos-initialized genetic algorithm is developed.The solution framework is validated by the simulation environment referring to the Fort-class carrier,exhibiting higher sortie efficiency when compared to existing strategies.And animation of the simulation results is available at www.bilibili.com/video/BV14t421A7Tt/.The study presents a promising supporting technique for autonomous flight deck operation in the foreseeable future,and can be easily extended to other supporting scenarios,e.g.,ammunition delivery and aircraft maintenance.
文摘In recent years,advancements in autonomous vehicle technology have accelerated,promising safer and more efficient transportation systems.However,achieving fully autonomous driving in challenging weather conditions,particularly in snowy environments,remains a challenge.Snow-covered roads introduce unpredictable surface conditions,occlusions,and reduced visibility,that require robust and adaptive path detection algorithms.This paper presents an enhanced road detection framework for snowy environments,leveraging Simple Framework forContrastive Learning of Visual Representations(SimCLR)for Self-Supervised pretraining,hyperparameter optimization,and uncertainty-aware object detection to improve the performance of YouOnly Look Once version 8(YOLOv8).Themodel is trained and evaluated on a custom-built dataset collected from snowy roads in Tromsø,Norway,which covers a range of snow textures,illumination conditions,and road geometries.The proposed framework achieves scores in terms of mAP@50 equal to 99%and mAP@50–95 equal to 97%,demonstrating the effectiveness of YOLOv8 for real-time road detection in extreme winter conditions.The findings contribute to the safe and reliable deployment of autonomous vehicles in Arctic environments,enabling robust decision-making in hazardous weather conditions.This research lays the groundwork for more resilient perceptionmodels in self-driving systems,paving the way for the future development of intelligent and adaptive transportation networks.
基金Supported by National Key Research and Development Program of China(Grant No.2022YFE0117100)National Science Foundation of China(Grant No.52102468,52325212)Fundamental Research Funds for the Central Universities。
文摘To solve problems of poor security guarantee and insufficient training efficiency in the conventional reinforcement learning methods for decision-making,this study proposes a hybrid framework to combine deep reinforcement learning with rule-based decision-making methods.A risk assessment model for lane-change maneuvers considering uncertain predictions of surrounding vehicles is established as a safety filter to improve learning efficiency while correcting dangerous actions for safety enhancement.On this basis,a Risk-fused DDQN is constructed utilizing the model-based risk assessment and supervision mechanism.The proposed reinforcement learning algorithm sets up a separate experience buffer for dangerous trials and punishes such actions,which is shown to improve the sampling efficiency and training outcomes.Compared with conventional DDQN methods,the proposed algorithm improves the convergence value of cumulated reward by 7.6%and 2.2%in the two constructed scenarios in the simulation study and reduces the number of training episodes by 52.2%and 66.8%respectively.The success rate of lane change is improved by 57.3%while the time headway is increased at least by 16.5%in real vehicle tests,which confirms the higher training efficiency,scenario adaptability,and security of the proposed Risk-fused DDQN.
文摘The fast growth of mobile autonomous machines from traditional equipment to unmanned autonomous vehicles has fueled the demand for accurate and reliable localization solutions in diverse application domains.Ultra Wide Band(UWB)technology has emerged as a promising candidate for addressing this need,offering high precision,immunity to multipath interference,and robust performance in challenging environments.In this comprehensive survey,we systematically explore UWB-based localization for mobile autonomous machines,spanning from fundamental principles to future trends.To the best of our knowledge,this review paper stands as the pioneer in systematically dissecting the algorithms of UWB-based localization for mobile autonomous machines,covering a spectrum from bottom-ranging schemes to advanced sensor fusion,error mitigation,and optimization techniques.By synthesizing existing knowledge,evaluating current methodologies,and highlighting future trends,this review aims to catalyze progress and innovation in the field,unlocking new opportunities for mobile autonomous machine applications across diverse industries and domains.Thus,it serves as a valuable resource for researchers,practitioners,and stakeholders interested in advancing the state-of-the-art UWB-based localization for mobile autonomous machines.
文摘The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment.The process of achieving autonomic resource management is identified to be a herculean task due to its huge distributed and heterogeneous environment.Moreover,the cloud network needs to provide autonomic resource management and deliver potential services to the clients by complying with the requirements of Quality-of-Service(QoS)without impacting the Service Level Agreements(SLAs).However,the existing autonomic cloud resource managing frameworks are not capable in handling the resources of the cloud with its dynamic requirements.In this paper,Coot Bird Behavior Model-based Workload Aware Autonomic Resource Management Scheme(CBBM-WARMS)is proposed for handling the dynamic requirements of cloud resources through the estimation of workload that need to be policed by the cloud environment.This CBBM-WARMS initially adopted the algorithm of adaptive density peak clustering for workloads clustering of the cloud.Then,it utilized the fuzzy logic during the process of workload scheduling for achieving the determining the availability of cloud resources.It further used CBBM for potential Virtual Machine(VM)deployment that attributes towards the provision of optimal resources.It is proposed with the capability of achieving optimal QoS with minimized time,energy consumption,SLA cost and SLA violation.The experimental validation of the proposed CBBMWARMS confirms minimized SLA cost of 19.21%and reduced SLA violation rate of 18.74%,better than the compared autonomic cloud resource managing frameworks.
文摘Acute mountain sickness(AMS) is an illness caused by hypoxia due to rapid ascent to altitudes above 2,500 m. Symptoms include headache,nausea, vomiting, and loss of appetite, all of which usually improve within 1 to 2 days. However,untreated AMS can progress to life-threatening conditions such as high-altitude cerebral and pulmonary edema(HACE and HAPE, respectively)^([1]).
文摘Xi Jinping,general secretary of the Communist Party of China(CPC)Central Committee,stressed that we should adhere to the“two integrations”(namely,integrating the basic tenets of Marxism with China’s specific realities and fine traditional culture),root ourselves in Chinese soil,carry forward the Chinese cultural heritage,and strengthen the academic foundation.We should accelerate the building of an independent knowledge system for Chinese philosophy and social sciences,and formulate original concepts and develop systems of academic discipline,research and discourse,drawing on China’s rich experience of advancing human rights.In the face of changes of a magnitude not seen in a century,in the historic process of advancing the great rejuvenation of the Chinese nation on all fronts through Chinese modernization,we should and must strengthen our theoretical self-consciousness and confidence in the path of Chinese modernization.We need to enhance human rights research,develop the human rights theoretical system and paradigm that are based on Chinese realities and express Chinese voice,and an independent Chinese knowledge system for human rights.
文摘Amyotrophic lateral sclerosis(ALS)is a neuromuscular condition resulting from the progressive degeneration of motor neurons in the cortex,brainstem,and spinal cord.While the typical clinical phenotype of ALS involves both upper and lower motor neurons,human and animal studies over the years have highlighted the potential spread to other motor and non-motor regions,expanding the phenotype of ALS.Although superoxide dismutase 1(SOD1)mutations represent a minority of ALS cases,the SOD1 gene remains a milestone in ALS research as it represents the first genetic target for personalized therapies.Despite numerous single case reports or case series exhibiting extramotor symptoms in patients with ALS mutations in SOD1(SOD1-ALS),no studies have comprehensively explored the full spectrum of extramotor neurological manifestations in this subpopulation.In this narrative review,we analyze and discuss the available literature on extrapyramidal and non-motor features during SOD1-ALS.The multifaceted expression of SOD1 could deepen our understanding of the pathogenic mechanisms,pointing towards a multidisciplinary approach for affected patients in light of new therapeutic strategies for SOD1-ALS.
