At present, Chinas civil aviation industry has entered a new stage of steady development, and various types of civil aircraft emerge in one after another. Aircraft as a special equipment, in order to ensure the safety...At present, Chinas civil aviation industry has entered a new stage of steady development, and various types of civil aircraft emerge in one after another. Aircraft as a special equipment, in order to ensure the safety of flight, airworthiness information management has extremely high requirements. In order to improve the level of airworthiness management, the introduction of information management means, make airworthiness management more scientific, consider from multiple goals and multiple dimensions, and improve the scientific nature and reliability of airworthiness management, so as to further improve the safety and stability of aircraft operation.展开更多
Submission.Papers appearing in the Journal comprise Editorials,Rapid Communications,Perspectives,Tutorials,Feature Articles,Reviews,Research Articles,which should contain original information,theoretical or experiment...Submission.Papers appearing in the Journal comprise Editorials,Rapid Communications,Perspectives,Tutorials,Feature Articles,Reviews,Research Articles,which should contain original information,theoretical or experimental,on any topics in the field of polymer science and polymer material science.Papers already published or scheduled to be published elsewhere should not be submitted and certainly will not be accepted.展开更多
People read many things online these days.It's an easy way to get a lot of information fast.They look at news,see posts and watch videos.But how much of the information is true?Some things online are fake.So it...People read many things online these days.It's an easy way to get a lot of information fast.They look at news,see posts and watch videos.But how much of the information is true?Some things online are fake.So it's important to check the facts before you believe or share anything.You can ask people or look at other sources first.Check newspapers or official websites.Always think carefully before you believe something online.展开更多
1 General information Journal of Geographical Sciences is an international academic journal that publishes papers of the highest quality in physical geography, natural resources, environmental sciences, geographic inf...1 General information Journal of Geographical Sciences is an international academic journal that publishes papers of the highest quality in physical geography, natural resources, environmental sciences, geographic information sciences, remote sensing and cartography. Manuscripts come from different parts of the world.展开更多
Human activities have significantly impacted the land surface temperature(LST),endangering human health;however,the relationship between these two factors has not been adequately quantified.This study comprehensively ...Human activities have significantly impacted the land surface temperature(LST),endangering human health;however,the relationship between these two factors has not been adequately quantified.This study comprehensively constructs a Human Activity Intensity(HAI)index and employs the Maximal Information Coefficient,four-quadrant model,and XGBoostSHAP model to investigate the spatiotemporal relationship and influencing factors of HAI-LST in the Yellow River Basin(YRB)from 2000 to 2020.The results indicated that from 2000 to 2020,as HAI and LST increased,the static HAI-LST relationship in the YRB showed a positive correlation that continued to strengthen.This dynamic relationship exhibited conflicting development,with the proportion of coordinated to conflicting regions shifting from 1:4 to 1:2,indicating a reduction in conflict intensity.Notably,only the degree of conflict in the source area decreased significantly,whereas it intensified in the upper and lower reaches.The key factors influencing the HAI-LST relationship include fractional vegetation cover,slope,precipitation,and evapotranspiration,along with region-specific factors such as PM_(2.5),biodiversity,and elevation.Based on these findings,region-specific ecological management strategies have been proposed to mitigate conflict-prone areas and alleviate thermal stress,thereby providing important guidance for promoting harmonious development between humans and nature.展开更多
Amphibious vehicles are more prone to attitude instability compared to ships,making it crucial to develop effective methods for monitoring instability risks.However,large inclination events,which can lead to instabili...Amphibious vehicles are more prone to attitude instability compared to ships,making it crucial to develop effective methods for monitoring instability risks.However,large inclination events,which can lead to instability,occur frequently in both experimental and operational data.This infrequency causes events to be overlooked by existing prediction models,which lack the precision to accurately predict inclination attitudes in amphibious vehicles.To address this gap in predicting attitudes near extreme inclination points,this study introduces a novel loss function,termed generalized extreme value loss.Subsequently,a deep learning model for improved waterborne attitude prediction,termed iInformer,was developed using a Transformer-based approach.During the embedding phase,a text prototype is created based on the vehicle’s operation log data is constructed to help the model better understand the vehicle’s operating environment.Data segmentation techniques are used to highlight local data variation features.Furthermore,to mitigate issues related to poor convergence and slow training speeds caused by the extreme value loss function,a teacher forcing mechanism is integrated into the model,enhancing its convergence capabilities.Experimental results validate the effectiveness of the proposed method,demonstrating its ability to handle data imbalance challenges.Specifically,the model achieves over a 60%improvement in root mean square error under extreme value conditions,with significant improvements observed across additional metrics.展开更多
Rock mass stability is significantly influenced by the heterogeneity of rock joint roughness and shear strength.While modern technology facilitates assessing roughness heterogeneity,evaluating shear strength heterogen...Rock mass stability is significantly influenced by the heterogeneity of rock joint roughness and shear strength.While modern technology facilitates assessing roughness heterogeneity,evaluating shear strength heterogeneity remains challenging.To address this,this study first captures the morphology of large-scale(1000 mm × 1000 mm) slate and granite joints via 3D laser scanning.Analysis of these surfaces and corresponding push/pull tests on carved specimens revealed a potential correlation between the heterogeneity of roughness and shear strength.A comparative evaluation of five statistical metrics identified information entropy(Hs) as the most robust indicator for quantifying rock joint heterogeneity.Further analysis using Hsreveals that the heterogeneity is anisotropic and,critically,that shear strength heterogeneity is governed not only by roughness heterogeneity but is also significantly influenced by the mean roughness value,normal stress,and intact rock tensile strength.Consequently,a simple comparison of roughness Hsvalues is insufficient for reliably comparing shear strength heterogeneity.To overcome this limitation,a theoretical framework is developed to explicitly map fundamental roughness statistics(mean and heterogeneity) to shear strength heterogeneity.This framework culminates in a practical workflow that allows for the rapid,field-based assessment of shear strength heterogeneity using readily obtainable rock joint roughness data.展开更多
Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of...Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of user preferences.To address this,we propose a Conditional Generative Adversarial Network(CGAN)that generates diverse and highly relevant itineraries.Our approach begins by constructing a conditional vector that encapsulates a user’s profile.This vector uniquely fuses embeddings from a Heterogeneous Information Network(HIN)to model complex user-place-route relationships,a Recurrent Neural Network(RNN)to capture sequential path dynamics,and Neural Collaborative Filtering(NCF)to incorporate collaborative signals from the wider user base.This comprehensive condition,further enhanced with features representing user interaction confidence and uncertainty,steers a CGAN stabilized by spectral normalization to generate high-fidelity latent route representations,effectively mitigating the data sparsity problem.Recommendations are then formulated using an Anchor-and-Expand algorithm,which selects relevant starting Points of Interest(POI)based on user history,then expands routes through latent similarity matching and geographic coherence optimization,culminating in Traveling Salesman Problem(TSP)-based route optimization for practical travel distances.Experiments on a real-world check-in dataset validate our model’s unique generative capability,achieving F1 scores ranging from 0.163 to 0.305,and near-zero pairs−F1 scores between 0.002 and 0.022.These results confirm the model’s success in generating novel travel routes by recommending new locations and sequences rather than replicating users’past itineraries.This work provides a robust solution for personalized travel planning,capable of generating novel and compelling routes for both new and existing users by learning from collective travel intelligence.展开更多
With the growing advancement of wireless communication technologies,WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution.Among the available signal types,Channel State I...With the growing advancement of wireless communication technologies,WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution.Among the available signal types,Channel State Information(CSI)offers fine-grained temporal,frequency,and spatial insights into multipath propagation,making it a crucial data source for human-centric sensing.Recently,the integration of deep learning has significantly improved the robustness and automation of feature extraction from CSI in complex environments.This paper provides a comprehensive review of deep learning-enhanced human sensing based on CSI.We first outline mainstream CSI acquisition tools and their hardware specifications,then provide a detailed discussion of preprocessing methods such as denoising,time–frequency transformation,data segmentation,and augmentation.Subsequently,we categorize deep learning approaches according to sensing tasks—namely detection,localization,and recognition—and highlight representative models across application scenarios.Finally,we examine key challenges including domain generalization,multi-user interference,and limited data availability,and we propose future research directions involving lightweight model deployment,multimodal data fusion,and semantic-level sensing.展开更多
Quantum control allows a wide range of quantum operations employed in molecular physics,nuclear magnetic resonance and quantum information processing.Thanks to the existing microelectronics industry,semiconducting qub...Quantum control allows a wide range of quantum operations employed in molecular physics,nuclear magnetic resonance and quantum information processing.Thanks to the existing microelectronics industry,semiconducting qubits,where quantum information is encoded in spin or charge degree freedom of electrons or nuclei in semiconductor quantum dots,constitute a highly competitive candidate for scalable solid-state quantum technologies.In quantum information processing,advanced control techniques are needed to realize quantum manipulations with both high precision and noise resilience.In this review,we first introduce the basics of various widely-used control methods,including resonant excitation,adabatic passage,shortcuts to adiabaticity,composite pulses,and quantum optimal control.Then we review the practical aspects in applying these methods to realize accurate and robust quantum gates for single semiconductor qubits,such as Loss–DiVincenzo spin qubit,spinglet-triplet qubit,exchange-only qubit and charge qubit.展开更多
Peripheral sensory neurons perceive external signals and convey signals to the central nervous system(CNS).Information transmission occurs via often extremely long axons and timely reactions of the animal require a fa...Peripheral sensory neurons perceive external signals and convey signals to the central nervous system(CNS).Information transmission occurs via often extremely long axons and timely reactions of the animal require a fast conductance velocity.This not only depends on axonal diameter and insulation by glial processes,but it requires the structural integrity of the axon.展开更多
为了保证运维阶段桥梁结构安全,提升桥梁运维工作的效率,开展公路混凝土梁式桥运维阶段建筑信息模型(building information modeling,BIM)技术应用研究。在对公路桥梁现行编码体系进行扩展的基础上,提出1种参数化快速建模方法,以快速完...为了保证运维阶段桥梁结构安全,提升桥梁运维工作的效率,开展公路混凝土梁式桥运维阶段建筑信息模型(building information modeling,BIM)技术应用研究。在对公路桥梁现行编码体系进行扩展的基础上,提出1种参数化快速建模方法,以快速完成桥梁构件族的创建与整体模型的集成。借助Autodesk Revit软件应用程序编程接口(application programming interface,API),采用C#语言,开发公路混凝土梁式桥智慧运维状态评估系统,以实际工程应用进行验证分析。研究结果表明:全面统一的桥梁信息编码体系,能够提高桥梁信息统计与检索效率;提出的快速建模方法能够显著减少建模工作量,建模时间较传统建模方法可减少60%,并保证模型的准确性与规范性;运维状态评估系统能够实现养护数据的充分利用与桥梁评定工作的自动化,通过对桥梁运维信息的有效组织,实现服役性能的长期追踪,从而确保运营期桥梁结构状态安全稳定。研究结果可为公路混凝土梁式桥运维管理提供技术支撑,提升桥梁运维的数字化水平。展开更多
Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure.Therefore,it is particularly significant to identify the structural modal parameters accordi...Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure.Therefore,it is particularly significant to identify the structural modal parameters according to the monitoring data information in the structural health monitoring(SHM)system,so as to provide a scientific basis for structural damage identification and dynamic model modification.In view of this,this paper reviews methods for identifying structural modal parameters under environmental excitation and briefly describes how to identify structural damages based on the derived modal parameters.The paper primarily introduces data-driven modal parameter recognition methods(e.g.,time-domain,frequency-domain,and time-frequency-domain methods,etc.),briefly describes damage identification methods based on the variations of modal parameters(e.g.,natural frequency,modal shapes,and curvature modal shapes,etc.)and modal validation methods(e.g.,Stability Diagram and Modal Assurance Criterion,etc.).The current status of the application of artificial intelligence(AI)methods in the direction of modal parameter recognition and damage identification is further discussed.Based on the pre-vious analysis,the main development trends of structural modal parameter recognition and damage identification methods are given to provide scientific references for the optimized design and functional upgrading of SHM systems.展开更多
This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble lear...This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.展开更多
With the development of smart cities and smart technologies,parks,as functional units of the city,are facing smart transformation.The development of smart parks can help address challenges of technology integration wi...With the development of smart cities and smart technologies,parks,as functional units of the city,are facing smart transformation.The development of smart parks can help address challenges of technology integration within urban spaces and serve as testbeds for exploring smart city planning and governance models.Information models facilitate the effective integration of technology into space.Building Information Modeling(BIM)and City Information Modeling(CIM)have been widely used in urban construction.However,the existing information models have limitations in the application of the park,so it is necessary to develop an information model suitable for the park.This paper first traces the evolution of park smart transformation,reviews the global landscape of smart park development,and identifies key trends and persistent challenges.Addressing the particularities of parks,the concept of Park Information Modeling(PIM)is proposed.PIM leverages smart technologies such as artificial intelligence,digital twins,and collaborative sensing to help form a‘space-technology-system’smart structure,enabling systematic management of diverse park spaces,addressing the deficiency in park-level information models,and aiming to achieve scale articulation between BIM and CIM.Finally,through a detailed top-level design application case study of the Nanjing Smart Education Park in China,this paper illustrates the translation process of the PIM concept into practice,showcasing its potential to provide smart management tools for park managers and enhance services for park stakeholders,although further empirical validation is required.展开更多
Unmanned aerial vehicles(UAVs)have become crucial tools in moving target tracking due to their agility and ability to operate in complex,dynamic environments.UAVs must meet several requirements to achieve stable track...Unmanned aerial vehicles(UAVs)have become crucial tools in moving target tracking due to their agility and ability to operate in complex,dynamic environments.UAVs must meet several requirements to achieve stable tracking,including maintaining continuous target visibility amidst occlusions,ensuring flight safety,and achieving smooth trajectory planning.This paper reviews the latest advancements in UAV-based target tracking,highlighting information prediction,tracking strategies,and swarm cooperation.To address challenges including target visibility and occlusion,real-time prediction and tracking in dynamic environments,flight safety and coordination,resource management and energy efficiency,the paper identifies future research directions aimed at improving the performance,reliability,and scalability of UAV tracking system.展开更多
In recent years,Volunteered Geographic Information(VGI)has emerged as a crucial source of mapping data,contributed by users through crowdsourcing platforms such as OpenStreetMap.This paper presents a novel approach th...In recent years,Volunteered Geographic Information(VGI)has emerged as a crucial source of mapping data,contributed by users through crowdsourcing platforms such as OpenStreetMap.This paper presents a novel approach that Integrates Large Language Models(LLMs)into a fully automated mapping workflow,utilizing VGI data.The process leverages Prompt Engineering,which involves designing and optimizing input instructions to ensure the LLM produces desired mapping outputs.By constructing precise and detailed prompts,LLM agents are able to accurately interpret mapping requirements,and autonomously extract,analyze,and process VGI geospatial data.They dynamically interact with mapping tools to automate the entire mapping process—from data acquisition to map generation.This approach significantly streamlines the creation of high-quality mapping outputs,reducing the time and resources typically required for such tasks.Moreover,the system lowers the barrier for non-expert users,enabling them to generate accurate maps without extensive technical expertise.Through various case studies,we demonstrate the LLM application across different mapping scenarios,highlighting its potential to enhance the efficiency,accuracy,and accessibility of map production.The results suggest that LLM-powered mapping systems can not only optimize VGI data processing but also expand the usability of ubiquitous mapping across diverse fields,including urban planning and infrastructure development.展开更多
The ongoing revolution in information technology is reshaping human life. In the realm of health behavior, wearable technology emerges as a leading digital solution,capturing physical behaviors (i.e., physical activit...The ongoing revolution in information technology is reshaping human life. In the realm of health behavior, wearable technology emerges as a leading digital solution,capturing physical behaviors (i.e., physical activity, sedentary habits, sleep patterns) within the 24-h cycle of daily life. Wearables are applied in research, clinical practice, and as lifestyle devices;most obvious, they promise to be a key element for increasing human physical activity, one of the biggest health challenges nowadays.展开更多
文摘At present, Chinas civil aviation industry has entered a new stage of steady development, and various types of civil aircraft emerge in one after another. Aircraft as a special equipment, in order to ensure the safety of flight, airworthiness information management has extremely high requirements. In order to improve the level of airworthiness management, the introduction of information management means, make airworthiness management more scientific, consider from multiple goals and multiple dimensions, and improve the scientific nature and reliability of airworthiness management, so as to further improve the safety and stability of aircraft operation.
文摘Submission.Papers appearing in the Journal comprise Editorials,Rapid Communications,Perspectives,Tutorials,Feature Articles,Reviews,Research Articles,which should contain original information,theoretical or experimental,on any topics in the field of polymer science and polymer material science.Papers already published or scheduled to be published elsewhere should not be submitted and certainly will not be accepted.
文摘People read many things online these days.It's an easy way to get a lot of information fast.They look at news,see posts and watch videos.But how much of the information is true?Some things online are fake.So it's important to check the facts before you believe or share anything.You can ask people or look at other sources first.Check newspapers or official websites.Always think carefully before you believe something online.
文摘1 General information Journal of Geographical Sciences is an international academic journal that publishes papers of the highest quality in physical geography, natural resources, environmental sciences, geographic information sciences, remote sensing and cartography. Manuscripts come from different parts of the world.
基金Shanxi Province Graduate Research Practice Innovation Project,No.2023KY465Project on the Reform of Graduate Education and Teaching in Shanxi Province,No.2021YJJG146+1 种基金Research Project of Shanxi Provincial Cultural Relics Bureau,No.22-8-14-1400-119National Key R&D Program of China,No.2021YFB3901300。
文摘Human activities have significantly impacted the land surface temperature(LST),endangering human health;however,the relationship between these two factors has not been adequately quantified.This study comprehensively constructs a Human Activity Intensity(HAI)index and employs the Maximal Information Coefficient,four-quadrant model,and XGBoostSHAP model to investigate the spatiotemporal relationship and influencing factors of HAI-LST in the Yellow River Basin(YRB)from 2000 to 2020.The results indicated that from 2000 to 2020,as HAI and LST increased,the static HAI-LST relationship in the YRB showed a positive correlation that continued to strengthen.This dynamic relationship exhibited conflicting development,with the proportion of coordinated to conflicting regions shifting from 1:4 to 1:2,indicating a reduction in conflict intensity.Notably,only the degree of conflict in the source area decreased significantly,whereas it intensified in the upper and lower reaches.The key factors influencing the HAI-LST relationship include fractional vegetation cover,slope,precipitation,and evapotranspiration,along with region-specific factors such as PM_(2.5),biodiversity,and elevation.Based on these findings,region-specific ecological management strategies have been proposed to mitigate conflict-prone areas and alleviate thermal stress,thereby providing important guidance for promoting harmonious development between humans and nature.
基金Supported by the National Defense Basic Scientific Research Program of China.
文摘Amphibious vehicles are more prone to attitude instability compared to ships,making it crucial to develop effective methods for monitoring instability risks.However,large inclination events,which can lead to instability,occur frequently in both experimental and operational data.This infrequency causes events to be overlooked by existing prediction models,which lack the precision to accurately predict inclination attitudes in amphibious vehicles.To address this gap in predicting attitudes near extreme inclination points,this study introduces a novel loss function,termed generalized extreme value loss.Subsequently,a deep learning model for improved waterborne attitude prediction,termed iInformer,was developed using a Transformer-based approach.During the embedding phase,a text prototype is created based on the vehicle’s operation log data is constructed to help the model better understand the vehicle’s operating environment.Data segmentation techniques are used to highlight local data variation features.Furthermore,to mitigate issues related to poor convergence and slow training speeds caused by the extreme value loss function,a teacher forcing mechanism is integrated into the model,enhancing its convergence capabilities.Experimental results validate the effectiveness of the proposed method,demonstrating its ability to handle data imbalance challenges.Specifically,the model achieves over a 60%improvement in root mean square error under extreme value conditions,with significant improvements observed across additional metrics.
基金supported by the National Natural Science Foundation of China (Nos.42422705,42207175,42177117 and 42577170)the Ningbo Youth Leading Talent Project (No.2024QL051)+1 种基金the Chinese Academy of Engineering Science and Technology Strategy Consulting Project (No.2025-XZ-57)the Central Government Funding Program for Guiding Local Science and Technology Development (No.2025ZY01028)。
文摘Rock mass stability is significantly influenced by the heterogeneity of rock joint roughness and shear strength.While modern technology facilitates assessing roughness heterogeneity,evaluating shear strength heterogeneity remains challenging.To address this,this study first captures the morphology of large-scale(1000 mm × 1000 mm) slate and granite joints via 3D laser scanning.Analysis of these surfaces and corresponding push/pull tests on carved specimens revealed a potential correlation between the heterogeneity of roughness and shear strength.A comparative evaluation of five statistical metrics identified information entropy(Hs) as the most robust indicator for quantifying rock joint heterogeneity.Further analysis using Hsreveals that the heterogeneity is anisotropic and,critically,that shear strength heterogeneity is governed not only by roughness heterogeneity but is also significantly influenced by the mean roughness value,normal stress,and intact rock tensile strength.Consequently,a simple comparison of roughness Hsvalues is insufficient for reliably comparing shear strength heterogeneity.To overcome this limitation,a theoretical framework is developed to explicitly map fundamental roughness statistics(mean and heterogeneity) to shear strength heterogeneity.This framework culminates in a practical workflow that allows for the rapid,field-based assessment of shear strength heterogeneity using readily obtainable rock joint roughness data.
基金supported by the Chung-Ang University Research Grants in 2023.Alsothe work is supported by the ELLIIT Excellence Center at Linköping–Lund in Information Technology in Sweden.
文摘Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of user preferences.To address this,we propose a Conditional Generative Adversarial Network(CGAN)that generates diverse and highly relevant itineraries.Our approach begins by constructing a conditional vector that encapsulates a user’s profile.This vector uniquely fuses embeddings from a Heterogeneous Information Network(HIN)to model complex user-place-route relationships,a Recurrent Neural Network(RNN)to capture sequential path dynamics,and Neural Collaborative Filtering(NCF)to incorporate collaborative signals from the wider user base.This comprehensive condition,further enhanced with features representing user interaction confidence and uncertainty,steers a CGAN stabilized by spectral normalization to generate high-fidelity latent route representations,effectively mitigating the data sparsity problem.Recommendations are then formulated using an Anchor-and-Expand algorithm,which selects relevant starting Points of Interest(POI)based on user history,then expands routes through latent similarity matching and geographic coherence optimization,culminating in Traveling Salesman Problem(TSP)-based route optimization for practical travel distances.Experiments on a real-world check-in dataset validate our model’s unique generative capability,achieving F1 scores ranging from 0.163 to 0.305,and near-zero pairs−F1 scores between 0.002 and 0.022.These results confirm the model’s success in generating novel travel routes by recommending new locations and sequences rather than replicating users’past itineraries.This work provides a robust solution for personalized travel planning,capable of generating novel and compelling routes for both new and existing users by learning from collective travel intelligence.
基金supported by National Natural Science Foundation of China(NSFC)under grant U23A20310.
文摘With the growing advancement of wireless communication technologies,WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution.Among the available signal types,Channel State Information(CSI)offers fine-grained temporal,frequency,and spatial insights into multipath propagation,making it a crucial data source for human-centric sensing.Recently,the integration of deep learning has significantly improved the robustness and automation of feature extraction from CSI in complex environments.This paper provides a comprehensive review of deep learning-enhanced human sensing based on CSI.We first outline mainstream CSI acquisition tools and their hardware specifications,then provide a detailed discussion of preprocessing methods such as denoising,time–frequency transformation,data segmentation,and augmentation.Subsequently,we categorize deep learning approaches according to sensing tasks—namely detection,localization,and recognition—and highlight representative models across application scenarios.Finally,we examine key challenges including domain generalization,multi-user interference,and limited data availability,and we propose future research directions involving lightweight model deployment,multimodal data fusion,and semantic-level sensing.
基金support by the National Natural Science Foundation of China(Nos.12174379,E31Q02BG)the Chinese Academy of Sciences(Nos.E0SEBB11,E27RBB11)+1 种基金the Innovation Program for Quantum Science and Technology(No.2021ZD0302300)Chinese Academy of Sciences Project for Young Scientists in Basic Research(No.YSBR-090)。
文摘Quantum control allows a wide range of quantum operations employed in molecular physics,nuclear magnetic resonance and quantum information processing.Thanks to the existing microelectronics industry,semiconducting qubits,where quantum information is encoded in spin or charge degree freedom of electrons or nuclei in semiconductor quantum dots,constitute a highly competitive candidate for scalable solid-state quantum technologies.In quantum information processing,advanced control techniques are needed to realize quantum manipulations with both high precision and noise resilience.In this review,we first introduce the basics of various widely-used control methods,including resonant excitation,adabatic passage,shortcuts to adiabaticity,composite pulses,and quantum optimal control.Then we review the practical aspects in applying these methods to realize accurate and robust quantum gates for single semiconductor qubits,such as Loss–DiVincenzo spin qubit,spinglet-triplet qubit,exchange-only qubit and charge qubit.
基金funded by a grant of the Deutsche Forschungsgemeinschaft(DFG)(SFB 1348,B5)to CK.
文摘Peripheral sensory neurons perceive external signals and convey signals to the central nervous system(CNS).Information transmission occurs via often extremely long axons and timely reactions of the animal require a fast conductance velocity.This not only depends on axonal diameter and insulation by glial processes,but it requires the structural integrity of the axon.
基金supported by the Innovation Foundation of Provincial Education Department of Gansu(2024B-005)the Gansu Province National Science Foundation(22YF7GA182)the Fundamental Research Funds for the Central Universities(No.lzujbky2022-kb01)。
文摘Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure.Therefore,it is particularly significant to identify the structural modal parameters according to the monitoring data information in the structural health monitoring(SHM)system,so as to provide a scientific basis for structural damage identification and dynamic model modification.In view of this,this paper reviews methods for identifying structural modal parameters under environmental excitation and briefly describes how to identify structural damages based on the derived modal parameters.The paper primarily introduces data-driven modal parameter recognition methods(e.g.,time-domain,frequency-domain,and time-frequency-domain methods,etc.),briefly describes damage identification methods based on the variations of modal parameters(e.g.,natural frequency,modal shapes,and curvature modal shapes,etc.)and modal validation methods(e.g.,Stability Diagram and Modal Assurance Criterion,etc.).The current status of the application of artificial intelligence(AI)methods in the direction of modal parameter recognition and damage identification is further discussed.Based on the pre-vious analysis,the main development trends of structural modal parameter recognition and damage identification methods are given to provide scientific references for the optimized design and functional upgrading of SHM systems.
基金the University of Transport Technology under the project entitled“Application of Machine Learning Algorithms in Landslide Susceptibility Mapping in Mountainous Areas”with grant number DTTD2022-16.
文摘This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.
基金Under the auspices of National Natural Science Foundation of China(No.42330510)。
文摘With the development of smart cities and smart technologies,parks,as functional units of the city,are facing smart transformation.The development of smart parks can help address challenges of technology integration within urban spaces and serve as testbeds for exploring smart city planning and governance models.Information models facilitate the effective integration of technology into space.Building Information Modeling(BIM)and City Information Modeling(CIM)have been widely used in urban construction.However,the existing information models have limitations in the application of the park,so it is necessary to develop an information model suitable for the park.This paper first traces the evolution of park smart transformation,reviews the global landscape of smart park development,and identifies key trends and persistent challenges.Addressing the particularities of parks,the concept of Park Information Modeling(PIM)is proposed.PIM leverages smart technologies such as artificial intelligence,digital twins,and collaborative sensing to help form a‘space-technology-system’smart structure,enabling systematic management of diverse park spaces,addressing the deficiency in park-level information models,and aiming to achieve scale articulation between BIM and CIM.Finally,through a detailed top-level design application case study of the Nanjing Smart Education Park in China,this paper illustrates the translation process of the PIM concept into practice,showcasing its potential to provide smart management tools for park managers and enhance services for park stakeholders,although further empirical validation is required.
基金financial support provided by the Natural Science Foundation of Hunan Province of China(Grant No.2021JJ10045)the Open Research Subject of State Key Laboratory of Intelligent Game(Grant No.ZBKF-24-01)+1 种基金the Postdoctoral Fellowship Program of CPSF(Grant No.GZB20240989)the China Postdoctoral Science Foundation(Grant No.2024M754304)。
文摘Unmanned aerial vehicles(UAVs)have become crucial tools in moving target tracking due to their agility and ability to operate in complex,dynamic environments.UAVs must meet several requirements to achieve stable tracking,including maintaining continuous target visibility amidst occlusions,ensuring flight safety,and achieving smooth trajectory planning.This paper reviews the latest advancements in UAV-based target tracking,highlighting information prediction,tracking strategies,and swarm cooperation.To address challenges including target visibility and occlusion,real-time prediction and tracking in dynamic environments,flight safety and coordination,resource management and energy efficiency,the paper identifies future research directions aimed at improving the performance,reliability,and scalability of UAV tracking system.
基金National Natural Science Foundation of china(No.42371446)Natural Science Foundatiorof Hubei Province(No.2024AFD412)Fundamental Research Funds for National Universities,China University of Geosciences(Wuhan)(No.2024XLA17).
文摘In recent years,Volunteered Geographic Information(VGI)has emerged as a crucial source of mapping data,contributed by users through crowdsourcing platforms such as OpenStreetMap.This paper presents a novel approach that Integrates Large Language Models(LLMs)into a fully automated mapping workflow,utilizing VGI data.The process leverages Prompt Engineering,which involves designing and optimizing input instructions to ensure the LLM produces desired mapping outputs.By constructing precise and detailed prompts,LLM agents are able to accurately interpret mapping requirements,and autonomously extract,analyze,and process VGI geospatial data.They dynamically interact with mapping tools to automate the entire mapping process—from data acquisition to map generation.This approach significantly streamlines the creation of high-quality mapping outputs,reducing the time and resources typically required for such tasks.Moreover,the system lowers the barrier for non-expert users,enabling them to generate accurate maps without extensive technical expertise.Through various case studies,we demonstrate the LLM application across different mapping scenarios,highlighting its potential to enhance the efficiency,accuracy,and accessibility of map production.The results suggest that LLM-powered mapping systems can not only optimize VGI data processing but also expand the usability of ubiquitous mapping across diverse fields,including urban planning and infrastructure development.
基金funded in part by the German Research Foundation(Grant reference:496846758).
文摘The ongoing revolution in information technology is reshaping human life. In the realm of health behavior, wearable technology emerges as a leading digital solution,capturing physical behaviors (i.e., physical activity, sedentary habits, sleep patterns) within the 24-h cycle of daily life. Wearables are applied in research, clinical practice, and as lifestyle devices;most obvious, they promise to be a key element for increasing human physical activity, one of the biggest health challenges nowadays.
