Bottom-up and top-down endogenous automobile clusters exhibit distinct evolutionary traits and driving mechanisms,yet their comparative analysis remains understudied.Therefore,using Taizhou automobile industry cluster...Bottom-up and top-down endogenous automobile clusters exhibit distinct evolutionary traits and driving mechanisms,yet their comparative analysis remains understudied.Therefore,using Taizhou automobile industry cluster(TAIC)and Wuhu automobile industry cluster(WAIC)as cases,using historical statistical data and field interview data from the 1980s to 2023,combined with qualitative research methods of thematic and diachronic analysis,and quantitative research methods of social network analysis,we compare both endogenous automobile clusters’evolutionary traits and driving mechanisms.The results confirm both clusters undergo multi-scale spatial reconfiguration,organizational complexification,and intelligent networking technological transformation,yet diverge fundamentally:TAIC evolves through market-driven progressive expansion,transitioning from single to dual-core structures via private enterprise networking,with innovation following market-integrated logic and institutional thickness built on demand-driven evolution.Conversely,WAIC follows planned expansion,maintaining state-led hierarchical single-core stability through policy-driven breakthrough innovation and supply-dominated institutional construction-though both ultimately require formal-informal system synergy.Their coevolution is driven by dynamic interactions of path dependence(weakening influence),learning-innovation(strengthening influence),and relationship selection(inverted U-shaped trajectory),with divergent development paths rooted in TAIC’s grassroots self-organization genes versus WAIC’s top-level design genes,amplified by core enterprises’strategic disparities.The research findings can not only provide decision-making support for China’s industrial upgrading,but also contribute China’s insights to global economic governance.展开更多
Landslides pose a formidable natural hazard across the Qinghai-Tibet Plateau(QTP),endangering both ecosystems and human life.Identifying the driving factors behind landslides and accurately assessing susceptibility ar...Landslides pose a formidable natural hazard across the Qinghai-Tibet Plateau(QTP),endangering both ecosystems and human life.Identifying the driving factors behind landslides and accurately assessing susceptibility are key to mitigating disaster risk.This study integrated multi-source historical landslide data with 15 predictive factors and used several machine learning models—Random Forest(RF),Gradient Boosting Regression Trees(GBRT),Extreme Gradient Boosting(XGBoost),and Categorical Boosting(CatBoost)—to generate susceptibility maps.The Shapley additive explanation(SHAP)method was applied to quantify factor importance and explore their nonlinear effects.The results showed that:(1)CatBoost was the best-performing model(CA=0.938,AUC=0.980)in assessing landslide susceptibility,with altitude emerging as the most significant factor,followed by distance to roads and earthquake sites,precipitation,and slope;(2)the SHAP method revealed critical nonlinear thresholds,demonstrating that historical landslides were concentrated at mid-altitudes(1400-4000 m)and decreased markedly above 4000 m,with a parallel reduction in probability beyond 700 m from roads;and(3)landslide-prone areas,comprising 13%of the QTP,were concentrated in the southeastern and northeastern parts of the plateau.By integrating machine learning and SHAP analysis,this study revealed landslide hazard-prone areas and their driving factors,providing insights to support disaster management strategies and sustainable regional planning.展开更多
In real-world autonomous driving tests,unexpected events such as pedestrians or wild animals suddenly entering the driving path can occur.Conducting actual test drives under various weather conditions may also lead to...In real-world autonomous driving tests,unexpected events such as pedestrians or wild animals suddenly entering the driving path can occur.Conducting actual test drives under various weather conditions may also lead to dangerous situations.Furthermore,autonomous vehicles may operate abnormally in bad weather due to limitations of their sensors and GPS.Driving simulators,which replicate driving conditions nearly identical to those in the real world,can drastically reduce the time and cost required for market entry validation;consequently,they have become widely used.In this paper,we design a virtual driving test environment capable of collecting and verifying SiLS data under adverse weather conditions using multi-source images.The proposed method generates a virtual testing environment that incorporates various events,including weather,time of day,and moving objects,that cannot be easily verified in real-world autonomous driving tests.By setting up scenario-based virtual environment events,multi-source image analysis and verification using real-world DCUs(Data Concentrator Units)with V2X-Car edge cloud can effectively address risk factors that may arise in real-world situations.We tested and validated the proposed method with scenarios employing V2X communication and multi-source image analysis.展开更多
The safe driving and operation of trains is a necessary condition for ensuring the safe operation of trains.In particular,heavy-haul trains are characterized by the difficulty in driving and operation.Considering the ...The safe driving and operation of trains is a necessary condition for ensuring the safe operation of trains.In particular,heavy-haul trains are characterized by the difficulty in driving and operation.Considering the uncertainties in train driving and operation,this paper analyzes the relationship between the safety of heavy-haul electric locomotive hauled trains and driving and operation.It studies the auxiliary intelligent driving safety operation control methods.Through K-means to identify the characteristics of drivers'driving manipulation,the hidden Markov model adaptively adjusts the train driving and operation sequence,and conducts auxiliary driving reconstruction for heavy-haul locomotive driving and operation.Based on the train running curve and the locomotive traction/braking characteristics,it smoothly controls the exertion of the traction/braking force of heavy-haul locomotives,thereby optimizing the driving safety control of heavy-haul trains in the vehicle-environment-track system.Finally,the train operation simulation and optimized driving verification are carried out by simulating some track sections.The results show that the proposed method can correct and pre-optimize driving operations,improving the smoothness of heavy-haul trains by approximately 10%.It verifies the effectiveness of the proposed train assisted driving control reconstruction method,facilitating the smooth and safe operation of heavy-haul trains.展开更多
Reliable detection of traffic signs and lights(TSLs)at long range and under varying illumination is essen-tial for improving the perception and safety of autonomous driving systems(ADS).Traditional object detection mo...Reliable detection of traffic signs and lights(TSLs)at long range and under varying illumination is essen-tial for improving the perception and safety of autonomous driving systems(ADS).Traditional object detection models often exhibit significant performance degradation in real-world environments characterized by high dynamic range and complex lighting conditions.To overcome these limitations,this research presents FED-YOLOv10s,an improved and lightweight object detection framework based on You Only look Once v10(YOLOv10).The proposed model integrates a C2f-Faster block derived from FasterNet to reduce parameters and floating-point operations,an Efficient Multiscale Attention(EMA)mechanism to improve TSL-invariant feature extraction,and a deformable Convolution Networks v4(DCNv4)module to enhance multiscale spatial adaptability.Experimental findings demonstrate that the proposed architecture achieves an optimal balance between computational efficiency and detection accuracy,attaining an F1-score of 91.8%,and mAP@0.5 of 95.1%,while reducing parameters to 8.13 million.Comparative analyses across multiple traffic sign detection benchmarks demonstrate that FED-YOLOv10s outperforms state-of-the-art models in precision,recall,and mAP.These results highlight FED-YOLOv10s as a robust,efficient,and deployable solution for intelligent traffic perception in ADS.展开更多
With the continuous progress of automatic driving technology,automatic driving technology standards are gradually affecting the determination of criminal responsibility for traffic accidents in China.At present,the ch...With the continuous progress of automatic driving technology,automatic driving technology standards are gradually affecting the determination of criminal responsibility for traffic accidents in China.At present,the characteristics and tendency of China's automatic driving technology standards present the situation of high policy relevance coexisting with low normative binding,professionalism coexist with barriers,forefront coexist with ambiguity.Therefore,challenges are presented both theoretically and practically on the determination of criminal responsibility based on automatic driving technology standard..In this regard,the misunderstanding should be clarified in theory:The legal order under the automatic driving technology standard has constitutionality and systematic,and there is a balance between the frontier of automatic driving technology development and the lagging of criminal law.The automatic driving technology risk level system should be built to clarify the boundary of the effectiveness of criminal law norms,seeking fora breakthrough in the application of the establishment of a comprehensive judgment system of the risks and accidents and the system of evidence to prove the system,which clarifies the determination of criminal responsibility under the automatic driving technology standard.This essay hopes to pursue breakthroughs in the application-to establish a comprehensive judgment system of risks and accidents as well as an evidence proof system,so as to clarify the determination of criminal responsibility under automatic driving technology standards.展开更多
Against the background of rapid urbanization and the“Dual Carbon”goals,analyzing the impact mechanisms of land use change on carbon metabolism is crucial for regional sustainable development.Taking the Guangdong-Hon...Against the background of rapid urbanization and the“Dual Carbon”goals,analyzing the impact mechanisms of land use change on carbon metabolism is crucial for regional sustainable development.Taking the Guangdong-Hong Kong-Macao Greater Bay Area(GBA)as the study area,we integrate energy consumption data and the Forest Carbon Sequestration(FCS)model to clarify the land use carbon metabolism status based on Ecological Network Analysis(ENA),and systematically analyze the spatiotemporal evolution patterns of urban land use carbon metabolism,interactions between land types,as well as its driving mechanisms in the GBA from 2000 to 2023.The results show that:(1)Over the past two decades,land use changes have exhibited a significant characteristic of“natural land retreat and construction land expansion”,with areas of cropland,forest,and waterbody shrank by 16%,4%,and 4% respectively,while urban land and industrial land increased by 50%and 438%respectively;76% of the reclaimed land was transferred to construction land.(2)The imbalance of carbon metabolism was jointly affected by land use patterns and land use change processes:carbon emissions from energy consumption surged by 116%,while land carbon sequestration capacity decreased by 12%;in most periods,the negative carbon flow from land use change exceeded positive flows,with both showing sharp fluctuations.(3)Construction land in various cities dominated the carbon flow network through control or exploitation relationships,and the mutual transfer between industrial land and cropland is the primary driver;ecological land protection policies(e.g.,the forest“in-out balance”scheme)effectively reduced the intensity of competition relationship.(4)The push-pull forces of land types demonstrate the dual effect of industrialization and urbanization,but their contribution has gradually weakened as the speed of urbanization declined in various cities;the proportion of the indirect carbon flow reached a maximum of 37%(2005-2010),indicating that the indirect impact of land use change cannot be ignored.This study deepens the understanding of the land-carbon interactions,reveals the implicit effects of the“policy implementation-land use change-carbon flow generation”transmission chain,and proposes a“construction land-cropland-ecological land”constraint system and a synergistic path of industrial land intensification and inefficient land ecological restoration.It provides methodological support for low-carbon governance at the urban agglomeration scale.展开更多
Studying the coupling coordination development of new energy vehicles(NEVs)and the ecological environment in China is helpful in promoting the development of NEVs in the country and is of great significance in promoti...Studying the coupling coordination development of new energy vehicles(NEVs)and the ecological environment in China is helpful in promoting the development of NEVs in the country and is of great significance in promoting high-quality development of new energy in China.This paper constructs an evaluation index system for the development of NEVs and the ecological environment.It uses game theory combining weighting model,particle swarm optimized projection tracking evaluation model,coupling coordination degree model,and machine learning algorithms to calculate and analyze the level of coupling coordination development of NEVs and the ecological environment in China from 2010 to 2021,and identifies the driving factors.The research results show that:(i)From 2010 to 2021,the development index of NEVs in China has steadily increased from 0.085 to 0.634,while the ecological environment level index significantly rose from 0.170 to 0.884,reflecting the continuous development of China in both NEVs and the ecological environment.(ii)From 2010 to 2012,the two systems—new energy vehicle(NEV)development and the ecological environment—were in a period of imbalance and decline.From 2013 to 2016,they underwent a transition period,and from 2017 to 2021,they entered a period of coordinated development showing a trend of benign and continuous improvement.By 2021,they reached a good level of coordination.(iii)Indicators such as the number of patents granted for NEVs,water consumption per unit of GDP,and energy consumption per unit of GDP are the main driving factors affecting the coupling coordination development of NEVs and the ecological environment in China.展开更多
BACKGROUND Acute respiratory distress syndrome(ARDS)is a critical condition characterized by acute hypoxemia,non-cardiogenic pulmonary edema,and decreased lung compliance.The Berlin definition,updated in 2012,classifi...BACKGROUND Acute respiratory distress syndrome(ARDS)is a critical condition characterized by acute hypoxemia,non-cardiogenic pulmonary edema,and decreased lung compliance.The Berlin definition,updated in 2012,classifies ARDS severity based on the partial pressure of arterial oxygen/fractional inspired oxygen fraction ratio.Despite various treatment strategies,ARDS remains a significant public health concern with high mortality rates.AIM To evaluate the implications of driving pressure(DP)in ARDS management and its potential as a protective lung strategy.METHODS We conducted a systematic review using databases including EbscoHost,MEDLINE,CINAHL,PubMed,and Google Scholar.The search was limited to articles published between January 2015 and September 2024.Twenty-three peer-reviewed articles were selected based on inclusion criteria focusing on adult ARDS patients undergoing mechanical ventilation and DP strategies.The literature review was conducted and reported according to PRISMA 2020 guidelines.RESULTS DP,the difference between plateau pressure and positive end-expiratory pressure,is crucial in ARDS management.Studies indicate that lower DP levels are significantly associated with improved survival rates in ARDS patients.DP is a better predictor of mortality than tidal volume or positive end-expiratory pressure alone.Adjusting DP by optimizing lung compliance and minimizing overdistension and collapse can reduce ventilator-induced lung injury.CONCLUSION DP is a valuable parameter in ARDS management,offering a more precise measure of lung stress and strain than traditional metrics.Implementing DP as a threshold for safety can enhance protective ventilation strategies,po-tentially reducing mortality in ARDS patients.Further research is needed to refine DP measurement techniques and validate its clinical application in diverse patient populations.展开更多
Scientifically understanding the evolution of urbanization and analysing the coupling mechanism of human-land systems are important foundations for solving spatial conflicts and promoting regional sustainable developm...Scientifically understanding the evolution of urbanization and analysing the coupling mechanism of human-land systems are important foundations for solving spatial conflicts and promoting regional sustainable development.This study analyzed the spatiotemporal evolution and landscape pattern change of construction land in the Yangtze River Delta(YRD)region from 1990 to 2018 by integrating Geographical Information System(GIS)spatial analysis and landscape pattern indices,and revealed its driving mechanism by XGBoost and SHapley Additive ex Planations(SHAP).Moreover,we compared the disparities in the core driving factors for construction land evolution in cities with diverse development orientations within the YRD region.Results show that:1)development intensity of construction land continued to increase from 7.54%in 1990 to 13.44%in 2018,primarily by occupying farmland.The landscape fragmentation of construction land in the YRD region decreased,and landscape dominance increased.Spatially,the eastern part of the YRD exhibits a high degree of spatial agglomeration of construction land,whereas the western part shows a high degree of fragmentation,revealing distinct spatial gradient differentiation characteristics.The landscape dominance of the construction land in the eastern region of the YRD is higher than that in the western and northern regions.2)Transportation and infrastructure exert the highest contribution rate on development intensity changes of construction land in the YRD.The industrial structure significantly influences the conversion of farmland to construction land.Additionally,infrastructure plays a crucial role in shaping the spatial agglomeration patterns of construction land.Population distribution is the dominant factor determining the regularity of the landscape shape of construction land.3)The core driving factors for the development intensity of construction land in central cities primarily lies in transportation,whereas for non-central cities,besides transportation,the year-end balance of per capita savings deposits of urban and rural residents also play a significant role.The area change of construction land occupying farmland in central and non-central cities is mainly driven by industrial structure and economic level,respectively.This study informs refined spatial optimization and regional high-quality integrated development.展开更多
To enhance the recuperation rate of the mine and comply with the stipulations of green mining technology, it is vital to expeditiously recuperate the coal pillar resources in the final stage, thus preventing the consi...To enhance the recuperation rate of the mine and comply with the stipulations of green mining technology, it is vital to expeditiously recuperate the coal pillar resources in the final stage, thus preventing the considerable squandering of resources. The coal pillar resource of the main roadway and its branch roadway constitutes a significant recovery subject. Its coal pillar shape is regular and possesses a considerable strike distance, facilitating the arrangement of the coal pillar recovery working face (CPRWF) for mining operations. However, for the remaining coal pillars with a thick and hard roof (THF) and multiple tectonic zones, CPRWF encounters challenges in selecting an appropriate layout, managing excessive roof pressure, and predicting mining stress. Aiming at the roadway coal pillar group with THF and multi-structural areas in specific projects, a method of constructing multi-stage CPRWF by one side gob-side entry driving (GSED) and one side roadway reusing is proposed. Through theoretical calculation of roof fracture and numerical simulation verification, combined with field engineering experience and economic analysis, the width of the narrow coal pillar (NCP) in the GSED is determined to be 10 m and the length of the CPRWF is 65 m. Concurrently, the potential safety hazard that the roof will fall asymmetrically and THF is difficult to break during CPRWF mining after GSED is analyzed and verified. Then, a control method involving the pre-cutting of the roof in the reused roadway before mining is proposed. This method has been shown to facilitate the complete collapse of THF, reduce the degree of mine pressure, and facilitate the symmetrical breaking of the roof. Accordingly, a roof-cutting scheme based on a directional drilling rig, bidirectional shaped polyvinyl chloride (PVC) pipe, and emulsion explosive was devised, and the pre-splitting of 8.2 m THF was accomplished. Field observations indicate that directional cracks are evident in the roof, the coal wall is flat during CPRWF mining, and the overall level of mining pressure is within the control range. Therefore, the combined application of GSED and roof-cutting technology for coal pillar recovery has been successfully implemented, thereby providing new insights and engineering references for the construction and pressure relief mining of CPRWF.展开更多
This study focuses on the spatiotemporal distribution,urban-rural variations,and driving factors of ammonia Vertical Column Densities(VCDs)in China’s Yangtze River Delta region(YRD)from 2008 to 2020.Utilizing data fr...This study focuses on the spatiotemporal distribution,urban-rural variations,and driving factors of ammonia Vertical Column Densities(VCDs)in China’s Yangtze River Delta region(YRD)from 2008 to 2020.Utilizing data from the Infrared Atmospheric Sounding Interfer-ometer(IASI),Generalized Additive Models(GAM),and the GEOS-Chem chemical transport model,we observed a significant increase of NH_(3)VCDs in the YRD between 2014 and 2020.The spatial distribution analysis revealed higher NH_(3)concentrations in the northern part of the YRD region,primarily due to lower precipitation,alkaline soil,and intensive agricul-tural activities.NH_(3)VCDs in the YRD region increased significantly(65.18%)from 2008 to 2020.The highest growth rate occurs in the summer,with an annual average growth rate of 7.2%during the period from 2014 to 2020.Agricultural emissions dominated NH_(3)VCDs during spring and summer,with high concentrations primarily located in the agricultural areas adjacent to densely populated urban zones.Regions within several large urban areas have been discovered to exhibit relatively stable variations in NH_(3)VCDs.The rise in NH_(3)VCDs within the YRD region was primarily driven by the reduction of acidic gases like SO_(2),as emphasized by GAM modeling and sensitivity tests using the GEOS-Chem model.The concentration changes of acidic gases contribute to over 80%of the interannual variations in NH_(3)VCDs.This emphasizes the crucial role of environmental policies targeting the reduction of these acidic gases.Effective emission control is urgent tomitigate environmental hazards and secondary particulate matter,especially in the northern YRD.展开更多
The scientific assessment of ecosystem ser-vice value(ESV)plays a critical role in regional ecologi-cal protection and management,rational land use planning,and the establishment of ecological security barriers.The ec...The scientific assessment of ecosystem ser-vice value(ESV)plays a critical role in regional ecologi-cal protection and management,rational land use planning,and the establishment of ecological security barriers.The ecosystem service value of the Northeast Forest Belt from 2005 to 2020 was assessed,focusing on spatial–temporal changes and the driving forces behind these dynamics.Using multi-source data,the equivalent factor method,and geo-graphic detectors,we analyzed natural and socio-economic factors affecting the region.which was crucial for effective ecological conservation and land-use planning.Enhanced the effectiveness of policy formulation and land use plan-ning.The results show that the ESV of the Northeast Forest Belt exhibits an overall increasing trend from 2005 to 2020,with forests and wetlands contributing the most.However,there are significant differences between forest belts.Driven by natural and socio-economic factors,the ESV of forest belts in Heilongjiang and Jilin provinces showed significant growth.In contrast,the ESV of Forest Belts in Liaoning and Inner Mongolia of China remains relatively stable,but the spatial differentiation within these regions is characterized by significant clustering of high-value and low-value areas.Furthermore,climate regulation and hydrological regulation services were identified as the most important ecological functions in the Northeast Forest Belt,contributing greatly to regional ecological stability and human well-being.The ESV in the Northeast Forest Belt is improved during the study period,but the stability of the ecosystem is still chal-lenged by the dual impacts of natural and socio-economic factors.To further optimize regional land use planning and ecological protection policies,it is recommended to prior-itize the conservation of high-ESV areas,enhance ecological restoration efforts for wetlands and forests,and reasonably control the spatial layout of urban expansion and agricul-tural development.Additionally,this study highlights the importance of tailored ecological compensation policies and strategic land-use planning to balance environmental protec-tion and economic growth.展开更多
BACKGROUND Return to work(RTW)and resumption of driving(ROD)are critical factors that influence postoperative quality of life in patients undergoing total hip arthroplasty(THA).However,few studies have focused on the ...BACKGROUND Return to work(RTW)and resumption of driving(ROD)are critical factors that influence postoperative quality of life in patients undergoing total hip arthroplasty(THA).However,few studies have focused on the minimally invasive(MIS)approach and its effect on these outcomes.AIM To investigate RTW and ROD's timelines and influencing factors following anterior MIS-THA.METHODS A retrospective analysis was conducted on 124 patients who underwent anterior MIS-THA.Data on the demographics,occupational physical demands,and RTW/ROD timelines were also collected.Clinical outcomes were measured using standardised scoring systems.Statistical analyses were performed to evaluate the differences between the groups based on employment status and physical workload.RESULTS Among employed patients,the RTW rate was 94.7%,with an average return time of five weeks.The average ROD time was 3.5 weeks across all patients.Despite similar postoperative clinical scores,RTW time was significantly influenced by occupations'physical workload,with heavier physical demands associated with delayed RTW.CONCLUSION Anterior MIS-THA facilitates early RTW and ROD,particularly in occupations with lower physical demands.These findings highlight the importance of considering occupational and physical workload in postoperative care planning to optimize recovery outcomes.展开更多
Amid ongoing global environmental change and the critical pursuit of sustainable development,human-environment systems are exhibiting increasingly complex dynamic evolutions and spatial relationships,underscoring an u...Amid ongoing global environmental change and the critical pursuit of sustainable development,human-environment systems are exhibiting increasingly complex dynamic evolutions and spatial relationships,underscoring an urgent need for innovative research frameworks.Integrated geography synthesizes physical geography,human geography,and geographic information science,providing key frameworks for understanding complex human-environment systems.This editorial proposes an emerging research framework for integrated geography—“Composite driving-System evolution-Coupling mechanism-Synergistic regulation(CSCS)”—based on key issues such as climate change,biodiversity loss,resource scarcity,and social-ecological interactions,which have been highlighted in both recent critical literature on human-environment systems and UN assessment reports.The framework starts with diverse composite driving forces,extends to the evolution of human-environment system structures,processes,and functions that these drivers induce,explores couplings within human-environment systems,and calls for regulation aimed at sustainable development in synergies.Major research frontiers include understanding the cascading“evolution-coupling”effects of shocks;measuring system resilience,thresholds,and safe and just operating space boundaries;clarifying linkage mechanisms across scales;and achieving synergistic outcomes for multi-objective sustainability.This framework will help promote the interdisciplinary integration and development of integrated geography,and provide geographical solutions for the global sustainable development agenda.展开更多
Trajectory prediction is a critical task in autonomous driving systems.It enables vehicles to anticipate the future movements of surrounding traffic participants,which facilitates safe and human-like decision-making i...Trajectory prediction is a critical task in autonomous driving systems.It enables vehicles to anticipate the future movements of surrounding traffic participants,which facilitates safe and human-like decision-making in the planning and control layers.However,most existing approaches rely on end-to-end deep learning architectures that overlook the influence of driving style on trajectory prediction.These methods often lack explicit modeling of semantic driving behavior and effective interaction mechanisms,leading to potentially unrealistic predictions.To address these limitations,we propose the Driving Style Guided Trajectory Prediction framework(DSG-TP),which incorporates a probabilistic representation of driving style into trajectory prediction.Our approach enhances the model’s ability to interact with vehicle behavior characteristics in complex traffic scenarios,significantly improving prediction reliability in critical decision-making situations by incorporating the driving style recognition module.Experimental evaluations on the Argoverse 1 dataset demonstrate that our method outperforms existing approaches in both prediction accuracy and computational efficiency.Through extensive ablation studies,we further validate the contribution of each module to overall performance.Notably,in decision-sensitive scenarios,DSG-TP more accurately captures vehicle behavior patterns and generates trajectory predictions that align with different driving styles,providing crucial support for safe decision-making in autonomous driving systems.展开更多
Vegetation not only plays a critical role in regulating regional climate,hydrological cycles,carbon sequestration,and oxygen release,but also is directly linked to ecosystem stability and regional sustainable developm...Vegetation not only plays a critical role in regulating regional climate,hydrological cycles,carbon sequestration,and oxygen release,but also is directly linked to ecosystem stability and regional sustainable development.In this study,based on the data of kNDVI in Jiangsu Province(an economically developed coastal region in eastern China)from 2002 to 2022,the spatial and temporal dynamics of vegetation in the province were systematically analyzed by using the Theil-Sen slope estimation and Mann-Kendall trend test methods.The results indicate that vegetation coverage in Jiangsu Province generally followed a trend of"fluctuation in the early period and improvement in the later period"from 2002 to 2022.Spatially,kNDVI changes exhibited clear heterogeneity,with an overall pattern of"decline in the south,increase in the north,and stability in the central region".Based on the 21-year mean of kNDVI,it is found that vegetation conditions were relatively better in northern and central Jiangsu,while lower mean of kNDVI was observed in southern Jiangsu(e.g.,Suzhou,Wuxi,and Changzhou),reflecting the pressure of accelerating urbanization on green space coverage.Further investigation into the driving factors of changes in vegetation reveals that social factors had the strongest influence,with a path coefficient of-0.86,followed by topographic and climatic factors.This spatial differentiation pattern and the identified driving factors highlight ongoing conflicts between the economic development and ecological conservation in Jiangsu Province.In the future,land use structure should be optimized based on local conditions,and coordinated development between ecological restoration and urban expansion should be strengthened.展开更多
Seagrass beds are crucial coastal ecosystems,functioning as vital blue carbon sinks and natural ecological barriers.However,these ecosystems are increasingly threatened by global climate events,coastal development,and...Seagrass beds are crucial coastal ecosystems,functioning as vital blue carbon sinks and natural ecological barriers.However,these ecosystems are increasingly threatened by global climate events,coastal development,and water eutrophication,making them some of the most endangered ecosystems worldwide.In the Yellow Sea and Bohai Sea regions,seagrass bed assessment and monitoring have been largely overlooked.Thus,strengthening research efforts is necessary to identify current distribution patterns and long-term changes in seagrass bed resources.This study focused on a seagrass bed in Sanggou Bay,Rongcheng,using remote sensing(RS)and geographic information system technologies to analyze multisource satellite data from the US Landsat and Chinese resource satellite series.By combining RS indexes with historical survey data,large-scale temporal and geographic distribution data for seagrass beds were obtained in the study area from 1985 to 2022.The spatial distribution and evolution trends of the seagrass bed were analyzed using a water depth inversion model,and the factors driving its degradation were identified.Results indicated that the seagrass bed area in Sanggou Bay fluctuated between 100 and 140 km^(2) from 1985 to 2010.During 2010–2013,dynamic changes in the seagrass bed area increased,with a considerable decrease in its overall size.After 2014,changes were minimal,indicating a notably stable state.Seagrass bed degradation in Sanggou Bay is influenced by high-intensity human activities,pollution from coastal land sources,raft cultures,underwater terrain conditions,and sedimentary environmental factors.The findings offer essential insights for developing seagrass restoration and protection strategies in Sanggou Bay and contribute to the broader scientific efforts for coastal ecosystem conservation and rehabilitation.展开更多
In recent years,the country has spent significant workforce and material resources to prevent traffic accidents,particularly those caused by fatigued driving.The current studies mainly concentrate on driver physiologi...In recent years,the country has spent significant workforce and material resources to prevent traffic accidents,particularly those caused by fatigued driving.The current studies mainly concentrate on driver physiological signals,driving behavior,and vehicle information.However,most of the approaches are computationally intensive and inconvenient for real-time detection.Therefore,this paper designs a network that combines precision,speed and lightweight and proposes an algorithm for facial fatigue detection based on multi-feature fusion.Specifically,the face detection model takes YOLOv8(You Only Look Once version 8)as the basic framework,and replaces its backbone network with MobileNetv3.To focus on the significant regions in the image,CPCA(Channel Prior Convolution Attention)is adopted to enhance the network’s capacity for feature extraction.Meanwhile,the network training phase employs the Focal-EIOU(Focal and Efficient Intersection Over Union)loss function,which makes the network lightweight and increases the accuracy of target detection.Ultimately,the Dlib toolkit was employed to annotate 68 facial feature points.This study established an evaluation metric for facial fatigue and developed a novel fatigue detection algorithm to assess the driver’s condition.A series of comparative experiments were carried out on the self-built dataset.The suggested method’s mAP(mean Average Precision)values for object detection and fatigue detection are 96.71%and 95.75%,respectively,as well as the detection speed is 47 FPS(Frames Per Second).This method can balance the contradiction between computational complexity and model accuracy.Furthermore,it can be transplanted to NVIDIA Jetson Orin NX and quickly detect the driver’s state while maintaining a high degree of accuracy.It contributes to the development of automobile safety systems and reduces the occurrence of traffic accidents.展开更多
Ensuring that autonomous vehicles maintain high precision and rapid response capabilities in complex and dynamic driving environments is a critical challenge in the field of autonomous driving.This study aims to enhan...Ensuring that autonomous vehicles maintain high precision and rapid response capabilities in complex and dynamic driving environments is a critical challenge in the field of autonomous driving.This study aims to enhance the learning efficiency ofmulti-sensor feature fusion in autonomous driving tasks,thereby improving the safety and responsiveness of the system.To achieve this goal,we propose an innovative multi-sensor feature fusion model that integrates three distinct modalities:visual,radar,and lidar data.The model optimizes the feature fusion process through the introduction of two novel mechanisms:Sparse Channel Pooling(SCP)and Residual Triplet-Attention(RTA).Firstly,the SCP mechanism enables the model to adaptively filter out salient feature channels while eliminating the interference of redundant features.This enhances the model’s emphasis on critical features essential for decisionmaking and strengthens its robustness to environmental variability.Secondly,the RTA mechanism addresses the issue of feature misalignment across different modalities by effectively aligning key cross-modal features.This alignment reduces the computational overhead associated with redundant features and enhances the overall efficiency of the system.Furthermore,this study incorporates a reinforcement learning module designed to optimize strategies within a continuous action space.By integrating thismodulewith the feature fusion learning process,the entire system is capable of learning efficient driving strategies in an end-to-end manner within the CARLA autonomous driving simulator.Experimental results demonstrate that the proposedmodel significantly enhances the perception and decision-making accuracy of the autonomous driving system in complex traffic scenarios while maintaining real-time responsiveness.This work provides a novel perspective and technical pathway for the application of multi-sensor data fusion in autonomous driving.展开更多
基金Under the auspices of National Natural Science Foundation of China(No.42571219)Key Project of Zhejiang Province Soft Science Research Plan(No.2023C25014)。
文摘Bottom-up and top-down endogenous automobile clusters exhibit distinct evolutionary traits and driving mechanisms,yet their comparative analysis remains understudied.Therefore,using Taizhou automobile industry cluster(TAIC)and Wuhu automobile industry cluster(WAIC)as cases,using historical statistical data and field interview data from the 1980s to 2023,combined with qualitative research methods of thematic and diachronic analysis,and quantitative research methods of social network analysis,we compare both endogenous automobile clusters’evolutionary traits and driving mechanisms.The results confirm both clusters undergo multi-scale spatial reconfiguration,organizational complexification,and intelligent networking technological transformation,yet diverge fundamentally:TAIC evolves through market-driven progressive expansion,transitioning from single to dual-core structures via private enterprise networking,with innovation following market-integrated logic and institutional thickness built on demand-driven evolution.Conversely,WAIC follows planned expansion,maintaining state-led hierarchical single-core stability through policy-driven breakthrough innovation and supply-dominated institutional construction-though both ultimately require formal-informal system synergy.Their coevolution is driven by dynamic interactions of path dependence(weakening influence),learning-innovation(strengthening influence),and relationship selection(inverted U-shaped trajectory),with divergent development paths rooted in TAIC’s grassroots self-organization genes versus WAIC’s top-level design genes,amplified by core enterprises’strategic disparities.The research findings can not only provide decision-making support for China’s industrial upgrading,but also contribute China’s insights to global economic governance.
基金The National Key Research and Development Program of China,No.2023YFC3206601。
文摘Landslides pose a formidable natural hazard across the Qinghai-Tibet Plateau(QTP),endangering both ecosystems and human life.Identifying the driving factors behind landslides and accurately assessing susceptibility are key to mitigating disaster risk.This study integrated multi-source historical landslide data with 15 predictive factors and used several machine learning models—Random Forest(RF),Gradient Boosting Regression Trees(GBRT),Extreme Gradient Boosting(XGBoost),and Categorical Boosting(CatBoost)—to generate susceptibility maps.The Shapley additive explanation(SHAP)method was applied to quantify factor importance and explore their nonlinear effects.The results showed that:(1)CatBoost was the best-performing model(CA=0.938,AUC=0.980)in assessing landslide susceptibility,with altitude emerging as the most significant factor,followed by distance to roads and earthquake sites,precipitation,and slope;(2)the SHAP method revealed critical nonlinear thresholds,demonstrating that historical landslides were concentrated at mid-altitudes(1400-4000 m)and decreased markedly above 4000 m,with a parallel reduction in probability beyond 700 m from roads;and(3)landslide-prone areas,comprising 13%of the QTP,were concentrated in the southeastern and northeastern parts of the plateau.By integrating machine learning and SHAP analysis,this study revealed landslide hazard-prone areas and their driving factors,providing insights to support disaster management strategies and sustainable regional planning.
基金supported by Institute of Information and Communications Technology Planning and Evaluation(IITP)grant funded by the Korean government(MSIT)(No.2019-0-01842,Artificial Intelligence Graduate School Program(GIST))supported by Korea Planning&Evaluation Institute of Industrial Technology(KEIT)grant funded by the Ministry of Trade,Industry&Energy(MOTIE,Republic of Korea)(RS-2025-25448249+1 种基金Automotive Industry Technology Development(R&D)Program)supported by the Regional Innovation System&Education(RISE)programthrough the(Gwangju RISE Center),funded by the Ministry of Education(MOE)and the Gwangju Metropolitan City,Republic of Korea(2025-RISE-05-001).
文摘In real-world autonomous driving tests,unexpected events such as pedestrians or wild animals suddenly entering the driving path can occur.Conducting actual test drives under various weather conditions may also lead to dangerous situations.Furthermore,autonomous vehicles may operate abnormally in bad weather due to limitations of their sensors and GPS.Driving simulators,which replicate driving conditions nearly identical to those in the real world,can drastically reduce the time and cost required for market entry validation;consequently,they have become widely used.In this paper,we design a virtual driving test environment capable of collecting and verifying SiLS data under adverse weather conditions using multi-source images.The proposed method generates a virtual testing environment that incorporates various events,including weather,time of day,and moving objects,that cannot be easily verified in real-world autonomous driving tests.By setting up scenario-based virtual environment events,multi-source image analysis and verification using real-world DCUs(Data Concentrator Units)with V2X-Car edge cloud can effectively address risk factors that may arise in real-world situations.We tested and validated the proposed method with scenarios employing V2X communication and multi-source image analysis.
基金Project(U2034211)supported by the National Natural Science Foundation of ChinaProject(20232ACE01013)supported by the Major Scientific and Technological Research and Development Special Project of Jiangxi Province,China。
文摘The safe driving and operation of trains is a necessary condition for ensuring the safe operation of trains.In particular,heavy-haul trains are characterized by the difficulty in driving and operation.Considering the uncertainties in train driving and operation,this paper analyzes the relationship between the safety of heavy-haul electric locomotive hauled trains and driving and operation.It studies the auxiliary intelligent driving safety operation control methods.Through K-means to identify the characteristics of drivers'driving manipulation,the hidden Markov model adaptively adjusts the train driving and operation sequence,and conducts auxiliary driving reconstruction for heavy-haul locomotive driving and operation.Based on the train running curve and the locomotive traction/braking characteristics,it smoothly controls the exertion of the traction/braking force of heavy-haul locomotives,thereby optimizing the driving safety control of heavy-haul trains in the vehicle-environment-track system.Finally,the train operation simulation and optimized driving verification are carried out by simulating some track sections.The results show that the proposed method can correct and pre-optimize driving operations,improving the smoothness of heavy-haul trains by approximately 10%.It verifies the effectiveness of the proposed train assisted driving control reconstruction method,facilitating the smooth and safe operation of heavy-haul trains.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia under Grant No.IPP:172-830-2025.
文摘Reliable detection of traffic signs and lights(TSLs)at long range and under varying illumination is essen-tial for improving the perception and safety of autonomous driving systems(ADS).Traditional object detection models often exhibit significant performance degradation in real-world environments characterized by high dynamic range and complex lighting conditions.To overcome these limitations,this research presents FED-YOLOv10s,an improved and lightweight object detection framework based on You Only look Once v10(YOLOv10).The proposed model integrates a C2f-Faster block derived from FasterNet to reduce parameters and floating-point operations,an Efficient Multiscale Attention(EMA)mechanism to improve TSL-invariant feature extraction,and a deformable Convolution Networks v4(DCNv4)module to enhance multiscale spatial adaptability.Experimental findings demonstrate that the proposed architecture achieves an optimal balance between computational efficiency and detection accuracy,attaining an F1-score of 91.8%,and mAP@0.5 of 95.1%,while reducing parameters to 8.13 million.Comparative analyses across multiple traffic sign detection benchmarks demonstrate that FED-YOLOv10s outperforms state-of-the-art models in precision,recall,and mAP.These results highlight FED-YOLOv10s as a robust,efficient,and deployable solution for intelligent traffic perception in ADS.
基金The National Social Science Foundation Youth Project of China:Research on the collaborative govemance path of administrative law and criminal law against dangerous driving behaviors in the digital-intelligent society(25CFX108)。
文摘With the continuous progress of automatic driving technology,automatic driving technology standards are gradually affecting the determination of criminal responsibility for traffic accidents in China.At present,the characteristics and tendency of China's automatic driving technology standards present the situation of high policy relevance coexisting with low normative binding,professionalism coexist with barriers,forefront coexist with ambiguity.Therefore,challenges are presented both theoretically and practically on the determination of criminal responsibility based on automatic driving technology standard..In this regard,the misunderstanding should be clarified in theory:The legal order under the automatic driving technology standard has constitutionality and systematic,and there is a balance between the frontier of automatic driving technology development and the lagging of criminal law.The automatic driving technology risk level system should be built to clarify the boundary of the effectiveness of criminal law norms,seeking fora breakthrough in the application of the establishment of a comprehensive judgment system of the risks and accidents and the system of evidence to prove the system,which clarifies the determination of criminal responsibility under the automatic driving technology standard.This essay hopes to pursue breakthroughs in the application-to establish a comprehensive judgment system of risks and accidents as well as an evidence proof system,so as to clarify the determination of criminal responsibility under automatic driving technology standards.
基金supported by the National Natural Science Foundation of China(Grant No.42371027).
文摘Against the background of rapid urbanization and the“Dual Carbon”goals,analyzing the impact mechanisms of land use change on carbon metabolism is crucial for regional sustainable development.Taking the Guangdong-Hong Kong-Macao Greater Bay Area(GBA)as the study area,we integrate energy consumption data and the Forest Carbon Sequestration(FCS)model to clarify the land use carbon metabolism status based on Ecological Network Analysis(ENA),and systematically analyze the spatiotemporal evolution patterns of urban land use carbon metabolism,interactions between land types,as well as its driving mechanisms in the GBA from 2000 to 2023.The results show that:(1)Over the past two decades,land use changes have exhibited a significant characteristic of“natural land retreat and construction land expansion”,with areas of cropland,forest,and waterbody shrank by 16%,4%,and 4% respectively,while urban land and industrial land increased by 50%and 438%respectively;76% of the reclaimed land was transferred to construction land.(2)The imbalance of carbon metabolism was jointly affected by land use patterns and land use change processes:carbon emissions from energy consumption surged by 116%,while land carbon sequestration capacity decreased by 12%;in most periods,the negative carbon flow from land use change exceeded positive flows,with both showing sharp fluctuations.(3)Construction land in various cities dominated the carbon flow network through control or exploitation relationships,and the mutual transfer between industrial land and cropland is the primary driver;ecological land protection policies(e.g.,the forest“in-out balance”scheme)effectively reduced the intensity of competition relationship.(4)The push-pull forces of land types demonstrate the dual effect of industrialization and urbanization,but their contribution has gradually weakened as the speed of urbanization declined in various cities;the proportion of the indirect carbon flow reached a maximum of 37%(2005-2010),indicating that the indirect impact of land use change cannot be ignored.This study deepens the understanding of the land-carbon interactions,reveals the implicit effects of the“policy implementation-land use change-carbon flow generation”transmission chain,and proposes a“construction land-cropland-ecological land”constraint system and a synergistic path of industrial land intensification and inefficient land ecological restoration.It provides methodological support for low-carbon governance at the urban agglomeration scale.
基金Supported by the Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX24_0102)the China Scholarship Council Program(202406190114)。
文摘Studying the coupling coordination development of new energy vehicles(NEVs)and the ecological environment in China is helpful in promoting the development of NEVs in the country and is of great significance in promoting high-quality development of new energy in China.This paper constructs an evaluation index system for the development of NEVs and the ecological environment.It uses game theory combining weighting model,particle swarm optimized projection tracking evaluation model,coupling coordination degree model,and machine learning algorithms to calculate and analyze the level of coupling coordination development of NEVs and the ecological environment in China from 2010 to 2021,and identifies the driving factors.The research results show that:(i)From 2010 to 2021,the development index of NEVs in China has steadily increased from 0.085 to 0.634,while the ecological environment level index significantly rose from 0.170 to 0.884,reflecting the continuous development of China in both NEVs and the ecological environment.(ii)From 2010 to 2012,the two systems—new energy vehicle(NEV)development and the ecological environment—were in a period of imbalance and decline.From 2013 to 2016,they underwent a transition period,and from 2017 to 2021,they entered a period of coordinated development showing a trend of benign and continuous improvement.By 2021,they reached a good level of coordination.(iii)Indicators such as the number of patents granted for NEVs,water consumption per unit of GDP,and energy consumption per unit of GDP are the main driving factors affecting the coupling coordination development of NEVs and the ecological environment in China.
文摘BACKGROUND Acute respiratory distress syndrome(ARDS)is a critical condition characterized by acute hypoxemia,non-cardiogenic pulmonary edema,and decreased lung compliance.The Berlin definition,updated in 2012,classifies ARDS severity based on the partial pressure of arterial oxygen/fractional inspired oxygen fraction ratio.Despite various treatment strategies,ARDS remains a significant public health concern with high mortality rates.AIM To evaluate the implications of driving pressure(DP)in ARDS management and its potential as a protective lung strategy.METHODS We conducted a systematic review using databases including EbscoHost,MEDLINE,CINAHL,PubMed,and Google Scholar.The search was limited to articles published between January 2015 and September 2024.Twenty-three peer-reviewed articles were selected based on inclusion criteria focusing on adult ARDS patients undergoing mechanical ventilation and DP strategies.The literature review was conducted and reported according to PRISMA 2020 guidelines.RESULTS DP,the difference between plateau pressure and positive end-expiratory pressure,is crucial in ARDS management.Studies indicate that lower DP levels are significantly associated with improved survival rates in ARDS patients.DP is a better predictor of mortality than tidal volume or positive end-expiratory pressure alone.Adjusting DP by optimizing lung compliance and minimizing overdistension and collapse can reduce ventilator-induced lung injury.CONCLUSION DP is a valuable parameter in ARDS management,offering a more precise measure of lung stress and strain than traditional metrics.Implementing DP as a threshold for safety can enhance protective ventilation strategies,po-tentially reducing mortality in ARDS patients.Further research is needed to refine DP measurement techniques and validate its clinical application in diverse patient populations.
基金Under the auspices of the National Natural Science Foundation of China(No.42301470,42171389)。
文摘Scientifically understanding the evolution of urbanization and analysing the coupling mechanism of human-land systems are important foundations for solving spatial conflicts and promoting regional sustainable development.This study analyzed the spatiotemporal evolution and landscape pattern change of construction land in the Yangtze River Delta(YRD)region from 1990 to 2018 by integrating Geographical Information System(GIS)spatial analysis and landscape pattern indices,and revealed its driving mechanism by XGBoost and SHapley Additive ex Planations(SHAP).Moreover,we compared the disparities in the core driving factors for construction land evolution in cities with diverse development orientations within the YRD region.Results show that:1)development intensity of construction land continued to increase from 7.54%in 1990 to 13.44%in 2018,primarily by occupying farmland.The landscape fragmentation of construction land in the YRD region decreased,and landscape dominance increased.Spatially,the eastern part of the YRD exhibits a high degree of spatial agglomeration of construction land,whereas the western part shows a high degree of fragmentation,revealing distinct spatial gradient differentiation characteristics.The landscape dominance of the construction land in the eastern region of the YRD is higher than that in the western and northern regions.2)Transportation and infrastructure exert the highest contribution rate on development intensity changes of construction land in the YRD.The industrial structure significantly influences the conversion of farmland to construction land.Additionally,infrastructure plays a crucial role in shaping the spatial agglomeration patterns of construction land.Population distribution is the dominant factor determining the regularity of the landscape shape of construction land.3)The core driving factors for the development intensity of construction land in central cities primarily lies in transportation,whereas for non-central cities,besides transportation,the year-end balance of per capita savings deposits of urban and rural residents also play a significant role.The area change of construction land occupying farmland in central and non-central cities is mainly driven by industrial structure and economic level,respectively.This study informs refined spatial optimization and regional high-quality integrated development.
基金Project(52204164) supported by the National Natural Science Foundation of ChinaProject(2023ZKPYSB01) supported by the Fundamental Research Funds for the Central Universities,China。
文摘To enhance the recuperation rate of the mine and comply with the stipulations of green mining technology, it is vital to expeditiously recuperate the coal pillar resources in the final stage, thus preventing the considerable squandering of resources. The coal pillar resource of the main roadway and its branch roadway constitutes a significant recovery subject. Its coal pillar shape is regular and possesses a considerable strike distance, facilitating the arrangement of the coal pillar recovery working face (CPRWF) for mining operations. However, for the remaining coal pillars with a thick and hard roof (THF) and multiple tectonic zones, CPRWF encounters challenges in selecting an appropriate layout, managing excessive roof pressure, and predicting mining stress. Aiming at the roadway coal pillar group with THF and multi-structural areas in specific projects, a method of constructing multi-stage CPRWF by one side gob-side entry driving (GSED) and one side roadway reusing is proposed. Through theoretical calculation of roof fracture and numerical simulation verification, combined with field engineering experience and economic analysis, the width of the narrow coal pillar (NCP) in the GSED is determined to be 10 m and the length of the CPRWF is 65 m. Concurrently, the potential safety hazard that the roof will fall asymmetrically and THF is difficult to break during CPRWF mining after GSED is analyzed and verified. Then, a control method involving the pre-cutting of the roof in the reused roadway before mining is proposed. This method has been shown to facilitate the complete collapse of THF, reduce the degree of mine pressure, and facilitate the symmetrical breaking of the roof. Accordingly, a roof-cutting scheme based on a directional drilling rig, bidirectional shaped polyvinyl chloride (PVC) pipe, and emulsion explosive was devised, and the pre-splitting of 8.2 m THF was accomplished. Field observations indicate that directional cracks are evident in the roof, the coal wall is flat during CPRWF mining, and the overall level of mining pressure is within the control range. Therefore, the combined application of GSED and roof-cutting technology for coal pillar recovery has been successfully implemented, thereby providing new insights and engineering references for the construction and pressure relief mining of CPRWF.
基金supported by the Joint Funds of the National Natural Science Foundation of China(No.U21A2027)the New Cornerstone Science Foundation through the XPLORER PRIZE(2023-1033).
文摘This study focuses on the spatiotemporal distribution,urban-rural variations,and driving factors of ammonia Vertical Column Densities(VCDs)in China’s Yangtze River Delta region(YRD)from 2008 to 2020.Utilizing data from the Infrared Atmospheric Sounding Interfer-ometer(IASI),Generalized Additive Models(GAM),and the GEOS-Chem chemical transport model,we observed a significant increase of NH_(3)VCDs in the YRD between 2014 and 2020.The spatial distribution analysis revealed higher NH_(3)concentrations in the northern part of the YRD region,primarily due to lower precipitation,alkaline soil,and intensive agricul-tural activities.NH_(3)VCDs in the YRD region increased significantly(65.18%)from 2008 to 2020.The highest growth rate occurs in the summer,with an annual average growth rate of 7.2%during the period from 2014 to 2020.Agricultural emissions dominated NH_(3)VCDs during spring and summer,with high concentrations primarily located in the agricultural areas adjacent to densely populated urban zones.Regions within several large urban areas have been discovered to exhibit relatively stable variations in NH_(3)VCDs.The rise in NH_(3)VCDs within the YRD region was primarily driven by the reduction of acidic gases like SO_(2),as emphasized by GAM modeling and sensitivity tests using the GEOS-Chem model.The concentration changes of acidic gases contribute to over 80%of the interannual variations in NH_(3)VCDs.This emphasizes the crucial role of environmental policies targeting the reduction of these acidic gases.Effective emission control is urgent tomitigate environmental hazards and secondary particulate matter,especially in the northern YRD.
基金funded by the Central University D Project(HFW230600022)National Natural Science Foundation of China(71973021)+1 种基金National Natural Science Foundation Youth Funding Project(72003022)Heilongjiang Province University Think Tank Open Topic(ZKKF2022173).
文摘The scientific assessment of ecosystem ser-vice value(ESV)plays a critical role in regional ecologi-cal protection and management,rational land use planning,and the establishment of ecological security barriers.The ecosystem service value of the Northeast Forest Belt from 2005 to 2020 was assessed,focusing on spatial–temporal changes and the driving forces behind these dynamics.Using multi-source data,the equivalent factor method,and geo-graphic detectors,we analyzed natural and socio-economic factors affecting the region.which was crucial for effective ecological conservation and land-use planning.Enhanced the effectiveness of policy formulation and land use plan-ning.The results show that the ESV of the Northeast Forest Belt exhibits an overall increasing trend from 2005 to 2020,with forests and wetlands contributing the most.However,there are significant differences between forest belts.Driven by natural and socio-economic factors,the ESV of forest belts in Heilongjiang and Jilin provinces showed significant growth.In contrast,the ESV of Forest Belts in Liaoning and Inner Mongolia of China remains relatively stable,but the spatial differentiation within these regions is characterized by significant clustering of high-value and low-value areas.Furthermore,climate regulation and hydrological regulation services were identified as the most important ecological functions in the Northeast Forest Belt,contributing greatly to regional ecological stability and human well-being.The ESV in the Northeast Forest Belt is improved during the study period,but the stability of the ecosystem is still chal-lenged by the dual impacts of natural and socio-economic factors.To further optimize regional land use planning and ecological protection policies,it is recommended to prior-itize the conservation of high-ESV areas,enhance ecological restoration efforts for wetlands and forests,and reasonably control the spatial layout of urban expansion and agricul-tural development.Additionally,this study highlights the importance of tailored ecological compensation policies and strategic land-use planning to balance environmental protec-tion and economic growth.
基金approved by the Institutional Ethics Committee of Shizuoka Red Cross Hospital(No.2023-36,approval date:January 12,2024).
文摘BACKGROUND Return to work(RTW)and resumption of driving(ROD)are critical factors that influence postoperative quality of life in patients undergoing total hip arthroplasty(THA).However,few studies have focused on the minimally invasive(MIS)approach and its effect on these outcomes.AIM To investigate RTW and ROD's timelines and influencing factors following anterior MIS-THA.METHODS A retrospective analysis was conducted on 124 patients who underwent anterior MIS-THA.Data on the demographics,occupational physical demands,and RTW/ROD timelines were also collected.Clinical outcomes were measured using standardised scoring systems.Statistical analyses were performed to evaluate the differences between the groups based on employment status and physical workload.RESULTS Among employed patients,the RTW rate was 94.7%,with an average return time of five weeks.The average ROD time was 3.5 weeks across all patients.Despite similar postoperative clinical scores,RTW time was significantly influenced by occupations'physical workload,with heavier physical demands associated with delayed RTW.CONCLUSION Anterior MIS-THA facilitates early RTW and ROD,particularly in occupations with lower physical demands.These findings highlight the importance of considering occupational and physical workload in postoperative care planning to optimize recovery outcomes.
基金supported by the National Natural Science Foundation of China(Grants No.W2412144,42271292)the 111 project,and the Fundamental Research Funds for the Central Universities of China.
文摘Amid ongoing global environmental change and the critical pursuit of sustainable development,human-environment systems are exhibiting increasingly complex dynamic evolutions and spatial relationships,underscoring an urgent need for innovative research frameworks.Integrated geography synthesizes physical geography,human geography,and geographic information science,providing key frameworks for understanding complex human-environment systems.This editorial proposes an emerging research framework for integrated geography—“Composite driving-System evolution-Coupling mechanism-Synergistic regulation(CSCS)”—based on key issues such as climate change,biodiversity loss,resource scarcity,and social-ecological interactions,which have been highlighted in both recent critical literature on human-environment systems and UN assessment reports.The framework starts with diverse composite driving forces,extends to the evolution of human-environment system structures,processes,and functions that these drivers induce,explores couplings within human-environment systems,and calls for regulation aimed at sustainable development in synergies.Major research frontiers include understanding the cascading“evolution-coupling”effects of shocks;measuring system resilience,thresholds,and safe and just operating space boundaries;clarifying linkage mechanisms across scales;and achieving synergistic outcomes for multi-objective sustainability.This framework will help promote the interdisciplinary integration and development of integrated geography,and provide geographical solutions for the global sustainable development agenda.
基金supported in part by the National Natural Science Foundation of China(NSFC)under Grant No.52267003.
文摘Trajectory prediction is a critical task in autonomous driving systems.It enables vehicles to anticipate the future movements of surrounding traffic participants,which facilitates safe and human-like decision-making in the planning and control layers.However,most existing approaches rely on end-to-end deep learning architectures that overlook the influence of driving style on trajectory prediction.These methods often lack explicit modeling of semantic driving behavior and effective interaction mechanisms,leading to potentially unrealistic predictions.To address these limitations,we propose the Driving Style Guided Trajectory Prediction framework(DSG-TP),which incorporates a probabilistic representation of driving style into trajectory prediction.Our approach enhances the model’s ability to interact with vehicle behavior characteristics in complex traffic scenarios,significantly improving prediction reliability in critical decision-making situations by incorporating the driving style recognition module.Experimental evaluations on the Argoverse 1 dataset demonstrate that our method outperforms existing approaches in both prediction accuracy and computational efficiency.Through extensive ablation studies,we further validate the contribution of each module to overall performance.Notably,in decision-sensitive scenarios,DSG-TP more accurately captures vehicle behavior patterns and generates trajectory predictions that align with different driving styles,providing crucial support for safe decision-making in autonomous driving systems.
文摘Vegetation not only plays a critical role in regulating regional climate,hydrological cycles,carbon sequestration,and oxygen release,but also is directly linked to ecosystem stability and regional sustainable development.In this study,based on the data of kNDVI in Jiangsu Province(an economically developed coastal region in eastern China)from 2002 to 2022,the spatial and temporal dynamics of vegetation in the province were systematically analyzed by using the Theil-Sen slope estimation and Mann-Kendall trend test methods.The results indicate that vegetation coverage in Jiangsu Province generally followed a trend of"fluctuation in the early period and improvement in the later period"from 2002 to 2022.Spatially,kNDVI changes exhibited clear heterogeneity,with an overall pattern of"decline in the south,increase in the north,and stability in the central region".Based on the 21-year mean of kNDVI,it is found that vegetation conditions were relatively better in northern and central Jiangsu,while lower mean of kNDVI was observed in southern Jiangsu(e.g.,Suzhou,Wuxi,and Changzhou),reflecting the pressure of accelerating urbanization on green space coverage.Further investigation into the driving factors of changes in vegetation reveals that social factors had the strongest influence,with a path coefficient of-0.86,followed by topographic and climatic factors.This spatial differentiation pattern and the identified driving factors highlight ongoing conflicts between the economic development and ecological conservation in Jiangsu Province.In the future,land use structure should be optimized based on local conditions,and coordinated development between ecological restoration and urban expansion should be strengthened.
基金supported by the Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology(Qingdao)(No.2022QNLM 050302-4)the Geological Survey Project of the China Geological Survey(No.DD20230071)+1 种基金the China Geological Survey Project‘Investigation and Monitoring of the Coastal Geological Environment of the Yangtze River Estuary’(No.DD20242714)the cooperation fund of Collaborative Research on Marine Geological Environment and Hazards in the Yangtze River Delta and Red River Delta.
文摘Seagrass beds are crucial coastal ecosystems,functioning as vital blue carbon sinks and natural ecological barriers.However,these ecosystems are increasingly threatened by global climate events,coastal development,and water eutrophication,making them some of the most endangered ecosystems worldwide.In the Yellow Sea and Bohai Sea regions,seagrass bed assessment and monitoring have been largely overlooked.Thus,strengthening research efforts is necessary to identify current distribution patterns and long-term changes in seagrass bed resources.This study focused on a seagrass bed in Sanggou Bay,Rongcheng,using remote sensing(RS)and geographic information system technologies to analyze multisource satellite data from the US Landsat and Chinese resource satellite series.By combining RS indexes with historical survey data,large-scale temporal and geographic distribution data for seagrass beds were obtained in the study area from 1985 to 2022.The spatial distribution and evolution trends of the seagrass bed were analyzed using a water depth inversion model,and the factors driving its degradation were identified.Results indicated that the seagrass bed area in Sanggou Bay fluctuated between 100 and 140 km^(2) from 1985 to 2010.During 2010–2013,dynamic changes in the seagrass bed area increased,with a considerable decrease in its overall size.After 2014,changes were minimal,indicating a notably stable state.Seagrass bed degradation in Sanggou Bay is influenced by high-intensity human activities,pollution from coastal land sources,raft cultures,underwater terrain conditions,and sedimentary environmental factors.The findings offer essential insights for developing seagrass restoration and protection strategies in Sanggou Bay and contribute to the broader scientific efforts for coastal ecosystem conservation and rehabilitation.
基金supported by the Science and Technology Bureau of Xi’an project(24KGDW0049)the Key Research and Development Programof Shaanxi(2023-YBGY-264)the Key Research and Development Program of Guangxi(GK-AB20159032).
文摘In recent years,the country has spent significant workforce and material resources to prevent traffic accidents,particularly those caused by fatigued driving.The current studies mainly concentrate on driver physiological signals,driving behavior,and vehicle information.However,most of the approaches are computationally intensive and inconvenient for real-time detection.Therefore,this paper designs a network that combines precision,speed and lightweight and proposes an algorithm for facial fatigue detection based on multi-feature fusion.Specifically,the face detection model takes YOLOv8(You Only Look Once version 8)as the basic framework,and replaces its backbone network with MobileNetv3.To focus on the significant regions in the image,CPCA(Channel Prior Convolution Attention)is adopted to enhance the network’s capacity for feature extraction.Meanwhile,the network training phase employs the Focal-EIOU(Focal and Efficient Intersection Over Union)loss function,which makes the network lightweight and increases the accuracy of target detection.Ultimately,the Dlib toolkit was employed to annotate 68 facial feature points.This study established an evaluation metric for facial fatigue and developed a novel fatigue detection algorithm to assess the driver’s condition.A series of comparative experiments were carried out on the self-built dataset.The suggested method’s mAP(mean Average Precision)values for object detection and fatigue detection are 96.71%and 95.75%,respectively,as well as the detection speed is 47 FPS(Frames Per Second).This method can balance the contradiction between computational complexity and model accuracy.Furthermore,it can be transplanted to NVIDIA Jetson Orin NX and quickly detect the driver’s state while maintaining a high degree of accuracy.It contributes to the development of automobile safety systems and reduces the occurrence of traffic accidents.
文摘Ensuring that autonomous vehicles maintain high precision and rapid response capabilities in complex and dynamic driving environments is a critical challenge in the field of autonomous driving.This study aims to enhance the learning efficiency ofmulti-sensor feature fusion in autonomous driving tasks,thereby improving the safety and responsiveness of the system.To achieve this goal,we propose an innovative multi-sensor feature fusion model that integrates three distinct modalities:visual,radar,and lidar data.The model optimizes the feature fusion process through the introduction of two novel mechanisms:Sparse Channel Pooling(SCP)and Residual Triplet-Attention(RTA).Firstly,the SCP mechanism enables the model to adaptively filter out salient feature channels while eliminating the interference of redundant features.This enhances the model’s emphasis on critical features essential for decisionmaking and strengthens its robustness to environmental variability.Secondly,the RTA mechanism addresses the issue of feature misalignment across different modalities by effectively aligning key cross-modal features.This alignment reduces the computational overhead associated with redundant features and enhances the overall efficiency of the system.Furthermore,this study incorporates a reinforcement learning module designed to optimize strategies within a continuous action space.By integrating thismodulewith the feature fusion learning process,the entire system is capable of learning efficient driving strategies in an end-to-end manner within the CARLA autonomous driving simulator.Experimental results demonstrate that the proposedmodel significantly enhances the perception and decision-making accuracy of the autonomous driving system in complex traffic scenarios while maintaining real-time responsiveness.This work provides a novel perspective and technical pathway for the application of multi-sensor data fusion in autonomous driving.
