Eco-driving behaviors have been recommended around the world because the transport is a key factor of energy use and pollution emissions.Therefore,based on the driving decision model,this paper introduces three aspect...Eco-driving behaviors have been recommended around the world because the transport is a key factor of energy use and pollution emissions.Therefore,based on the driving decision model,this paper introduces three aspects of the driving decisions(strategic decision,tactical decision and operation decision)to analyze the economy of vehicle energy.The analytic hierarchy process(AHP)is used to assign the weight of the internal evaluation indexes,so as to form a complete assessment for drivers'eco-driving behaviors.The research result can not only quantitatively describe the energy-saving effect of drivers'decisions,but also put forward targeted driving suggestions to optimize drivers'eco-driving behaviors.This assessment model helps to clarify the potential of eco-driving on energy economy of transportation in a hierarchical way,and provides a valuable theoretical basis for the further promotion and application of eco-driving education.展开更多
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
Analyzing the driving behavior of autonomous vehicles(AV)in mixed traffic conditions at urban intersections has become increasingly important for improving intersection design,providing infrastructure-based guidance i...Analyzing the driving behavior of autonomous vehicles(AV)in mixed traffic conditions at urban intersections has become increasingly important for improving intersection design,providing infrastructure-based guidance information,and developing capability-enhanced AV perception systems.This study investigated the contributing factors affecting AV driving behavior using theWaymo Open Dataset.Binarized autonomous driving stability metrics,derived via a kernel density estimation,served as the target variables for a random forest classification model.The model’s input variables included 15 factors divided into four types:intersection-related,surrounding object-related,road infrastructure-related,and time-of-day-related types.The random forest classification model was employed to identify the key factors affecting autonomous driving behavior.In addition,the identified factors were further ranked based on feature importance.SHAP analysis was utilized to enhance model interpretability by quantifying the contribution of each factor and identifying their directional impacts.The type of intersection factor was found to have an importance of 0.243 and was the most influential factor on autonomous driving behavior.On average,intersection-related factors had an importance of 0.196,which is approximately a 31.1%margin over the average importance of surrounding object-related factors.Additionally,the surrounding object-related factors that were collected through sensors on the autonomous vehicle had a high degree of feature importance,especially with the number of pedestrians having the highest importance(0.107)of the types of objects.The correlation between these findings can contribute to the development of various treatments to improvemore harmonized AVs’maneuvering with other road users and facilities in urban mixed traffic environments.展开更多
To address soil salinization’s significant impact on human production and livelihood in arid regions,especially in high-salinity areas like salt lake regions,this study used multi-source remote sensing data to extrac...To address soil salinization’s significant impact on human production and livelihood in arid regions,especially in high-salinity areas like salt lake regions,this study used multi-source remote sensing data to extract 52 surface factors.Combined with measured soil salinity data,correlation analysis,multicollinearity testing,and projection importance analysis identified eight dominant factors.Subsequently,four machine learning algorithms were applied for modeling,and the optimal models were selected to study the spatiotemporal variation of soil salinization.The results indicate that the average soil salt content in the study area was 20.74%in 2020.LST(land surface temperature)can effectively identify areas with high salinity,such as saline-alkali land and salt flats.Among inversion models,the GBDT(gradient boosting decision trees)model demonstrated the highest predictive ability and minimal errors.The optimal inversion results revealed that soil salinization distribution was influenced by topographic elevation,distance from Qarhan Salt Lake,and river network density.Over the past 21 years,there was significant fluctuation in soil salinity observed in the concentrated area of grassland within the groundwater overflow zone,indicating strong variation in salinization.This fluctuation correlates with changes in groundwater levels in the groundwater overflow zone,which are influenced by temperature variations that determine the amount of snow and ice meltwater,and the precipitation in the upstream area.This study enhances understanding of soil salinization and its drivers in extremely arid salt lake regions.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
The development of human settlements(HS)in coastal cities is an integral component and a vital pathway toward building a strong marine power.It is also an essential requirement for achieving the coordinated developmen...The development of human settlements(HS)in coastal cities is an integral component and a vital pathway toward building a strong marine power.It is also an essential requirement for achieving the coordinated development of HS systems in these cities.In this study,we constructed an indicator system to analyze the coupling coordination degree(CCD)of HS systems in coastal cities in the Bohai Rim region of China(CCBRR).This study is based on five systems and employs methods such as the entropy weight method,CCD model,spatial trend surface analysis,and geographic detector to examine comprehensively the spatial and temporal patterns of CCD in 17 CCBRR during the period 2011–2022,as well as to explore their influencing factors.The findings are as follows:(1)Temporally,the CCD is high and exhibits a slow increasing trend,with distinct stage characteristics.(2)Spatially,the distribution of CCD reveals a“one core,many strengths”structural pattern.(3)Moreover,socioeconomic factors are the dominant force driving the CCD of the internal HS systems in the CCBRR.(4)Finally,we constructed a coupling coordination driving mechanism for HS in the CCBRR with the aim of providing scientific references and path choices for the high-quality and coordinated development of the CCBRR along with the implementation of the new quality productive forces regionalization.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
This article studies the problem of image segmentation-based semantic communication in autonomous driving.In real traffic scenes,the detecting of objects(e.g.,vehicles and pedestrians)is more important to guarantee dr...This article studies the problem of image segmentation-based semantic communication in autonomous driving.In real traffic scenes,the detecting of objects(e.g.,vehicles and pedestrians)is more important to guarantee driving safety,which is always ignored in existing works.Therefore,we propose a vehicular image segmentation-oriented semantic communication system,termed VIS-SemCom,focusing on transmitting and recovering image semantic features of high-important objects to reduce transmission redundancy.First,we develop a semantic codec based on Swin Transformer architecture,which expands the perceptual field thus improving the segmentation accuracy.To highlight the important objects'accuracy,we propose a multi-scale semantic extraction method by assigning the number of Swin Transformer blocks for diverse resolution semantic features.Also,an importance-aware loss incorporating important levels is devised,and an online hard example mining(OHEM)strategy is proposed to handle small sample issues in the dataset.Finally,experimental results demonstrate that the proposed VIS-SemCom can achieve a significant mean intersection over union(mIoU)performance in the SNR regions,a reduction of transmitted data volume by about 60%at 60%mIoU,and improve the segmentation accuracy of important objects,compared to baseline image communication.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
This study aimed to enhance the performance of semantic segmentation for autonomous driving by improving the 2DPASS model.Two novel improvements were proposed and implemented in this paper:dynamically adjusting the lo...This study aimed to enhance the performance of semantic segmentation for autonomous driving by improving the 2DPASS model.Two novel improvements were proposed and implemented in this paper:dynamically adjusting the loss function ratio and integrating an attention mechanism(CBAM).First,the loss function weights were adjusted dynamically.The grid search method is used for deciding the best ratio of 7:3.It gives greater emphasis to the cross-entropy loss,which resulted in better segmentation performance.Second,CBAM was applied at different layers of the 2Dencoder.Heatmap analysis revealed that introducing it after the second block of 2D image encoding produced the most effective enhancement of important feature representation.The training epoch was chosen for optimizing the best value by experiments,which improved model convergence and overall accuracy.To evaluate the proposed approach,experiments were conducted based on the SemanticKITTI database.The results showed that the improved model achieved higher segmentation accuracy by 64.31%,improved 11.47% in mIoU compared with the conventional 2DPASS model(baseline:52.84%).It was more effective at detecting small and distant objects and clearly identifying boundaries between different classes.Issues such as noise and variations in data distribution affected its accuracy,indicating the need for further refinement.Overall,the proposed improvements to the 2DPASS model demonstrated the potential to advance semantic segmentation technology and contributed to a more reliable perception of complex,dynamic environments in autonomous vehicles.Accurate segmentation enhances the vehicle’s ability to distinguish different objects,and this improvement directly supports safer navigation,robust decision-making,and efficient path planning,making it highly applicable to real-world deployment of autonomous systems in urban and highway settings.展开更多
Ukraine,as one of the world’s largest agricultural producers and exporters,plays a critical role in global food security.It is essential to understand the spatiotemporal dynamics and drivers of productive cropland in...Ukraine,as one of the world’s largest agricultural producers and exporters,plays a critical role in global food security.It is essential to understand the spatiotemporal dynamics and drivers of productive cropland in Ukraine,particularly in the context of the 2022 Russia-Ukraine conflict.We provide the first comprehensive assessment of both conflict-and non-conflict-related factors that influenced the distribution and productivity of Ukraine’s cropland from 2013 to 2023.In addition,we propose a novel method using machine learning models to isolate the impact of conflict on cropland.Our findings reveal that,prior to the conflict,the spatial pattern of Ukraine’s mean cultivation rate was primarily shaped by natural factors—such as climate,soil properties,and elevation—whereas socio-economic factors(e.g.,GDP and population size)exerted a weaker influence.Interannual dynamics in productive cropland area were largely driven by climate variability.The onset of conflict in 2022 dramatically altered this landscape,with nearly half of the cropland grid cells experiencing a conflict-induced reduction.Notably,almost half of the interannual reduction in productive cropland in 2022 was attributed to climate change.Remarkably,in 2023,the return of displaced populations and favorable climatic conditions in many oblasts contributed to a positive trend in cropland reclamation.Despite this,the total area of productive cropland in 2023 remained below expected levels,due to ongoing conflict and localized droughts.Finally,we highlight the urgent need to adopt a two-pronged approach that addresses both the immediate impacts of conflict and the ongoing threats posed by climate change to ensure the resilience and sustainability of agricultural systems in post-conflict areas.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
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
In order to give a new way for modeling driving behavior, identifying road traffic accident causation and solving a variety of road traffic safety problems such as driving errors prevention and driving behavior analys...In order to give a new way for modeling driving behavior, identifying road traffic accident causation and solving a variety of road traffic safety problems such as driving errors prevention and driving behavior analysis, a new driving behavior shaping model is proposed, which could be used to assess the degree of effect of driving error upon road traffic safety. Driver behavior shaping model based on driving reliability and safety analysis could be used to identify the road traffic accident causation, to supply data for driver's behavior training, to evaluate driving procedures, to human factor design of road traffic system.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
文摘Eco-driving behaviors have been recommended around the world because the transport is a key factor of energy use and pollution emissions.Therefore,based on the driving decision model,this paper introduces three aspects of the driving decisions(strategic decision,tactical decision and operation decision)to analyze the economy of vehicle energy.The analytic hierarchy process(AHP)is used to assign the weight of the internal evaluation indexes,so as to form a complete assessment for drivers'eco-driving behaviors.The research result can not only quantitatively describe the energy-saving effect of drivers'decisions,but also put forward targeted driving suggestions to optimize drivers'eco-driving behaviors.This assessment model helps to clarify the potential of eco-driving on energy economy of transportation in a hierarchical way,and provides a valuable theoretical basis for the further promotion and application of eco-driving education.
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supported by Korea Institute of Police Technology(KIPoT)grant funded by the Korea government(KNPA)(Project Name:Development of Lv.4 Driving Ability Evaluation Technology for Autonomous Vehicles Based on Real Roads/Project Number:RS-2023-00238253).
文摘Analyzing the driving behavior of autonomous vehicles(AV)in mixed traffic conditions at urban intersections has become increasingly important for improving intersection design,providing infrastructure-based guidance information,and developing capability-enhanced AV perception systems.This study investigated the contributing factors affecting AV driving behavior using theWaymo Open Dataset.Binarized autonomous driving stability metrics,derived via a kernel density estimation,served as the target variables for a random forest classification model.The model’s input variables included 15 factors divided into four types:intersection-related,surrounding object-related,road infrastructure-related,and time-of-day-related types.The random forest classification model was employed to identify the key factors affecting autonomous driving behavior.In addition,the identified factors were further ranked based on feature importance.SHAP analysis was utilized to enhance model interpretability by quantifying the contribution of each factor and identifying their directional impacts.The type of intersection factor was found to have an importance of 0.243 and was the most influential factor on autonomous driving behavior.On average,intersection-related factors had an importance of 0.196,which is approximately a 31.1%margin over the average importance of surrounding object-related factors.Additionally,the surrounding object-related factors that were collected through sensors on the autonomous vehicle had a high degree of feature importance,especially with the number of pedestrians having the highest importance(0.107)of the types of objects.The correlation between these findings can contribute to the development of various treatments to improvemore harmonized AVs’maneuvering with other road users and facilities in urban mixed traffic environments.
基金The Second Tibetan Plateau Scientific Expedition and Research Program,No.2019QZKK0805-02The Innovation Team Foundation of Qinghai Office of Science and Technology,No.2022-ZJ-903+2 种基金The Comprehensive Development and Utilization of Salt Lake Resources,No.2023ZXKYA05100The Special Research Assistant of Chinese Academy of Sciences(Han Jinjun)The Kunlun Talented People of Qinghai Province,High-end Innovation and Entrepreneurship Talents,2023(Han Jinjun)。
文摘To address soil salinization’s significant impact on human production and livelihood in arid regions,especially in high-salinity areas like salt lake regions,this study used multi-source remote sensing data to extract 52 surface factors.Combined with measured soil salinity data,correlation analysis,multicollinearity testing,and projection importance analysis identified eight dominant factors.Subsequently,four machine learning algorithms were applied for modeling,and the optimal models were selected to study the spatiotemporal variation of soil salinization.The results indicate that the average soil salt content in the study area was 20.74%in 2020.LST(land surface temperature)can effectively identify areas with high salinity,such as saline-alkali land and salt flats.Among inversion models,the GBDT(gradient boosting decision trees)model demonstrated the highest predictive ability and minimal errors.The optimal inversion results revealed that soil salinization distribution was influenced by topographic elevation,distance from Qarhan Salt Lake,and river network density.Over the past 21 years,there was significant fluctuation in soil salinity observed in the concentrated area of grassland within the groundwater overflow zone,indicating strong variation in salinization.This fluctuation correlates with changes in groundwater levels in the groundwater overflow zone,which are influenced by temperature variations that determine the amount of snow and ice meltwater,and the precipitation in the upstream area.This study enhances understanding of soil salinization and its drivers in extremely arid salt lake regions.
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National Natural Science Foundation of China,No.42201221,No.42471246Liaoning Province Natural Science Foundation Project,No.2023-MS-254+1 种基金Liaoning Province Social Science Planning Fund Project,No.L22CJY016Dalian Federation of Social Sciences,No.2022dlskzd037。
文摘The development of human settlements(HS)in coastal cities is an integral component and a vital pathway toward building a strong marine power.It is also an essential requirement for achieving the coordinated development of HS systems in these cities.In this study,we constructed an indicator system to analyze the coupling coordination degree(CCD)of HS systems in coastal cities in the Bohai Rim region of China(CCBRR).This study is based on five systems and employs methods such as the entropy weight method,CCD model,spatial trend surface analysis,and geographic detector to examine comprehensively the spatial and temporal patterns of CCD in 17 CCBRR during the period 2011–2022,as well as to explore their influencing factors.The findings are as follows:(1)Temporally,the CCD is high and exhibits a slow increasing trend,with distinct stage characteristics.(2)Spatially,the distribution of CCD reveals a“one core,many strengths”structural pattern.(3)Moreover,socioeconomic factors are the dominant force driving the CCD of the internal HS systems in the CCBRR.(4)Finally,we constructed a coupling coordination driving mechanism for HS in the CCBRR with the aim of providing scientific references and path choices for the high-quality and coordinated development of the CCBRR along with the implementation of the new quality productive forces regionalization.
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National Natural Science Foundation of China under Grants No.62171047,U22B2001,62271065,62001051Beijing Natural Science Foundation under Grant L223027BUPT Excellent Ph.D Students Foundation under Grants CX2021114。
文摘This article studies the problem of image segmentation-based semantic communication in autonomous driving.In real traffic scenes,the detecting of objects(e.g.,vehicles and pedestrians)is more important to guarantee driving safety,which is always ignored in existing works.Therefore,we propose a vehicular image segmentation-oriented semantic communication system,termed VIS-SemCom,focusing on transmitting and recovering image semantic features of high-important objects to reduce transmission redundancy.First,we develop a semantic codec based on Swin Transformer architecture,which expands the perceptual field thus improving the segmentation accuracy.To highlight the important objects'accuracy,we propose a multi-scale semantic extraction method by assigning the number of Swin Transformer blocks for diverse resolution semantic features.Also,an importance-aware loss incorporating important levels is devised,and an online hard example mining(OHEM)strategy is proposed to handle small sample issues in the dataset.Finally,experimental results demonstrate that the proposed VIS-SemCom can achieve a significant mean intersection over union(mIoU)performance in the SNR regions,a reduction of transmitted data volume by about 60%at 60%mIoU,and improve the segmentation accuracy of important objects,compared to baseline image communication.
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.
文摘This study aimed to enhance the performance of semantic segmentation for autonomous driving by improving the 2DPASS model.Two novel improvements were proposed and implemented in this paper:dynamically adjusting the loss function ratio and integrating an attention mechanism(CBAM).First,the loss function weights were adjusted dynamically.The grid search method is used for deciding the best ratio of 7:3.It gives greater emphasis to the cross-entropy loss,which resulted in better segmentation performance.Second,CBAM was applied at different layers of the 2Dencoder.Heatmap analysis revealed that introducing it after the second block of 2D image encoding produced the most effective enhancement of important feature representation.The training epoch was chosen for optimizing the best value by experiments,which improved model convergence and overall accuracy.To evaluate the proposed approach,experiments were conducted based on the SemanticKITTI database.The results showed that the improved model achieved higher segmentation accuracy by 64.31%,improved 11.47% in mIoU compared with the conventional 2DPASS model(baseline:52.84%).It was more effective at detecting small and distant objects and clearly identifying boundaries between different classes.Issues such as noise and variations in data distribution affected its accuracy,indicating the need for further refinement.Overall,the proposed improvements to the 2DPASS model demonstrated the potential to advance semantic segmentation technology and contributed to a more reliable perception of complex,dynamic environments in autonomous vehicles.Accurate segmentation enhances the vehicle’s ability to distinguish different objects,and this improvement directly supports safer navigation,robust decision-making,and efficient path planning,making it highly applicable to real-world deployment of autonomous systems in urban and highway settings.
基金supported in part by the National Natural Science Foundation of China(Grants No.41971284 and 42371321)the Key Research and Development Program of Hubei Province(Grant No.2025BAB024).
文摘Ukraine,as one of the world’s largest agricultural producers and exporters,plays a critical role in global food security.It is essential to understand the spatiotemporal dynamics and drivers of productive cropland in Ukraine,particularly in the context of the 2022 Russia-Ukraine conflict.We provide the first comprehensive assessment of both conflict-and non-conflict-related factors that influenced the distribution and productivity of Ukraine’s cropland from 2013 to 2023.In addition,we propose a novel method using machine learning models to isolate the impact of conflict on cropland.Our findings reveal that,prior to the conflict,the spatial pattern of Ukraine’s mean cultivation rate was primarily shaped by natural factors—such as climate,soil properties,and elevation—whereas socio-economic factors(e.g.,GDP and population size)exerted a weaker influence.Interannual dynamics in productive cropland area were largely driven by climate variability.The onset of conflict in 2022 dramatically altered this landscape,with nearly half of the cropland grid cells experiencing a conflict-induced reduction.Notably,almost half of the interannual reduction in productive cropland in 2022 was attributed to climate change.Remarkably,in 2023,the return of displaced populations and favorable climatic conditions in many oblasts contributed to a positive trend in cropland reclamation.Despite this,the total area of productive cropland in 2023 remained below expected levels,due to ongoing conflict and localized droughts.Finally,we highlight the urgent need to adopt a two-pronged approach that addresses both the immediate impacts of conflict and the ongoing threats posed by climate change to ensure the resilience and sustainability of agricultural systems in post-conflict areas.
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 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In order to give a new way for modeling driving behavior, identifying road traffic accident causation and solving a variety of road traffic safety problems such as driving errors prevention and driving behavior analysis, a new driving behavior shaping model is proposed, which could be used to assess the degree of effect of driving error upon road traffic safety. Driver behavior shaping model based on driving reliability and safety analysis could be used to identify the road traffic accident causation, to supply data for driver's behavior training, to evaluate driving procedures, to human factor design of road traffic system.
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.
