As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods ge...As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods generally have problems such as insufficient 3D scene description capability and low dynamic update efficiency,which are difficult to meet the demand of real-time accurate management.For this reason,this paper proposes a vehicle twin modeling method for road tunnels.This approach starts from the actual management needs,and supports multi-level dynamic modeling from vehicle type,size to color by constructing a vehicle model library that can be flexibly invoked;at the same time,semantic constraint rules with geometric layout,behavioral attributes,and spatial relationships are designed to ensure that the virtual model matches with the real model with a high degree of similarity;ultimately,the prototype system is constructed and the case region is selected for the case study,and the dynamic vehicle status in the tunnel is realized by integrating real-time monitoring data with semantic constraints for precise virtual-real mapping.Finally,the prototype system is constructed and case experiments are conducted in selected case areas,which are combined with real-time monitoring data to realize dynamic updating and three-dimensional visualization of vehicle states in tunnels.The experiments show that the proposed method can run smoothly with an average rendering efficiency of 17.70 ms while guaranteeing the modeling accuracy(composite similarity of 0.867),which significantly improves the real-time and intuitive tunnel management.The research results provide reliable technical support for intelligent operation and emergency response of road tunnels,and offer new ideas for digital twin modeling of complex scenes.展开更多
The umbilical,a key component in offshore energy extraction,plays a vital role in ensuring the stable operation of the entire production system.The extensive variety of cross-sectional components creates highly comple...The umbilical,a key component in offshore energy extraction,plays a vital role in ensuring the stable operation of the entire production system.The extensive variety of cross-sectional components creates highly complex layout combinations.Furthermore,due to constraints in component quantity and geometry within the cross-sectional layout,filler bodies must be incorporated to maintain cross-section performance.Conventional design approaches based on manual experience suffer from inefficiency,high variability,and difficulties in quantification.This paper presents a multi-level automatic filling optimization design method for umbilical cross-sectional layouts to address these limitations.Initially,the research establishes a multi-objective optimization model that considers compactness,balance,and wear resistance of the cross-section,employing an enhanced genetic algorithm to achieve a near-optimal layout.Subsequently,the study implements an image processing-based vacancy detection technique to accurately identify cross-sectional gaps.To manage the variability and diversity of these vacant regions,the research introduces a multi-level filling method that strategically selects and places filler bodies of varying dimensions,overcoming the constraints of uniform-size fillers.Additionally,the method incorporates a hierarchical strategy that subdivides the complex cross-section into multiple layers,enabling layer-by-layer optimization and filling.This approach reduces manufac-turing equipment requirements while ensuring practical production process feasibility.The methodology is validated through a specific umbilical case study.The results demonstrate improvements in compactness,balance,and wear resistance compared with the initial cross-section,offering novel insights and valuable references for filler design in umbilical cross-sections.展开更多
Soil fugitive dust(SFD)is characterized by a variety of sources and considerable spatialtemporal variability,exerting a significant impact on environmental air quality and ecological systems in cities across northern ...Soil fugitive dust(SFD)is characterized by a variety of sources and considerable spatialtemporal variability,exerting a significant impact on environmental air quality and ecological systems in cities across northern China.Multiple factors can shape SFD emission.Nevertheless,the current comprehension of its critical impact factors and quantitative methodologies remains constrained.This study utilizes interpretable machine learning techniques to identify the principal impact factors of SFD and their interactions while delineating their action thresholds.The findings reveal seasonal variations in impact factors and emphasize the substantial effect of bare soil source strength on SFD,including parameters such as bare soil area and soil moisture.Consequently,the Wind Erosion Equation model is optimized following these findings to localize its parameters and improve its capability to calculate hourly SFD emissions.The case application is validated using observational data,demonstrating the reliability and precision of the optimized methodology.This study provides insights and solutions for the local optimization of SFD parameterization schemes and further supports the formulation of precise prevention and control policies for SFD.展开更多
Carbonyl sulfide(COS)is an effective tracer for estimating Gross Primary Productivity(GPP)in the carbon cycle.As the largest contribution to the atmosphere,anthropogenic COS emissions must be accurately quantified.In ...Carbonyl sulfide(COS)is an effective tracer for estimating Gross Primary Productivity(GPP)in the carbon cycle.As the largest contribution to the atmosphere,anthropogenic COS emissions must be accurately quantified.In this study,an anthropogenic COS emission inventory from 2015 to 2021 was constructed by applying the bottom-up approach based on activity data from emission sources.China’s anthropogenic COS emissions increased from approximately 171 to 198 Gg S yr^(-1)from 2015-2021,differing from the trends of other pollutants.Despite an initial decline in COS emissions across sectors during the early stage of the COVID-19 pandemic,a rapid rebound in emissions occurred following the resumption of economic activities.In 2021,industrial sources,coal combustion,agriculture and vehicle exhaust accounted for 76.8%,12.3%,10.5%and 0.4%of total COS emissions,respectively.The aluminum industry was the primary COS emitter among industrial sources,contributing40.7% of total emissions.Shandong,Shanxi,and Zhejiang were the top three provinces in terms of anthropogenic COS emissions,reaching 39,21 and 17 Gg S yr-1,respectively.Provincial-level regions(hereafter province)with high COS emissions are observed mainly in the eastern and coastal regions of China,which,together with the wind direction,helps explain the pattern of high COS concentrations in the Western Pacific Ocean in winter.The Green Contribution Coefficient of COS(GCCCOS)was used to assess the relationship between GDP and COS emissions,highlighting the disparity between GDP and COS contributions to green development.As part of this analysis,relevant recommendations are proposed to address this disparity.The COS emission inventory in our study can be used as input for the Sulfur Transport and Deposition Model(STEM),reducing uncertainties in the atmospheric COS source?sink budget and promoting understanding of the atmosphere sulfur cycle.展开更多
Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,ther...Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,there is limited research on the spatiotemporal characteristics of landslide deformation.This paper proposes a novel Multi-Relation Spatiotemporal Graph Residual Network with Multi-Level Feature Attention(MFA-MRSTGRN)that effectively improves the prediction performance of landslide displacement through spatiotemporal fusion.This model integrates internal seepage factors as data feature enhancements with external triggering factors,allowing for accurate capture of the complex spatiotemporal characteristics of landslide displacement and the construction of a multi-source heterogeneous dataset.The MFA-MRSTGRN model incorporates dynamic graph theory and four key modules:multilevel feature attention,temporal-residual decomposition,spatial multi-relational graph convolution,and spatiotemporal fusion prediction.This comprehensive approach enables the efficient analyses of multi-source heterogeneous datasets,facilitating adaptive exploration of the evolving multi-relational,multi-dimensional spatiotemporal complexities in landslides.When applying this model to predict the displacement of the Liangshuijing landslide,we demonstrate that the MFA-MRSTGRN model surpasses traditional models,such as random forest(RF),long short-term memory(LSTM),and spatial temporal graph convolutional networks(ST-GCN)models in terms of various evaluation metrics including mean absolute error(MAE=1.27 mm),root mean square error(RMSE=1.49 mm),mean absolute percentage error(MAPE=0.026),and R-squared(R^(2)=0.88).Furthermore,feature ablation experiments indicate that incorporating internal seepage factors improves the predictive performance of landslide displacement models.This research provides an advanced and reliable method for landslide displacement prediction.展开更多
Seyitgazi and Han districts,located in the south of Eskişehir in Central Anatolia,in western Türkiye,host interesting landforms,such as steep slopes,mesas and butte structures,fault-guided slopes,valleys,fairy ch...Seyitgazi and Han districts,located in the south of Eskişehir in Central Anatolia,in western Türkiye,host interesting landforms,such as steep slopes,mesas and butte structures,fault-guided slopes,valleys,fairy chimneys,castle koppies,pillars,weathered rock blocks,perched rocks,cavernous weathering features,grooves,and gnammas,formed on tuffs in semi-arid to semi-humid climatic conditions,as well as geoarchaeological remains belonging to various civilisations,primarily the Phrygians(including rock-cut tombs and settlements,fortresses,rock churches,façades,altars,and niches).This study aims at identifying these remarkable landforms that host cultural heritage and revealing the geoheritage value and geotourism potential of the region.The data obtained from the fieldwork were evaluated using the methodology proposed by Pereira and Pereira in 2010,and 26 geomorphosites were selected from 61 potential sites using this method.The analysis results revealed that although the region hosts numerous geomorphosites with high scientific,cultural,aesthetic,and ecological value,the overall levels of protection and touristic use of these landforms are generally low.Indeed,the area,which has the potential to be an important tourism region in the future,faces problems such as infrastructure deficiencies,transportation difficulties,lack of promotion,weaknesses in accommodation services,and destruction of geoheritage.These results highlight the importance of implementing sustainable geotourism strategies that are compatible with the region’s unique geoheritage.In this respect,this study is among the first to comprehensively inventory and assess the geomorphosites of Mountainous Phrygia,contributing to regional geoconservation and sustainable tourism development.展开更多
The Ili River is a typical transboundary river between China and Kazakhstan,with glaciers within its basin serving as a crucial solid water resource.Recently,we compiled the Chinese Glacier Inventory of Xinjiang in 20...The Ili River is a typical transboundary river between China and Kazakhstan,with glaciers within its basin serving as a crucial solid water resource.Recently,we compiled the Chinese Glacier Inventory of Xinjiang in 2020(CGI-XJ2020)using high-resolution satellite imagery(<2 m),based on visual interpretation.This study presented the state of glaciers in the Ili River Basin in 2020 by utilizing the data from CGI-XJ2020.It quantified glacier changes in 1960s–2020 based on CGI-XJ2020 and revised datasets from the First and Second Chinese Glacier Inventories.The results indicated that in 2020,the Ili River Basin contained 2,177 glaciers,totaling 1,433.19 km^(2)in area.Among them,213 glaciers were covered by 57.43 km^(2)of debris.The total uncertainty in glacier area was 46.43 km^(2),accounting for approximately 3.2%of the total area.Mapped glacier areas varied from 0.003 to 74.67 km^(2),with an average area of 0.66 km^(2)and a median area of 0.15 km^(2).Glaciers<0.5 km^(2)in size dominated in numbers,accounting for 75.1%of the total.Glaciers in the basin have undergone significant retreat during 1960s–2020,with their total area decreasing by 589.38 km^(2)(29.15%).A total of 495 glaciers(with an area of 49.67 km^(2))disappeared.The average annual glacier area retreat rates for 1960s-2007 and 2007–2020 were 10.86 km^(2)/a(0.54%/a)and 9.41 km^(2)/a(0.61%/a),respectively,showing a continued acceleration in glacier shrinkage,despite a slight decrease in absolute retreat rates.展开更多
As we look ahead to future lunar exploration missions, such as crewed lunar exploration and establishing lunar scientific research stations, the lunar rovers will need to cover vast distances. These distances could ra...As we look ahead to future lunar exploration missions, such as crewed lunar exploration and establishing lunar scientific research stations, the lunar rovers will need to cover vast distances. These distances could range from kilometers to tens of kilometers, and even hundreds and thousands of kilometers. Therefore, it is crucial to develop effective long-range path planning for lunar rovers to meet the demands of lunar patrol exploration. This paper presents a hierarchical map model path planning method that utilizes the existing high-resolution images, digital elevation models and mineral abundance maps. The objective is to address the issue of the construction of lunar rover travel costs in the absence of large-scale, high-resolution digital elevation models. This method models the reference and semantic layers using the middle- and low-resolution remote sensing data. The multi-scale obstacles on the lunar surface are extracted by combining the deep learning algorithm on the high-resolution image, and the obstacle avoidance layer is modeled. A two-stage exploratory path planning decision is employed for long-distance driving path planning on a global–local scale. The proposed method analyzes the long-distance accessibility of various areas of scientific significance, such as Rima Bode. A high-precision digital elevation model is created using stereo images to validate the method. Based on the findings, it can be observed that the entire route spans a distance of 930.32 km. The route demonstrates an impressive ability to avoid meter-level impact craters and linear structures while maintaining an average slope of less than 8°. This paper explores scientific research by traversing at least seven basalt units, uncovering the secrets of lunar volcanic activities, and establishing ‘golden spike’ reference points for lunar stratigraphy. The final result of path planning can serve as a valuable reference for the design, mission demonstration, and subsequent project implementation of the new manned lunar rover.展开更多
This study presents an emission inventory for 2022,focusing on assessing the emissions of PM_(2.5),PM_(10),NO_(x),SO_(2),CO,and VOC from India's road transport,residential,and thermal power sectors.Road transport ...This study presents an emission inventory for 2022,focusing on assessing the emissions of PM_(2.5),PM_(10),NO_(x),SO_(2),CO,and VOC from India's road transport,residential,and thermal power sectors.Road transport emissions were estimated using a vehicle kilometer traveled methodology derived from a survey of 200,000 vehicles.A regression analysis was conducted to assess residential fuel usage,considering recent changes in consumption patterns and updated data on cleaner fuels.Estimates for the thermal power sector were based on emission monitoring data.The residential sector is the predominant source of PM_(2.5)(1112 kt),PM_(10)(1678 kt),CO(10630 kt),and VOC(2558 kt).The thermal power sector is the predominant source of secondary air pollutant precursors such as NO_(x)(2328 kt)and SO_(2)(4694 kt).India has the highest emission intensity per gross domestic product(GDP)across sectors compared to other countries.For example,PM_(2.5)emissions per GDP from the roads in India are 14,21,and 10 times that of those in China,the USA,and Europe.The southern(29%),eastern(30%),and central(36%)regions were the notable contributors to emissions from transport,residential,and thermal power sectors.Urban areas contributed 5%of the total residential sector emissions across India but 25%of the total road transport sector emissions nationwide.Moreover,power plants within or near the non-attainment cities were responsible for 12%of the overall thermal power pollution recorded across India.The study identifies unequal emission burdens,with economically disadvantaged regions bearing the brunt.展开更多
Deep learning networks are increasingly exploited in the field of neuronal soma segmentation.However,annotating dataset is also an expensive and time-consuming task.Unsupervised domain adaptation is an effective metho...Deep learning networks are increasingly exploited in the field of neuronal soma segmentation.However,annotating dataset is also an expensive and time-consuming task.Unsupervised domain adaptation is an effective method to mitigate the problem,which is able to learn an adaptive segmentation model by transferring knowledge from a rich-labeled source domain.In this paper,we propose a multi-level distribution alignment-based unsupervised domain adaptation network(MDA-Net)for segmentation of 3D neuronal soma images.Distribution alignment is performed in both feature space and output space.In the feature space,features from different scales are adaptively fused to enhance the feature extraction capability for small target somata and con-strained to be domain invariant by adversarial adaptation strategy.In the output space,local discrepancy maps that can reveal the spatial structures of somata are constructed on the predicted segmentation results.Then thedistribution alignment is performed on the local discrepancies maps across domains to obtain a superior discrepancy map in the target domain,achieving refined segmentation performance of neuronal somata.Additionally,after a period of distribution align-ment procedure,a portion of target samples with high confident pseudo-labels are selected as training data,which assist in learning a more adaptive segmentation network.We verified the superiority of the proposed algorithm by comparing several domain adaptation networks on two 3D mouse brain neuronal somata datasets and one macaque brain neuronal soma dataset.展开更多
Numerous studies documented the occurrence of organophosphate tri-esters(tri-OPEs)and di-esters(di-OPEs)in the environment.Little information is available on their occurrence in waste consumer products,reservoirs and ...Numerous studies documented the occurrence of organophosphate tri-esters(tri-OPEs)and di-esters(di-OPEs)in the environment.Little information is available on their occurrence in waste consumer products,reservoirs and sources of these chemicals.This study collected and analyzed 92 waste consumer products manufactured from diverse polymers,including polyurethane foam(PUF),polystyrene(PS),acrylonitrile butadiene styrene(ABS),polypropylene(PP),and polyethylene(PE)to obtain information on the occurrence and profiles of 16 tri-OPEs and 10 di-OPEs.Total concentrations of di-OPEs(18−370,000 ng/g,median 1,700 ng/g)were one order of magnitude lower than those of tri-OPEs(94−4,500,000 ng/g,median 5,400 ng/g).The concentrations of both tri-and di-OPEs in products made of PUF,PS,and ABS were orders of magnitude higher than those made of PP and PE.The compositional patterns of OPEs varied among different polymer types but were generally dominated by bisphenol A bis(diphenyl phosphate),triphenyl phosphate,tris(1-chloro-2-propyl)phosphate,di-phenyl phosphate(DPHP),and bis(2-ethylhexyl)phosphate.Two industrially applied di-OPEs(di-n-butyl phosphate and DPHP)exhibited higher levels than their respective tri-OPEs,contrary to their production volumes.Some non-industrially applied chlorinated di-OPEs were also detected,with concentrations up to 97,000 ng/g.These findings suggest that degradation of tri-OPEs during the manufacturing and use of products is an important source of di-OPEs.The mass inventories of tri-OPEs and di-OPEs in consumer products were estimated at 3,100 and 750 tons/year,respectively.This study highlights the importance of consumer products as emission sources of a broad suite of OPEs.展开更多
Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and treatment.However,achieving precise segmentation remains a challenge due to vari...Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and treatment.However,achieving precise segmentation remains a challenge due to various factors,including scattering noise,low contrast,and limited resolution in ultrasound images.Although existing segmentation models have made progress,they still suffer from several limitations,such as high error rates,low generalizability,overfitting,limited feature learning capability,etc.To address these challenges,this paper proposes a Multi-level Relation Transformer-based U-Net(MLRT-UNet)to improve thyroid nodule segmentation.The MLRTUNet leverages a novel Relation Transformer,which processes images at multiple scales,overcoming the limitations of traditional encoding methods.This transformer integrates both local and global features effectively through selfattention and cross-attention units,capturing intricate relationships within the data.The approach also introduces a Co-operative Transformer Fusion(CTF)module to combine multi-scale features from different encoding layers,enhancing the model’s ability to capture complex patterns in the data.Furthermore,the Relation Transformer block enhances long-distance dependencies during the decoding process,improving segmentation accuracy.Experimental results showthat the MLRT-UNet achieves high segmentation accuracy,reaching 98.2% on the Digital Database Thyroid Image(DDT)dataset,97.8% on the Thyroid Nodule 3493(TG3K)dataset,and 98.2% on the Thyroid Nodule3K(TN3K)dataset.These findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation,addressing the limitations of existing models.展开更多
The rapid development of vocational colleges in China brings about the explosive growth of the number and category of state-owned assets that guarantees the development of vocational colleges. The special equipment an...The rapid development of vocational colleges in China brings about the explosive growth of the number and category of state-owned assets that guarantees the development of vocational colleges. The special equipment and the general equipment included in the state-owned assets of vocational colleges are increasing at the fastest rate. Based on the problems from equipment inventory, this paper analyzes the problems of state-owned management, and puts forward countermeasures to improve the management of state-owned assets from formulating regulations and rules, strengthening the unified institution, applying the information technologies in building the team of administrators.展开更多
Based on China's high-resolution satellite imagery series(2 m resolution),the Chinese Glacier Inventory of Xinjiang in 2020(CGI-XJ2020)was compiled,with 2020 as the baseline year.CGI-XJ2020 has five key features:1...Based on China's high-resolution satellite imagery series(2 m resolution),the Chinese Glacier Inventory of Xinjiang in 2020(CGI-XJ2020)was compiled,with 2020 as the baseline year.CGI-XJ2020 has five key features:1)improved accuracy in glacier boundary delineation and optimized inventory attributes through highresolution satellite imagery and field validation of 38 glaciers;2)established an area-volume formula for Xinjiang glaciers using ground-penetrating radar(GPR)thickness data from 23 glaciers in the study region;3)the use of high-resolution satellite imagery has reduced the minimum glacier area threshold,enabling the identification of more small glaciers;4)enhanced accuracy in delineating supraglacial debris coverage;5)upgraded administrative division from prefecture-level(CGI-2)to county-level.According to CGI-XJ2020 data,Xinjiang contains 24,202 glaciers in 2020,covering 23,629.28 km^(2)with an average size of 0.98 km^(2)per glacier and a total ice volume of 1,608.94 km^(3).Among these,1,612 debris-covered glaciers occupy 1,163.32 km^(2)(4.9%of the total glacierized area).Glaciers larger than 10 km^(2)(296 in total)cover 9,881.69 km^(2)with a volume of 1,053.17 km^(3),accounting for 41.82%of total area and 65.46%of total ice volume,respectively.The Kunlun Mountains host the most glaciers,followed by the Tianshan Mountains.The Tarim river basin contains the largest concentration(15,860 glaciers,18,594.24 km^(2),1,347.17 km^(3)).The Hotan Prefecture has the highest glacier density.展开更多
Recently,the transportation sector in China has gradually become the main source of urban air pollution and primary driver of carbon emissions growth.Considering air pollutants and greenhouse gases come from the same ...Recently,the transportation sector in China has gradually become the main source of urban air pollution and primary driver of carbon emissions growth.Considering air pollutants and greenhouse gases come from the same emission sources,it is necessary to establish an updated high-resolution emission inventory for the transportation sector in Central China,themost polluted region in China.The inventory includes on-road mobile,non-roadmobile,oil storage and transportation,and covers 9 types of air pollutants and 3 types of greenhouse gases.Based on the Long-range Energy Alternatives Planning System(LEAP)model,the emissions of pollutants were predicted for the period from2020 to 2035 in different scenarios.Results showed that in 2020,emissions of SO_(2),NO_(x),CO,PM_(10),PM_(2.5),VOCs,NH_(3),BC,OC,CO_(2),CH_(4),and N_(2)O in Henan Province were 27.5,503.2,878.6,20.1,17.4,222.1,21.5,9.4,2.9,92,077.9,6.0,and 10.4 kilotons,respectively.Energy demand and pollutant emissions in Henan Province are simulated under four scenarios(Baseline Scenario(BS),Pollution Abatement Scenario(PA),Green Transportation Scenario(GT),and Reinforcing Low Carbon Scenario(RLC)).The collaborative emission reduction effect is most significant in the RLC scenario,followed by the GT scenario.By 2035,under the RLC scenario,energy consumption and emissions of SO_(2),NO_(x),CO,PM_(10),PM_(2.5),VOCs,NH_(3),CO_(2),CH_(4),and N_(2)O are projected to decrease by 72.0%,30.0%,55.6%,56.0%,38.6%,39.7%,51.5%,66.1%,65.5%,55.4%,and 52.8%,respectively.This study provides fundamental data support for subsequent numerical simulations.展开更多
Accurate,reliable,and regularly updated information is necessary for targeted management of forest stands.This information is usually obtained from sample-based field inventory data.Due to the time-consuming and costl...Accurate,reliable,and regularly updated information is necessary for targeted management of forest stands.This information is usually obtained from sample-based field inventory data.Due to the time-consuming and costly procedure of forest inventory,it is imperative to generate and use the resulting data optimally.Integrating field inventory information with remote sensing data increases the value of field approaches,such as national forest inventories.This study investigated the optimal integration of forest inventory data with aerial image-based canopy height models(CHM)for forest growing stock estimation.For this purpose,fixed-area and angle-count plots from a forest area in Austria were used to assess which type of inventory system is more suitable when the field data is integrated with aerial image analysis.Although a higher correlation was observed between remotely predicted growing stocks and field inventory values for fixed-area plots,the paired t-test results revealed no statistical difference between the two methods.The R2 increased by 0.08 points and the RMSE decreased by 7.7 percentage points(24.8m^(3)·ha^(−1))using fixed-area plots.Since tree height is the most critical variable essential for modeling forest growing stock using aerial images,we also compared the tree heights obtained from CHM to those from the typical field inventory approach.The result shows a high correlation(R^(2)=0.781)between the tree heights extracted from the CHM and those measured in the field.However,the correlation decreased by 0.113 points and the RMSE increased by 4.2 percentage points(1.04m)when the allometrically derived tree heights were analyzed.Moreover,the results of the paired t-test revealed that there is no significant statistical difference between the tree heights extracted from CHM and those measured in the field,but there is a significant statistical difference when the CHM-derived and the allometrically-derived heights were compared.This proved that image-based CHM can obtain more accurate tree height information than field inventory estimations.Overall,the results of this study demonstrated that image-based CHM can be integrated into the forest inventory data at large scales and provide reliable information on forest growing stock.The produced maps reflect the variability of growth conditions and developmental stages of different forest stands.This information is required to characterize the status and changes,e.g.,in forest structure diversity,parameters for volume,and can be used for forest aboveground biomass estimation,which plays an important role in managing and controlling forest resources in mid-term forest management.This is of particular interest to forest managers and forest ecologists.展开更多
In the self-built fruit and vegetable sorting warehouse of Lushang Group,the system is automatically scanning the QR code for agricultural product and conducts pesticide residue testing on fruits and vegetables;new pr...In the self-built fruit and vegetable sorting warehouse of Lushang Group,the system is automatically scanning the QR code for agricultural product and conducts pesticide residue testing on fruits and vegetables;new products from foreign trade factories will be directly transported to Ginza Supermarket through the“Direct Express for Domestic Product”channel,and will be shelved on the“Lushang Life”service platform within 48 hours;in front of the campus intelligent milk cabinet independently launched by Lushang Technology,students receive pasteurized milk by brushing their faces,and the data about milk source farms and sterilization parameters are sent to the parents simultaneously.This is the daily scenario of digital applications by Lushang Group’s full supply chain management.展开更多
By inducing the typical inventory control problem - the bullwhip effect, this paper presents vendor managed inventory (VMI) control methods on the basis of traditional methods of inventory management methods, construc...By inducing the typical inventory control problem - the bullwhip effect, this paper presents vendor managed inventory (VMI) control methods on the basis of traditional methods of inventory management methods, constructs a VMI mathematics model, and analyzes the influence of VMI on inventory cost and channel profit. Finally, a special case is studied to verify that VMI is an effective supply chain strategy that can not only increase channel profit of supplier and customer but also improve full channel coordination, thereby reducing the bullwhip effect.展开更多
基金National Natural Science Foundation of China(Nos.42301473,42271424,42171397)Chinese Postdoctoral Innovation Talents Support Program(No.BX20230299)+2 种基金China Postdoctoral Science Foundation(No.2023M742884)Natural Science Foundation of Sichuan Province(Nos.24NSFSC2264,2025ZNSFSC0322)Key Research and Development Project of Sichuan Province(No.24ZDYF0633).
文摘As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods generally have problems such as insufficient 3D scene description capability and low dynamic update efficiency,which are difficult to meet the demand of real-time accurate management.For this reason,this paper proposes a vehicle twin modeling method for road tunnels.This approach starts from the actual management needs,and supports multi-level dynamic modeling from vehicle type,size to color by constructing a vehicle model library that can be flexibly invoked;at the same time,semantic constraint rules with geometric layout,behavioral attributes,and spatial relationships are designed to ensure that the virtual model matches with the real model with a high degree of similarity;ultimately,the prototype system is constructed and the case region is selected for the case study,and the dynamic vehicle status in the tunnel is realized by integrating real-time monitoring data with semantic constraints for precise virtual-real mapping.Finally,the prototype system is constructed and case experiments are conducted in selected case areas,which are combined with real-time monitoring data to realize dynamic updating and three-dimensional visualization of vehicle states in tunnels.The experiments show that the proposed method can run smoothly with an average rendering efficiency of 17.70 ms while guaranteeing the modeling accuracy(composite similarity of 0.867),which significantly improves the real-time and intuitive tunnel management.The research results provide reliable technical support for intelligent operation and emergency response of road tunnels,and offer new ideas for digital twin modeling of complex scenes.
基金financially supported by Guangdong Province Basic and Applied Basic Research Fund Project(Grant No.2022B1515250009)Liaoning Provincial Natural Science Foundation-Doctoral Research Start-up Fund Project(Grant No.2024-BSBA-05)+1 种基金Major Science and Technology Innovation Project in Shandong Province(Grant No.2024CXGC010803)the National Natural Science Foundation of China(Grant Nos.52271269 and 12302147).
文摘The umbilical,a key component in offshore energy extraction,plays a vital role in ensuring the stable operation of the entire production system.The extensive variety of cross-sectional components creates highly complex layout combinations.Furthermore,due to constraints in component quantity and geometry within the cross-sectional layout,filler bodies must be incorporated to maintain cross-section performance.Conventional design approaches based on manual experience suffer from inefficiency,high variability,and difficulties in quantification.This paper presents a multi-level automatic filling optimization design method for umbilical cross-sectional layouts to address these limitations.Initially,the research establishes a multi-objective optimization model that considers compactness,balance,and wear resistance of the cross-section,employing an enhanced genetic algorithm to achieve a near-optimal layout.Subsequently,the study implements an image processing-based vacancy detection technique to accurately identify cross-sectional gaps.To manage the variability and diversity of these vacant regions,the research introduces a multi-level filling method that strategically selects and places filler bodies of varying dimensions,overcoming the constraints of uniform-size fillers.Additionally,the method incorporates a hierarchical strategy that subdivides the complex cross-section into multiple layers,enabling layer-by-layer optimization and filling.This approach reduces manufac-turing equipment requirements while ensuring practical production process feasibility.The methodology is validated through a specific umbilical case study.The results demonstrate improvements in compactness,balance,and wear resistance compared with the initial cross-section,offering novel insights and valuable references for filler design in umbilical cross-sections.
基金supported by the General Program of National Natural Science Foundation of China(No.42275190)。
文摘Soil fugitive dust(SFD)is characterized by a variety of sources and considerable spatialtemporal variability,exerting a significant impact on environmental air quality and ecological systems in cities across northern China.Multiple factors can shape SFD emission.Nevertheless,the current comprehension of its critical impact factors and quantitative methodologies remains constrained.This study utilizes interpretable machine learning techniques to identify the principal impact factors of SFD and their interactions while delineating their action thresholds.The findings reveal seasonal variations in impact factors and emphasize the substantial effect of bare soil source strength on SFD,including parameters such as bare soil area and soil moisture.Consequently,the Wind Erosion Equation model is optimized following these findings to localize its parameters and improve its capability to calculate hourly SFD emissions.The case application is validated using observational data,demonstrating the reliability and precision of the optimized methodology.This study provides insights and solutions for the local optimization of SFD parameterization schemes and further supports the formulation of precise prevention and control policies for SFD.
基金National Natural Science Foundation of China,No.42250205“CUG Scholar”Scientific Research Funds at China University of Geosciences,No.2019004+1 种基金Strategic Priority Research Program of the Chinese Academy of Sciences,No.XDA23100202Scientific Research Foundation of China University of Geosciences,No.162301192642。
文摘Carbonyl sulfide(COS)is an effective tracer for estimating Gross Primary Productivity(GPP)in the carbon cycle.As the largest contribution to the atmosphere,anthropogenic COS emissions must be accurately quantified.In this study,an anthropogenic COS emission inventory from 2015 to 2021 was constructed by applying the bottom-up approach based on activity data from emission sources.China’s anthropogenic COS emissions increased from approximately 171 to 198 Gg S yr^(-1)from 2015-2021,differing from the trends of other pollutants.Despite an initial decline in COS emissions across sectors during the early stage of the COVID-19 pandemic,a rapid rebound in emissions occurred following the resumption of economic activities.In 2021,industrial sources,coal combustion,agriculture and vehicle exhaust accounted for 76.8%,12.3%,10.5%and 0.4%of total COS emissions,respectively.The aluminum industry was the primary COS emitter among industrial sources,contributing40.7% of total emissions.Shandong,Shanxi,and Zhejiang were the top three provinces in terms of anthropogenic COS emissions,reaching 39,21 and 17 Gg S yr-1,respectively.Provincial-level regions(hereafter province)with high COS emissions are observed mainly in the eastern and coastal regions of China,which,together with the wind direction,helps explain the pattern of high COS concentrations in the Western Pacific Ocean in winter.The Green Contribution Coefficient of COS(GCCCOS)was used to assess the relationship between GDP and COS emissions,highlighting the disparity between GDP and COS contributions to green development.As part of this analysis,relevant recommendations are proposed to address this disparity.The COS emission inventory in our study can be used as input for the Sulfur Transport and Deposition Model(STEM),reducing uncertainties in the atmospheric COS source?sink budget and promoting understanding of the atmosphere sulfur cycle.
基金the funding support from the National Natural Science Foundation of China(Grant No.52308340)Chongqing Talent Innovation and Entrepreneurship Demonstration Team Project(Grant No.cstc2024ycjh-bgzxm0012)the Science and Technology Projects supported by China Coal Technology and Engineering Chongqing Design and Research Institute(Group)Co.,Ltd.(Grant No.H20230317).
文摘Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,there is limited research on the spatiotemporal characteristics of landslide deformation.This paper proposes a novel Multi-Relation Spatiotemporal Graph Residual Network with Multi-Level Feature Attention(MFA-MRSTGRN)that effectively improves the prediction performance of landslide displacement through spatiotemporal fusion.This model integrates internal seepage factors as data feature enhancements with external triggering factors,allowing for accurate capture of the complex spatiotemporal characteristics of landslide displacement and the construction of a multi-source heterogeneous dataset.The MFA-MRSTGRN model incorporates dynamic graph theory and four key modules:multilevel feature attention,temporal-residual decomposition,spatial multi-relational graph convolution,and spatiotemporal fusion prediction.This comprehensive approach enables the efficient analyses of multi-source heterogeneous datasets,facilitating adaptive exploration of the evolving multi-relational,multi-dimensional spatiotemporal complexities in landslides.When applying this model to predict the displacement of the Liangshuijing landslide,we demonstrate that the MFA-MRSTGRN model surpasses traditional models,such as random forest(RF),long short-term memory(LSTM),and spatial temporal graph convolutional networks(ST-GCN)models in terms of various evaluation metrics including mean absolute error(MAE=1.27 mm),root mean square error(RMSE=1.49 mm),mean absolute percentage error(MAPE=0.026),and R-squared(R^(2)=0.88).Furthermore,feature ablation experiments indicate that incorporating internal seepage factors improves the predictive performance of landslide displacement models.This research provides an advanced and reliable method for landslide displacement prediction.
文摘Seyitgazi and Han districts,located in the south of Eskişehir in Central Anatolia,in western Türkiye,host interesting landforms,such as steep slopes,mesas and butte structures,fault-guided slopes,valleys,fairy chimneys,castle koppies,pillars,weathered rock blocks,perched rocks,cavernous weathering features,grooves,and gnammas,formed on tuffs in semi-arid to semi-humid climatic conditions,as well as geoarchaeological remains belonging to various civilisations,primarily the Phrygians(including rock-cut tombs and settlements,fortresses,rock churches,façades,altars,and niches).This study aims at identifying these remarkable landforms that host cultural heritage and revealing the geoheritage value and geotourism potential of the region.The data obtained from the fieldwork were evaluated using the methodology proposed by Pereira and Pereira in 2010,and 26 geomorphosites were selected from 61 potential sites using this method.The analysis results revealed that although the region hosts numerous geomorphosites with high scientific,cultural,aesthetic,and ecological value,the overall levels of protection and touristic use of these landforms are generally low.Indeed,the area,which has the potential to be an important tourism region in the future,faces problems such as infrastructure deficiencies,transportation difficulties,lack of promotion,weaknesses in accommodation services,and destruction of geoheritage.These results highlight the importance of implementing sustainable geotourism strategies that are compatible with the region’s unique geoheritage.In this respect,this study is among the first to comprehensively inventory and assess the geomorphosites of Mountainous Phrygia,contributing to regional geoconservation and sustainable tourism development.
基金supported by Third Xinjiang Scientific Expedition Program(Grant No.2022xjkk0101)Second Qinghai-Tibet Scientific Expedition Program(Grant No.2019 QZKK0201)+2 种基金Third Xinjiang Sci-entific Expedition Program(Grant No.2021xjkk0401)National Natural Science Foundation of China(Grant No.42301166)National Natural Science Foundation of China(Grant No.42371148)。
文摘The Ili River is a typical transboundary river between China and Kazakhstan,with glaciers within its basin serving as a crucial solid water resource.Recently,we compiled the Chinese Glacier Inventory of Xinjiang in 2020(CGI-XJ2020)using high-resolution satellite imagery(<2 m),based on visual interpretation.This study presented the state of glaciers in the Ili River Basin in 2020 by utilizing the data from CGI-XJ2020.It quantified glacier changes in 1960s–2020 based on CGI-XJ2020 and revised datasets from the First and Second Chinese Glacier Inventories.The results indicated that in 2020,the Ili River Basin contained 2,177 glaciers,totaling 1,433.19 km^(2)in area.Among them,213 glaciers were covered by 57.43 km^(2)of debris.The total uncertainty in glacier area was 46.43 km^(2),accounting for approximately 3.2%of the total area.Mapped glacier areas varied from 0.003 to 74.67 km^(2),with an average area of 0.66 km^(2)and a median area of 0.15 km^(2).Glaciers<0.5 km^(2)in size dominated in numbers,accounting for 75.1%of the total.Glaciers in the basin have undergone significant retreat during 1960s–2020,with their total area decreasing by 589.38 km^(2)(29.15%).A total of 495 glaciers(with an area of 49.67 km^(2))disappeared.The average annual glacier area retreat rates for 1960s-2007 and 2007–2020 were 10.86 km^(2)/a(0.54%/a)and 9.41 km^(2)/a(0.61%/a),respectively,showing a continued acceleration in glacier shrinkage,despite a slight decrease in absolute retreat rates.
基金co-supported by the National Key Research and Development Program of China(No.2022YFF0503100)the Youth Innovation Project of Pandeng Program of National Space Science Center,Chinese Academy of Sciences(No.E3PD40012S).
文摘As we look ahead to future lunar exploration missions, such as crewed lunar exploration and establishing lunar scientific research stations, the lunar rovers will need to cover vast distances. These distances could range from kilometers to tens of kilometers, and even hundreds and thousands of kilometers. Therefore, it is crucial to develop effective long-range path planning for lunar rovers to meet the demands of lunar patrol exploration. This paper presents a hierarchical map model path planning method that utilizes the existing high-resolution images, digital elevation models and mineral abundance maps. The objective is to address the issue of the construction of lunar rover travel costs in the absence of large-scale, high-resolution digital elevation models. This method models the reference and semantic layers using the middle- and low-resolution remote sensing data. The multi-scale obstacles on the lunar surface are extracted by combining the deep learning algorithm on the high-resolution image, and the obstacle avoidance layer is modeled. A two-stage exploratory path planning decision is employed for long-distance driving path planning on a global–local scale. The proposed method analyzes the long-distance accessibility of various areas of scientific significance, such as Rima Bode. A high-precision digital elevation model is created using stereo images to validate the method. Based on the findings, it can be observed that the entire route spans a distance of 930.32 km. The route demonstrates an impressive ability to avoid meter-level impact craters and linear structures while maintaining an average slope of less than 8°. This paper explores scientific research by traversing at least seven basalt units, uncovering the secrets of lunar volcanic activities, and establishing ‘golden spike’ reference points for lunar stratigraphy. The final result of path planning can serve as a valuable reference for the design, mission demonstration, and subsequent project implementation of the new manned lunar rover.
文摘This study presents an emission inventory for 2022,focusing on assessing the emissions of PM_(2.5),PM_(10),NO_(x),SO_(2),CO,and VOC from India's road transport,residential,and thermal power sectors.Road transport emissions were estimated using a vehicle kilometer traveled methodology derived from a survey of 200,000 vehicles.A regression analysis was conducted to assess residential fuel usage,considering recent changes in consumption patterns and updated data on cleaner fuels.Estimates for the thermal power sector were based on emission monitoring data.The residential sector is the predominant source of PM_(2.5)(1112 kt),PM_(10)(1678 kt),CO(10630 kt),and VOC(2558 kt).The thermal power sector is the predominant source of secondary air pollutant precursors such as NO_(x)(2328 kt)and SO_(2)(4694 kt).India has the highest emission intensity per gross domestic product(GDP)across sectors compared to other countries.For example,PM_(2.5)emissions per GDP from the roads in India are 14,21,and 10 times that of those in China,the USA,and Europe.The southern(29%),eastern(30%),and central(36%)regions were the notable contributors to emissions from transport,residential,and thermal power sectors.Urban areas contributed 5%of the total residential sector emissions across India but 25%of the total road transport sector emissions nationwide.Moreover,power plants within or near the non-attainment cities were responsible for 12%of the overall thermal power pollution recorded across India.The study identifies unequal emission burdens,with economically disadvantaged regions bearing the brunt.
基金supported by the Fund of Key Laboratory of Biomedical Engineering of Hainan Province(No.BME20240001)the STI2030-Major Projects(No.2021ZD0200104)the National Natural Science Foundations of China under Grant 61771437.
文摘Deep learning networks are increasingly exploited in the field of neuronal soma segmentation.However,annotating dataset is also an expensive and time-consuming task.Unsupervised domain adaptation is an effective method to mitigate the problem,which is able to learn an adaptive segmentation model by transferring knowledge from a rich-labeled source domain.In this paper,we propose a multi-level distribution alignment-based unsupervised domain adaptation network(MDA-Net)for segmentation of 3D neuronal soma images.Distribution alignment is performed in both feature space and output space.In the feature space,features from different scales are adaptively fused to enhance the feature extraction capability for small target somata and con-strained to be domain invariant by adversarial adaptation strategy.In the output space,local discrepancy maps that can reveal the spatial structures of somata are constructed on the predicted segmentation results.Then thedistribution alignment is performed on the local discrepancies maps across domains to obtain a superior discrepancy map in the target domain,achieving refined segmentation performance of neuronal somata.Additionally,after a period of distribution align-ment procedure,a portion of target samples with high confident pseudo-labels are selected as training data,which assist in learning a more adaptive segmentation network.We verified the superiority of the proposed algorithm by comparing several domain adaptation networks on two 3D mouse brain neuronal somata datasets and one macaque brain neuronal soma dataset.
基金supported by the National Key Research and Development Project of China(No.2020YFC1808201)the National Natural Science Foundation of China(No.U1906224).
文摘Numerous studies documented the occurrence of organophosphate tri-esters(tri-OPEs)and di-esters(di-OPEs)in the environment.Little information is available on their occurrence in waste consumer products,reservoirs and sources of these chemicals.This study collected and analyzed 92 waste consumer products manufactured from diverse polymers,including polyurethane foam(PUF),polystyrene(PS),acrylonitrile butadiene styrene(ABS),polypropylene(PP),and polyethylene(PE)to obtain information on the occurrence and profiles of 16 tri-OPEs and 10 di-OPEs.Total concentrations of di-OPEs(18−370,000 ng/g,median 1,700 ng/g)were one order of magnitude lower than those of tri-OPEs(94−4,500,000 ng/g,median 5,400 ng/g).The concentrations of both tri-and di-OPEs in products made of PUF,PS,and ABS were orders of magnitude higher than those made of PP and PE.The compositional patterns of OPEs varied among different polymer types but were generally dominated by bisphenol A bis(diphenyl phosphate),triphenyl phosphate,tris(1-chloro-2-propyl)phosphate,di-phenyl phosphate(DPHP),and bis(2-ethylhexyl)phosphate.Two industrially applied di-OPEs(di-n-butyl phosphate and DPHP)exhibited higher levels than their respective tri-OPEs,contrary to their production volumes.Some non-industrially applied chlorinated di-OPEs were also detected,with concentrations up to 97,000 ng/g.These findings suggest that degradation of tri-OPEs during the manufacturing and use of products is an important source of di-OPEs.The mass inventories of tri-OPEs and di-OPEs in consumer products were estimated at 3,100 and 750 tons/year,respectively.This study highlights the importance of consumer products as emission sources of a broad suite of OPEs.
文摘Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and treatment.However,achieving precise segmentation remains a challenge due to various factors,including scattering noise,low contrast,and limited resolution in ultrasound images.Although existing segmentation models have made progress,they still suffer from several limitations,such as high error rates,low generalizability,overfitting,limited feature learning capability,etc.To address these challenges,this paper proposes a Multi-level Relation Transformer-based U-Net(MLRT-UNet)to improve thyroid nodule segmentation.The MLRTUNet leverages a novel Relation Transformer,which processes images at multiple scales,overcoming the limitations of traditional encoding methods.This transformer integrates both local and global features effectively through selfattention and cross-attention units,capturing intricate relationships within the data.The approach also introduces a Co-operative Transformer Fusion(CTF)module to combine multi-scale features from different encoding layers,enhancing the model’s ability to capture complex patterns in the data.Furthermore,the Relation Transformer block enhances long-distance dependencies during the decoding process,improving segmentation accuracy.Experimental results showthat the MLRT-UNet achieves high segmentation accuracy,reaching 98.2% on the Digital Database Thyroid Image(DDT)dataset,97.8% on the Thyroid Nodule 3493(TG3K)dataset,and 98.2% on the Thyroid Nodule3K(TN3K)dataset.These findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation,addressing the limitations of existing models.
基金Supported by College-level Research Project of Hangzhou Vocational&Technical College(ky202514).
文摘The rapid development of vocational colleges in China brings about the explosive growth of the number and category of state-owned assets that guarantees the development of vocational colleges. The special equipment and the general equipment included in the state-owned assets of vocational colleges are increasing at the fastest rate. Based on the problems from equipment inventory, this paper analyzes the problems of state-owned management, and puts forward countermeasures to improve the management of state-owned assets from formulating regulations and rules, strengthening the unified institution, applying the information technologies in building the team of administrators.
基金supported by the Third Xinjiang Scientific Expedition Program(Grant No.2022xjkk0101)the Second Qinghai-Tibet Scientific Expedition Program(Grant No.2019QZKK0201).
文摘Based on China's high-resolution satellite imagery series(2 m resolution),the Chinese Glacier Inventory of Xinjiang in 2020(CGI-XJ2020)was compiled,with 2020 as the baseline year.CGI-XJ2020 has five key features:1)improved accuracy in glacier boundary delineation and optimized inventory attributes through highresolution satellite imagery and field validation of 38 glaciers;2)established an area-volume formula for Xinjiang glaciers using ground-penetrating radar(GPR)thickness data from 23 glaciers in the study region;3)the use of high-resolution satellite imagery has reduced the minimum glacier area threshold,enabling the identification of more small glaciers;4)enhanced accuracy in delineating supraglacial debris coverage;5)upgraded administrative division from prefecture-level(CGI-2)to county-level.According to CGI-XJ2020 data,Xinjiang contains 24,202 glaciers in 2020,covering 23,629.28 km^(2)with an average size of 0.98 km^(2)per glacier and a total ice volume of 1,608.94 km^(3).Among these,1,612 debris-covered glaciers occupy 1,163.32 km^(2)(4.9%of the total glacierized area).Glaciers larger than 10 km^(2)(296 in total)cover 9,881.69 km^(2)with a volume of 1,053.17 km^(3),accounting for 41.82%of total area and 65.46%of total ice volume,respectively.The Kunlun Mountains host the most glaciers,followed by the Tianshan Mountains.The Tarim river basin contains the largest concentration(15,860 glaciers,18,594.24 km^(2),1,347.17 km^(3)).The Hotan Prefecture has the highest glacier density.
基金supported by the 2020 National Supercomputing Zhengzhou Center Innovation Ecosystem Construction Technology Project(No.201400210700)Zhengzhou PM2.5 and O3 Collaborative Control and Monitoring Project(No.20220347A).
文摘Recently,the transportation sector in China has gradually become the main source of urban air pollution and primary driver of carbon emissions growth.Considering air pollutants and greenhouse gases come from the same emission sources,it is necessary to establish an updated high-resolution emission inventory for the transportation sector in Central China,themost polluted region in China.The inventory includes on-road mobile,non-roadmobile,oil storage and transportation,and covers 9 types of air pollutants and 3 types of greenhouse gases.Based on the Long-range Energy Alternatives Planning System(LEAP)model,the emissions of pollutants were predicted for the period from2020 to 2035 in different scenarios.Results showed that in 2020,emissions of SO_(2),NO_(x),CO,PM_(10),PM_(2.5),VOCs,NH_(3),BC,OC,CO_(2),CH_(4),and N_(2)O in Henan Province were 27.5,503.2,878.6,20.1,17.4,222.1,21.5,9.4,2.9,92,077.9,6.0,and 10.4 kilotons,respectively.Energy demand and pollutant emissions in Henan Province are simulated under four scenarios(Baseline Scenario(BS),Pollution Abatement Scenario(PA),Green Transportation Scenario(GT),and Reinforcing Low Carbon Scenario(RLC)).The collaborative emission reduction effect is most significant in the RLC scenario,followed by the GT scenario.By 2035,under the RLC scenario,energy consumption and emissions of SO_(2),NO_(x),CO,PM_(10),PM_(2.5),VOCs,NH_(3),CO_(2),CH_(4),and N_(2)O are projected to decrease by 72.0%,30.0%,55.6%,56.0%,38.6%,39.7%,51.5%,66.1%,65.5%,55.4%,and 52.8%,respectively.This study provides fundamental data support for subsequent numerical simulations.
基金supported by grants provided within the research project»EO4Forest:Use of multi-temporal Sentinel-2 and VHR Pleiades stereo data for sustainable forest monitoring and management«funded by the Austrian Federal Ministry for Climate Action,Environ-ment,Energy,Mobility,Innovation and Technology(BMK)within the FFG Austrian Space Applications Program ASAP 12(grant agreement number 854027).
文摘Accurate,reliable,and regularly updated information is necessary for targeted management of forest stands.This information is usually obtained from sample-based field inventory data.Due to the time-consuming and costly procedure of forest inventory,it is imperative to generate and use the resulting data optimally.Integrating field inventory information with remote sensing data increases the value of field approaches,such as national forest inventories.This study investigated the optimal integration of forest inventory data with aerial image-based canopy height models(CHM)for forest growing stock estimation.For this purpose,fixed-area and angle-count plots from a forest area in Austria were used to assess which type of inventory system is more suitable when the field data is integrated with aerial image analysis.Although a higher correlation was observed between remotely predicted growing stocks and field inventory values for fixed-area plots,the paired t-test results revealed no statistical difference between the two methods.The R2 increased by 0.08 points and the RMSE decreased by 7.7 percentage points(24.8m^(3)·ha^(−1))using fixed-area plots.Since tree height is the most critical variable essential for modeling forest growing stock using aerial images,we also compared the tree heights obtained from CHM to those from the typical field inventory approach.The result shows a high correlation(R^(2)=0.781)between the tree heights extracted from the CHM and those measured in the field.However,the correlation decreased by 0.113 points and the RMSE increased by 4.2 percentage points(1.04m)when the allometrically derived tree heights were analyzed.Moreover,the results of the paired t-test revealed that there is no significant statistical difference between the tree heights extracted from CHM and those measured in the field,but there is a significant statistical difference when the CHM-derived and the allometrically-derived heights were compared.This proved that image-based CHM can obtain more accurate tree height information than field inventory estimations.Overall,the results of this study demonstrated that image-based CHM can be integrated into the forest inventory data at large scales and provide reliable information on forest growing stock.The produced maps reflect the variability of growth conditions and developmental stages of different forest stands.This information is required to characterize the status and changes,e.g.,in forest structure diversity,parameters for volume,and can be used for forest aboveground biomass estimation,which plays an important role in managing and controlling forest resources in mid-term forest management.This is of particular interest to forest managers and forest ecologists.
文摘In the self-built fruit and vegetable sorting warehouse of Lushang Group,the system is automatically scanning the QR code for agricultural product and conducts pesticide residue testing on fruits and vegetables;new products from foreign trade factories will be directly transported to Ginza Supermarket through the“Direct Express for Domestic Product”channel,and will be shelved on the“Lushang Life”service platform within 48 hours;in front of the campus intelligent milk cabinet independently launched by Lushang Technology,students receive pasteurized milk by brushing their faces,and the data about milk source farms and sterilization parameters are sent to the parents simultaneously.This is the daily scenario of digital applications by Lushang Group’s full supply chain management.
文摘By inducing the typical inventory control problem - the bullwhip effect, this paper presents vendor managed inventory (VMI) control methods on the basis of traditional methods of inventory management methods, constructs a VMI mathematics model, and analyzes the influence of VMI on inventory cost and channel profit. Finally, a special case is studied to verify that VMI is an effective supply chain strategy that can not only increase channel profit of supplier and customer but also improve full channel coordination, thereby reducing the bullwhip effect.
