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
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
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
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
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
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
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
The influences of biological,chemical,and flow processes on soil structure through microbially induced carbonate precipitation(MICP)are not yet fully understood.In this study,we use a multi-level thresholding segmenta...The influences of biological,chemical,and flow processes on soil structure through microbially induced carbonate precipitation(MICP)are not yet fully understood.In this study,we use a multi-level thresholding segmentation algorithm,genetic algorithm(GA)enhanced Kapur entropy(KE)(GAE-KE),to accomplish quantitative characterization of sandy soil structure altered by MICP cementation.A sandy soil sample was treated using MICP method and scanned by the synchrotron radiation(SR)micro-CT with a resolution of 6.5 mm.After validation,tri-level thresholding segmentation using GAE-KE successfully separated the precipitated calcium carbonate crystals from sand particles and pores.The spatial distributions of porosity,pore structure parameters,and flow characteristics were calculated for quantitative characterization.The results offer pore-scale insights into the MICP treatment effect,and the quantitative understanding confirms the feasibility of the GAE-KE multi-level thresholding segmentation algorithm.展开更多
Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible blindness.Existing research has failed to fully explore effective DR lesion information in fundus maps.Besides,traditional ...Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible blindness.Existing research has failed to fully explore effective DR lesion information in fundus maps.Besides,traditional attention schemes have not considered the impact of lesion type differences on grading,resulting in unreasonable extraction of important lesion features.Therefore,this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator(MPAG)and a lesion localization module(LLM).Firstly,MPAGis used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained in the patches,fully considering the impact of lesion type differences on grading,solving the problem that the attention maps of lesions cannot be further refined and then adapted to the final DR diagnosis task.Secondly,the LLM generates a global attention map based on localization.Finally,the weighted attention map and global attention map are weighted with the fundus map to fully explore effective DR lesion information and increase the attention of the classification network to lesion details.This paper demonstrates the effectiveness of the proposed method through extensive experiments on the public DDR dataset,obtaining an accuracy of 0.8064.展开更多
Remote sensing(RS)facilitates forest inventory across a wide range of variables required by the UNFCCC as well as by other agreements and processes.The Conventional model-based(CMB)estimator supports wall-to-wall RS d...Remote sensing(RS)facilitates forest inventory across a wide range of variables required by the UNFCCC as well as by other agreements and processes.The Conventional model-based(CMB)estimator supports wall-to-wall RS data,while Hybrid estimators support surveys where RS data are available as a sample.However,the connection between these two types of monitoring procedures has been unclear,hindering the reconciliation of wall-to-wall and non-wall-to-wall use of RS data in practical applications and thus potentially impeding cost-efficient deployment of high-end sensing instruments for large area monitoring.Consequently,our objectives are to(1)shed further light on the connections between different types of Hybrid estimators,and between CMB and Hybrid estimators,through mathematical analyses and Monte Carlo simulations;and(2)compare the effects and explore the tradeoffs related to the RS sampling design,coverage rate,and cluster size on estimation precision.Primary findings are threefold:(1)the CMB estimator represents a special case of Hybrid estimators,signifying that wallto-wall RS data is a particular instance of sample-based RS data;(2)the precision of estimators in forest inventory can be greater for stratified non-wall-to-wall RS data compared to wall-to-wall RS data;(3)otherwise costprohibitive sensing,such as LiDAR and UAV,can support large scale monitoring through collecting RS data as a sample.These conclusions may reconcile different perspectives regarding choice of RS instruments,data acquisition,and cost for continuous observations,particularly in the context of surveys aiming at providing data for mitigating climate change.展开更多
This paper analyzes how artificial intelligence (AI) automation can improve warehouse management compared to emerging technologies like drone usage. Specifically, we evaluate AI’s impact on crucial warehouse function...This paper analyzes how artificial intelligence (AI) automation can improve warehouse management compared to emerging technologies like drone usage. Specifically, we evaluate AI’s impact on crucial warehouse functions—inventory tracking, order fulfillment, and logistics efficiency. Our findings indicate AI automation enables real-time inventory visibility, optimized picking routes, and dynamic delivery scheduling, which drones cannot match. AI better leverages data insights for intelligent decision-making across warehouse operations, supporting improved productivity and lower operating costs.展开更多
The combination of nucleic acid and small-molecule drugs in tumor treatment holds significant promise;however,the precise delivery and controlled release of drugs within the cytoplasm encounter substantial obstacles,i...The combination of nucleic acid and small-molecule drugs in tumor treatment holds significant promise;however,the precise delivery and controlled release of drugs within the cytoplasm encounter substantial obstacles,impeding the advancement of formulations.To surmount the challenges associated with precise drug delivery and controlled release,we have developed a multi-level p H-responsive co-loaded drug lipid nanoplatform.This platform first employs cyclic cell-penetrating peptides to exert a multi-level pH response,thereby enhancing the uptake efficiency of tumor cells and endow the nanosystem with effective endosomal/lysosomal escape.Subsequently,small interferring RNA(siRNA)complexes are formed by compacting siRNA with stearic acid octahistidine,which is capable of responding to the lysosome-tocytoplasm pH gradient and facilitate siRNA release.The siRNA complexes and docetaxel are simultaneously encapsulated into liposomes,thereby creating a lipid nanoplatform capable of co-delivering nucleic acid and small-molecule drugs.The efficacy of this platform has been validated through both in vitro and in vivo experiments,affirming its significant potential for practical applications in the co-delivery of nucleic acids and small-molecule drugs.展开更多
Existing glass segmentation networks have high computational complexity and large memory occupation,leading to high hardware requirements and time overheads for model inference,which is not conducive to efficiency-see...Existing glass segmentation networks have high computational complexity and large memory occupation,leading to high hardware requirements and time overheads for model inference,which is not conducive to efficiency-seeking real-time tasks such as autonomous driving.The inefficiency of the models is mainly due to employing homogeneous modules to process features of different layers.These modules require computationally intensive convolutions and weight calculation branches with numerous parameters to accommodate the differences in information across layers.We propose an efficient glass segmentation network(EGSNet)based on multi-level heterogeneous architecture and boundary awareness to balance the model performance and efficiency.EGSNet divides the feature layers from different stages into low-level understanding,semantic-level understanding,and global understanding with boundary guidance.Based on the information differences among the different layers,we further propose the multi-angle collaborative enhancement(MCE)module,which extracts the detailed information from shallow features,and the large-scale contextual feature extraction(LCFE)module to understand semantic logic through deep features.The models are trained and evaluated on the glass segmentation datasets HSO(Home-Scene-Oriented)and Trans10k-stuff,respectively,and EGSNet achieves the best efficiency and performance compared to advanced methods.In the HSO test set results,the IoU,Fβ,MAE(Mean Absolute Error),and BER(Balance Error Rate)of EGSNet are 0.804,0.847,0.084,and 0.085,and the GFLOPs(Giga Floating Point Operations Per Second)are only 27.15.Experimental results show that EGSNet significantly improves the efficiency of the glass segmentation task with better performance.展开更多
Island ecosystems support diverse aquatic invertebrate communities comprising endemic taxa.Documentation of existing species is important for conservation.In this study,a checklist of marine opisthobranch from the Rep...Island ecosystems support diverse aquatic invertebrate communities comprising endemic taxa.Documentation of existing species is important for conservation.In this study,a checklist of marine opisthobranch from the Republic of Mauritius is presented.A combination of benthic surveys(50 m×5 m in triplicates),rover diving techniques and photo documentation were used over two years(2018–2020)within 35 sheltered and unsheltered lagoons.Morphological and molecular analysis were used for identification.Species composition within sheltered and unsheltered areas in Mauritius was estimated using the Bray-Curtis similarity.The checklist featured 117 species belonging to 61 genera and 28families,of which 13 are new records.The findings increased the knowledge of opisthobranch diversity from the Mauritius by 15.4%.Among the listed species,the distribution range of Cyerce nigra,Actinocyclus papillatus,and Phyllidia picta extended from the Western Pacific to the South Western Indian Ocean.Molecular analysis of the undescribed Gymnodoris sp.showed it resembled Gymnodoris sp.from Hawaii and were different by a genetic distance value of 10.6%.The species richness and evenness were higher within the sheltered regions of Mauritius which harboured the food resource of opisthobranch.These areas as compared to unsheltered regions were heavily populated,suggesting the probable influence of wave actions on opisthobranch diversity and abundance.The order Nudibranchia was reported as most speciose,with 86 species.The Sacoglossa and Nudibranchia were observed only on macroalgae and sponges respectively.High abundance was also recorded on shipwrecks which are the most common form of artificial reefs.With the inclusion of observations from previous studies,201species belonging to 94 genera and 36 families are now known from the Mauritius.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.
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.
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 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.
基金supported by the National Natural Science Foundation of China(Grant Nos.42077232 and 42077235)the Key Research and Development Plan of Jiangsu Province(Grant No.BE2022156).
文摘The influences of biological,chemical,and flow processes on soil structure through microbially induced carbonate precipitation(MICP)are not yet fully understood.In this study,we use a multi-level thresholding segmentation algorithm,genetic algorithm(GA)enhanced Kapur entropy(KE)(GAE-KE),to accomplish quantitative characterization of sandy soil structure altered by MICP cementation.A sandy soil sample was treated using MICP method and scanned by the synchrotron radiation(SR)micro-CT with a resolution of 6.5 mm.After validation,tri-level thresholding segmentation using GAE-KE successfully separated the precipitated calcium carbonate crystals from sand particles and pores.The spatial distributions of porosity,pore structure parameters,and flow characteristics were calculated for quantitative characterization.The results offer pore-scale insights into the MICP treatment effect,and the quantitative understanding confirms the feasibility of the GAE-KE multi-level thresholding segmentation algorithm.
基金supported in part by the Research on the Application of Multimodal Artificial Intelligence in Diagnosis and Treatment of Type 2 Diabetes under Grant No.2020SK50910in part by the Hunan Provincial Natural Science Foundation of China under Grant 2023JJ60020.
文摘Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible blindness.Existing research has failed to fully explore effective DR lesion information in fundus maps.Besides,traditional attention schemes have not considered the impact of lesion type differences on grading,resulting in unreasonable extraction of important lesion features.Therefore,this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator(MPAG)and a lesion localization module(LLM).Firstly,MPAGis used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained in the patches,fully considering the impact of lesion type differences on grading,solving the problem that the attention maps of lesions cannot be further refined and then adapted to the final DR diagnosis task.Secondly,the LLM generates a global attention map based on localization.Finally,the weighted attention map and global attention map are weighted with the fundus map to fully explore effective DR lesion information and increase the attention of the classification network to lesion details.This paper demonstrates the effectiveness of the proposed method through extensive experiments on the public DDR dataset,obtaining an accuracy of 0.8064.
基金supported by the National Social Science Fund of China(No.22BTJ005)the Key Project of National Key Research and Development Plan(No.2023YFF1304002-05)+1 种基金supported by the National Natural Science Foundation of China(No.32001252)the International Center for Bamboo and Rattan(Nos.1632022024,1632020029,1632021024).
文摘Remote sensing(RS)facilitates forest inventory across a wide range of variables required by the UNFCCC as well as by other agreements and processes.The Conventional model-based(CMB)estimator supports wall-to-wall RS data,while Hybrid estimators support surveys where RS data are available as a sample.However,the connection between these two types of monitoring procedures has been unclear,hindering the reconciliation of wall-to-wall and non-wall-to-wall use of RS data in practical applications and thus potentially impeding cost-efficient deployment of high-end sensing instruments for large area monitoring.Consequently,our objectives are to(1)shed further light on the connections between different types of Hybrid estimators,and between CMB and Hybrid estimators,through mathematical analyses and Monte Carlo simulations;and(2)compare the effects and explore the tradeoffs related to the RS sampling design,coverage rate,and cluster size on estimation precision.Primary findings are threefold:(1)the CMB estimator represents a special case of Hybrid estimators,signifying that wallto-wall RS data is a particular instance of sample-based RS data;(2)the precision of estimators in forest inventory can be greater for stratified non-wall-to-wall RS data compared to wall-to-wall RS data;(3)otherwise costprohibitive sensing,such as LiDAR and UAV,can support large scale monitoring through collecting RS data as a sample.These conclusions may reconcile different perspectives regarding choice of RS instruments,data acquisition,and cost for continuous observations,particularly in the context of surveys aiming at providing data for mitigating climate change.
文摘This paper analyzes how artificial intelligence (AI) automation can improve warehouse management compared to emerging technologies like drone usage. Specifically, we evaluate AI’s impact on crucial warehouse functions—inventory tracking, order fulfillment, and logistics efficiency. Our findings indicate AI automation enables real-time inventory visibility, optimized picking routes, and dynamic delivery scheduling, which drones cannot match. AI better leverages data insights for intelligent decision-making across warehouse operations, supporting improved productivity and lower operating costs.
基金supported by the grants from the National Natural Science Foundation of China(Nos.81973251 and 81302725)Hebei Province Funding Project for Introduced Overseas Personnel(Nos.C20230351 and C20220345)+3 种基金Key Research and Development Program of Hebei Province(No.22372701D)Hebei Province Natural Science Fund(No.H2020206610)Hebei Provincial Health Commission Government-Funded Clinical Medicine Talent Program(No.ZF2024048)Hebei Medical University Undergraduate Innovative Experiment Program(No.USIP2023008)。
文摘The combination of nucleic acid and small-molecule drugs in tumor treatment holds significant promise;however,the precise delivery and controlled release of drugs within the cytoplasm encounter substantial obstacles,impeding the advancement of formulations.To surmount the challenges associated with precise drug delivery and controlled release,we have developed a multi-level p H-responsive co-loaded drug lipid nanoplatform.This platform first employs cyclic cell-penetrating peptides to exert a multi-level pH response,thereby enhancing the uptake efficiency of tumor cells and endow the nanosystem with effective endosomal/lysosomal escape.Subsequently,small interferring RNA(siRNA)complexes are formed by compacting siRNA with stearic acid octahistidine,which is capable of responding to the lysosome-tocytoplasm pH gradient and facilitate siRNA release.The siRNA complexes and docetaxel are simultaneously encapsulated into liposomes,thereby creating a lipid nanoplatform capable of co-delivering nucleic acid and small-molecule drugs.The efficacy of this platform has been validated through both in vitro and in vivo experiments,affirming its significant potential for practical applications in the co-delivery of nucleic acids and small-molecule drugs.
文摘Existing glass segmentation networks have high computational complexity and large memory occupation,leading to high hardware requirements and time overheads for model inference,which is not conducive to efficiency-seeking real-time tasks such as autonomous driving.The inefficiency of the models is mainly due to employing homogeneous modules to process features of different layers.These modules require computationally intensive convolutions and weight calculation branches with numerous parameters to accommodate the differences in information across layers.We propose an efficient glass segmentation network(EGSNet)based on multi-level heterogeneous architecture and boundary awareness to balance the model performance and efficiency.EGSNet divides the feature layers from different stages into low-level understanding,semantic-level understanding,and global understanding with boundary guidance.Based on the information differences among the different layers,we further propose the multi-angle collaborative enhancement(MCE)module,which extracts the detailed information from shallow features,and the large-scale contextual feature extraction(LCFE)module to understand semantic logic through deep features.The models are trained and evaluated on the glass segmentation datasets HSO(Home-Scene-Oriented)and Trans10k-stuff,respectively,and EGSNet achieves the best efficiency and performance compared to advanced methods.In the HSO test set results,the IoU,Fβ,MAE(Mean Absolute Error),and BER(Balance Error Rate)of EGSNet are 0.804,0.847,0.084,and 0.085,and the GFLOPs(Giga Floating Point Operations Per Second)are only 27.15.Experimental results show that EGSNet significantly improves the efficiency of the glass segmentation task with better performance.
文摘Island ecosystems support diverse aquatic invertebrate communities comprising endemic taxa.Documentation of existing species is important for conservation.In this study,a checklist of marine opisthobranch from the Republic of Mauritius is presented.A combination of benthic surveys(50 m×5 m in triplicates),rover diving techniques and photo documentation were used over two years(2018–2020)within 35 sheltered and unsheltered lagoons.Morphological and molecular analysis were used for identification.Species composition within sheltered and unsheltered areas in Mauritius was estimated using the Bray-Curtis similarity.The checklist featured 117 species belonging to 61 genera and 28families,of which 13 are new records.The findings increased the knowledge of opisthobranch diversity from the Mauritius by 15.4%.Among the listed species,the distribution range of Cyerce nigra,Actinocyclus papillatus,and Phyllidia picta extended from the Western Pacific to the South Western Indian Ocean.Molecular analysis of the undescribed Gymnodoris sp.showed it resembled Gymnodoris sp.from Hawaii and were different by a genetic distance value of 10.6%.The species richness and evenness were higher within the sheltered regions of Mauritius which harboured the food resource of opisthobranch.These areas as compared to unsheltered regions were heavily populated,suggesting the probable influence of wave actions on opisthobranch diversity and abundance.The order Nudibranchia was reported as most speciose,with 86 species.The Sacoglossa and Nudibranchia were observed only on macroalgae and sponges respectively.High abundance was also recorded on shipwrecks which are the most common form of artificial reefs.With the inclusion of observations from previous studies,201species belonging to 94 genera and 36 families are now known from the Mauritius.
