Powdery mildew negatively impacts wheat yield and quality.Emmer wheat(Triticum dicoccum),an ancestral species of common wheat,is a gene donor for wheat improvement.Cultivated emmer accession H1-707 exhibited all-stage...Powdery mildew negatively impacts wheat yield and quality.Emmer wheat(Triticum dicoccum),an ancestral species of common wheat,is a gene donor for wheat improvement.Cultivated emmer accession H1-707 exhibited all-stage resistance to powdery mildew over consecutive years.Genetic analysis of H1-707 at the seedling stage revealed a dominant monogenic inheritance pattern,and the underlying gene was designated Pm71.By employing bulked segregant exome sequencing(BSE-Seq)and using 2000 F2:3 families,Pm71 was fine mapped to a 336-kb interval on chromosome arm 6AS by referencing to the durum cv.Svevo RefSeq 1.0.Collinearity analysis revealed high homology in the candidate interval between Svevo and six Triticum species.Among six high-confidence genes annotated within this interval,TRITD6Av1G005050 encoding a GDSL esterase/lipase was identified as a key candidate for Pm71.展开更多
目的基于T2^(*)mapping定量分析业余马拉松运动员足踝部关节软骨的T2^(*)值,并分析其与性别、年龄、身体质量指数(body mass index,BMI)、跑龄、跑量之间的相关性。材料与方法于2023年7月份至2023年9月份招募重庆市长跑运动爱好者48名,...目的基于T2^(*)mapping定量分析业余马拉松运动员足踝部关节软骨的T2^(*)值,并分析其与性别、年龄、身体质量指数(body mass index,BMI)、跑龄、跑量之间的相关性。材料与方法于2023年7月份至2023年9月份招募重庆市长跑运动爱好者48名,其中跑量<300 km/月的36例(中低跑量组),跑量≥300 km/月的12例(高跑量组)。所有受试者均进行单侧无症状踝关节的MRI扫描,扫描序列包括T2^(*)mapping多回波自旋回波(spin echo,SE)序列矢状位、质子密度加权成像脂肪抑制(proton density-weighted imaging fat-saturated,PDWI-FS)序列矢状位、冠状位、横轴位以及T1加权脂肪抑制成像(T1-weighted imaging fat-saturated,T1WI-FS)序列横轴位。沿关节软骨轮廓边缘勾画距骨穹窿、跟骰关节跟骨面、骰骨面及后距下关节跟骨面、距骨面软骨作为感兴趣区(region of interest,ROI),获得相应的T2^(*)值。采用线性回归分析软骨T2^(*)值与年龄、BMI、跑龄的相关性,采用独立样本t检验分析不同跑量及不同性别间的软骨T2^(*)值差异。结果(1)距骨穹窿、跟骰关节跟骨面及骰骨面、后距下关节跟骨面及距骨面软骨T2^(*)值在性别上的差异均具有统计学意义(P=0.001、P<0.001、P=0.002、P=0.008、P=0.004);(2)高跑量组的距骨穹窿、后距下关节跟骨面软骨T2^(*)值高于中低跑量组(P=0.014、0.023),不同跑量的跟骰关节跟骨面及骰骨面、后距下关节距骨面软骨T2^(*)值的差异均无统计学意义(P=0.987、0.072、0.724);(3)距骨穹窿、跟骰关节跟骨面及骰骨面、后距下关节跟骨面、距骨面软骨T2^(*)值均与BMI呈正相关(r=0.376、0.384、0.300、0.422、0.455,P=0.005、0.004、0.019、0.001、0.001)。结论在业余马拉松运动员这一跑步群体中,与中低跑量相比,高跑量更有可能导致距骨穹窿、后距下关节跟骨面软骨损伤;而与较低的BMI相比,高BMI增加了距骨穹窿、跟骰关节跟骨面、骰骨面及后距下关节跟骨面、距骨面软骨损伤的风险。展开更多
This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble lear...This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.展开更多
A distinctive feature of scholarly communities today is exploring topics and concepts in interdisciplinary and international contexts. This observation is increasingly apparent and visible in advancing our thinking an...A distinctive feature of scholarly communities today is exploring topics and concepts in interdisciplinary and international contexts. This observation is increasingly apparent and visible in advancing our thinking and policies related to human/environmental worlds at local, regional, and global scales. Maps are an important part of these innovative and ongoing research approaches. In this context, we consider urban forests a topic meriting more attention of scholars studying the geographic and environmental intersections of the natural sciences with the social sciences and humanities. We construct two innovative knowledge bases, one a conceptual framework based on major themes and concepts related to mapping urban forests using key words of the first 100 results of a Google Scholar query and a second using the number of Google Scholar hyperlinks about mapping urban forests in 244 capital cities. We discovered that the constructed world maps reveal vast global unevenness in our knowledge about urban forests in hyperlink numbers and ratios, results that merit further attention by disciplinary, international and interdisciplinary scholarly communities.展开更多
Silicosis is a chronic interstitial lung disease caused by prolonged exposure to inhalable silica particles.The initiation and progression of silicosis involve a dynamic process encompassing various cell types and mol...Silicosis is a chronic interstitial lung disease caused by prolonged exposure to inhalable silica particles.The initiation and progression of silicosis involve a dynamic process encompassing various cell types and molecules.Although the effector cells in different stages of silicosis undergo constant switching with disease progression,immune cells play a dominant role throughout the entire process.1 Therefore,comprehensively exploring cellular and molecular mechanisms underlying immune responses in silicosis becomes a prerequisite for unraveling its pathogenesis.Immune checkpoints(ICs)are crucial immunomodulatory factors that contribute significantly to the maintenance of immune tolerance and homeostasis.展开更多
Dear Editor,The mammalian brain exhibits cross-scale complexity in neuronal morphology and connectivity,the study of which demands high-resolution morphological reconstruction of individual neurons across the entire b...Dear Editor,The mammalian brain exhibits cross-scale complexity in neuronal morphology and connectivity,the study of which demands high-resolution morphological reconstruction of individual neurons across the entire brain[1-4].Current commonly used approaches for such mesoscale brain mapping include two main types of three-dimensional fluorescence microscopy:the block-face methods,and the lightsheet-based methods[5,6].In general,the high imaging speed and light efficiency of light-sheet microscopy make it a suitable tool for high-throughput volumetric imaging,especially when combined with tissue-clearing techniques.However,large brain samples pose major challenges to this approach.展开更多
Objective:To evaluate the value of rehabilitation nursing based on mind mapping model combined with psychological intervention for patients with nephrotic syndrome(NS).Methods:A total of 60 patients with NS who visite...Objective:To evaluate the value of rehabilitation nursing based on mind mapping model combined with psychological intervention for patients with nephrotic syndrome(NS).Methods:A total of 60 patients with NS who visited our hospital from January 2024 to December 2024 were selected as samples and randomly divided into groups.The observation group received rehabilitation nursing based on the mind mapping model combined with psychological intervention,while the control group received routine intervention.The differences in emotional scores,self-care ability scores,compliance,and complications were compared between the two groups.Results:The anxiety(SAS)and depression(SDS)scores of the observation group were lower than those of the control group,while the self-care ability scale(ESCA)score was higher than that of the control group(P<0.05).The compliance rate of the observation group was higher than that of the control group(P<0.05).The complication rate of NS in the observation group was lower than that in the control group(P<0.05).Conclusion:Rehabilitation nursing based on the mind mapping model combined with psychological intervention can enhance self-care ability,reduce negative emotions,and reduce complications in NS nursing,which is efficient and feasible.展开更多
This paper presents a standardised workflow for conducting hazard assessments of mass wasting processes in remote mountain areas with limited data.The methodology integrates geomorphological mapping and remote sensing...This paper presents a standardised workflow for conducting hazard assessments of mass wasting processes in remote mountain areas with limited data.The methodology integrates geomorphological mapping and remote sensing techniques and is adaptable to different national standards,thus ensuring its applicability in a variety of contexts.The principal objective is to guarantee the safety of mountainous regions,particularly in the vicinity of essential infrastructure,where the scope for implementing structural measures is restricted.The framework commences with comprehensive geomorphological mapping,which facilitates the identification of past hazardous processes and potential future hazards.New technologies,such as uncrewed aerial vehicles(UAVs),are employed to create high-resolution DEMs,which are particularly beneficial in regions with limited data availability.These models facilitate the assessment of potential hazards and inform decisions regarding protective measures.The utilisation of UAVs enhances the accuracy and efficiency of data collection,particularly in remote mountainous regions where alternative remotely sensed information may be unavailable.The integration of modern approaches into traditional hazard assessment methods allows for a comprehensive analysis of the spatial distribution of factors driving mass wasting processes.This workflow provides valuable insights that assist in the prioritisation of interventions and the optimisation of risk reduction in high mountainous areas.展开更多
This study proposes a novel methodology to employ discrete point spectra as input variable for digital mapping of soil organic carbon(SOC).Accordingly,two SOC modeling approaches were used in three agricultural sites ...This study proposes a novel methodology to employ discrete point spectra as input variable for digital mapping of soil organic carbon(SOC).Accordingly,two SOC modeling approaches were used in three agricultural sites in Czech Republic:i)machine learning(ML)including partial least squares regression(PLSR),cubist,random forest(RF),and support vector regression(SVR),and ii)regression kriging(RK)by the combination of ordinary kriging(OK)and PLSR(PLSR-K),cubist(cubist-K),RF(RF-K),and SVR(SVRK).Models were developed on environmental predictor covariates(EPCs)and thirty genetic algorithms(GA)-selected visible,near-infrared,and shortwave-infrared(VNIR-SWIR)wavelengths spectra,individually and combined.Thirty rasters were then created using interpolation of the selected spectra and served as the input variables e with and without EPCs e to test and compare the developed models and SOC predictive maps with each other and with those retrieved from the third approach:iii)kriging using OK of the measured and ML-predicted SOC.The impact of employing selected wavelengths’spectra and EPCs on models'performance was investigated using independent test samples and the uncertainty associated with the produced maps.Using interpolated spectra as the only input variable yielded a relatively acceptable accuracy(Nova Ves:RMSE=0.19%,Udrnice:RMSE=0.12%,Klucov:RMSE=0.13%).In comparison,the interpolated spectra coupled with EPCs enhanced the results.Regarding the uncertainty,however,the ML-based SOC maps were more reliable,than RK-based ones.Furthermore,maps produced using both spectra and EPCs showed less uncertainty than those constructed on the individual datasets.展开更多
Vegetation maps are crucial for ecologists and decision-makers, providing essential information on the spatial distribution of various vegetation types to support ecosystem exploration and management. Despite advancem...Vegetation maps are crucial for ecologists and decision-makers, providing essential information on the spatial distribution of various vegetation types to support ecosystem exploration and management. Despite advancements in Earth observation and machine learning enabling large-scale vegetation mapping, creating detailed and accurate maps in biodiversity hotspots remains challenging due to significant environmental heterogeneity and frequent human disturbances. The lack of sufficient ground-based data and complex climate-vegetation interactions further limits mapping accuracy. In this study, we developed an integrated framework for multi-source data fusion to enhance vegetation mapping and validation in Yunnan Province, a global biodiversity hotspot region in Southwest China. The mapping process involved four key steps:(1) vegetation classification using random forest and Landsat imagery,(2) boundary calibration based on a locally calibrated static climatevegetation model,(3) patch correction with independent forest inventory data, and(4) validation using adequate field observations. This approach enabled the mapping of 17 vegetation types and 44 subtypes in Yunnan Province(1:50000), categorized based on the growth-form composition of dominant species of the community. The overall accuracies were 0.747 and0.710 for natural vegetation types and subtypes, and 0.905 and 0.891 for artificial types and subtypes. This high-resolution map enhances our understanding of vegetation distribution and ecological complexity in this region, offering valuable insights for policymakers to support conservation efforts and sustainable management strategies.展开更多
Due to the high-order B-spline basis functions utilized in isogeometric analysis(IGA)and the repeatedly updating global stiffness matrix of topology optimization,Isogeometric topology optimization(ITO)intrinsically su...Due to the high-order B-spline basis functions utilized in isogeometric analysis(IGA)and the repeatedly updating global stiffness matrix of topology optimization,Isogeometric topology optimization(ITO)intrinsically suffers from the computationally demanding process.In this work,we address the efficiency problem existing in the assembling stiffness matrix and sensitivity analysis using B˙ezier element stiffness mapping.The Element-wise and Interaction-wise parallel computing frameworks for updating the global stiffness matrix are proposed for ITO with B˙ezier element stiffness mapping,which differs from these ones with the traditional Gaussian integrals utilized.Since the explicit stiffness computation formula derived from B˙ezier element stiffness mapping possesses a typical parallel structure,the presented GPU-enabled ITO method can greatly accelerate the computation speed while maintaining its high memory efficiency unaltered.Numerical examples demonstrate threefold speedup:1)the assembling stiffness matrix is accelerated by 10×maximumly with the proposed GPU strategy;2)the solution efficiency of a sparse linear system is enhanced by up to 30×with Eigen replaced by AMGCL;3)the efficiency of sensitivity analysis is promoted by 100×with GPU applied.Therefore,the proposed method is a promising way to enhance the numerical efficiency of ITO for both single-patch and multiple-patch design problems.展开更多
In floristic research,the grid mapping method is a crucial and highly effective tool for investigating the flora of specific regions.This methodology aids in the collection of comprehensive data,thereby promoting a th...In floristic research,the grid mapping method is a crucial and highly effective tool for investigating the flora of specific regions.This methodology aids in the collection of comprehensive data,thereby promoting a thorough understanding of regional plant diversity.This paper presents findings from a grid mapping study conducted in the Surkhan-Sherabad botanical-geographic region(SShBGR),acknowledged as one of the major floristic areas in southwestern Uzbekistan.Using an expansive dataset of 14,317 records comprised of herbarium specimens and field diary entries collected from 1897 to 2023,we evaluated the stages and seasonal dynamics of data accumulation,species richness(SR),and collection density(CD)within 5 km×5 km grid cells.We further examined the taxonomic and life form composition of the region's flora.Our analysis revealed that the grid mapping phase(2021–2023)produced a significantly greater volume of specimens and taxonomic diversity compared with other periods(1897–1940,1941–1993,and 1994–2020).Field research spanned 206 grid cells during 2021–2023,resulting in 11,883 samples,including 6469 herbarium specimens and 5414 field records.Overall,fieldwork covered 251 of the 253 grid cells within the SShBGR.Notably,the highest species diversity was documented in the B198 grid cell,recording 160 species.In terms of collection density,the E198 grid cell produced 475 samples.Overall,we identified 1053 species distributed across 439 genera and 78 families in the SShBGR.The flora of this region aligned significantly with the dominant families commonly found in the Holarctic,highlighting vital ecological connections.Among our findings,the Asteraceae family was the most polymorphic,with 147 species,followed by the continually stable and diverse Poaceae,Fabaceae,Brassicaceae,and Amaranthaceae.Besides,our analysis revealed a predominance of therophyte life forms,which constituted 52%(552 species)of the total flora.The findings underscore the necessity for continual data collection efforts to further enhance our understanding of the biodiversity in the SShBGR.The results of this study demonstrated that the application of grid-based mapping in floristic studies proves to be an effective tool for assessing biodiversity and identifying key taxonomic groups.展开更多
基金financially supported by National Natural Science Foundation of China(32301800,32301923 and 32072053)Wheat Industrial Technology System of Shandong Province(SDAIT-01-01)Key Research and Development Project of Shandong Province(2022LZG002-4,2023LZGC009-4-4).
文摘Powdery mildew negatively impacts wheat yield and quality.Emmer wheat(Triticum dicoccum),an ancestral species of common wheat,is a gene donor for wheat improvement.Cultivated emmer accession H1-707 exhibited all-stage resistance to powdery mildew over consecutive years.Genetic analysis of H1-707 at the seedling stage revealed a dominant monogenic inheritance pattern,and the underlying gene was designated Pm71.By employing bulked segregant exome sequencing(BSE-Seq)and using 2000 F2:3 families,Pm71 was fine mapped to a 336-kb interval on chromosome arm 6AS by referencing to the durum cv.Svevo RefSeq 1.0.Collinearity analysis revealed high homology in the candidate interval between Svevo and six Triticum species.Among six high-confidence genes annotated within this interval,TRITD6Av1G005050 encoding a GDSL esterase/lipase was identified as a key candidate for Pm71.
基金the University of Transport Technology under the project entitled“Application of Machine Learning Algorithms in Landslide Susceptibility Mapping in Mountainous Areas”with grant number DTTD2022-16.
文摘This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.
文摘A distinctive feature of scholarly communities today is exploring topics and concepts in interdisciplinary and international contexts. This observation is increasingly apparent and visible in advancing our thinking and policies related to human/environmental worlds at local, regional, and global scales. Maps are an important part of these innovative and ongoing research approaches. In this context, we consider urban forests a topic meriting more attention of scholars studying the geographic and environmental intersections of the natural sciences with the social sciences and humanities. We construct two innovative knowledge bases, one a conceptual framework based on major themes and concepts related to mapping urban forests using key words of the first 100 results of a Google Scholar query and a second using the number of Google Scholar hyperlinks about mapping urban forests in 244 capital cities. We discovered that the constructed world maps reveal vast global unevenness in our knowledge about urban forests in hyperlink numbers and ratios, results that merit further attention by disciplinary, international and interdisciplinary scholarly communities.
基金supported by the National Natural Science Foundation of China(No.U1904209,82241093).
文摘Silicosis is a chronic interstitial lung disease caused by prolonged exposure to inhalable silica particles.The initiation and progression of silicosis involve a dynamic process encompassing various cell types and molecules.Although the effector cells in different stages of silicosis undergo constant switching with disease progression,immune cells play a dominant role throughout the entire process.1 Therefore,comprehensively exploring cellular and molecular mechanisms underlying immune responses in silicosis becomes a prerequisite for unraveling its pathogenesis.Immune checkpoints(ICs)are crucial immunomodulatory factors that contribute significantly to the maintenance of immune tolerance and homeostasis.
基金supported by the STI 2030-Major Project(2021ZD0204400,2022ZD0205203,2021ZD0200104,2022ZD0211900)the Shenzhen Science and Technology Program(RCYX20210706092100003,RCBS20221008093311027)+3 种基金the Shenzhen Medical Research Funds(A2303005)the Youth Innovation Promotion Association CAS(2022367)the National Natural Science Foundation of China(32100896)NSFC-Guangdong Joint Fund(U20A6005).
文摘Dear Editor,The mammalian brain exhibits cross-scale complexity in neuronal morphology and connectivity,the study of which demands high-resolution morphological reconstruction of individual neurons across the entire brain[1-4].Current commonly used approaches for such mesoscale brain mapping include two main types of three-dimensional fluorescence microscopy:the block-face methods,and the lightsheet-based methods[5,6].In general,the high imaging speed and light efficiency of light-sheet microscopy make it a suitable tool for high-throughput volumetric imaging,especially when combined with tissue-clearing techniques.However,large brain samples pose major challenges to this approach.
文摘Objective:To evaluate the value of rehabilitation nursing based on mind mapping model combined with psychological intervention for patients with nephrotic syndrome(NS).Methods:A total of 60 patients with NS who visited our hospital from January 2024 to December 2024 were selected as samples and randomly divided into groups.The observation group received rehabilitation nursing based on the mind mapping model combined with psychological intervention,while the control group received routine intervention.The differences in emotional scores,self-care ability scores,compliance,and complications were compared between the two groups.Results:The anxiety(SAS)and depression(SDS)scores of the observation group were lower than those of the control group,while the self-care ability scale(ESCA)score was higher than that of the control group(P<0.05).The compliance rate of the observation group was higher than that of the control group(P<0.05).The complication rate of NS in the observation group was lower than that in the control group(P<0.05).Conclusion:Rehabilitation nursing based on the mind mapping model combined with psychological intervention can enhance self-care ability,reduce negative emotions,and reduce complications in NS nursing,which is efficient and feasible.
基金Open access funding provided by University of Natural Resources and Life Sciences Vienna(BOKU).
文摘This paper presents a standardised workflow for conducting hazard assessments of mass wasting processes in remote mountain areas with limited data.The methodology integrates geomorphological mapping and remote sensing techniques and is adaptable to different national standards,thus ensuring its applicability in a variety of contexts.The principal objective is to guarantee the safety of mountainous regions,particularly in the vicinity of essential infrastructure,where the scope for implementing structural measures is restricted.The framework commences with comprehensive geomorphological mapping,which facilitates the identification of past hazardous processes and potential future hazards.New technologies,such as uncrewed aerial vehicles(UAVs),are employed to create high-resolution DEMs,which are particularly beneficial in regions with limited data availability.These models facilitate the assessment of potential hazards and inform decisions regarding protective measures.The utilisation of UAVs enhances the accuracy and efficiency of data collection,particularly in remote mountainous regions where alternative remotely sensed information may be unavailable.The integration of modern approaches into traditional hazard assessment methods allows for a comprehensive analysis of the spatial distribution of factors driving mass wasting processes.This workflow provides valuable insights that assist in the prioritisation of interventions and the optimisation of risk reduction in high mountainous areas.
基金supported by the Czech Ministry of Education,Youth and Sports and an internal grant No.SV22-9-21130 of the Faculty of Agrobiology,Food and Natural Resources of the Czech University of Life Sciences Prague(CZU)The support from the EJP Soil(grant agreement No.862695 of the European Union's Horizon 2020 research and innovation programme)is also acknowledged.
文摘This study proposes a novel methodology to employ discrete point spectra as input variable for digital mapping of soil organic carbon(SOC).Accordingly,two SOC modeling approaches were used in three agricultural sites in Czech Republic:i)machine learning(ML)including partial least squares regression(PLSR),cubist,random forest(RF),and support vector regression(SVR),and ii)regression kriging(RK)by the combination of ordinary kriging(OK)and PLSR(PLSR-K),cubist(cubist-K),RF(RF-K),and SVR(SVRK).Models were developed on environmental predictor covariates(EPCs)and thirty genetic algorithms(GA)-selected visible,near-infrared,and shortwave-infrared(VNIR-SWIR)wavelengths spectra,individually and combined.Thirty rasters were then created using interpolation of the selected spectra and served as the input variables e with and without EPCs e to test and compare the developed models and SOC predictive maps with each other and with those retrieved from the third approach:iii)kriging using OK of the measured and ML-predicted SOC.The impact of employing selected wavelengths’spectra and EPCs on models'performance was investigated using independent test samples and the uncertainty associated with the produced maps.Using interpolated spectra as the only input variable yielded a relatively acceptable accuracy(Nova Ves:RMSE=0.19%,Udrnice:RMSE=0.12%,Klucov:RMSE=0.13%).In comparison,the interpolated spectra coupled with EPCs enhanced the results.Regarding the uncertainty,however,the ML-based SOC maps were more reliable,than RK-based ones.Furthermore,maps produced using both spectra and EPCs showed less uncertainty than those constructed on the individual datasets.
基金supported by the Major Program for Basic Research Project of Yunnan Province (Grant No. 202101BC070002)the Second Comprehensive Scientific Expedition of the Tibetan Plateau (Grant No. 2019QZKK04020101)。
文摘Vegetation maps are crucial for ecologists and decision-makers, providing essential information on the spatial distribution of various vegetation types to support ecosystem exploration and management. Despite advancements in Earth observation and machine learning enabling large-scale vegetation mapping, creating detailed and accurate maps in biodiversity hotspots remains challenging due to significant environmental heterogeneity and frequent human disturbances. The lack of sufficient ground-based data and complex climate-vegetation interactions further limits mapping accuracy. In this study, we developed an integrated framework for multi-source data fusion to enhance vegetation mapping and validation in Yunnan Province, a global biodiversity hotspot region in Southwest China. The mapping process involved four key steps:(1) vegetation classification using random forest and Landsat imagery,(2) boundary calibration based on a locally calibrated static climatevegetation model,(3) patch correction with independent forest inventory data, and(4) validation using adequate field observations. This approach enabled the mapping of 17 vegetation types and 44 subtypes in Yunnan Province(1:50000), categorized based on the growth-form composition of dominant species of the community. The overall accuracies were 0.747 and0.710 for natural vegetation types and subtypes, and 0.905 and 0.891 for artificial types and subtypes. This high-resolution map enhances our understanding of vegetation distribution and ecological complexity in this region, offering valuable insights for policymakers to support conservation efforts and sustainable management strategies.
基金supported by the National Key R&D Program of China(2023YFB2504601)National Natural Science Foundation of China(52205267).
文摘Due to the high-order B-spline basis functions utilized in isogeometric analysis(IGA)and the repeatedly updating global stiffness matrix of topology optimization,Isogeometric topology optimization(ITO)intrinsically suffers from the computationally demanding process.In this work,we address the efficiency problem existing in the assembling stiffness matrix and sensitivity analysis using B˙ezier element stiffness mapping.The Element-wise and Interaction-wise parallel computing frameworks for updating the global stiffness matrix are proposed for ITO with B˙ezier element stiffness mapping,which differs from these ones with the traditional Gaussian integrals utilized.Since the explicit stiffness computation formula derived from B˙ezier element stiffness mapping possesses a typical parallel structure,the presented GPU-enabled ITO method can greatly accelerate the computation speed while maintaining its high memory efficiency unaltered.Numerical examples demonstrate threefold speedup:1)the assembling stiffness matrix is accelerated by 10×maximumly with the proposed GPU strategy;2)the solution efficiency of a sparse linear system is enhanced by up to 30×with Eigen replaced by AMGCL;3)the efficiency of sensitivity analysis is promoted by 100×with GPU applied.Therefore,the proposed method is a promising way to enhance the numerical efficiency of ITO for both single-patch and multiple-patch design problems.
基金supported by the grant from the State Programs"Grid Mapping of the Flora of Uzbekistan'during 2020–2024"the grant from the State Programs"Creation of the Digital Platform of the Plant World of Central Uzbekistan"during 2025–2029the State Research Project"Taxonomic Revision of Polymorphic Plant Families of the Flora of Uzbekistan"from the Institute of Botany,Academy of Sciences of the Republic of Uzbekistan (A-FA-2021-427)
文摘In floristic research,the grid mapping method is a crucial and highly effective tool for investigating the flora of specific regions.This methodology aids in the collection of comprehensive data,thereby promoting a thorough understanding of regional plant diversity.This paper presents findings from a grid mapping study conducted in the Surkhan-Sherabad botanical-geographic region(SShBGR),acknowledged as one of the major floristic areas in southwestern Uzbekistan.Using an expansive dataset of 14,317 records comprised of herbarium specimens and field diary entries collected from 1897 to 2023,we evaluated the stages and seasonal dynamics of data accumulation,species richness(SR),and collection density(CD)within 5 km×5 km grid cells.We further examined the taxonomic and life form composition of the region's flora.Our analysis revealed that the grid mapping phase(2021–2023)produced a significantly greater volume of specimens and taxonomic diversity compared with other periods(1897–1940,1941–1993,and 1994–2020).Field research spanned 206 grid cells during 2021–2023,resulting in 11,883 samples,including 6469 herbarium specimens and 5414 field records.Overall,fieldwork covered 251 of the 253 grid cells within the SShBGR.Notably,the highest species diversity was documented in the B198 grid cell,recording 160 species.In terms of collection density,the E198 grid cell produced 475 samples.Overall,we identified 1053 species distributed across 439 genera and 78 families in the SShBGR.The flora of this region aligned significantly with the dominant families commonly found in the Holarctic,highlighting vital ecological connections.Among our findings,the Asteraceae family was the most polymorphic,with 147 species,followed by the continually stable and diverse Poaceae,Fabaceae,Brassicaceae,and Amaranthaceae.Besides,our analysis revealed a predominance of therophyte life forms,which constituted 52%(552 species)of the total flora.The findings underscore the necessity for continual data collection efforts to further enhance our understanding of the biodiversity in the SShBGR.The results of this study demonstrated that the application of grid-based mapping in floristic studies proves to be an effective tool for assessing biodiversity and identifying key taxonomic groups.
