The composition of animal species and interactions among them are widely known to shape ecological communities and fine-scale(e.g.,<1 km)monitoring of animal communities is essential for understanding the relations...The composition of animal species and interactions among them are widely known to shape ecological communities and fine-scale(e.g.,<1 km)monitoring of animal communities is essential for understanding the relationships among animals and plants.Although the co-existence of large-and medium-sized species has been studied across different scales,research on fine-scale interactions of herbivores in deciduous broadleaf forests is limited.Camera trapping of large-and medium-sized mammals was carried out over a 1 year period within a 25 ha deciduous broadleaf forest dynamics plot in the Qinling Mountains,China.Fourteen species of large-and medium-sized mammals,including six carnivores,six ungulates,one primate and one rodent species were found.Kernel density estimations were used to analyse the diel or 24 h activity patterns of all species with more than 40 independent detections and general linear models were developed to explore the spatial relationships among the species.The combination of overlapping diel activity patterns and spatial associations showed obvious niche separation among six species:giant panda(Ailuropoda melanoleuca David),takin(Budorcas taxicolor Hodgson),Reeves’s muntjac(Muntiacus reevesi Ogilby),tufted deer(Elaphodus cephalophus Milne-Edwards),Chinese serow(Capricornis milneedwardsii David)and wild boar(Sus scrofa Linnaeus).Long-term fine-scale monitoring is useful for providing information about the co-existence of species and their interactions.The results demonstrate the importance for fine-scale monitoring of animals and plants for improving understanding of species interactions and community dynamics.展开更多
The Gulong shale demonstrates high clay content and pronounced thin laminations,with limited vertical variability in log curves,complicating lithofacies classification.To comprehend the distribution and compositional ...The Gulong shale demonstrates high clay content and pronounced thin laminations,with limited vertical variability in log curves,complicating lithofacies classification.To comprehend the distribution and compositional features of lithofacies in the Gulong shale for optimal sweet spot selection and reservoir stimulation,this study introduced a lithofacies classification scheme and a log-based lithofacies evaluation method.Specifically,theΔlgR method was utilized for accurately determining the total organic carbon(TOC)content;a multi-mineral model based on element-to-mineral content conversion coefficients was developed to enhance mineral composition prediction accuracy,and the microresistivity curve variations derived from formation micro-image(FMI)log were used to compute lamination density,offering insights into sedimentary structures.Using this method,integrating TOC content,sedimentary structures,and mineral compositions,the Qingshankou Formation is classified into four lithofacies and 12 sublithofacies,displaying 90.6%accuracy compared to core description outcomes.The classification results reveal that the northern portion of the study area exhibits more prevalent fissile felsic shales,siltstone interlayers,shell limestones,and dolomites.Vertically,the upper section primarily exhibits organic-rich felsic shale and siltstone interlayers,the middle part is characterized by moderate organic quartz-feldspathic shale and siltstone/carbonate interlayers,and the lower section predominantly features organic-rich fissile felsic/clayey felsic shales.Analyzing various sublithofacies in relation to seven petrophysical parameters,oil test production,and fracturing operation conditions indicates that the organic-rich felsic shales in the upper section and the organic-rich/clayey felsic shales in the lower section possess superior physical properties and oil content,contributing to smoother fracturing operation and enhanced production,thus emerging as dominant sublithofacies.Conversely,thin interlayers such as siltstones and limestones,while producing oil,demonstrate higher brittleness and pose great fracturing operation challenges.The methodology and insights in this study will provide a valuable guide for sweet spot identification and horizontal well-based exploitation of the Gulong shale.展开更多
Based on the quality and analysis of the medium carbon product whose grade was 81%-82%, the emphasis of the study was placed on the orthogonal experiments of roughing flotation reagent and single factors, such as abra...Based on the quality and analysis of the medium carbon product whose grade was 81%-82%, the emphasis of the study was placed on the orthogonal experiments of roughing flotation reagent and single factors, such as abrasive grain, pulp concentration and the rotating speed of flotation machine. The result with improvement in the grade of concentrate to 95% through the routine floatation method was achieved.展开更多
The state-of-art Computational Fluid Dynamics （CFD） codes FLUENT is applied in a fine-scale simulation of the wind field over a complex terrain. Several numerical tests are performed to validate the capability of FL...The state-of-art Computational Fluid Dynamics （CFD） codes FLUENT is applied in a fine-scale simulation of the wind field over a complex terrain. Several numerical tests are performed to validate the capability of FLUENT on describing the wind field details over a complex terrain. The results of the numerical tests show that FLUENT can simulate the wind field over extremely complex terrain, which cannot be simulated by mesoscale models. The reason why FLUENT can cope with extremely complex terrain, which can not be coped with by mesoscale models, relies on some particular techniques adopted by FLUENT, such as computer-aided design （CAD） technique, unstructured grid technique and finite volume method. Compared with mesoscale models, FLUENT can describe terrain in much more accurate details and can provide wind simulation results with higher resolution and more accuracy.展开更多
Quantifying the relationship between light and stands or individual trees is of great significance in understanding tree competition,improving forest productivity,and comprehending ecological processes.However,accurat...Quantifying the relationship between light and stands or individual trees is of great significance in understanding tree competition,improving forest productivity,and comprehending ecological processes.However,accurately depicting the spatiotemporal variability of light under complex forest structural conditions poses a challenge,especially for precise forest management decisions that require a quantitative study of the relationship between fine-scale individual tree structure and light.3D RTMs(3-dimensional radiative transfer models),which accurately characterize the interaction between solar radiation and detailed forest scenes,provide a reliable means for depicting such relationships.展开更多
The community composition and activity-density of termites can influence nutrient cycling and other ecological functions.However,the spatial distribution and the activity-density of termites on a fine-scale in tropica...The community composition and activity-density of termites can influence nutrient cycling and other ecological functions.However,the spatial distribution and the activity-density of termites on a fine-scale in tropical forests are still unknown.We checked the spatial distribution patterns of the feeding groups and species of termites and their co-occurrence pattern in a 1-ha(100 m×100 m)plot,and their correlatiion with the environmental factors.We used a standard protocol to collect termite assemblages and classified them into five feeding groups based on their preferrred diet:fungus growers,litter feeders,soil feeders,soil-wood feeders,and wood feeders.We measured the environmental factors:soil pH,litter mass,aboveground plant biomass,and topographic position index(TPI).Soil-wood feeders showed the highest activity-density,followed by wood feeders,fungus growers,soil feeders,and litter feeders.Soil-wood feeders and fungus growers demonstated a strong correlation while litter feeders showed weak correlations with other feeding groups.Termite feeding groups and most of the termite species displayed a positive association with the high TPI and the low soil pH patches.Our results indicated that the examined environmental factors influenced the termite community assemblages and distribution patterns on a fine-scale in tropical rainforests.展开更多
A total of 892 individuals sampled from a wild soybean population in a natural reserve near the Yellow River estuary located in Kenli of Shandong Province(China)were investigated.Seventeen SSR(simple sequence repeat)p...A total of 892 individuals sampled from a wild soybean population in a natural reserve near the Yellow River estuary located in Kenli of Shandong Province(China)were investigated.Seventeen SSR(simple sequence repeat)primer pairs from cultivated soybeans were used to estimate the genetic diversity of the population and its variation pattern versus changes of the sample size(sub-samples),in addition to investigating the fine-scale spatial genetic structure within the population.The results showed relatively high genetic diversity of the population with the mean value of allele number(A)being 2.88,expected heterozygosity(He)0.431,Shannon diversity index(I)0.699,and percentage of poly-morphic loci(P)100%.Sub-samples of different sizes(ten groups)were randomly drawn from the population and their genetic diversity was calculated by computer simulation.The regression model of the four diversity indexes with the change of sample sizes was computed.As a result,27-52 individuals can reach 95%of total genetic variability of the population.Spatial autocorrelation analysis revealed that the genetic patch size of this wild soybean population is about 18 m.The study provided a scientific basis for the sampling strategy of wild soybean populations.展开更多
With the rapid development of functional magnetic resonance imaging (fMRI) technology, the spatial resolution of fMRI data is continuously growing. This pro- vides us the possibility to detect the fine-scale patterns ...With the rapid development of functional magnetic resonance imaging (fMRI) technology, the spatial resolution of fMRI data is continuously growing. This pro- vides us the possibility to detect the fine-scale patterns of brain activities. The es- tablished univariate and multivariate methods to analyze fMRI data mostly focus on detecting the activation blobs without considering the distributed fine-scale pat- terns within the blobs. To improve the sensitivity of the activation detection, in this paper, multivariate statistical method and univariate statistical method are com- bined to discover the fine-grained activity patterns. For one voxel in the brain, a local homogenous region is constructed. Then, time courses from the local ho- mogenous region are integrated with multivariate statistical method. Univariate statistical method is finally used to construct the interests of statistic for that voxel. The approach has explicitly taken into account the structures of both activity pat- terns and existing noise of local brain regions. Therefore, it could highlight the fine-scale activity patterns of the local regions. Experiments with simulated and real fMRI data demonstrate that the proposed method dramatically increases the sensitivity of detection of fine-scale brain activity patterns which contain the subtle information about experimental conditions.展开更多
Background Fine-scale mapping of schistosomiasis to guide micro-targeting of interventions will gain importance in elimination settings,where the heterogeneity of transmission is often pronounced.Novel mobile applicat...Background Fine-scale mapping of schistosomiasis to guide micro-targeting of interventions will gain importance in elimination settings,where the heterogeneity of transmission is often pronounced.Novel mobile applications offer new opportunities for disease mapping.We provide a practical introduction and documentation of the strengths and shortcomings of GPS-based household identification and participant recruitment using tablet-based applications for fine-scale schistosomiasis mapping at sub-district level in a remote area in Pemba,Tanzania.Methods A community-based household survey for urogenital schistosomiasis assessment was conducted from November 2020 until February 2021 in 20 small administrative areas in Pemba.For the survey,1400 housing structures were prospectively and randomly selected from shapefile data.To identify pre-selected structures and collect survey-related data,field enumerators searched for the houses’geolocation using the mobile applications Open Data Kit(ODK)and MAPS.ME.The number of inhabited and uninhabited structures,the median distance between the pre-selected and recorded locations,and the dropout rates due to non-participation or non-submission of urine samples of sufficient volume for schistosomiasis testing was assessed.Results Among the 1400 randomly selected housing structures,1396(99.7%)were identified by the enumerators.The median distance between the pre-selected and recorded structures was 5.4 m.A total of 1098(78.7%)were residential houses.Among them,99(9.0%)were dropped due to continuous absence of residents and 40(3.6%)households refused to participate.In 797(83.1%)among the 959 participating households,all eligible household members or all but one provided a urine sample of sufficient volume.Conclusions The fine-scale mapping approach using a combination of ODK and an offline navigation application installed on tablet computers allows a very precise identification of housing structures.Dropouts due to non-residential housing structures,absence,non-participation and lack of urine need to be considered in survey designs.Our findings can guide the planning and implementation of future household-based mapping or longitudinal surveys and thus support micro-targeting and follow-up of interventions for schistosomiasis control and elimination in remote areas.展开更多
Afforestation is helpful to improve soil functions and increase soil organic carbon(SOC)sequestration in semiarid deserts.However,the fine-scale(around a single plant)spatial distribution of SOC and its liable organic...Afforestation is helpful to improve soil functions and increase soil organic carbon(SOC)sequestration in semiarid deserts.However,the fine-scale(around a single plant)spatial distribution of SOC and its liable organic carbon(LOC)fractions after afforestation in semiarid deserts are poorly understood.Pinus sylvestris and Salix psammophila afforested on shifting sandy land(Sland)were selected to quantify fine-scale(at 20,80,150 and 240 cm away from the trees)spatial distribution of SOC and its LOC fractions in the southeast edge of Mu Us Desert,China.The results showed that the afforested S.psammophila and P.sylvestris significantly increased SOC,total nitrogen,dissolved organic carbon,microbial biomass carbon and readily oxidized organic carbon(ROOC).At 20 cm distance,SOC storage of P.sylvestris was 27.21%higher than S.psammophila in 0-100 cm soil layers,and SOC storage of S.psammophila at 80 and 150 cm distances was 5.50%and 5.66%higher than P.sylvestris,respectively.Compared with Sland,SOC storage under S.psammophila and P.sylvestris significantly increased by 94.90%,39.50%,27.10%and 18.50%at 20,80,150 and 240 cm distance,respectively.ROOC accounted for 14.09%and 18.93%of SOC under S.psammophila and P.sylvestris,respectively.Our results suggest that afforestation can promote SOC accumulation at different distances from the plants,and that P.sylvestris allocates more organic matter to the closer soil compared with S.psammophila(<80 cm from the tree).展开更多
A straight-line wind case was observed in Tianjin on 13 June 2005,which was caused by a gust front from a squall line.Mesoscale analyses based on observations from in-situ surface stations,sounding,and in-situ radar a...A straight-line wind case was observed in Tianjin on 13 June 2005,which was caused by a gust front from a squall line.Mesoscale analyses based on observations from in-situ surface stations,sounding,and in-situ radar as well as fine-scale analyses based on observation tower data were performed.The mesoscale characteristics of the gust front determined its shape and fine-scale internal structures.Based on the scale and wavelet analyses,the fine-scale structures within the gust front were distinguished from the classical mesoscale structures,and such fine-scale structures were associated with the distribution of straight-line wind zones.A series of cross-frontal fine-scale circulations at the lowest levels of the gust front was discovered,which caused a relatively weak wind zone within the frontal strong wind zone.The downdraft at the rear of the head region of the gust front was more intense than in the classical model,and similar to the microburst,a series of vertical vortices propagated from the rear region to the frontal region.In addition,strong tangential fine-scale instability was detected in the frontal region.Finally,a fine-scale gust front model with straight-line wind zones is presented.展开更多
Fine-scale structures can be observed in small field-of-view(FOV)auroral observations,but they are often overlooked because they appear only sporadically in all-sky observations.Such forms are of great interest becaus...Fine-scale structures can be observed in small field-of-view(FOV)auroral observations,but they are often overlooked because they appear only sporadically in all-sky observations.Such forms are of great interest because they may embody specific magnetosphere-ionosphere coupling processes,reveal localized energy deposition pathways,and provide new insights into cross-scale plasma dynamics and instabilities.However,their limited spatial extent,transient occurrence,and scarcity in wide-FOV observations make systematic investigation challenging.Traditional manual analysis struggles to capture these subtle structures within vast all-sky datasets,while automated detection faces severe data imbalance and morphological ambiguity.To address these challenges,we propose a synthetic-to-real progressive learning framework for cross-FOV retrieval of rare auroral forms.A Generative Adversarial Network(GAN)is employed to perform cross-FOV transformation between unpaired small-FOV images containing rare aurora forms and all-sky images(ASI)without such structures,thereby generating large numbers of synthetic ASI with rare auroral morphology.These synthetic samples are used to train an initial detection model,which subsequently undergoes iterative fine-tuning through feedback-guided learning:The model performs inference on new all-sky data,and the progressively accumulated real detections are incorporated into the training set.Experimental results demonstrate that the proposed method achieves over 92%detection accuracy on ASI,enabling high-precision retrieval of small-scale auroral structures across large-scale observations.This framework provides a scalable and effective approach to rediscovering rare auroral phenomena in continuous all-sky monitoring,offering new opportunities for exploring the fine-scale dynamics of the upper atmosphere.展开更多
在细粒度图像检索领域,现有研究成果主要集中于采用深层网络实现判别特征提取与精准定位,忽略了浅层特征信息的重要性,且无法消除背景中的复杂噪声干扰,限制了检索性能的提升。有鉴于此,提出了一种基于多层次特征提取的细粒度图像哈希...在细粒度图像检索领域,现有研究成果主要集中于采用深层网络实现判别特征提取与精准定位,忽略了浅层特征信息的重要性,且无法消除背景中的复杂噪声干扰,限制了检索性能的提升。有鉴于此,提出了一种基于多层次特征提取的细粒度图像哈希检索方法(Fine-grained Deep Hashing image retrieval method based on Multi-level Feature Extraction, FDH-MFE)。该方法主要关注不同层次间特征的关联性,并增强了局部特征的提取能力。首先,提出了一个特征提取模块,旨在从网络的不同阶段提取细粒度特征,并通过图神经网络揭示其潜在的长距离依赖关系,为后续阶段提供更全面和精细的特征表示。其次,设计了一种代理损失算法,使得哈希码分布更加均匀,从而提升细粒度特征的区分能力。最后,通过设计背景抑制算法并结合三元组损失,增强了模型拟合全局分布的能力,使得所提出的方法在细粒度图像检索任务中表现出色。实验结果表明:该方法在4个公开数据集上的平均检索精度相较于次先进方法分别提高了15.03%、10.94%、9.98%和9.78%。展开更多
基金This work was supported by the National Natural Science Foundation of China project(No 41671183).
文摘The composition of animal species and interactions among them are widely known to shape ecological communities and fine-scale(e.g.,<1 km)monitoring of animal communities is essential for understanding the relationships among animals and plants.Although the co-existence of large-and medium-sized species has been studied across different scales,research on fine-scale interactions of herbivores in deciduous broadleaf forests is limited.Camera trapping of large-and medium-sized mammals was carried out over a 1 year period within a 25 ha deciduous broadleaf forest dynamics plot in the Qinling Mountains,China.Fourteen species of large-and medium-sized mammals,including six carnivores,six ungulates,one primate and one rodent species were found.Kernel density estimations were used to analyse the diel or 24 h activity patterns of all species with more than 40 independent detections and general linear models were developed to explore the spatial relationships among the species.The combination of overlapping diel activity patterns and spatial associations showed obvious niche separation among six species:giant panda(Ailuropoda melanoleuca David),takin(Budorcas taxicolor Hodgson),Reeves’s muntjac(Muntiacus reevesi Ogilby),tufted deer(Elaphodus cephalophus Milne-Edwards),Chinese serow(Capricornis milneedwardsii David)and wild boar(Sus scrofa Linnaeus).Long-term fine-scale monitoring is useful for providing information about the co-existence of species and their interactions.The results demonstrate the importance for fine-scale monitoring of animals and plants for improving understanding of species interactions and community dynamics.
基金research is funded by China Petroleum Major Science and Tech-nology Project-Study on Reservoir Formation Theory and Key technology of Gulong Shale Oil(2021ZZ10-01)Petrochina Oil and Gas major project-Research on Production and exploration and development technology of large-scale Increase of Continental shale oil storage(2023ZZ15-02).
文摘The Gulong shale demonstrates high clay content and pronounced thin laminations,with limited vertical variability in log curves,complicating lithofacies classification.To comprehend the distribution and compositional features of lithofacies in the Gulong shale for optimal sweet spot selection and reservoir stimulation,this study introduced a lithofacies classification scheme and a log-based lithofacies evaluation method.Specifically,theΔlgR method was utilized for accurately determining the total organic carbon(TOC)content;a multi-mineral model based on element-to-mineral content conversion coefficients was developed to enhance mineral composition prediction accuracy,and the microresistivity curve variations derived from formation micro-image(FMI)log were used to compute lamination density,offering insights into sedimentary structures.Using this method,integrating TOC content,sedimentary structures,and mineral compositions,the Qingshankou Formation is classified into four lithofacies and 12 sublithofacies,displaying 90.6%accuracy compared to core description outcomes.The classification results reveal that the northern portion of the study area exhibits more prevalent fissile felsic shales,siltstone interlayers,shell limestones,and dolomites.Vertically,the upper section primarily exhibits organic-rich felsic shale and siltstone interlayers,the middle part is characterized by moderate organic quartz-feldspathic shale and siltstone/carbonate interlayers,and the lower section predominantly features organic-rich fissile felsic/clayey felsic shales.Analyzing various sublithofacies in relation to seven petrophysical parameters,oil test production,and fracturing operation conditions indicates that the organic-rich felsic shales in the upper section and the organic-rich/clayey felsic shales in the lower section possess superior physical properties and oil content,contributing to smoother fracturing operation and enhanced production,thus emerging as dominant sublithofacies.Conversely,thin interlayers such as siltstones and limestones,while producing oil,demonstrate higher brittleness and pose great fracturing operation challenges.The methodology and insights in this study will provide a valuable guide for sweet spot identification and horizontal well-based exploitation of the Gulong shale.
文摘Based on the quality and analysis of the medium carbon product whose grade was 81%-82%, the emphasis of the study was placed on the orthogonal experiments of roughing flotation reagent and single factors, such as abrasive grain, pulp concentration and the rotating speed of flotation machine. The result with improvement in the grade of concentrate to 95% through the routine floatation method was achieved.
基金supported by the National Natural Science Foundation of China(40805004, 40705039 and 90715031)the "Mini-projecton detailed survey and evaluation of wind energy resources"supported by National Climate Center of Chinese Meteoro-logical Administration (CWERA2010002)
文摘The state-of-art Computational Fluid Dynamics （CFD） codes FLUENT is applied in a fine-scale simulation of the wind field over a complex terrain. Several numerical tests are performed to validate the capability of FLUENT on describing the wind field details over a complex terrain. The results of the numerical tests show that FLUENT can simulate the wind field over extremely complex terrain, which cannot be simulated by mesoscale models. The reason why FLUENT can cope with extremely complex terrain, which can not be coped with by mesoscale models, relies on some particular techniques adopted by FLUENT, such as computer-aided design （CAD） technique, unstructured grid technique and finite volume method. Compared with mesoscale models, FLUENT can describe terrain in much more accurate details and can provide wind simulation results with higher resolution and more accuracy.
基金supported by the National Natural Science Foundation of China(Grant Nos.41930111,42130111,and 42001279).
文摘Quantifying the relationship between light and stands or individual trees is of great significance in understanding tree competition,improving forest productivity,and comprehending ecological processes.However,accurately depicting the spatiotemporal variability of light under complex forest structural conditions poses a challenge,especially for precise forest management decisions that require a quantitative study of the relationship between fine-scale individual tree structure and light.3D RTMs(3-dimensional radiative transfer models),which accurately characterize the interaction between solar radiation and detailed forest scenes,provide a reliable means for depicting such relationships.
基金supported by the National Natural Science Foundation of China(41977057,41877064)NSFCUNEP(42061144005)+2 种基金the Youth Innovation Promotion Association of the Chinese Academy of Sciences(2019387)Yunnan Applied Basic Research Projects(202001AW070014)the CAS 135 program(2017 XTBG-T01).
文摘The community composition and activity-density of termites can influence nutrient cycling and other ecological functions.However,the spatial distribution and the activity-density of termites on a fine-scale in tropical forests are still unknown.We checked the spatial distribution patterns of the feeding groups and species of termites and their co-occurrence pattern in a 1-ha(100 m×100 m)plot,and their correlatiion with the environmental factors.We used a standard protocol to collect termite assemblages and classified them into five feeding groups based on their preferrred diet:fungus growers,litter feeders,soil feeders,soil-wood feeders,and wood feeders.We measured the environmental factors:soil pH,litter mass,aboveground plant biomass,and topographic position index(TPI).Soil-wood feeders showed the highest activity-density,followed by wood feeders,fungus growers,soil feeders,and litter feeders.Soil-wood feeders and fungus growers demonstated a strong correlation while litter feeders showed weak correlations with other feeding groups.Termite feeding groups and most of the termite species displayed a positive association with the high TPI and the low soil pH patches.Our results indicated that the examined environmental factors influenced the termite community assemblages and distribution patterns on a fine-scale in tropical rainforests.
基金This work was supported by the National Basic Research Program of China(No.2006CB403305).
文摘A total of 892 individuals sampled from a wild soybean population in a natural reserve near the Yellow River estuary located in Kenli of Shandong Province(China)were investigated.Seventeen SSR(simple sequence repeat)primer pairs from cultivated soybeans were used to estimate the genetic diversity of the population and its variation pattern versus changes of the sample size(sub-samples),in addition to investigating the fine-scale spatial genetic structure within the population.The results showed relatively high genetic diversity of the population with the mean value of allele number(A)being 2.88,expected heterozygosity(He)0.431,Shannon diversity index(I)0.699,and percentage of poly-morphic loci(P)100%.Sub-samples of different sizes(ten groups)were randomly drawn from the population and their genetic diversity was calculated by computer simulation.The regression model of the four diversity indexes with the change of sample sizes was computed.As a result,27-52 individuals can reach 95%of total genetic variability of the population.Spatial autocorrelation analysis revealed that the genetic patch size of this wild soybean population is about 18 m.The study provided a scientific basis for the sampling strategy of wild soybean populations.
基金Chair Professors of Changjiang Scholars Program and CAS Hundred Talents ProgramNational Program on Key Basic Research Projects (Grant No. 2006CB705700)+4 种基金National High-Tech R&D Program of China (Grant No.2006AA04Z216)National Key Technology R&D Program (Grant No. 2006BAH02A25) Joint Research Fund for Overseas Chinese Young Scholars (Grant No.30528027),National Natural Science Foundation of China (Grant Nos.30600151, 30500131 and 60532050) Natural Science Foundation of Beijing (Grant Nos. 4051002 and 4071003)
文摘With the rapid development of functional magnetic resonance imaging (fMRI) technology, the spatial resolution of fMRI data is continuously growing. This pro- vides us the possibility to detect the fine-scale patterns of brain activities. The es- tablished univariate and multivariate methods to analyze fMRI data mostly focus on detecting the activation blobs without considering the distributed fine-scale pat- terns within the blobs. To improve the sensitivity of the activation detection, in this paper, multivariate statistical method and univariate statistical method are com- bined to discover the fine-grained activity patterns. For one voxel in the brain, a local homogenous region is constructed. Then, time courses from the local ho- mogenous region are integrated with multivariate statistical method. Univariate statistical method is finally used to construct the interests of statistic for that voxel. The approach has explicitly taken into account the structures of both activity pat- terns and existing noise of local brain regions. Therefore, it could highlight the fine-scale activity patterns of the local regions. Experiments with simulated and real fMRI data demonstrate that the proposed method dramatically increases the sensitivity of detection of fine-scale brain activity patterns which contain the subtle information about experimental conditions.
文摘Background Fine-scale mapping of schistosomiasis to guide micro-targeting of interventions will gain importance in elimination settings,where the heterogeneity of transmission is often pronounced.Novel mobile applications offer new opportunities for disease mapping.We provide a practical introduction and documentation of the strengths and shortcomings of GPS-based household identification and participant recruitment using tablet-based applications for fine-scale schistosomiasis mapping at sub-district level in a remote area in Pemba,Tanzania.Methods A community-based household survey for urogenital schistosomiasis assessment was conducted from November 2020 until February 2021 in 20 small administrative areas in Pemba.For the survey,1400 housing structures were prospectively and randomly selected from shapefile data.To identify pre-selected structures and collect survey-related data,field enumerators searched for the houses’geolocation using the mobile applications Open Data Kit(ODK)and MAPS.ME.The number of inhabited and uninhabited structures,the median distance between the pre-selected and recorded locations,and the dropout rates due to non-participation or non-submission of urine samples of sufficient volume for schistosomiasis testing was assessed.Results Among the 1400 randomly selected housing structures,1396(99.7%)were identified by the enumerators.The median distance between the pre-selected and recorded structures was 5.4 m.A total of 1098(78.7%)were residential houses.Among them,99(9.0%)were dropped due to continuous absence of residents and 40(3.6%)households refused to participate.In 797(83.1%)among the 959 participating households,all eligible household members or all but one provided a urine sample of sufficient volume.Conclusions The fine-scale mapping approach using a combination of ODK and an offline navigation application installed on tablet computers allows a very precise identification of housing structures.Dropouts due to non-residential housing structures,absence,non-participation and lack of urine need to be considered in survey designs.Our findings can guide the planning and implementation of future household-based mapping or longitudinal surveys and thus support micro-targeting and follow-up of interventions for schistosomiasis control and elimination in remote areas.
基金supported by the National Natural Science Foundation of China(41877541,41471222).
文摘Afforestation is helpful to improve soil functions and increase soil organic carbon(SOC)sequestration in semiarid deserts.However,the fine-scale(around a single plant)spatial distribution of SOC and its liable organic carbon(LOC)fractions after afforestation in semiarid deserts are poorly understood.Pinus sylvestris and Salix psammophila afforested on shifting sandy land(Sland)were selected to quantify fine-scale(at 20,80,150 and 240 cm away from the trees)spatial distribution of SOC and its LOC fractions in the southeast edge of Mu Us Desert,China.The results showed that the afforested S.psammophila and P.sylvestris significantly increased SOC,total nitrogen,dissolved organic carbon,microbial biomass carbon and readily oxidized organic carbon(ROOC).At 20 cm distance,SOC storage of P.sylvestris was 27.21%higher than S.psammophila in 0-100 cm soil layers,and SOC storage of S.psammophila at 80 and 150 cm distances was 5.50%and 5.66%higher than P.sylvestris,respectively.Compared with Sland,SOC storage under S.psammophila and P.sylvestris significantly increased by 94.90%,39.50%,27.10%and 18.50%at 20,80,150 and 240 cm distance,respectively.ROOC accounted for 14.09%and 18.93%of SOC under S.psammophila and P.sylvestris,respectively.Our results suggest that afforestation can promote SOC accumulation at different distances from the plants,and that P.sylvestris allocates more organic matter to the closer soil compared with S.psammophila(<80 cm from the tree).
基金Supported by the China Meteorological Administration Special Public Welfare Research Fund(GYHY200906011,GYHY201006007,and GYHY201106004)
文摘A straight-line wind case was observed in Tianjin on 13 June 2005,which was caused by a gust front from a squall line.Mesoscale analyses based on observations from in-situ surface stations,sounding,and in-situ radar as well as fine-scale analyses based on observation tower data were performed.The mesoscale characteristics of the gust front determined its shape and fine-scale internal structures.Based on the scale and wavelet analyses,the fine-scale structures within the gust front were distinguished from the classical mesoscale structures,and such fine-scale structures were associated with the distribution of straight-line wind zones.A series of cross-frontal fine-scale circulations at the lowest levels of the gust front was discovered,which caused a relatively weak wind zone within the frontal strong wind zone.The downdraft at the rear of the head region of the gust front was more intense than in the classical model,and similar to the microburst,a series of vertical vortices propagated from the rear region to the frontal region.In addition,strong tangential fine-scale instability was detected in the frontal region.Finally,a fine-scale gust front model with straight-line wind zones is presented.
基金supported by the National Natural Science Foundation of China(Grant no.41874173).
文摘Fine-scale structures can be observed in small field-of-view(FOV)auroral observations,but they are often overlooked because they appear only sporadically in all-sky observations.Such forms are of great interest because they may embody specific magnetosphere-ionosphere coupling processes,reveal localized energy deposition pathways,and provide new insights into cross-scale plasma dynamics and instabilities.However,their limited spatial extent,transient occurrence,and scarcity in wide-FOV observations make systematic investigation challenging.Traditional manual analysis struggles to capture these subtle structures within vast all-sky datasets,while automated detection faces severe data imbalance and morphological ambiguity.To address these challenges,we propose a synthetic-to-real progressive learning framework for cross-FOV retrieval of rare auroral forms.A Generative Adversarial Network(GAN)is employed to perform cross-FOV transformation between unpaired small-FOV images containing rare aurora forms and all-sky images(ASI)without such structures,thereby generating large numbers of synthetic ASI with rare auroral morphology.These synthetic samples are used to train an initial detection model,which subsequently undergoes iterative fine-tuning through feedback-guided learning:The model performs inference on new all-sky data,and the progressively accumulated real detections are incorporated into the training set.Experimental results demonstrate that the proposed method achieves over 92%detection accuracy on ASI,enabling high-precision retrieval of small-scale auroral structures across large-scale observations.This framework provides a scalable and effective approach to rediscovering rare auroral phenomena in continuous all-sky monitoring,offering new opportunities for exploring the fine-scale dynamics of the upper atmosphere.
文摘在细粒度图像检索领域,现有研究成果主要集中于采用深层网络实现判别特征提取与精准定位,忽略了浅层特征信息的重要性,且无法消除背景中的复杂噪声干扰,限制了检索性能的提升。有鉴于此,提出了一种基于多层次特征提取的细粒度图像哈希检索方法(Fine-grained Deep Hashing image retrieval method based on Multi-level Feature Extraction, FDH-MFE)。该方法主要关注不同层次间特征的关联性,并增强了局部特征的提取能力。首先,提出了一个特征提取模块,旨在从网络的不同阶段提取细粒度特征,并通过图神经网络揭示其潜在的长距离依赖关系,为后续阶段提供更全面和精细的特征表示。其次,设计了一种代理损失算法,使得哈希码分布更加均匀,从而提升细粒度特征的区分能力。最后,通过设计背景抑制算法并结合三元组损失,增强了模型拟合全局分布的能力,使得所提出的方法在细粒度图像检索任务中表现出色。实验结果表明:该方法在4个公开数据集上的平均检索精度相较于次先进方法分别提高了15.03%、10.94%、9.98%和9.78%。
