Accurate fine-grained geospatial scene classification using remote sensing imagery is essential for a wide range of applications.However,existing approaches often rely on manually zooming remote sensing images at diff...Accurate fine-grained geospatial scene classification using remote sensing imagery is essential for a wide range of applications.However,existing approaches often rely on manually zooming remote sensing images at different scales to create typical scene samples.This approach fails to adequately support the fixed-resolution image interpretation requirements in real-world scenarios.To address this limitation,we introduce the million-scale fine-grained geospatial scene classification dataset(MEET),which contains over 1.03 million zoom-free remote sensing scene samples,manually annotated into 80 fine-grained categories.In MEET,each scene sample follows a scene-in-scene layout,where the central scene serves as the reference,and auxiliary scenes provide crucial spatial context for fine-grained classification.Moreover,to tackle the emerging challenge of scene-in-scene classification,we present the context-aware transformer(CAT),a model specifically designed for this task,which adaptively fuses spatial context to accurately classify the scene samples.CAT adaptively fuses spatial context to accurately classify the scene samples by learning attentional features that capture the relationships between the center and auxiliary scenes.Based on MEET,we establish a comprehensive benchmark for fine-grained geospatial scene classification,evaluating CAT against 11 competitive baselines.The results demonstrate that CAT significantly outperforms these baselines,achieving a 1.88%higher balanced accuracy(BA)with the Swin-Large backbone,and a notable 7.87%improvement with the Swin-Huge backbone.Further experiments validate the effectiveness of each module in CAT and show the practical applicability of CAT in the urban functional zone mapping.The source code and dataset will be publicly available at https://jerrywyn.github.io/project/MEET.html.展开更多
Fine-grained sediments are widely distributed and constitute the most abundant component in sedi-mentary systems,thus the research on their genesis and distribution is of great significance.In recent years,fine-graine...Fine-grained sediments are widely distributed and constitute the most abundant component in sedi-mentary systems,thus the research on their genesis and distribution is of great significance.In recent years,fine-grained sediment gravity-flows(FGSGF)have been recognized as an important transportation and depositional mechanism for accumulating thick successions of fine-grained sediments.Through a comprehensive review and synthesis of global research on FGSGF deposition,the characteristics,depositional mechanisms,and distribution patterns of fine-grained sediment gravity-flow deposits(FGSGFD)are discussed,and future research prospects are clarified.In addition to the traditionally recognized low-density turbidity current and muddy debris flow,wave-enhanced gravity flow,low-density muddy hyperpycnal flow,and hypopycnal plumes can all form widely distributed FGSGFD.At the same time,the evolution of FGSGF during transportation can result in transitional and hybrid gravity-flow deposits.The combination of multiple triggering mechanisms promotes the widespread develop-ment of FGSGFD,without temporal and spatial limitations.Different types and concentrations of clay minerals,organic matters,and organo-clay complexes are the keys to controlling the flow transformation of FGSGF from low-concentration turbidity currents to high-concentration muddy debris flows.Further study is needed on the interaction mechanism of FGSGF caused by different initiations,the evolution of FGSGF with the effect of organic-inorganic synergy,and the controlling factors of the distribution pat-terns of FGSGFD.The study of FGSGFD can shed some new light on the formation of widely developed thin-bedded siltstones within shales.At the same time,these insights may broaden the exploration scope of shale oil and gas,which have important geological significances for unconventional shale oil and gas.展开更多
Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimo...Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimodal Aspect-oriented Sentiment Classification(MASC).Currently,most existing models for JMASA only perform text and image feature encoding from a basic level,but often neglect the in-depth analysis of unimodal intrinsic features,which may lead to the low accuracy of aspect term extraction and the poor ability of sentiment prediction due to the insufficient learning of intra-modal features.Given this problem,we propose a Text-Image Feature Fine-grained Learning(TIFFL)model for JMASA.First,we construct an enhanced adjacency matrix of word dependencies and adopt graph convolutional network to learn the syntactic structure features for text,which addresses the context interference problem of identifying different aspect terms.Then,the adjective-noun pairs extracted from image are introduced to enable the semantic representation of visual features more intuitive,which addresses the ambiguous semantic extraction problem during image feature learning.Thereby,the model performance of aspect term extraction and sentiment polarity prediction can be further optimized and enhanced.Experiments on two Twitter benchmark datasets demonstrate that TIFFL achieves competitive results for JMASA,MATE and MASC,thus validating the effectiveness of our proposed methods.展开更多
The ongoing revolution in information technology is reshaping human life. In the realm of health behavior, wearable technology emerges as a leading digital solution,capturing physical behaviors (i.e., physical activit...The ongoing revolution in information technology is reshaping human life. In the realm of health behavior, wearable technology emerges as a leading digital solution,capturing physical behaviors (i.e., physical activity, sedentary habits, sleep patterns) within the 24-h cycle of daily life. Wearables are applied in research, clinical practice, and as lifestyle devices;most obvious, they promise to be a key element for increasing human physical activity, one of the biggest health challenges nowadays.展开更多
OBJECTIVE:To explore the potential molecular mechanism of Qigu capsule(芪骨胶囊,QGC) in the treatment of sarcopenia through network pharmacology and to verify it experimentally.METHODS:The active compounds of QGC and ...OBJECTIVE:To explore the potential molecular mechanism of Qigu capsule(芪骨胶囊,QGC) in the treatment of sarcopenia through network pharmacology and to verify it experimentally.METHODS:The active compounds of QGC and common targets between QGC and sarcopenia were screened from databases.Then the herbs-compounds-targets network,and protein-protein interaction(PPI) network was constructed.Gene ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analysis were performed by R software.Next,we used a dexamethasone-induced sarcopenia mouse model to evaluate the anti-sarcopenic mechanism of QGC.RESULTS:A total of 57 common targets of QGC and sarcopenia were obtained.Based on the enrichment analysis of GO and KEGG,we took the phosphatidylinositol 3-kinase(PI3K)/protein kinase B(Akt) signaling pathway as a key target to explore the mechanism of QGC on sarcopenia.Animal experiments showed that QGC could increase muscle strength and inhibit muscle fiber atrophy.In the model group,the expression of muscle ring finger-1 and Atrogin-1 were increased,while myosin heavy chain was decreased,QGC treatment reversed these changes.Moreover,compared with the model group,the expressions of pPI3K,p-Akt,p-mammalian target of rapamycin and pForkhead box O3 in the QGC group were all upregulated.CONCLUSION:QGC exerts an anti-sarcopenic effect by activating PI3K/Akt signaling pathway to regulate skeletal muscle protein metabolism.展开更多
Based on recent advancements in shale oil exploration within the Ordos Basin,this study presents a comprehensive investigation of the paleoenvironment,lithofacies assemblages and distribution,depositional mechanisms,a...Based on recent advancements in shale oil exploration within the Ordos Basin,this study presents a comprehensive investigation of the paleoenvironment,lithofacies assemblages and distribution,depositional mechanisms,and reservoir characteristics of shale oil of fine-grained sediment deposition in continental freshwater lacustrine basins,with a focus on the Chang 7_(3) sub-member of Triassic Yanchang Formation.The research integrates a variety of exploration data,including field outcrops,drilling,logging,core samples,geochemical analyses,and flume simulation.The study indicates that:(1)The paleoenvironment of the Chang 7_(3) deposition is characterized by a warm and humid climate,frequent monsoon events,and a large water depth of freshwater lacustrine basin.The paleogeomorphology exhibits an asymmetrical pattern,with steep slopes in the southwest and gentle slopes in the northeast,which can be subdivided into microgeomorphological units,including depressions and ridges in lakebed,as well as ancient channels.(2)The Chang 7_(3) sub-member is characterized by a diverse array of fine-grained sediments,including very fine sandstone,siltstone,mudstone and tuff.These sediments are primarily distributed in thin interbedded and laminated arrangements vertically.The overall grain size of the sandstone predominantly falls below 62.5μm,with individual layer thicknesses of 0.05–0.64 m.The deposits contain intact plant fragments and display various sedimentary structure,such as wavy bedding,inverse-to-normal grading sequence,and climbing ripple bedding,which indicating a depositional origin associated with density flows.(3)Flume simulation experiments have successfully replicated the transport processes and sedimentary characteristics associated with density flows.The initial phase is characterized by a density-velocity differential,resulting in a thicker,coarser sediment layer at the flow front,while the upper layers are thinner and finer in grain size.During the mid-phase,sliding water effects cause the fluid front to rise and facilitate rapid forward transport.This process generates multiple“new fronts”,enabling the long-distance transport of fine-grained sandstones,such as siltstone and argillaceous siltstone,into the center of the lake basin.(4)A sedimentary model primarily controlled by hyperpynal flows was established for the southwestern part of the basin,highlighting that the frequent occurrence of flood events and the steep slope topography in this area are primary controlling factors for the development of hyperpynal flows.(5)Sandstone and mudstone in the Chang 7_(3) sub-member exhibit micro-and nano-scale pore-throat systems,shale oil is present in various lithologies,while the content of movable oil varies considerably,with sandstone exhibiting the highest content of movable oil.(6)The fine-grained sediment complexes formed by multiple episodes of sandstones and mudstones associated with density flow in the Chang 7_(3) formation exhibit characteristics of“overall oil-bearing with differential storage capacity”.The combination of mudstone with low total organic carbon content(TOC)and siltstone is identified as the most favorable exploration target at present.展开更多
In this paper,we propose hierarchical attention dual network(DNet)for fine-grained image classification.The DNet can randomly select pairs of inputs from the dataset and compare the differences between them through hi...In this paper,we propose hierarchical attention dual network(DNet)for fine-grained image classification.The DNet can randomly select pairs of inputs from the dataset and compare the differences between them through hierarchical attention feature learning,which are used simultaneously to remove noise and retain salient features.In the loss function,it considers the losses of difference in paired images according to the intra-variance and inter-variance.In addition,we also collect the disaster scene dataset from remote sensing images and apply the proposed method to disaster scene classification,which contains complex scenes and multiple types of disasters.Compared to other methods,experimental results show that the DNet with hierarchical attention is robust to different datasets and performs better.展开更多
Objective:Pelvic floor dysfunction is common among pregnant and postpartum women and significantly impacts quality of life.This study aims to translate the German Pelvic Floor Questionnaire for Pregnant and Postpartum...Objective:Pelvic floor dysfunction is common among pregnant and postpartum women and significantly impacts quality of life.This study aims to translate the German Pelvic Floor Questionnaire for Pregnant and Postpartum Women into Chinese and to evaluate its reliability and validity in the Chinese population.Methods:The questionnaire was translated using the Brislin model.A cross-sectional study was conducted among pregnant and postpartum women to assess the content validity,construct validity,Cronbach’sαcoefficient,test-retest reliability,and split-half reliability of the Chinese version.Results:A total of 72 women were included,with 6.9% being pregnant and 93.1% postpartum;the age was(32.3±3.6)years.The Chinese version of the questionnaire contains 4 dimensions and 45 items.The content validity index of individual items ranged from 0.833 to 1.000,with a scale-level content validity index of 0.977 and intraclass correlation coefficients(ICCs)exceeding 0.90.The overall Cronbach’s α coefficient was 0.891,with subscale coefficients ranging from 0.732 to 0.884(all ICCs>0.70).The testretest reliability of the total scale was 0.833,and for the 4 dimensions,bladder,bowel,prolapse,and sexual function,the values were 0.776,0.579,0.732,and 0.645,respectively.The split-half reliability was 0.74.Conclusion:The Chinese version of the questionnaire demonstrated good reliability and validity,indicating its applicability in assessing pelvic floor dysfunction and associated risk factors during pregnancy and postpartum.展开更多
The purpose of this paper is to look into how reliable and valid the Persian version of the Cannabis Use Disorder Identification Test-Revised (CUDIT-R-Pr) is. It will also compare the screening features of the CUDIT-R...The purpose of this paper is to look into how reliable and valid the Persian version of the Cannabis Use Disorder Identification Test-Revised (CUDIT-R-Pr) is. It will also compare the screening features of the CUDIT-R with those of the DSM-5 criteria for cannabis use disorder (CUD) based on the SCID-5-CT in a group of university students in Tehran, Iran. The study used the stratified random sampling technique to collect data from 541 students (19 to 24 years old) who used cannabis in Tehran universities in 2024. Confirmatory factor analysis confirmed the uni-dimensionality of the CUDIT-R-Pr. We checked the reliability of the CUDIT-R-Pr using Cronbach Alpha, split-half, inter-rater, test-retest stability over time, and parallel testing equivalence. The results indicated that CUDIT-R-Pr is reliable, reproducible, and responsive, with substantial agreement and adequate interpretability. The CUDIT-R shows that it can tell the difference between different levels of cannabis use severity, which is known as discriminant validity. Receiver operating characteristic analyses confirmed this, using an area under the receiver operating characteristics curve (AUC = 0.95) at a cutoff of ten or less. This allowed CUDIT-R-Pr to accurately predict any DSM-5 based on the highest correctly classified value (0.89), demonstrating high levels of sensitivity (0.96), specificity (0.69), and Youden value (0.65). The exact maximum Youden index (0.72) showed that CUDIT-R-Pr could also predict moderate DSM-5 with a cutoff of twelve or less. To validate and generalize the CUDIT-R-Pr for use among Iranian cannabis users, we need more research.展开更多
Soil responds to cavity expansion is inherently rate-dependent,especially in the case of fine-grained soils.To better understand such rate effects,self-boring pressuremeter tests were conducted on Kunming peaty soil w...Soil responds to cavity expansion is inherently rate-dependent,especially in the case of fine-grained soils.To better understand such rate effects,self-boring pressuremeter tests were conducted on Kunming peaty soil within a strain rate range of 0.1%/min to 5.0%/min.The results showed a clear dependence of cavity pressure and excess pore pressure(EPP)on strain ratesdboth increased with higher rates for a given radial displacement.In light of the experimental results,three cases of cylindrical cavity expansion were investigated using the finite element method and analytical method,partially drained expansion in Modified Cam-Clay(MCC)soil,and undrained and partially drained expansion in elastoviscoplastic(EVP)soil.The EVP behavior was and modeled using the MCC model and the overstress viscoplastic theory.The results indicated that over the strain rate range of 0.0001%/min and 50%/min,the rate response of cavity pressure for the case of partially drained expansion in MCC soil(permeability coefficient ranging from 5×10^(-6) m/s to 2.5×10^(-11) m/s)is not obvious,while the EPP response during undrained expansion in EVP soil shows rate-independent.Only the partially drained solution for cavity expansion in EVP soil captured the rate-sensitive responses of both cavity pressure and EPP,confirmed by the pressuremeter tests on the Kunming peaty soil,Saint-Herblain clay,and Burswood clay.This suggests that the rate effect results from a combination of drainage-related and time-dependent soil behavior.Parametric studies further demonstrated that both viscous behavior and the overconsolidation ratio significantly influence cylindrical cavity expansion response,and the drainage conditions during expansion can be assessed using a nondimensional velocity.展开更多
Climate change is a global phenomenon that has profound impacts on ecological dynamics and biodiversity,shaping the interactions between species and their environment.To gain a deeper understanding of the mechanisms d...Climate change is a global phenomenon that has profound impacts on ecological dynamics and biodiversity,shaping the interactions between species and their environment.To gain a deeper understanding of the mechanisms driving climate change,phenological monitoring is essential.Traditional methods of defining phenological phases often rely on fixed thresholds.However,with the development of technology,deep learning-based classification models are now able to more accurately delineate phenological phases from images,enabling phenological monitoring.Despite the significant advancements these models have made in phenological monitoring,they still face challenges in fully capturing the complexity of biotic-environmental interactions,which can limit the fine-grained accuracy of phenological phase identification.To address this,we propose a novel deep learning model,RESformer,designed to monitor tree phenology at a fine-grained level using PhenoCam images.RESformer features a lightweight structure,making it suitable for deployment in resource-constrained environments.It incorporates a dual-branch routing mechanism that considers both global and local information,thereby improving the accuracy of phenological monitoring.To validate the effectiveness of RESformer,we conducted a case study involving 82,118 images taken over two years from four different locations in Wisconsin,focusing on the phenology of Acer.The images were classified into seven distinct phenological stages,with RESformer achieving an overall monitoring accuracy of 96.02%.Furthermore,we compared RESformer with a phenological monitoring approach based on the Green Chromatic Coordinate(GCC)index and ten popular classification models.The results showed that RESformer excelled in fine-grained monitoring,effectively capturing and identifying changes in phenological stages.This finding not only provides strong support for monitoring the phenology of Acer species but also offers valuable insights for understanding ecological trends and developing more effective ecosystem conservation and management strategies.展开更多
The spray-deposition was used to produce billets of Mg-4Al-1.5Zn-3Ca-1Nd(A alloy)and Mg-13Al-3Zn-3Ca-1Nd(B alloy),and evolution of deformation substructure and Mg_(x)Zn_(y)Ca_(z)metastable phase in fine-grained(3μm)M...The spray-deposition was used to produce billets of Mg-4Al-1.5Zn-3Ca-1Nd(A alloy)and Mg-13Al-3Zn-3Ca-1Nd(B alloy),and evolution of deformation substructure and Mg_(x)Zn_(y)Ca_(z)metastable phase in fine-grained(3μm)Mg alloys was investigated by scanning electron microscopy(SEM),transmission electron microscopy(TEM),X-ray diffraction(XRD),and electron backscattered diffraction(EBSD).It was found that different dislocation configurations were formed in A and B alloys.Redundant free dislocations(RFDs)and dislocation tangles were the ways to form deformation substructure in A alloy,no RFDs except dislocation tangles were found in B alloy.The interaction between nano-scale second phase particles(nano-scale C15 andβ-Mg_(17)(Al,Zn)_(12)phase)and different dislocation configurations had a significant effect on the deformation substructures formation.The mass transfer of Mg_(x)Zn_(y)Ca_(z)metastable phases and the stacking order of stacking faults were conducive to the Mg-Nd-Zn typed long period stacking ordered(LPSO)phases formation.Nano-scale C15 phases,Mg-Nd-Zn typed LPSO phases,c/a ratio,β-Mg_(17)(Al,Zn)_(12)phases were the key factors influencing the formation of textures.Different textures and grain boundary features(GB features)had a significant effect on k-value.The non-basal textures were the main factor affecting k-value in A alloy,while the high-angle grain boundary(HAGB)was the main factor affecting k-value in B alloy.展开更多
Bird monitoring and protection are essential for maintaining biodiversity,and fine-grained bird classification has become a key focus in this field.Audio-visual modalities provide critical cues for this task,but robus...Bird monitoring and protection are essential for maintaining biodiversity,and fine-grained bird classification has become a key focus in this field.Audio-visual modalities provide critical cues for this task,but robust feature extraction and efficient fusion remain major challenges.We introduce a multi-stage fine-grained audiovisual fusion network(MSFG-AVFNet) for fine-grained bird species classification,which addresses these challenges through two key components:(1) the audiovisual feature extraction module,which adopts a multi-stage finetuning strategy to provide high-quality unimodal features,laying a solid foundation for modality fusion;(2) the audiovisual feature fusion module,which combines a max pooling aggregation strategy with a novel audiovisual loss function to achieve effective and robust feature fusion.Experiments were conducted on the self-built AVB81and the publicly available SSW60 datasets,which contain data from 81 and 60 bird species,respectively.Comprehensive experiments demonstrate that our approach achieves notable performance gains,outperforming existing state-of-the-art methods.These results highlight its effectiveness in leveraging audiovisual modalities for fine-grained bird classification and its potential to support ecological monitoring and biodiversity research.展开更多
Fine-grained aircraft target detection in remote sensing holds significant research valueand practical applications,particularly in military defense and precision strikes.Given the complex-ity of remote sensing images...Fine-grained aircraft target detection in remote sensing holds significant research valueand practical applications,particularly in military defense and precision strikes.Given the complex-ity of remote sensing images,where targets are often small and similar within categories,detectingthese fine-grained targets is challenging.To address this,we constructed a fine-grained dataset ofremotely sensed airplanes;for the problems of remote sensing fine-grained targets with obvious head-to-tail distributions and large variations in target sizes,we proposed the DWDet fine-grained tar-get detection and recognition algorithm.First,for the problem of unbalanced category distribution,we adopt an adaptive sampling strategy.In addition,we construct a deformable convolutional blockand improve the decoupling head structure to improve the detection effect of the model ondeformed targets.Then,we design a localization loss function,which is used to improve the model’slocalization ability for targets of different scales.The experimental results show that our algorithmimproves the overall accuracy of the model by 4.1%compared to the baseline model,and improvesthe detection accuracy of small targets by 12.2%.The ablation and comparison experiments alsoprove the effectiveness of our algorithm.展开更多
Ecosystem services(ES)mapping and models have advanced in recent years.Improvements were made,and the assessments have transitioned from qualitative to quantitative.Although this is an important advancement,the ES map...Ecosystem services(ES)mapping and models have advanced in recent years.Improvements were made,and the assessments have transitioned from qualitative to quantitative.Although this is an important advancement,the ES mapping and modelling validation step has been overlooked,and this raises an important question in the credibility of the outcomes.This has been an important and unsolved issue in the ES research community that needs to be tackled.This highlight paper discusses the importance of validating single ES mapping and models.Conducting this using field or proximal/remote sensing raw data and not data from other models or stakeholder evaluation is important.A validation step should be mandatory in ES frameworks since it can assess the models’veracity,contribute to identifying the model’s weaknesses/strengths and ultimately represent a scientific advance in the field.This is easier to apply to the biophysical mapping and models of regulating and provisioning ES than to cultural ES,as the latter rely more on perception and cultural contexts.Also,ES supply models are easier to validate than demand and flow models.Robust and well-grounded models are essential for ensuring the reliability of individual ES maps and models and should be integrated into decision-making processes.Although several challenges arise related to the costs of data collection,in several cases prohibitive,and the time and the expertise needed to conduct this sampling and analysis,this is likely an imperative step that needs to be considered in the future.This will be beneficial in establishing ES research and improving decision-making and wellbeing.展开更多
Fine-grained Image Recognition(FGIR)task is dedicated to distinguishing similar sub-categories that belong to the same super-category,such as bird species and car types.In order to highlight visual differences,existin...Fine-grained Image Recognition(FGIR)task is dedicated to distinguishing similar sub-categories that belong to the same super-category,such as bird species and car types.In order to highlight visual differences,existing FGIR works often follow two steps:discriminative sub-region localization and local feature representation.However,these works pay less attention on global context information.They neglect a fact that the subtle visual difference in challenging scenarios can be highlighted through exploiting the spatial relationship among different subregions from a global view point.Therefore,in this paper,we consider both global and local information for FGIR,and propose a collaborative teacher-student strategy to reinforce and unity the two types of information.Our framework is implemented mainly by convolutional neural network,referred to Teacher-Student Based Attention Convolutional Neural Network(T-S-ACNN).For fine-grained local information,we choose the classic Multi-Attention Network(MA-Net)as our baseline,and propose a type of boundary constraint to further reduce background noises in the local attention maps.In this way,the discriminative sub-regions tend to appear in the area occupied by fine-grained objects,leading to more accurate sub-region localization.For fine-grained global information,we design a graph convolution based Global Attention Network(GA-Net),which can combine extracted local attention maps from MA-Net with non-local techniques to explore spatial relationship among subregions.At last,we develop a collaborative teacher-student strategy to adaptively determine the attended roles and optimization modes,so as to enhance the cooperative reinforcement of MA-Net and GA-Net.Extensive experiments on CUB-200-2011,Stanford Cars and FGVC Aircraft datasets illustrate the promising performance of our framework.展开更多
Objectives This study aimed to validate the Russian version of the Copenhagen Burnout Inventory(R-CBI)among nurses in Kazakhstan and Kyrgyzstan and explored factors contributing to burnout.Methods The original Copenha...Objectives This study aimed to validate the Russian version of the Copenhagen Burnout Inventory(R-CBI)among nurses in Kazakhstan and Kyrgyzstan and explored factors contributing to burnout.Methods The original Copenhagen Burnout Inventory(CBI)was translated into the R-CBI using a rigorous forward-backward method and reviewed by experts.Between July and November 2022,1,530 nurses were recruited through convenience sampling method from various nursing settings in Kazakhstan and Kyrgyzstan to test the scale’s reliability and validity,including confirmatory factor analysis(CFA),internal consistency reliability,and concurrent validity.A linear regression analysis was conducted to identify influencing factors of burnout.Results The content of the R-CBI is consistent with the original CBI,consisting of 19 items with three dimensions.The Cronbach’sαcoefficient is 0.926 in Kazakhstan and 0.922 in Kyrgyzstan,ranging from 0.830 to 0.898 for three dimensions.The CFA results among nurses in Kazakhstan and Kyrgyzstan supported the three-factor structure of R-CBI with good fit indices.Concurrent validity was established through significant correlations(P<0.001)with job satisfaction questionnaire(r=−0.457),Depression Anxiety Stress Scales(r=0.506 in depression,r=0.485 in anxiety,r=0.564 in stress),and WHO-5 Well-Being Index(r=−0.528).The overall burnout level was 36.1±17.6 and 37.5±17.4 in Kazakhstani and Kyrgyzstani nurses,respectively.Significant influencing factors of burnout included gender,age,educational level,and COVID-19 infection history.Conclusions The R-CBI was proved to be a reliable and valid tool for assessing nurses’burnout in Kazakhstan and Kyrgyzstan.展开更多
Objective:To translate,adapt,and validate the Indonesian version of the Prenatal Health Behavior Scale.Methods:This cross-sectional,cross-cultural adaptation study was conducted between September 2024 and October 2024...Objective:To translate,adapt,and validate the Indonesian version of the Prenatal Health Behavior Scale.Methods:This cross-sectional,cross-cultural adaptation study was conducted between September 2024 and October 2024 in Ngrambe and Sine,subdistricts in Ngawi,East Java,Indonesia.We selected participants using purposive convenience sampling and matched them with inclusion and exclusion criteria.We collected sociodemographic,Prenatal Health Behavior Scale,and anthropometrics(height,weight,body mass index,and middle-upper arm circumference)data.We analyzed the content validity using the content validity index and Gwet's chance-corrected Agreement Coefficient 2,face validity by pilot-testing on several pregnant women,and construct validity using exploratory factor analysis.We measured reliability using McDonald's omega coefficient.Results:We recruited 183 pregnant women in this study(median age 28 years).The item-content validity index(I-CVI)of all items was 1.00,with Gwet's chance-corrected Agreement Coefficient 2 was 0.945.The face validity resulted in a clear statement of all items.The exploratory factor analysis showed the two-factor model best suited to the questionnaire.Omega coefficients for the overall scale,health-impairing,and health-promoting domains were 0.696,0.507,and 0.678,respectively.Conclusions:The Indonesian version of the Prenatal Health Behavior Scale is a valid and reliable instrument to assess prenatal health behavior in Indonesian-speaking pregnant women.Future studies may implement this scale in community and clinical settings.展开更多
To solve the problem of the lack of reference material(RM)for determination of allergenic ingredients in food,a RM of cashew nut powder was developed in the study.Cashew nut powder was prepared from cashew nut kernel ...To solve the problem of the lack of reference material(RM)for determination of allergenic ingredients in food,a RM of cashew nut powder was developed in the study.Cashew nut powder was prepared from cashew nut kernel by selecting,cleaning,crushing,n-hexane degreasing and sieving treatment.The reliability and traceability of RM was verified using real-time quantitative polymerase chain reaction(qPCR)and phylogenetic tree analysis.The cashew nut powder RM showed good homogeneity,and good stability under long-term storage at 4℃and short-term simulated transportation from-20 to 45℃.The RM was determined jointly by 8 collaborative laboratories,and the characteristic CT value was 24.732,and the extended uncertainty was 1.052%(k=2).The RM was applied to verify the amplification efficiency and the limit of detection for qPCR assay,and showed good applicability.The RM could be used for method validation and quality control,for the determination of allergenic ingredients in food mixed with trace amounts of cashew nut.展开更多
BACKGROUND The Victorian institute of sports assessment for patellar tendons questionnaire(VISA-P),a valid tool for patellar tendinopathy,has been used for patellofemoral pain(PFP).AIM To validate VISA-P in PFP.METHOD...BACKGROUND The Victorian institute of sports assessment for patellar tendons questionnaire(VISA-P),a valid tool for patellar tendinopathy,has been used for patellofemoral pain(PFP).AIM To validate VISA-P in PFP.METHODS Study of validity,responsiveness and feasibility following COSMIN.Inclusion criteria:Subjects with PFP,aged 18 to 55.Agreement among 10 experts on the relevance and clarity of each item using Aiken's V coefficient determined content validity.An exploratory factorial analysis established structural validity.The correlation of VISA-P with knee injury and osteoarthritis outcome score for PFP and Osteoarthritis(KOOS-PF)and Kujala patellofemoral score(KPS;specific for PFP)analyzed the construct validity.Internal consistency was calculated with Cronbach'sαand test-retest reliability with the intraclass correlation coefficient(ICC).Feasibility considered the subjects'self-completion time.RESULTS The sample consisted of 103 knees from 73 subjects(47 female/26 male;aged 34.9±13 SD).The items were relevant and clear,with the exception of item-8,which didn't reach an acceptable level of agreement on clarity.Exploratory factorial analysis found a 2-factor solution,which explained 63.48%of the variance.VISAP achieved a strong and significant correlation with KOOS-PF(Spearman rho=0.826;P<0.001)and KPS(Spearman rho=0.771;P<0.001).The questionnaire showed adequate reliability(Cronbach'sα:0.752;ICC:0.934;P<0.0001;95%CI:0.902-0.955).The mean self-completion time was 232±0.52 SD seconds.CONCLUSION VISA-P proved to be valid and reliable to functionally assess PFP and/or chondromalacia patella.VISA-P is a feasible tool in the clinical and research environment,quick and easy to complete.展开更多
基金supported by the National Natural Science Foundation of China(42030102,42371321).
文摘Accurate fine-grained geospatial scene classification using remote sensing imagery is essential for a wide range of applications.However,existing approaches often rely on manually zooming remote sensing images at different scales to create typical scene samples.This approach fails to adequately support the fixed-resolution image interpretation requirements in real-world scenarios.To address this limitation,we introduce the million-scale fine-grained geospatial scene classification dataset(MEET),which contains over 1.03 million zoom-free remote sensing scene samples,manually annotated into 80 fine-grained categories.In MEET,each scene sample follows a scene-in-scene layout,where the central scene serves as the reference,and auxiliary scenes provide crucial spatial context for fine-grained classification.Moreover,to tackle the emerging challenge of scene-in-scene classification,we present the context-aware transformer(CAT),a model specifically designed for this task,which adaptively fuses spatial context to accurately classify the scene samples.CAT adaptively fuses spatial context to accurately classify the scene samples by learning attentional features that capture the relationships between the center and auxiliary scenes.Based on MEET,we establish a comprehensive benchmark for fine-grained geospatial scene classification,evaluating CAT against 11 competitive baselines.The results demonstrate that CAT significantly outperforms these baselines,achieving a 1.88%higher balanced accuracy(BA)with the Swin-Large backbone,and a notable 7.87%improvement with the Swin-Huge backbone.Further experiments validate the effectiveness of each module in CAT and show the practical applicability of CAT in the urban functional zone mapping.The source code and dataset will be publicly available at https://jerrywyn.github.io/project/MEET.html.
基金supported by National Natural Science Foundation of China(Grant Nos.42072126,42372139)the Natural Science Foundation of Sichuan Province(Grant Nos.2022NSFSC0990).
文摘Fine-grained sediments are widely distributed and constitute the most abundant component in sedi-mentary systems,thus the research on their genesis and distribution is of great significance.In recent years,fine-grained sediment gravity-flows(FGSGF)have been recognized as an important transportation and depositional mechanism for accumulating thick successions of fine-grained sediments.Through a comprehensive review and synthesis of global research on FGSGF deposition,the characteristics,depositional mechanisms,and distribution patterns of fine-grained sediment gravity-flow deposits(FGSGFD)are discussed,and future research prospects are clarified.In addition to the traditionally recognized low-density turbidity current and muddy debris flow,wave-enhanced gravity flow,low-density muddy hyperpycnal flow,and hypopycnal plumes can all form widely distributed FGSGFD.At the same time,the evolution of FGSGF during transportation can result in transitional and hybrid gravity-flow deposits.The combination of multiple triggering mechanisms promotes the widespread develop-ment of FGSGFD,without temporal and spatial limitations.Different types and concentrations of clay minerals,organic matters,and organo-clay complexes are the keys to controlling the flow transformation of FGSGF from low-concentration turbidity currents to high-concentration muddy debris flows.Further study is needed on the interaction mechanism of FGSGF caused by different initiations,the evolution of FGSGF with the effect of organic-inorganic synergy,and the controlling factors of the distribution pat-terns of FGSGFD.The study of FGSGFD can shed some new light on the formation of widely developed thin-bedded siltstones within shales.At the same time,these insights may broaden the exploration scope of shale oil and gas,which have important geological significances for unconventional shale oil and gas.
基金supported by the Science and Technology Project of Henan Province(No.222102210081).
文摘Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimodal Aspect-oriented Sentiment Classification(MASC).Currently,most existing models for JMASA only perform text and image feature encoding from a basic level,but often neglect the in-depth analysis of unimodal intrinsic features,which may lead to the low accuracy of aspect term extraction and the poor ability of sentiment prediction due to the insufficient learning of intra-modal features.Given this problem,we propose a Text-Image Feature Fine-grained Learning(TIFFL)model for JMASA.First,we construct an enhanced adjacency matrix of word dependencies and adopt graph convolutional network to learn the syntactic structure features for text,which addresses the context interference problem of identifying different aspect terms.Then,the adjective-noun pairs extracted from image are introduced to enable the semantic representation of visual features more intuitive,which addresses the ambiguous semantic extraction problem during image feature learning.Thereby,the model performance of aspect term extraction and sentiment polarity prediction can be further optimized and enhanced.Experiments on two Twitter benchmark datasets demonstrate that TIFFL achieves competitive results for JMASA,MATE and MASC,thus validating the effectiveness of our proposed methods.
基金funded in part by the German Research Foundation(Grant reference:496846758).
文摘The ongoing revolution in information technology is reshaping human life. In the realm of health behavior, wearable technology emerges as a leading digital solution,capturing physical behaviors (i.e., physical activity, sedentary habits, sleep patterns) within the 24-h cycle of daily life. Wearables are applied in research, clinical practice, and as lifestyle devices;most obvious, they promise to be a key element for increasing human physical activity, one of the biggest health challenges nowadays.
基金Shanghai Clinical Research Center for Chronic Musculoskeletal Diseases (20MC1920600)Shanghai Key Clinical Specialty "Traditional Chinese Medicine Orthopaedic Traumatology"(shslczdzk03901)+3 种基金The Second Round of Construction Project of National TCM Academic School Inheritance Studio "Shi's Trauma Department"[Letter of the People's Education of Traditional Chinese Medicine (2019) No.62]Shanghai High-level Local Universities "Chronic Muscle and Bone Damage Research and Transformation" Innovation Team [No.3 of Shanghai Education Commission (2022)]Program for Shanghai High-Level Local University Innovation Team (SZY20220315)Shanghai Shenkang Hospital Development Center Clinical Three-year Action Plan (SHDC2020CR3090B)。
文摘OBJECTIVE:To explore the potential molecular mechanism of Qigu capsule(芪骨胶囊,QGC) in the treatment of sarcopenia through network pharmacology and to verify it experimentally.METHODS:The active compounds of QGC and common targets between QGC and sarcopenia were screened from databases.Then the herbs-compounds-targets network,and protein-protein interaction(PPI) network was constructed.Gene ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analysis were performed by R software.Next,we used a dexamethasone-induced sarcopenia mouse model to evaluate the anti-sarcopenic mechanism of QGC.RESULTS:A total of 57 common targets of QGC and sarcopenia were obtained.Based on the enrichment analysis of GO and KEGG,we took the phosphatidylinositol 3-kinase(PI3K)/protein kinase B(Akt) signaling pathway as a key target to explore the mechanism of QGC on sarcopenia.Animal experiments showed that QGC could increase muscle strength and inhibit muscle fiber atrophy.In the model group,the expression of muscle ring finger-1 and Atrogin-1 were increased,while myosin heavy chain was decreased,QGC treatment reversed these changes.Moreover,compared with the model group,the expressions of pPI3K,p-Akt,p-mammalian target of rapamycin and pForkhead box O3 in the QGC group were all upregulated.CONCLUSION:QGC exerts an anti-sarcopenic effect by activating PI3K/Akt signaling pathway to regulate skeletal muscle protein metabolism.
基金Supported by the CNPC Major Science and Technology Project(2021DJ1806).
文摘Based on recent advancements in shale oil exploration within the Ordos Basin,this study presents a comprehensive investigation of the paleoenvironment,lithofacies assemblages and distribution,depositional mechanisms,and reservoir characteristics of shale oil of fine-grained sediment deposition in continental freshwater lacustrine basins,with a focus on the Chang 7_(3) sub-member of Triassic Yanchang Formation.The research integrates a variety of exploration data,including field outcrops,drilling,logging,core samples,geochemical analyses,and flume simulation.The study indicates that:(1)The paleoenvironment of the Chang 7_(3) deposition is characterized by a warm and humid climate,frequent monsoon events,and a large water depth of freshwater lacustrine basin.The paleogeomorphology exhibits an asymmetrical pattern,with steep slopes in the southwest and gentle slopes in the northeast,which can be subdivided into microgeomorphological units,including depressions and ridges in lakebed,as well as ancient channels.(2)The Chang 7_(3) sub-member is characterized by a diverse array of fine-grained sediments,including very fine sandstone,siltstone,mudstone and tuff.These sediments are primarily distributed in thin interbedded and laminated arrangements vertically.The overall grain size of the sandstone predominantly falls below 62.5μm,with individual layer thicknesses of 0.05–0.64 m.The deposits contain intact plant fragments and display various sedimentary structure,such as wavy bedding,inverse-to-normal grading sequence,and climbing ripple bedding,which indicating a depositional origin associated with density flows.(3)Flume simulation experiments have successfully replicated the transport processes and sedimentary characteristics associated with density flows.The initial phase is characterized by a density-velocity differential,resulting in a thicker,coarser sediment layer at the flow front,while the upper layers are thinner and finer in grain size.During the mid-phase,sliding water effects cause the fluid front to rise and facilitate rapid forward transport.This process generates multiple“new fronts”,enabling the long-distance transport of fine-grained sandstones,such as siltstone and argillaceous siltstone,into the center of the lake basin.(4)A sedimentary model primarily controlled by hyperpynal flows was established for the southwestern part of the basin,highlighting that the frequent occurrence of flood events and the steep slope topography in this area are primary controlling factors for the development of hyperpynal flows.(5)Sandstone and mudstone in the Chang 7_(3) sub-member exhibit micro-and nano-scale pore-throat systems,shale oil is present in various lithologies,while the content of movable oil varies considerably,with sandstone exhibiting the highest content of movable oil.(6)The fine-grained sediment complexes formed by multiple episodes of sandstones and mudstones associated with density flow in the Chang 7_(3) formation exhibit characteristics of“overall oil-bearing with differential storage capacity”.The combination of mudstone with low total organic carbon content(TOC)and siltstone is identified as the most favorable exploration target at present.
基金Supported by the National Natural Science Foundation of China(61601176)。
文摘In this paper,we propose hierarchical attention dual network(DNet)for fine-grained image classification.The DNet can randomly select pairs of inputs from the dataset and compare the differences between them through hierarchical attention feature learning,which are used simultaneously to remove noise and retain salient features.In the loss function,it considers the losses of difference in paired images according to the intra-variance and inter-variance.In addition,we also collect the disaster scene dataset from remote sensing images and apply the proposed method to disaster scene classification,which contains complex scenes and multiple types of disasters.Compared to other methods,experimental results show that the DNet with hierarchical attention is robust to different datasets and performs better.
基金supported by the Natural Science Foundation of Hunan Province(2024JJ6626)the Hunan Provincial Key Laboratory of Regional Hereditary Birth Defects Prevention and Control(HPKL202320),China.
文摘Objective:Pelvic floor dysfunction is common among pregnant and postpartum women and significantly impacts quality of life.This study aims to translate the German Pelvic Floor Questionnaire for Pregnant and Postpartum Women into Chinese and to evaluate its reliability and validity in the Chinese population.Methods:The questionnaire was translated using the Brislin model.A cross-sectional study was conducted among pregnant and postpartum women to assess the content validity,construct validity,Cronbach’sαcoefficient,test-retest reliability,and split-half reliability of the Chinese version.Results:A total of 72 women were included,with 6.9% being pregnant and 93.1% postpartum;the age was(32.3±3.6)years.The Chinese version of the questionnaire contains 4 dimensions and 45 items.The content validity index of individual items ranged from 0.833 to 1.000,with a scale-level content validity index of 0.977 and intraclass correlation coefficients(ICCs)exceeding 0.90.The overall Cronbach’s α coefficient was 0.891,with subscale coefficients ranging from 0.732 to 0.884(all ICCs>0.70).The testretest reliability of the total scale was 0.833,and for the 4 dimensions,bladder,bowel,prolapse,and sexual function,the values were 0.776,0.579,0.732,and 0.645,respectively.The split-half reliability was 0.74.Conclusion:The Chinese version of the questionnaire demonstrated good reliability and validity,indicating its applicability in assessing pelvic floor dysfunction and associated risk factors during pregnancy and postpartum.
文摘The purpose of this paper is to look into how reliable and valid the Persian version of the Cannabis Use Disorder Identification Test-Revised (CUDIT-R-Pr) is. It will also compare the screening features of the CUDIT-R with those of the DSM-5 criteria for cannabis use disorder (CUD) based on the SCID-5-CT in a group of university students in Tehran, Iran. The study used the stratified random sampling technique to collect data from 541 students (19 to 24 years old) who used cannabis in Tehran universities in 2024. Confirmatory factor analysis confirmed the uni-dimensionality of the CUDIT-R-Pr. We checked the reliability of the CUDIT-R-Pr using Cronbach Alpha, split-half, inter-rater, test-retest stability over time, and parallel testing equivalence. The results indicated that CUDIT-R-Pr is reliable, reproducible, and responsive, with substantial agreement and adequate interpretability. The CUDIT-R shows that it can tell the difference between different levels of cannabis use severity, which is known as discriminant validity. Receiver operating characteristic analyses confirmed this, using an area under the receiver operating characteristics curve (AUC = 0.95) at a cutoff of ten or less. This allowed CUDIT-R-Pr to accurately predict any DSM-5 based on the highest correctly classified value (0.89), demonstrating high levels of sensitivity (0.96), specificity (0.69), and Youden value (0.65). The exact maximum Youden index (0.72) showed that CUDIT-R-Pr could also predict moderate DSM-5 with a cutoff of twelve or less. To validate and generalize the CUDIT-R-Pr for use among Iranian cannabis users, we need more research.
基金The financial support of the National Natural Science Foundation of China(Grant Nos.41972293,42272337)the Science Fund for Distinguished Young Scholars of Hubei Province(Grant No.2023AFA078)are gratefully acknowledged.
文摘Soil responds to cavity expansion is inherently rate-dependent,especially in the case of fine-grained soils.To better understand such rate effects,self-boring pressuremeter tests were conducted on Kunming peaty soil within a strain rate range of 0.1%/min to 5.0%/min.The results showed a clear dependence of cavity pressure and excess pore pressure(EPP)on strain ratesdboth increased with higher rates for a given radial displacement.In light of the experimental results,three cases of cylindrical cavity expansion were investigated using the finite element method and analytical method,partially drained expansion in Modified Cam-Clay(MCC)soil,and undrained and partially drained expansion in elastoviscoplastic(EVP)soil.The EVP behavior was and modeled using the MCC model and the overstress viscoplastic theory.The results indicated that over the strain rate range of 0.0001%/min and 50%/min,the rate response of cavity pressure for the case of partially drained expansion in MCC soil(permeability coefficient ranging from 5×10^(-6) m/s to 2.5×10^(-11) m/s)is not obvious,while the EPP response during undrained expansion in EVP soil shows rate-independent.Only the partially drained solution for cavity expansion in EVP soil captured the rate-sensitive responses of both cavity pressure and EPP,confirmed by the pressuremeter tests on the Kunming peaty soil,Saint-Herblain clay,and Burswood clay.This suggests that the rate effect results from a combination of drainage-related and time-dependent soil behavior.Parametric studies further demonstrated that both viscous behavior and the overconsolidation ratio significantly influence cylindrical cavity expansion response,and the drainage conditions during expansion can be assessed using a nondimensional velocity.
基金supported by the National Natural Science Foundation of China(32171777)the Natural Science Foundation of Heilongjiang for Distinguished Young Scientists(JQ2023F002)the Fundamental Research Funds for Central Universities(2572023CT16).
文摘Climate change is a global phenomenon that has profound impacts on ecological dynamics and biodiversity,shaping the interactions between species and their environment.To gain a deeper understanding of the mechanisms driving climate change,phenological monitoring is essential.Traditional methods of defining phenological phases often rely on fixed thresholds.However,with the development of technology,deep learning-based classification models are now able to more accurately delineate phenological phases from images,enabling phenological monitoring.Despite the significant advancements these models have made in phenological monitoring,they still face challenges in fully capturing the complexity of biotic-environmental interactions,which can limit the fine-grained accuracy of phenological phase identification.To address this,we propose a novel deep learning model,RESformer,designed to monitor tree phenology at a fine-grained level using PhenoCam images.RESformer features a lightweight structure,making it suitable for deployment in resource-constrained environments.It incorporates a dual-branch routing mechanism that considers both global and local information,thereby improving the accuracy of phenological monitoring.To validate the effectiveness of RESformer,we conducted a case study involving 82,118 images taken over two years from four different locations in Wisconsin,focusing on the phenology of Acer.The images were classified into seven distinct phenological stages,with RESformer achieving an overall monitoring accuracy of 96.02%.Furthermore,we compared RESformer with a phenological monitoring approach based on the Green Chromatic Coordinate(GCC)index and ten popular classification models.The results showed that RESformer excelled in fine-grained monitoring,effectively capturing and identifying changes in phenological stages.This finding not only provides strong support for monitoring the phenology of Acer species but also offers valuable insights for understanding ecological trends and developing more effective ecosystem conservation and management strategies.
基金financial support by the National Natural Science Foundation of China(No.51364032)the Inner Mongolia Natural Science Foundation(No.2022MS05028)。
文摘The spray-deposition was used to produce billets of Mg-4Al-1.5Zn-3Ca-1Nd(A alloy)and Mg-13Al-3Zn-3Ca-1Nd(B alloy),and evolution of deformation substructure and Mg_(x)Zn_(y)Ca_(z)metastable phase in fine-grained(3μm)Mg alloys was investigated by scanning electron microscopy(SEM),transmission electron microscopy(TEM),X-ray diffraction(XRD),and electron backscattered diffraction(EBSD).It was found that different dislocation configurations were formed in A and B alloys.Redundant free dislocations(RFDs)and dislocation tangles were the ways to form deformation substructure in A alloy,no RFDs except dislocation tangles were found in B alloy.The interaction between nano-scale second phase particles(nano-scale C15 andβ-Mg_(17)(Al,Zn)_(12)phase)and different dislocation configurations had a significant effect on the deformation substructures formation.The mass transfer of Mg_(x)Zn_(y)Ca_(z)metastable phases and the stacking order of stacking faults were conducive to the Mg-Nd-Zn typed long period stacking ordered(LPSO)phases formation.Nano-scale C15 phases,Mg-Nd-Zn typed LPSO phases,c/a ratio,β-Mg_(17)(Al,Zn)_(12)phases were the key factors influencing the formation of textures.Different textures and grain boundary features(GB features)had a significant effect on k-value.The non-basal textures were the main factor affecting k-value in A alloy,while the high-angle grain boundary(HAGB)was the main factor affecting k-value in B alloy.
基金supported by the Beijing Natural Science Foundation(No.5252014)the Open Fund of The Key Laboratory of Urban Ecological Environment Simulation and Protection,Ministry of Ecology and Environment of the People's Republic of China (No.UEESP-202502)the National Natural Science Foundation of China (No.62303063&32371874)。
文摘Bird monitoring and protection are essential for maintaining biodiversity,and fine-grained bird classification has become a key focus in this field.Audio-visual modalities provide critical cues for this task,but robust feature extraction and efficient fusion remain major challenges.We introduce a multi-stage fine-grained audiovisual fusion network(MSFG-AVFNet) for fine-grained bird species classification,which addresses these challenges through two key components:(1) the audiovisual feature extraction module,which adopts a multi-stage finetuning strategy to provide high-quality unimodal features,laying a solid foundation for modality fusion;(2) the audiovisual feature fusion module,which combines a max pooling aggregation strategy with a novel audiovisual loss function to achieve effective and robust feature fusion.Experiments were conducted on the self-built AVB81and the publicly available SSW60 datasets,which contain data from 81 and 60 bird species,respectively.Comprehensive experiments demonstrate that our approach achieves notable performance gains,outperforming existing state-of-the-art methods.These results highlight its effectiveness in leveraging audiovisual modalities for fine-grained bird classification and its potential to support ecological monitoring and biodiversity research.
基金supported by National Natural Science Foundation of China(No.62471034)Hebei Natural Science Foundation(No.F2023105001).
文摘Fine-grained aircraft target detection in remote sensing holds significant research valueand practical applications,particularly in military defense and precision strikes.Given the complex-ity of remote sensing images,where targets are often small and similar within categories,detectingthese fine-grained targets is challenging.To address this,we constructed a fine-grained dataset ofremotely sensed airplanes;for the problems of remote sensing fine-grained targets with obvious head-to-tail distributions and large variations in target sizes,we proposed the DWDet fine-grained tar-get detection and recognition algorithm.First,for the problem of unbalanced category distribution,we adopt an adaptive sampling strategy.In addition,we construct a deformable convolutional blockand improve the decoupling head structure to improve the detection effect of the model ondeformed targets.Then,we design a localization loss function,which is used to improve the model’slocalization ability for targets of different scales.The experimental results show that our algorithmimproves the overall accuracy of the model by 4.1%compared to the baseline model,and improvesthe detection accuracy of small targets by 12.2%.The ablation and comparison experiments alsoprove the effectiveness of our algorithm.
基金supported by the project Monetary valuation of soil ecosystem services and creation of initiatives to invest in soil health:setting a framework for the inclusion of soil health in business and in the policy making process(InBestSoil)(Horizon Europe)Grant agreement ID:101091099。
文摘Ecosystem services(ES)mapping and models have advanced in recent years.Improvements were made,and the assessments have transitioned from qualitative to quantitative.Although this is an important advancement,the ES mapping and modelling validation step has been overlooked,and this raises an important question in the credibility of the outcomes.This has been an important and unsolved issue in the ES research community that needs to be tackled.This highlight paper discusses the importance of validating single ES mapping and models.Conducting this using field or proximal/remote sensing raw data and not data from other models or stakeholder evaluation is important.A validation step should be mandatory in ES frameworks since it can assess the models’veracity,contribute to identifying the model’s weaknesses/strengths and ultimately represent a scientific advance in the field.This is easier to apply to the biophysical mapping and models of regulating and provisioning ES than to cultural ES,as the latter rely more on perception and cultural contexts.Also,ES supply models are easier to validate than demand and flow models.Robust and well-grounded models are essential for ensuring the reliability of individual ES maps and models and should be integrated into decision-making processes.Although several challenges arise related to the costs of data collection,in several cases prohibitive,and the time and the expertise needed to conduct this sampling and analysis,this is likely an imperative step that needs to be considered in the future.This will be beneficial in establishing ES research and improving decision-making and wellbeing.
基金supported by the National Natural Science Foundation of China,China (Grants No.62171232)the Priority Academic Program Development of Jiangsu Higher Education Institutions,China。
文摘Fine-grained Image Recognition(FGIR)task is dedicated to distinguishing similar sub-categories that belong to the same super-category,such as bird species and car types.In order to highlight visual differences,existing FGIR works often follow two steps:discriminative sub-region localization and local feature representation.However,these works pay less attention on global context information.They neglect a fact that the subtle visual difference in challenging scenarios can be highlighted through exploiting the spatial relationship among different subregions from a global view point.Therefore,in this paper,we consider both global and local information for FGIR,and propose a collaborative teacher-student strategy to reinforce and unity the two types of information.Our framework is implemented mainly by convolutional neural network,referred to Teacher-Student Based Attention Convolutional Neural Network(T-S-ACNN).For fine-grained local information,we choose the classic Multi-Attention Network(MA-Net)as our baseline,and propose a type of boundary constraint to further reduce background noises in the local attention maps.In this way,the discriminative sub-regions tend to appear in the area occupied by fine-grained objects,leading to more accurate sub-region localization.For fine-grained global information,we design a graph convolution based Global Attention Network(GA-Net),which can combine extracted local attention maps from MA-Net with non-local techniques to explore spatial relationship among subregions.At last,we develop a collaborative teacher-student strategy to adaptively determine the attended roles and optimization modes,so as to enhance the cooperative reinforcement of MA-Net and GA-Net.Extensive experiments on CUB-200-2011,Stanford Cars and FGVC Aircraft datasets illustrate the promising performance of our framework.
文摘Objectives This study aimed to validate the Russian version of the Copenhagen Burnout Inventory(R-CBI)among nurses in Kazakhstan and Kyrgyzstan and explored factors contributing to burnout.Methods The original Copenhagen Burnout Inventory(CBI)was translated into the R-CBI using a rigorous forward-backward method and reviewed by experts.Between July and November 2022,1,530 nurses were recruited through convenience sampling method from various nursing settings in Kazakhstan and Kyrgyzstan to test the scale’s reliability and validity,including confirmatory factor analysis(CFA),internal consistency reliability,and concurrent validity.A linear regression analysis was conducted to identify influencing factors of burnout.Results The content of the R-CBI is consistent with the original CBI,consisting of 19 items with three dimensions.The Cronbach’sαcoefficient is 0.926 in Kazakhstan and 0.922 in Kyrgyzstan,ranging from 0.830 to 0.898 for three dimensions.The CFA results among nurses in Kazakhstan and Kyrgyzstan supported the three-factor structure of R-CBI with good fit indices.Concurrent validity was established through significant correlations(P<0.001)with job satisfaction questionnaire(r=−0.457),Depression Anxiety Stress Scales(r=0.506 in depression,r=0.485 in anxiety,r=0.564 in stress),and WHO-5 Well-Being Index(r=−0.528).The overall burnout level was 36.1±17.6 and 37.5±17.4 in Kazakhstani and Kyrgyzstani nurses,respectively.Significant influencing factors of burnout included gender,age,educational level,and COVID-19 infection history.Conclusions The R-CBI was proved to be a reliable and valid tool for assessing nurses’burnout in Kazakhstan and Kyrgyzstan.
文摘Objective:To translate,adapt,and validate the Indonesian version of the Prenatal Health Behavior Scale.Methods:This cross-sectional,cross-cultural adaptation study was conducted between September 2024 and October 2024 in Ngrambe and Sine,subdistricts in Ngawi,East Java,Indonesia.We selected participants using purposive convenience sampling and matched them with inclusion and exclusion criteria.We collected sociodemographic,Prenatal Health Behavior Scale,and anthropometrics(height,weight,body mass index,and middle-upper arm circumference)data.We analyzed the content validity using the content validity index and Gwet's chance-corrected Agreement Coefficient 2,face validity by pilot-testing on several pregnant women,and construct validity using exploratory factor analysis.We measured reliability using McDonald's omega coefficient.Results:We recruited 183 pregnant women in this study(median age 28 years).The item-content validity index(I-CVI)of all items was 1.00,with Gwet's chance-corrected Agreement Coefficient 2 was 0.945.The face validity resulted in a clear statement of all items.The exploratory factor analysis showed the two-factor model best suited to the questionnaire.Omega coefficients for the overall scale,health-impairing,and health-promoting domains were 0.696,0.507,and 0.678,respectively.Conclusions:The Indonesian version of the Prenatal Health Behavior Scale is a valid and reliable instrument to assess prenatal health behavior in Indonesian-speaking pregnant women.Future studies may implement this scale in community and clinical settings.
基金supported by the National Key Research and Development Pro-gram of China(2021YFF0601902)the National Reference Material Development Project(S2022234).
文摘To solve the problem of the lack of reference material(RM)for determination of allergenic ingredients in food,a RM of cashew nut powder was developed in the study.Cashew nut powder was prepared from cashew nut kernel by selecting,cleaning,crushing,n-hexane degreasing and sieving treatment.The reliability and traceability of RM was verified using real-time quantitative polymerase chain reaction(qPCR)and phylogenetic tree analysis.The cashew nut powder RM showed good homogeneity,and good stability under long-term storage at 4℃and short-term simulated transportation from-20 to 45℃.The RM was determined jointly by 8 collaborative laboratories,and the characteristic CT value was 24.732,and the extended uncertainty was 1.052%(k=2).The RM was applied to verify the amplification efficiency and the limit of detection for qPCR assay,and showed good applicability.The RM could be used for method validation and quality control,for the determination of allergenic ingredients in food mixed with trace amounts of cashew nut.
文摘BACKGROUND The Victorian institute of sports assessment for patellar tendons questionnaire(VISA-P),a valid tool for patellar tendinopathy,has been used for patellofemoral pain(PFP).AIM To validate VISA-P in PFP.METHODS Study of validity,responsiveness and feasibility following COSMIN.Inclusion criteria:Subjects with PFP,aged 18 to 55.Agreement among 10 experts on the relevance and clarity of each item using Aiken's V coefficient determined content validity.An exploratory factorial analysis established structural validity.The correlation of VISA-P with knee injury and osteoarthritis outcome score for PFP and Osteoarthritis(KOOS-PF)and Kujala patellofemoral score(KPS;specific for PFP)analyzed the construct validity.Internal consistency was calculated with Cronbach'sαand test-retest reliability with the intraclass correlation coefficient(ICC).Feasibility considered the subjects'self-completion time.RESULTS The sample consisted of 103 knees from 73 subjects(47 female/26 male;aged 34.9±13 SD).The items were relevant and clear,with the exception of item-8,which didn't reach an acceptable level of agreement on clarity.Exploratory factorial analysis found a 2-factor solution,which explained 63.48%of the variance.VISAP achieved a strong and significant correlation with KOOS-PF(Spearman rho=0.826;P<0.001)and KPS(Spearman rho=0.771;P<0.001).The questionnaire showed adequate reliability(Cronbach'sα:0.752;ICC:0.934;P<0.0001;95%CI:0.902-0.955).The mean self-completion time was 232±0.52 SD seconds.CONCLUSION VISA-P proved to be valid and reliable to functionally assess PFP and/or chondromalacia patella.VISA-P is a feasible tool in the clinical and research environment,quick and easy to complete.
