Texture features have played an essential role in the field of medical imaging for computer-aided diagnosis.The gray-level co-occurrence matrix(GLCM)-based texture descriptor has emerged to become one of the most succ...Texture features have played an essential role in the field of medical imaging for computer-aided diagnosis.The gray-level co-occurrence matrix(GLCM)-based texture descriptor has emerged to become one of the most successful feature sets for these applications.This study aims to increase the potential of these features by introducing multi-scale analysis into the construction of GLCM texture descriptor.In this study,we first introduce a new parameter-stride,to explore the definition of GLCM.Then we propose three multi-scaling GLCM models according to its three parameters,(1)learning model by multiple displacements,(2)learning model by multiple strides(LMS),and(3)learning model by multiple angles.These models increase the texture information by introducing more texture patterns and mitigate direction sparsity and dense sampling problems presented in the traditional Haralick model.To further analyze the three parameters,we test the three models by performing classification on a dataset of 63 large polyp masses obtained from computed tomography colonoscopy consisting of 32 adenocarcinomas and 31 benign adenomas.Finally,the proposed methods are compared to several typical GLCM-texture descriptors and one deep learning model.LMS obtains the highest performance and enhances the prediction power to 0.9450 with standard deviation 0.0285 by area under the curve of receiver operating characteristics score which is a significant improvement.展开更多
In this study,a novel polysaccharide GPA-G 2-H was derived from ginseng.Furthermore,the coherent study of its structural characteristics,fermented characteristics in vitro,as well as antioxidant mechanism of fermented...In this study,a novel polysaccharide GPA-G 2-H was derived from ginseng.Furthermore,the coherent study of its structural characteristics,fermented characteristics in vitro,as well as antioxidant mechanism of fermented product FGPA-G 2-H on Aβ25-35-induced PC 12 cells were explored.The structure of GPA-G 2-H was determined by means of zeta potential analysis,FTIR,HPLC,XRD,GC-MS and NMR.The backbone of GPA-G 2-H was mainly composed of→4)-α-D-Glcp-(1→with branches substituted at O-3.Notably,GPA-G 2-H was degraded by intestinal microbiota in vitro with total sugar content and pH value decreasing,and short-chain fatty acids(SCFAs)increasing.Moreover,GPA-G 2-H significantly promoted the proliferation of Lactobacillus,Muribaculaceae and Weissella,thereby making positive alterations in intestinal microbiota composition.Additionally,FGPA-G 2-H activated the Nrf 2/HO-1 signaling pathway,enhanced HO-1,NQO 1,SOD and GSH-Px,while inhabited Keap 1,MDA and LDH,which alleviated Aβ-induced oxidative stress in PC 12 cells.These provide a solid theoretical basis for the further development of ginseng polysaccharides as functional food and antioxidant drugs.展开更多
Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approach...Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approaches,while effective in global illumination modeling,often struggle to simultaneously suppress noise and preserve structural details,especially under heterogeneous lighting.Furthermore,misalignment between luminance and color channels introduces additional challenges to accurate enhancement.In response to the aforementioned difficulties,we introduce a single-stage framework,M2ATNet,using the multi-scale multi-attention and Transformer architecture.First,to address the problems of texture blurring and residual noise,we design a multi-scale multi-attention denoising module(MMAD),which is applied separately to the luminance and color channels to enhance the structural and texture modeling capabilities.Secondly,to solve the non-alignment problem of the luminance and color channels,we introduce the multi-channel feature fusion Transformer(CFFT)module,which effectively recovers the dark details and corrects the color shifts through cross-channel alignment and deep feature interaction.To guide the model to learn more stably and efficiently,we also fuse multiple types of loss functions to form a hybrid loss term.We extensively evaluate the proposed method on various standard datasets,including LOL-v1,LOL-v2,DICM,LIME,and NPE.Evaluation in terms of numerical metrics and visual quality demonstrate that M2ATNet consistently outperforms existing advanced approaches.Ablation studies further confirm the critical roles played by the MMAD and CFFT modules to detail preservation and visual fidelity under challenging illumination-deficient environments.展开更多
Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectra...Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectral similarity between buildings and backgrounds,sensor variations,and insufficient computational efficiency.To address these challenges,this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network(MewCDNet),which integrates the advantages of Convolutional Neural Networks and Transformers,balances computational costs,and achieves high-performance building change detection.The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction,integrates multi-level feature maps through a multi-scale fusion strategy,and incorporates two key modules:Cross-temporal Difference Detection(CTDD)and Cross-scale Wavelet Refinement(CSWR).CTDD adopts a dual-branch architecture that combines pixel-wise differencing with semanticaware Euclidean distance weighting to enhance the distinction between true changes and background noise.CSWR integrates Haar-based Discrete Wavelet Transform with multi-head cross-attention mechanisms,enabling cross-scale feature fusion while significantly improving edge localization and suppressing spurious changes.Extensive experiments on four benchmark datasets demonstrate MewCDNet’s superiority over comparison methods:achieving F1 scores of 91.54%on LEVIR,93.70%on WHUCD,and 64.96%on S2Looking for building change detection.Furthermore,MewCDNet exhibits optimal performance on the multi-class⋅SYSU dataset(F1:82.71%),highlighting its exceptional generalization capability.展开更多
The growing volume of end-of-life lithium-ion batteries(LIBs)represents both an urgent environmental challenge and a critical resource opportunity,especially for cathode materials.Among commercial cathodes,LiFePO4(LFP...The growing volume of end-of-life lithium-ion batteries(LIBs)represents both an urgent environmental challenge and a critical resource opportunity,especially for cathode materials.Among commercial cathodes,LiFePO4(LFP)dominates the market due to its favorable properties;thus,a substantial amount of LFP cathode materials is expected to retire in the near future.The conventional hydrometallurgical method suffers from high costs and serious pollution.Direct regeneration technologies,especially solid-state sintering,provide a more efficient and environmentally benign alternative by repairing cathode structures through high-temperature solid-phase reactions without extra chemical reagents.Traditional solid-state sintering faces challenges in processing spent LFP from diverse sources,struggling to achieve the homogenization of physical–chemical properties and electrochemical performance.To address the limitations above,phase homogenization with a lattice reconstruction strategy has been investigated,which can enable effective lattice reconstruction and microstructural homogenization,demonstrating robust adaptability to spent samples from variable sources.This review systematically summarizes the mechanisms,detailed steps,characterization techniques,and advances in pre-oxidation optimization(including ion-doping and coated carbon layer modification),as well as future research directions for sustainable LFP recycling.Given this,this review is expected to offer theoretical guidance for achieving homogeneous regeneration of LFP cathode.展开更多
With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods ...With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods face numerous challenges in practical deployment,including scale variation handling,feature degradation,and complex backgrounds.To address these issues,we propose Edge-enhanced and Detail-Capturing You Only Look Once(EHDC-YOLO),a novel framework for object detection in Unmanned Aerial Vehicle(UAV)imagery.Based on the You Only Look Once version 11 nano(YOLOv11n)baseline,EHDC-YOLO systematically introduces several architectural enhancements:(1)a Multi-Scale Edge Enhancement(MSEE)module that leverages multi-scale pooling and edge information to enhance boundary feature extraction;(2)an Enhanced Feature Pyramid Network(EFPN)that integrates P2-level features with Cross Stage Partial(CSP)structures and OmniKernel convolutions for better fine-grained representation;and(3)Dynamic Head(DyHead)with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability.Comprehensive experiments on the Vision meets Drones for Detection(VisDrone-DET)2019 dataset demonstrate that EHDC-YOLO achieves significant improvements,increasing mean Average Precision(mAP)@0.5 from 33.2%to 46.1%(an absolute improvement of 12.9 percentage points)and mAP@0.5:0.95 from 19.5%to 28.0%(an absolute improvement of 8.5 percentage points)compared with the YOLOv11n baseline,while maintaining a reasonable parameter count(2.81 M vs the baseline’s 2.58 M).Further ablation studies confirm the effectiveness of each proposed component,while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios.展开更多
Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious an...Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious and they are numerous,resulting in low detection accuracy by deep learning models.Therefore,we proposed a new multi-scale fusion crater detection algorithm(MSF-CDA)based on the YOLO11 to improve the accuracy of lunar impact crater detection,especially for small craters with a diameter of<1 km.Using the images taken by the LROC(Lunar Reconnaissance Orbiter Camera)at the Chang’e-4(CE-4)landing area,we constructed three separate datasets for craters with diameters of 0-70 m,70-140 m,and>140 m.We then trained three submodels separately with these three datasets.Additionally,we designed a slicing-amplifying-slicing strategy to enhance the ability to extract features from small craters.To handle redundant predictions,we proposed a new Non-Maximum Suppression with Area Filtering method to fuse the results in overlapping targets within the multi-scale submodels.Finally,our new MSF-CDA method achieved high detection performance,with the Precision,Recall,and F1 score having values of 0.991,0.987,and 0.989,respectively,perfectly addressing the problems induced by the lesser features and sample imbalance of small craters.Our MSF-CDA can provide strong data support for more in-depth study of the geological evolution of the lunar surface and finer geological age estimations.This strategy can also be used to detect other small objects with lesser features and sample imbalance problems.We detected approximately 500,000 impact craters in an area of approximately 214 km2 around the CE-4 landing area.By statistically analyzing the new data,we updated the distribution function of the number and diameter of impact craters.Finally,we identified the most suitable lighting conditions for detecting impact crater targets by analyzing the effect of different lighting conditions on the detection accuracy.展开更多
The identification of rock mass hazard sources is fundamental for preventing rockfall and landslide disasters in mountainous regions,with rock mass structural characteristics playing a vital role in hazard assessment....The identification of rock mass hazard sources is fundamental for preventing rockfall and landslide disasters in mountainous regions,with rock mass structural characteristics playing a vital role in hazard assessment.In this study,terrestrial laser scanning(TLS)and unmanned aerial vehicle(UAV)technologies were integrated to enhance the evaluation methodology for rock mass hazard sources,focusing on the Sichuan Yanjiang Expressway project in China.The findings demonstrate that TLS-UAV technology enhanced both spatial coverage and data density in slope modeling.Through integrated algorithmic analysis,rock discontinuities within heterogeneous datasets were systematically identified,enabling quantitative extraction and statistical analysis of key geometric parameters,including orientation,trace length,spacing,and roughness.Furthermore,quantitative models were developed for cohesion,friction angle and the morphology parameter M of in situ discontinuities,respectively,facilitating efficient mechanical parameter acquisition.A novel rock mass hazard index(RHI)was developed incorporating discontinuity geometric rating(DGR),discontinuity mechanical rating(DMR),and slope mass rating(SMR).Field validation confirmed the methodology's effectiveness in evaluating risk levels and spatial heterogeneity of rock mass hazard sources,revealing the contribution of different discontinuity sets to the rock mass hazard and identifying the primary discontinuity sets controlling instability mechanisms.This study is of great significance for evaluating discontinuity-controlled rock mass hazard sources and preventing rockfall disasters.展开更多
Microstructural characteristics and mechanical behavior of hot extruded Al5083/B4C nanocomposites were studied.Al5083and Al5083/B4C powders were milled for50h under argon atmosphere in attrition mill with rotational s...Microstructural characteristics and mechanical behavior of hot extruded Al5083/B4C nanocomposites were studied.Al5083and Al5083/B4C powders were milled for50h under argon atmosphere in attrition mill with rotational speed of400r/min.For increasing the elongation,milled powders were mixed with30%and50%unmilled aluminum powder(mass fraction)with meanparticle size of>100μm and<100μm and then consolidated by hot pressing and hot extrusion with9:1extrusion ratio.Hot extrudedsamples were studied by optical microscopy,scanning electron microscopy(SEM),energy dispersive spectroscopy(EDS),transmission electron microscopy(TEM),tensile and hardness tests.The results showed that mechanical milling process andpresence of B4C particles increase the yield strength of Al5083alloy from130to566MPa but strongly decrease elongation(from11.3%to0.49%).Adding<100μm unmilled particles enhanced the ductility and reduced tensile strength and hardness,but usingthe>100μm unmilled particles reduced the tensile strength and ductility at the same time.By increasing the content of unmilledparticles failure mechanism changed from brittle to ductile.展开更多
Because of the challenge of compounding lightweight,high-strength Ti/Al alloys due to their considerable disparity in properties,Al 6063 as intermediate layer was proposed to fabricate TC4/Al 6063/Al 7075 three-layer ...Because of the challenge of compounding lightweight,high-strength Ti/Al alloys due to their considerable disparity in properties,Al 6063 as intermediate layer was proposed to fabricate TC4/Al 6063/Al 7075 three-layer composite plate by explosive welding.The microscopic properties of each bonding interface were elucidated through field emission scanning electron microscope and electron backscattered diffraction(EBSD).A methodology combining finite element method-smoothed particle hydrodynamics(FEM-SPH)and molecular dynamics(MD)was proposed for the analysis of the forming and evolution characteristics of explosive welding interfaces at multi-scale.The results demonstrate that the bonding interface morphologies of TC4/Al 6063 and Al 6063/Al 7075 exhibit a flat and wavy configuration,without discernible defects or cracks.The phenomenon of grain refinement is observed in the vicinity of the two bonding interfaces.Furthermore,the degree of plastic deformation of TC4 and Al 7075 is more pronounced than that of Al 6063 in the intermediate layer.The interface morphology characteristics obtained by FEM-SPH simulation exhibit a high degree of similarity to the experimental results.MD simulations reveal that the diffusion of interfacial elements predominantly occurs during the unloading phase,and the simulated thickness of interfacial diffusion aligns well with experimental outcomes.The introduction of intermediate layer in the explosive welding process can effectively produce high-quality titanium/aluminum alloy composite plates.Furthermore,this approach offers a multi-scale simulation strategy for the study of explosive welding bonding interfaces.展开更多
Well-developed pores and cracks in coal reservoirs are the main venues for gas storage and migration.To investigate the multi-scale pore fractal characteristics,six coal samples of different rankings were studied usin...Well-developed pores and cracks in coal reservoirs are the main venues for gas storage and migration.To investigate the multi-scale pore fractal characteristics,six coal samples of different rankings were studied using high-pressure mercury injection(HPMI),low-pressure nitrogen adsorption(LPGA-N2),and scanning electron microscopy(SEM)test methods.Based on the Frankel,Halsey and Hill(FHH)fractal theory,the Menger sponge model,Pores and Cracks Analysis System(PCAS),pore volume complexity(D_(v)),coal surface irregularity(Ds)and pore distribution heterogeneity(D_(p))were studied and evaluated,respectively.The effect of three fractal dimensions on the gas adsorption ability was also analyzed with high-pressure isothermal gas adsorption experiments.Results show that pore structures within these coal samples have obvious fractal characteristics.A noticeable segmentation effect appears in the Dv1and Dv2fitting process,with the boundary size ranging from 36.00 to 182.95 nm,which helps differentiate diffusion pores and seepage fractures.The D values show an asymmetric U-shaped trend as the coal metamorphism increases,demonstrating that coalification greatly affects the pore fractal dimensions.The three fractal dimensions can characterize the difference in coal microstructure and reflect their influence on gas adsorption ability.Langmuir volume(V_(L))has an evident and positive correlation with Dsvalues,whereas Langmuir pressure(P_(L))is mainly affected by the combined action of Dvand Dp.This study will provide valuable knowledge for the appraisal of coal seam gas reservoirs of differently ranked coals.展开更多
The turbulence data are decomposed to multi-scales and its respective fractal dimensions are computed. The conclusions are drawn from investigating the variation of fractal dimensions. With the level of decomposition ...The turbulence data are decomposed to multi-scales and its respective fractal dimensions are computed. The conclusions are drawn from investigating the variation of fractal dimensions. With the level of decomposition increasing, the low-frequency part extracted from the turbulence signals tends to be simple and smooth, the dimensions decrease; the high-frequency part shows complex, the dimensions are fixed, about 1.70 on the average, which indicates clear self-similarity characteristics.展开更多
Objective This study reports the first imported case of Lassa fever(LF)in China.Laboratory detection and molecular epidemiological analysis of the Lassa virus(LASV)from this case offer valuable insights for the preven...Objective This study reports the first imported case of Lassa fever(LF)in China.Laboratory detection and molecular epidemiological analysis of the Lassa virus(LASV)from this case offer valuable insights for the prevention and control of LF.Methods Samples of cerebrospinal fluid(CSF),blood,urine,saliva,and environmental materials were collected from the patient and their close contacts for LASV nucleotide detection.Whole-genome sequencing was performed on positive samples to analyze the genetic characteristics of the virus.Results LASV was detected in the patient’s CSF,blood,and urine,while all samples from close contacts and the environment tested negative.The virus belongs to the lineage IV strain and shares the highest homology with strains from Sierra Leone.The variability in the glycoprotein complex(GPC)among different strains ranged from 3.9%to 15.1%,higher than previously reported for the seven known lineages.Amino acid mutation analysis revealed multiple mutations within the GPC immunogenic epitopes,increasing strain diversity and potentially impacting immune response.Conclusion The case was confirmed through nucleotide detection,with no evidence of secondary transmission or viral spread.The LASV strain identified belongs to lineage IV,with broader GPC variability than previously reported.Mutations in the immune-related sites of GPC may affect immune responses,necessitating heightened vigilance regarding the virus.展开更多
Improving the volumetric energy density of supercapacitors is essential for practical applications,which highly relies on the dense storage of ions in carbon-based electrodes.The functional units of carbon-based elect...Improving the volumetric energy density of supercapacitors is essential for practical applications,which highly relies on the dense storage of ions in carbon-based electrodes.The functional units of carbon-based electrode exhibit multi-scale structural characteristics including macroscopic electrode morphologies,mesoscopic microcrystals and pores,and microscopic defects and dopants in the carbon basal plane.Therefore,the ordered combination of multi-scale structures of carbon electrode is crucial for achieving dense energy storage and high volumetric performance by leveraging the functions of various scale structu re.Considering that previous reviews have focused more on the discussion of specific scale structu re of carbon electrodes,this review takes a multi-scale perspective in which recent progresses regarding the structureperformance relationship,underlying mechanism and directional design of carbon-based multi-scale structures including carbon morphology,pore structure,carbon basal plane micro-environment and electrode technology on dense energy storage and volumetric property of supercapacitors are systematically discussed.We analyzed in detail the effects of the morphology,pore,and micro-environment of carbon electrode materials on ion dense storage,summarized the specific effects of different scale structures on volumetric property and recent research progress,and proposed the mutual influence and trade-off relationship between various scale structures.In addition,the challenges and outlooks for improving the dense storage and volumetric performance of carbon-based supercapacitors are analyzed,which can provide feasible technical reference and guidance for the design and manufacture of dense carbon-based electrode materials.展开更多
Convolutional neural networks(CNNs)-based medical image segmentation technologies have been widely used in medical image segmentation because of their strong representation and generalization abilities.However,due to ...Convolutional neural networks(CNNs)-based medical image segmentation technologies have been widely used in medical image segmentation because of their strong representation and generalization abilities.However,due to the inability to effectively capture global information from images,CNNs can easily lead to loss of contours and textures in segmentation results.Notice that the transformer model can effectively capture the properties of long-range dependencies in the image,and furthermore,combining the CNN and the transformer can effectively extract local details and global contextual features of the image.Motivated by this,we propose a multi-branch and multi-scale attention network(M2ANet)for medical image segmentation,whose architecture consists of three components.Specifically,in the first component,we construct an adaptive multi-branch patch module for parallel extraction of image features to reduce information loss caused by downsampling.In the second component,we apply residual block to the well-known convolutional block attention module to enhance the network’s ability to recognize important features of images and alleviate the phenomenon of gradient vanishing.In the third component,we design a multi-scale feature fusion module,in which we adopt adaptive average pooling and position encoding to enhance contextual features,and then multi-head attention is introduced to further enrich feature representation.Finally,we validate the effectiveness and feasibility of the proposed M2ANet method through comparative experiments on four benchmark medical image segmentation datasets,particularly in the context of preserving contours and textures.展开更多
A large number of nanopores and complex fracture structures in shale reservoirs results in multi-scale flow of oil. With the development of shale oil reservoirs, the permeability of multi-scale media undergoes changes...A large number of nanopores and complex fracture structures in shale reservoirs results in multi-scale flow of oil. With the development of shale oil reservoirs, the permeability of multi-scale media undergoes changes due to stress sensitivity, which plays a crucial role in controlling pressure propagation and oil flow. This paper proposes a multi-scale coupled flow mathematical model of matrix nanopores, induced fractures, and hydraulic fractures. In this model, the micro-scale effects of shale oil flow in fractal nanopores, fractal induced fracture network, and stress sensitivity of multi-scale media are considered. We solved the model iteratively using Pedrosa transform, semi-analytic Segmented Bessel function, Laplace transform. The results of this model exhibit good agreement with the numerical solution and field production data, confirming the high accuracy of the model. As well, the influence of stress sensitivity on permeability, pressure and production is analyzed. It is shown that the permeability and production decrease significantly when induced fractures are weakly supported. Closed induced fractures can inhibit interporosity flow in the stimulated reservoir volume (SRV). It has been shown in sensitivity analysis that hydraulic fractures are beneficial to early production, and induced fractures in SRV are beneficial to middle production. The model can characterize multi-scale flow characteristics of shale oil, providing theoretical guidance for rapid productivity evaluation.展开更多
Prediction of production decline and evaluation of the adsorbed/free gas ratio are critical for determining the lifespan and production status of shale gas wells.Traditional production prediction methods have some sho...Prediction of production decline and evaluation of the adsorbed/free gas ratio are critical for determining the lifespan and production status of shale gas wells.Traditional production prediction methods have some shortcomings because of the low permeability and tightness of shale,complex gas flow behavior of multi-scale gas transport regions and multiple gas transport mechanism superpositions,and complex and variable production regimes of shale gas wells.Recent research has demonstrated the existence of a multi-stage isotope fractionation phenomenon during shale gas production,with the fractionation characteristics of each stage associated with the pore structure,gas in place(GIP),adsorption/desorption,and gas production process.This study presents a new approach for estimating shale gas well production and evaluating the adsorbed/free gas ratio throughout production using isotope fractionation techniques.A reservoir-scale carbon isotope fractionation(CIF)model applicable to the production process of shale gas wells was developed for the first time in this research.In contrast to the traditional model,this model improves production prediction accuracy by simultaneously fitting the gas production rate and δ^(13)C_(1) data and provides a new evaluation method of the adsorbed/free gas ratio during shale gas production.The results indicate that the diffusion and adsorption/desorption properties of rock,bottom-hole flowing pressure(BHP)of gas well,and multi-scale gas transport regions of the reservoir all affect isotope fractionation,with the diffusion and adsorption/desorption parameters of rock having the greatest effect on isotope fractionation being D∗/D,PL,VL,α,and others in that order.We effectively tested the universality of the four-stage isotope fractionation feature and revealed a unique isotope fractionation mechanism caused by the superimposed coupling of multi-scale gas transport regions during shale gas well production.Finally,we applied the established CIF model to a shale gas well in the Sichuan Basin,China,and calculated the estimated ultimate recovery(EUR)of the well to be 3.33×10^(8) m^(3);the adsorbed gas ratio during shale gas production was 1.65%,10.03%,and 23.44%in the first,fifth,and tenth years,respectively.The findings are significant for understanding the isotope fractionation mechanism during natural gas transport in complex systems and for formulating and optimizing unconventional natural gas development strategies.展开更多
With the ongoing depletion of fossil fuels,energy and environmental issues have become increasingly critical,necessitating the search for effective solutions.Catalysis,being one of the hallmarks of modern industry,off...With the ongoing depletion of fossil fuels,energy and environmental issues have become increasingly critical,necessitating the search for effective solutions.Catalysis,being one of the hallmarks of modern industry,offers a promising avenue for researchers.However,the question of how to significantly enhance the performance of catalysts has gradually drawn the attention of scholars.Defect engineering,a commonly employed and effective approach to improve catalyst activity,has become a significant research focus in the catalysis field in recent years.Nonmetal vacancies have received extensive attention due to their simple form.Consequently,exploration of metal vacancies has remained stagnant for a considerable period,resulting in a scarcity of comprehensive reviews on this topic.Therefore,based on the latest research findings,this paper summarizes and consolidates the construction strategies for metal vacancies,characterization techniques,and their roles in typical energy and environmental catalytic reactions.Additionally,it outlines potential challenges in the future,aiming to provide valuable references for researchers interested in investigating metal vacancies.展开更多
In recent years,gait-based emotion recognition has been widely applied in the field of computer vision.However,existing gait emotion recognition methods typically rely on complete human skeleton data,and their accurac...In recent years,gait-based emotion recognition has been widely applied in the field of computer vision.However,existing gait emotion recognition methods typically rely on complete human skeleton data,and their accuracy significantly declines when the data is occluded.To enhance the accuracy of gait emotion recognition under occlusion,this paper proposes a Multi-scale Suppression Graph ConvolutionalNetwork(MS-GCN).TheMS-GCN consists of three main components:Joint Interpolation Module(JI Moudle),Multi-scale Temporal Convolution Network(MS-TCN),and Suppression Graph Convolutional Network(SGCN).The JI Module completes the spatially occluded skeletal joints using the(K-Nearest Neighbors)KNN interpolation method.The MS-TCN employs convolutional kernels of various sizes to comprehensively capture the emotional information embedded in the gait,compensating for the temporal occlusion of gait information.The SGCN extracts more non-prominent human gait features by suppressing the extraction of key body part features,thereby reducing the negative impact of occlusion on emotion recognition results.The proposed method is evaluated on two comprehensive datasets:Emotion-Gait,containing 4227 real gaits from sources like BML,ICT-Pollick,and ELMD,and 1000 synthetic gaits generated using STEP-Gen technology,and ELMB,consisting of 3924 gaits,with 1835 labeled with emotions such as“Happy,”“Sad,”“Angry,”and“Neutral.”On the standard datasets Emotion-Gait and ELMB,the proposed method achieved accuracies of 0.900 and 0.896,respectively,attaining performance comparable to other state-ofthe-artmethods.Furthermore,on occlusion datasets,the proposedmethod significantly mitigates the performance degradation caused by occlusion compared to other methods,the accuracy is significantly higher than that of other methods.展开更多
Given the advances in satellite altimetry and multibeam bathymetry,benthic terrain classification based on digital bathymetric models(DBMs)has been widely used for the mapping of benthic topographies.For instance,coba...Given the advances in satellite altimetry and multibeam bathymetry,benthic terrain classification based on digital bathymetric models(DBMs)has been widely used for the mapping of benthic topographies.For instance,cobaltrich crusts(CRCs)are important mineral resources found on seamounts and guyots in the western Pacific Ocean.Thick,plate-like CRCs are known to form on the summit and slopes of seamounts at the 1000–3000 m depth,while the relationship between seamount topography and spatial distribution of CRCs remains unclear.The benthic terrain classification of seamounts can solve this problem,thereby,facilitating the rapid exploration of seamount CRCs.Our study used an EM122 multibeam echosounder to retrieve high-resolution bathymetry data in the CRCs contract license area of China,i.e.,the Jiaxie Guyots in 2015 and 2016.Based on the DBM construted by bathymetirc data,broad-and fine-scale bathymetric position indices were utilized for quantitative classification of the terrain units of the Jiaxie Guyots on multiple scales.The classification revealed four first-order terrain units(e.g.,flat,crest,slope,and depression)and eleven second-order terrain units(e.g.,local crests,depressions on crests,gentle slopes,crests on slopes,and local depressions,etc.).Furthermore,the classification of the terrain and geological analysis indicated that the Weijia Guyot has a large flat summit,with local crests at the southern summit,whereas most of the guyot flanks were covered by gentle slopes.“Radial”mountain ridges have developed on the eastern side,while large-scale gravitational landslides have developed on the western and southern flanks.Additionally,landslide masses can be observed at the bottom of these slopes.The coverage of local crests on the seamount is∼1000 km^(2),and the local crests on the peak and flanks of the guyots may be the areas where thick and continuous plate-like CRCs are likely to occur.展开更多
基金This work was supported by the NIH/NCI,No.CA206171.
文摘Texture features have played an essential role in the field of medical imaging for computer-aided diagnosis.The gray-level co-occurrence matrix(GLCM)-based texture descriptor has emerged to become one of the most successful feature sets for these applications.This study aims to increase the potential of these features by introducing multi-scale analysis into the construction of GLCM texture descriptor.In this study,we first introduce a new parameter-stride,to explore the definition of GLCM.Then we propose three multi-scaling GLCM models according to its three parameters,(1)learning model by multiple displacements,(2)learning model by multiple strides(LMS),and(3)learning model by multiple angles.These models increase the texture information by introducing more texture patterns and mitigate direction sparsity and dense sampling problems presented in the traditional Haralick model.To further analyze the three parameters,we test the three models by performing classification on a dataset of 63 large polyp masses obtained from computed tomography colonoscopy consisting of 32 adenocarcinomas and 31 benign adenomas.Finally,the proposed methods are compared to several typical GLCM-texture descriptors and one deep learning model.LMS obtains the highest performance and enhances the prediction power to 0.9450 with standard deviation 0.0285 by area under the curve of receiver operating characteristics score which is a significant improvement.
基金Supported by the National Key Research and Development Program of Traditional Chinese Medicine Modernization Project,China(No.2023YFC3504000)the Science and Technology Development Project of Jilin Province,China(No.20240404043ZP)the Science and Technology Innovation Cooperation Project of Changchun Science and Technology Bureau and Chinese Academy of Sciences,China(No.23SH14)。
文摘In this study,a novel polysaccharide GPA-G 2-H was derived from ginseng.Furthermore,the coherent study of its structural characteristics,fermented characteristics in vitro,as well as antioxidant mechanism of fermented product FGPA-G 2-H on Aβ25-35-induced PC 12 cells were explored.The structure of GPA-G 2-H was determined by means of zeta potential analysis,FTIR,HPLC,XRD,GC-MS and NMR.The backbone of GPA-G 2-H was mainly composed of→4)-α-D-Glcp-(1→with branches substituted at O-3.Notably,GPA-G 2-H was degraded by intestinal microbiota in vitro with total sugar content and pH value decreasing,and short-chain fatty acids(SCFAs)increasing.Moreover,GPA-G 2-H significantly promoted the proliferation of Lactobacillus,Muribaculaceae and Weissella,thereby making positive alterations in intestinal microbiota composition.Additionally,FGPA-G 2-H activated the Nrf 2/HO-1 signaling pathway,enhanced HO-1,NQO 1,SOD and GSH-Px,while inhabited Keap 1,MDA and LDH,which alleviated Aβ-induced oxidative stress in PC 12 cells.These provide a solid theoretical basis for the further development of ginseng polysaccharides as functional food and antioxidant drugs.
基金funded by the National Natural Science Foundation of China,grant numbers 52374156 and 62476005。
文摘Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approaches,while effective in global illumination modeling,often struggle to simultaneously suppress noise and preserve structural details,especially under heterogeneous lighting.Furthermore,misalignment between luminance and color channels introduces additional challenges to accurate enhancement.In response to the aforementioned difficulties,we introduce a single-stage framework,M2ATNet,using the multi-scale multi-attention and Transformer architecture.First,to address the problems of texture blurring and residual noise,we design a multi-scale multi-attention denoising module(MMAD),which is applied separately to the luminance and color channels to enhance the structural and texture modeling capabilities.Secondly,to solve the non-alignment problem of the luminance and color channels,we introduce the multi-channel feature fusion Transformer(CFFT)module,which effectively recovers the dark details and corrects the color shifts through cross-channel alignment and deep feature interaction.To guide the model to learn more stably and efficiently,we also fuse multiple types of loss functions to form a hybrid loss term.We extensively evaluate the proposed method on various standard datasets,including LOL-v1,LOL-v2,DICM,LIME,and NPE.Evaluation in terms of numerical metrics and visual quality demonstrate that M2ATNet consistently outperforms existing advanced approaches.Ablation studies further confirm the critical roles played by the MMAD and CFFT modules to detail preservation and visual fidelity under challenging illumination-deficient environments.
基金supported by the Henan Province Key R&D Project under Grant 241111210400the Henan Provincial Science and Technology Research Project under Grants 252102211047,252102211062,252102211055 and 232102210069+2 种基金the Jiangsu Provincial Scheme Double Initiative Plan JSS-CBS20230474,the XJTLU RDF-21-02-008the Science and Technology Innovation Project of Zhengzhou University of Light Industry under Grant 23XNKJTD0205the Higher Education Teaching Reform Research and Practice Project of Henan Province under Grant 2024SJGLX0126。
文摘Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectral similarity between buildings and backgrounds,sensor variations,and insufficient computational efficiency.To address these challenges,this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network(MewCDNet),which integrates the advantages of Convolutional Neural Networks and Transformers,balances computational costs,and achieves high-performance building change detection.The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction,integrates multi-level feature maps through a multi-scale fusion strategy,and incorporates two key modules:Cross-temporal Difference Detection(CTDD)and Cross-scale Wavelet Refinement(CSWR).CTDD adopts a dual-branch architecture that combines pixel-wise differencing with semanticaware Euclidean distance weighting to enhance the distinction between true changes and background noise.CSWR integrates Haar-based Discrete Wavelet Transform with multi-head cross-attention mechanisms,enabling cross-scale feature fusion while significantly improving edge localization and suppressing spurious changes.Extensive experiments on four benchmark datasets demonstrate MewCDNet’s superiority over comparison methods:achieving F1 scores of 91.54%on LEVIR,93.70%on WHUCD,and 64.96%on S2Looking for building change detection.Furthermore,MewCDNet exhibits optimal performance on the multi-class⋅SYSU dataset(F1:82.71%),highlighting its exceptional generalization capability.
基金financially supported by National Natural Science Key Foundation of China(52534010)National Natural Science Foundation of China(52374288,52204298)+2 种基金Young Elite Scientists Sponsorship Program by China Association for Science and Technology(2022QNRC001)National Key Research and Development Program of China(2022YFC3900805-4/7)Collaborative Innovation Centre for Clean and Efficient Utilization of Strategic Metal Mineral Resources,Found of State Key Laboratory of Mineral Processing(BGRIMM-KJSKL-2017-13).
文摘The growing volume of end-of-life lithium-ion batteries(LIBs)represents both an urgent environmental challenge and a critical resource opportunity,especially for cathode materials.Among commercial cathodes,LiFePO4(LFP)dominates the market due to its favorable properties;thus,a substantial amount of LFP cathode materials is expected to retire in the near future.The conventional hydrometallurgical method suffers from high costs and serious pollution.Direct regeneration technologies,especially solid-state sintering,provide a more efficient and environmentally benign alternative by repairing cathode structures through high-temperature solid-phase reactions without extra chemical reagents.Traditional solid-state sintering faces challenges in processing spent LFP from diverse sources,struggling to achieve the homogenization of physical–chemical properties and electrochemical performance.To address the limitations above,phase homogenization with a lattice reconstruction strategy has been investigated,which can enable effective lattice reconstruction and microstructural homogenization,demonstrating robust adaptability to spent samples from variable sources.This review systematically summarizes the mechanisms,detailed steps,characterization techniques,and advances in pre-oxidation optimization(including ion-doping and coated carbon layer modification),as well as future research directions for sustainable LFP recycling.Given this,this review is expected to offer theoretical guidance for achieving homogeneous regeneration of LFP cathode.
文摘With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods face numerous challenges in practical deployment,including scale variation handling,feature degradation,and complex backgrounds.To address these issues,we propose Edge-enhanced and Detail-Capturing You Only Look Once(EHDC-YOLO),a novel framework for object detection in Unmanned Aerial Vehicle(UAV)imagery.Based on the You Only Look Once version 11 nano(YOLOv11n)baseline,EHDC-YOLO systematically introduces several architectural enhancements:(1)a Multi-Scale Edge Enhancement(MSEE)module that leverages multi-scale pooling and edge information to enhance boundary feature extraction;(2)an Enhanced Feature Pyramid Network(EFPN)that integrates P2-level features with Cross Stage Partial(CSP)structures and OmniKernel convolutions for better fine-grained representation;and(3)Dynamic Head(DyHead)with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability.Comprehensive experiments on the Vision meets Drones for Detection(VisDrone-DET)2019 dataset demonstrate that EHDC-YOLO achieves significant improvements,increasing mean Average Precision(mAP)@0.5 from 33.2%to 46.1%(an absolute improvement of 12.9 percentage points)and mAP@0.5:0.95 from 19.5%to 28.0%(an absolute improvement of 8.5 percentage points)compared with the YOLOv11n baseline,while maintaining a reasonable parameter count(2.81 M vs the baseline’s 2.58 M).Further ablation studies confirm the effectiveness of each proposed component,while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios.
基金the National Key Research and Development Program of China (Grant No.2022YFF0711400)the National Space Science Data Center Youth Open Project (Grant No. NSSDC2302001)
文摘Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious and they are numerous,resulting in low detection accuracy by deep learning models.Therefore,we proposed a new multi-scale fusion crater detection algorithm(MSF-CDA)based on the YOLO11 to improve the accuracy of lunar impact crater detection,especially for small craters with a diameter of<1 km.Using the images taken by the LROC(Lunar Reconnaissance Orbiter Camera)at the Chang’e-4(CE-4)landing area,we constructed three separate datasets for craters with diameters of 0-70 m,70-140 m,and>140 m.We then trained three submodels separately with these three datasets.Additionally,we designed a slicing-amplifying-slicing strategy to enhance the ability to extract features from small craters.To handle redundant predictions,we proposed a new Non-Maximum Suppression with Area Filtering method to fuse the results in overlapping targets within the multi-scale submodels.Finally,our new MSF-CDA method achieved high detection performance,with the Precision,Recall,and F1 score having values of 0.991,0.987,and 0.989,respectively,perfectly addressing the problems induced by the lesser features and sample imbalance of small craters.Our MSF-CDA can provide strong data support for more in-depth study of the geological evolution of the lunar surface and finer geological age estimations.This strategy can also be used to detect other small objects with lesser features and sample imbalance problems.We detected approximately 500,000 impact craters in an area of approximately 214 km2 around the CE-4 landing area.By statistically analyzing the new data,we updated the distribution function of the number and diameter of impact craters.Finally,we identified the most suitable lighting conditions for detecting impact crater targets by analyzing the effect of different lighting conditions on the detection accuracy.
基金support from the National Natural Science Foundation of China(Grant Nos.42177142 and 52378477)the Key Research and Development Program of Shaanxi(Grant No.2023-YBSF-486).
文摘The identification of rock mass hazard sources is fundamental for preventing rockfall and landslide disasters in mountainous regions,with rock mass structural characteristics playing a vital role in hazard assessment.In this study,terrestrial laser scanning(TLS)and unmanned aerial vehicle(UAV)technologies were integrated to enhance the evaluation methodology for rock mass hazard sources,focusing on the Sichuan Yanjiang Expressway project in China.The findings demonstrate that TLS-UAV technology enhanced both spatial coverage and data density in slope modeling.Through integrated algorithmic analysis,rock discontinuities within heterogeneous datasets were systematically identified,enabling quantitative extraction and statistical analysis of key geometric parameters,including orientation,trace length,spacing,and roughness.Furthermore,quantitative models were developed for cohesion,friction angle and the morphology parameter M of in situ discontinuities,respectively,facilitating efficient mechanical parameter acquisition.A novel rock mass hazard index(RHI)was developed incorporating discontinuity geometric rating(DGR),discontinuity mechanical rating(DMR),and slope mass rating(SMR).Field validation confirmed the methodology's effectiveness in evaluating risk levels and spatial heterogeneity of rock mass hazard sources,revealing the contribution of different discontinuity sets to the rock mass hazard and identifying the primary discontinuity sets controlling instability mechanisms.This study is of great significance for evaluating discontinuity-controlled rock mass hazard sources and preventing rockfall disasters.
文摘Microstructural characteristics and mechanical behavior of hot extruded Al5083/B4C nanocomposites were studied.Al5083and Al5083/B4C powders were milled for50h under argon atmosphere in attrition mill with rotational speed of400r/min.For increasing the elongation,milled powders were mixed with30%and50%unmilled aluminum powder(mass fraction)with meanparticle size of>100μm and<100μm and then consolidated by hot pressing and hot extrusion with9:1extrusion ratio.Hot extrudedsamples were studied by optical microscopy,scanning electron microscopy(SEM),energy dispersive spectroscopy(EDS),transmission electron microscopy(TEM),tensile and hardness tests.The results showed that mechanical milling process andpresence of B4C particles increase the yield strength of Al5083alloy from130to566MPa but strongly decrease elongation(from11.3%to0.49%).Adding<100μm unmilled particles enhanced the ductility and reduced tensile strength and hardness,but usingthe>100μm unmilled particles reduced the tensile strength and ductility at the same time.By increasing the content of unmilledparticles failure mechanism changed from brittle to ductile.
基金Opening Foundation of Key Laboratory of Explosive Energy Utilization and Control,Anhui Province(BP20240104)Graduate Innovation Program of China University of Mining and Technology(2024WLJCRCZL049)Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX24_2701)。
文摘Because of the challenge of compounding lightweight,high-strength Ti/Al alloys due to their considerable disparity in properties,Al 6063 as intermediate layer was proposed to fabricate TC4/Al 6063/Al 7075 three-layer composite plate by explosive welding.The microscopic properties of each bonding interface were elucidated through field emission scanning electron microscope and electron backscattered diffraction(EBSD).A methodology combining finite element method-smoothed particle hydrodynamics(FEM-SPH)and molecular dynamics(MD)was proposed for the analysis of the forming and evolution characteristics of explosive welding interfaces at multi-scale.The results demonstrate that the bonding interface morphologies of TC4/Al 6063 and Al 6063/Al 7075 exhibit a flat and wavy configuration,without discernible defects or cracks.The phenomenon of grain refinement is observed in the vicinity of the two bonding interfaces.Furthermore,the degree of plastic deformation of TC4 and Al 7075 is more pronounced than that of Al 6063 in the intermediate layer.The interface morphology characteristics obtained by FEM-SPH simulation exhibit a high degree of similarity to the experimental results.MD simulations reveal that the diffusion of interfacial elements predominantly occurs during the unloading phase,and the simulated thickness of interfacial diffusion aligns well with experimental outcomes.The introduction of intermediate layer in the explosive welding process can effectively produce high-quality titanium/aluminum alloy composite plates.Furthermore,this approach offers a multi-scale simulation strategy for the study of explosive welding bonding interfaces.
基金The first author would like to express sincere appreciation for the scholarship provided by China Scholarship Council(No.202006430006)and University of Wollongongfinancially supported by the ACARP Project C28006+1 种基金the National Key Research and Development Program of China(No.2018YFC0808301)the Natural Science Foundation of Beijing Municipality,China(No.8192036)。
文摘Well-developed pores and cracks in coal reservoirs are the main venues for gas storage and migration.To investigate the multi-scale pore fractal characteristics,six coal samples of different rankings were studied using high-pressure mercury injection(HPMI),low-pressure nitrogen adsorption(LPGA-N2),and scanning electron microscopy(SEM)test methods.Based on the Frankel,Halsey and Hill(FHH)fractal theory,the Menger sponge model,Pores and Cracks Analysis System(PCAS),pore volume complexity(D_(v)),coal surface irregularity(Ds)and pore distribution heterogeneity(D_(p))were studied and evaluated,respectively.The effect of three fractal dimensions on the gas adsorption ability was also analyzed with high-pressure isothermal gas adsorption experiments.Results show that pore structures within these coal samples have obvious fractal characteristics.A noticeable segmentation effect appears in the Dv1and Dv2fitting process,with the boundary size ranging from 36.00 to 182.95 nm,which helps differentiate diffusion pores and seepage fractures.The D values show an asymmetric U-shaped trend as the coal metamorphism increases,demonstrating that coalification greatly affects the pore fractal dimensions.The three fractal dimensions can characterize the difference in coal microstructure and reflect their influence on gas adsorption ability.Langmuir volume(V_(L))has an evident and positive correlation with Dsvalues,whereas Langmuir pressure(P_(L))is mainly affected by the combined action of Dvand Dp.This study will provide valuable knowledge for the appraisal of coal seam gas reservoirs of differently ranked coals.
基金This research is supported by the Key Project of National Natural Science Foundation of China (No.40035010
文摘The turbulence data are decomposed to multi-scales and its respective fractal dimensions are computed. The conclusions are drawn from investigating the variation of fractal dimensions. With the level of decomposition increasing, the low-frequency part extracted from the turbulence signals tends to be simple and smooth, the dimensions decrease; the high-frequency part shows complex, the dimensions are fixed, about 1.70 on the average, which indicates clear self-similarity characteristics.
基金supported by Public Health Talent Training and Surport Plan(National Administration of Disease Prevention and Control)Research and application of new technology for rapid monitoring and tracing of emergent infectious diseases among entry-exit population(2024YFFK0056)Monitoring,Early warning and Response of Major Infectious Diseases(2022ZDZX0017).
文摘Objective This study reports the first imported case of Lassa fever(LF)in China.Laboratory detection and molecular epidemiological analysis of the Lassa virus(LASV)from this case offer valuable insights for the prevention and control of LF.Methods Samples of cerebrospinal fluid(CSF),blood,urine,saliva,and environmental materials were collected from the patient and their close contacts for LASV nucleotide detection.Whole-genome sequencing was performed on positive samples to analyze the genetic characteristics of the virus.Results LASV was detected in the patient’s CSF,blood,and urine,while all samples from close contacts and the environment tested negative.The virus belongs to the lineage IV strain and shares the highest homology with strains from Sierra Leone.The variability in the glycoprotein complex(GPC)among different strains ranged from 3.9%to 15.1%,higher than previously reported for the seven known lineages.Amino acid mutation analysis revealed multiple mutations within the GPC immunogenic epitopes,increasing strain diversity and potentially impacting immune response.Conclusion The case was confirmed through nucleotide detection,with no evidence of secondary transmission or viral spread.The LASV strain identified belongs to lineage IV,with broader GPC variability than previously reported.Mutations in the immune-related sites of GPC may affect immune responses,necessitating heightened vigilance regarding the virus.
基金funded by the Joint Fund for Regional Innovation and Development of National Natural Science Foundation of China(U21A20143)the National Science Fund for Excellent Young Scholars(52322607)the Excellent Youth Foundation of Heilongjiang Scientific Committee(YQ2022E028)。
文摘Improving the volumetric energy density of supercapacitors is essential for practical applications,which highly relies on the dense storage of ions in carbon-based electrodes.The functional units of carbon-based electrode exhibit multi-scale structural characteristics including macroscopic electrode morphologies,mesoscopic microcrystals and pores,and microscopic defects and dopants in the carbon basal plane.Therefore,the ordered combination of multi-scale structures of carbon electrode is crucial for achieving dense energy storage and high volumetric performance by leveraging the functions of various scale structu re.Considering that previous reviews have focused more on the discussion of specific scale structu re of carbon electrodes,this review takes a multi-scale perspective in which recent progresses regarding the structureperformance relationship,underlying mechanism and directional design of carbon-based multi-scale structures including carbon morphology,pore structure,carbon basal plane micro-environment and electrode technology on dense energy storage and volumetric property of supercapacitors are systematically discussed.We analyzed in detail the effects of the morphology,pore,and micro-environment of carbon electrode materials on ion dense storage,summarized the specific effects of different scale structures on volumetric property and recent research progress,and proposed the mutual influence and trade-off relationship between various scale structures.In addition,the challenges and outlooks for improving the dense storage and volumetric performance of carbon-based supercapacitors are analyzed,which can provide feasible technical reference and guidance for the design and manufacture of dense carbon-based electrode materials.
基金supported by the Natural Science Foundation of the Anhui Higher Education Institutions of China(Grant Nos.2023AH040149 and 2024AH051915)the Anhui Provincial Natural Science Foundation(Grant No.2208085MF168)+1 种基金the Science and Technology Innovation Tackle Plan Project of Maanshan(Grant No.2024RGZN001)the Scientific Research Fund Project of Anhui Medical University(Grant No.2023xkj122).
文摘Convolutional neural networks(CNNs)-based medical image segmentation technologies have been widely used in medical image segmentation because of their strong representation and generalization abilities.However,due to the inability to effectively capture global information from images,CNNs can easily lead to loss of contours and textures in segmentation results.Notice that the transformer model can effectively capture the properties of long-range dependencies in the image,and furthermore,combining the CNN and the transformer can effectively extract local details and global contextual features of the image.Motivated by this,we propose a multi-branch and multi-scale attention network(M2ANet)for medical image segmentation,whose architecture consists of three components.Specifically,in the first component,we construct an adaptive multi-branch patch module for parallel extraction of image features to reduce information loss caused by downsampling.In the second component,we apply residual block to the well-known convolutional block attention module to enhance the network’s ability to recognize important features of images and alleviate the phenomenon of gradient vanishing.In the third component,we design a multi-scale feature fusion module,in which we adopt adaptive average pooling and position encoding to enhance contextual features,and then multi-head attention is introduced to further enrich feature representation.Finally,we validate the effectiveness and feasibility of the proposed M2ANet method through comparative experiments on four benchmark medical image segmentation datasets,particularly in the context of preserving contours and textures.
基金This study was supported by the National Natural Science Foundation of China(U22B2075,52274056,51974356).
文摘A large number of nanopores and complex fracture structures in shale reservoirs results in multi-scale flow of oil. With the development of shale oil reservoirs, the permeability of multi-scale media undergoes changes due to stress sensitivity, which plays a crucial role in controlling pressure propagation and oil flow. This paper proposes a multi-scale coupled flow mathematical model of matrix nanopores, induced fractures, and hydraulic fractures. In this model, the micro-scale effects of shale oil flow in fractal nanopores, fractal induced fracture network, and stress sensitivity of multi-scale media are considered. We solved the model iteratively using Pedrosa transform, semi-analytic Segmented Bessel function, Laplace transform. The results of this model exhibit good agreement with the numerical solution and field production data, confirming the high accuracy of the model. As well, the influence of stress sensitivity on permeability, pressure and production is analyzed. It is shown that the permeability and production decrease significantly when induced fractures are weakly supported. Closed induced fractures can inhibit interporosity flow in the stimulated reservoir volume (SRV). It has been shown in sensitivity analysis that hydraulic fractures are beneficial to early production, and induced fractures in SRV are beneficial to middle production. The model can characterize multi-scale flow characteristics of shale oil, providing theoretical guidance for rapid productivity evaluation.
基金supported by the Natural Science Foundation of China(Grant No.42302170)National Postdoctoral Innovative Talent Support Program(Grant No.BX20220062)+3 种基金CNPC Innovation Found(Grant No.2022DQ02-0104)National Science Foundation of Heilongjiang Province of China(Grant No.YQ2023D001)Postdoctoral Science Foundation of Heilongjiang Province of China(Grant No.LBH-Z22091)the Natural Science Foundation of Shandong Province(Grant No.ZR2022YQ30).
文摘Prediction of production decline and evaluation of the adsorbed/free gas ratio are critical for determining the lifespan and production status of shale gas wells.Traditional production prediction methods have some shortcomings because of the low permeability and tightness of shale,complex gas flow behavior of multi-scale gas transport regions and multiple gas transport mechanism superpositions,and complex and variable production regimes of shale gas wells.Recent research has demonstrated the existence of a multi-stage isotope fractionation phenomenon during shale gas production,with the fractionation characteristics of each stage associated with the pore structure,gas in place(GIP),adsorption/desorption,and gas production process.This study presents a new approach for estimating shale gas well production and evaluating the adsorbed/free gas ratio throughout production using isotope fractionation techniques.A reservoir-scale carbon isotope fractionation(CIF)model applicable to the production process of shale gas wells was developed for the first time in this research.In contrast to the traditional model,this model improves production prediction accuracy by simultaneously fitting the gas production rate and δ^(13)C_(1) data and provides a new evaluation method of the adsorbed/free gas ratio during shale gas production.The results indicate that the diffusion and adsorption/desorption properties of rock,bottom-hole flowing pressure(BHP)of gas well,and multi-scale gas transport regions of the reservoir all affect isotope fractionation,with the diffusion and adsorption/desorption parameters of rock having the greatest effect on isotope fractionation being D∗/D,PL,VL,α,and others in that order.We effectively tested the universality of the four-stage isotope fractionation feature and revealed a unique isotope fractionation mechanism caused by the superimposed coupling of multi-scale gas transport regions during shale gas well production.Finally,we applied the established CIF model to a shale gas well in the Sichuan Basin,China,and calculated the estimated ultimate recovery(EUR)of the well to be 3.33×10^(8) m^(3);the adsorbed gas ratio during shale gas production was 1.65%,10.03%,and 23.44%in the first,fifth,and tenth years,respectively.The findings are significant for understanding the isotope fractionation mechanism during natural gas transport in complex systems and for formulating and optimizing unconventional natural gas development strategies.
基金financially supported by National Key R&D Program of China(2021YFB3500702)National Natural Science Foundation of China(Nos.21677010 and 51808037)Special fund of Beijing Key Laboratory of Indoor Air Quality Evaluation and Control(No.BZ0344KF21-04).
文摘With the ongoing depletion of fossil fuels,energy and environmental issues have become increasingly critical,necessitating the search for effective solutions.Catalysis,being one of the hallmarks of modern industry,offers a promising avenue for researchers.However,the question of how to significantly enhance the performance of catalysts has gradually drawn the attention of scholars.Defect engineering,a commonly employed and effective approach to improve catalyst activity,has become a significant research focus in the catalysis field in recent years.Nonmetal vacancies have received extensive attention due to their simple form.Consequently,exploration of metal vacancies has remained stagnant for a considerable period,resulting in a scarcity of comprehensive reviews on this topic.Therefore,based on the latest research findings,this paper summarizes and consolidates the construction strategies for metal vacancies,characterization techniques,and their roles in typical energy and environmental catalytic reactions.Additionally,it outlines potential challenges in the future,aiming to provide valuable references for researchers interested in investigating metal vacancies.
基金supported by the National Natural Science Foundation of China(62272049,62236006,62172045)the Key Projects of Beijing Union University(ZKZD202301).
文摘In recent years,gait-based emotion recognition has been widely applied in the field of computer vision.However,existing gait emotion recognition methods typically rely on complete human skeleton data,and their accuracy significantly declines when the data is occluded.To enhance the accuracy of gait emotion recognition under occlusion,this paper proposes a Multi-scale Suppression Graph ConvolutionalNetwork(MS-GCN).TheMS-GCN consists of three main components:Joint Interpolation Module(JI Moudle),Multi-scale Temporal Convolution Network(MS-TCN),and Suppression Graph Convolutional Network(SGCN).The JI Module completes the spatially occluded skeletal joints using the(K-Nearest Neighbors)KNN interpolation method.The MS-TCN employs convolutional kernels of various sizes to comprehensively capture the emotional information embedded in the gait,compensating for the temporal occlusion of gait information.The SGCN extracts more non-prominent human gait features by suppressing the extraction of key body part features,thereby reducing the negative impact of occlusion on emotion recognition results.The proposed method is evaluated on two comprehensive datasets:Emotion-Gait,containing 4227 real gaits from sources like BML,ICT-Pollick,and ELMD,and 1000 synthetic gaits generated using STEP-Gen technology,and ELMB,consisting of 3924 gaits,with 1835 labeled with emotions such as“Happy,”“Sad,”“Angry,”and“Neutral.”On the standard datasets Emotion-Gait and ELMB,the proposed method achieved accuracies of 0.900 and 0.896,respectively,attaining performance comparable to other state-ofthe-artmethods.Furthermore,on occlusion datasets,the proposedmethod significantly mitigates the performance degradation caused by occlusion compared to other methods,the accuracy is significantly higher than that of other methods.
基金The National Natural Science Foundation of China under contract Nos 42072324 and 91958202the Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory(Guangzhou)under contract No.GML2019ZD0106+1 种基金the Resource&Environment Project of China Ocean Mineral Resources R&D Association under contract No.DY135-C1-1-03the Geological Survey Project of China Geological Survey under contract No.DD20190629.
文摘Given the advances in satellite altimetry and multibeam bathymetry,benthic terrain classification based on digital bathymetric models(DBMs)has been widely used for the mapping of benthic topographies.For instance,cobaltrich crusts(CRCs)are important mineral resources found on seamounts and guyots in the western Pacific Ocean.Thick,plate-like CRCs are known to form on the summit and slopes of seamounts at the 1000–3000 m depth,while the relationship between seamount topography and spatial distribution of CRCs remains unclear.The benthic terrain classification of seamounts can solve this problem,thereby,facilitating the rapid exploration of seamount CRCs.Our study used an EM122 multibeam echosounder to retrieve high-resolution bathymetry data in the CRCs contract license area of China,i.e.,the Jiaxie Guyots in 2015 and 2016.Based on the DBM construted by bathymetirc data,broad-and fine-scale bathymetric position indices were utilized for quantitative classification of the terrain units of the Jiaxie Guyots on multiple scales.The classification revealed four first-order terrain units(e.g.,flat,crest,slope,and depression)and eleven second-order terrain units(e.g.,local crests,depressions on crests,gentle slopes,crests on slopes,and local depressions,etc.).Furthermore,the classification of the terrain and geological analysis indicated that the Weijia Guyot has a large flat summit,with local crests at the southern summit,whereas most of the guyot flanks were covered by gentle slopes.“Radial”mountain ridges have developed on the eastern side,while large-scale gravitational landslides have developed on the western and southern flanks.Additionally,landslide masses can be observed at the bottom of these slopes.The coverage of local crests on the seamount is∼1000 km^(2),and the local crests on the peak and flanks of the guyots may be the areas where thick and continuous plate-like CRCs are likely to occur.
