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
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
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
Objective weather classification methods have been extensively applied to identify dominant ozone-favorable synoptic weather patterns(SWPs),however,the consistency of different classification methods is rarely examine...Objective weather classification methods have been extensively applied to identify dominant ozone-favorable synoptic weather patterns(SWPs),however,the consistency of different classification methods is rarely examined.In this study,we apply two widely-used objective methods,the self-organizing map(SOM)and K-means clustering analysis,to derive ozone-favorable SWPs at four Chinese megacities in 2015-2022.We find that the two algorithms are largely consistent in recognizing dominant ozone-favorable SWPs for four Chinese megacities.In the case of classifying six SWPs,the derived circulation fields are highly similar with a spatial correlation of 0.99 between the two methods,and the difference in themean frequency of each SWP is less than 7%.The six dominant ozone-favorable SWPs in Guangzhou are all characterized by anomaly higher radiation and temperature,lower cloud cover,relative humidity,and wind speed,and stronger subsidence compared to climatology mean.We find that during 2015-2022,the occurrence of ozone-favorable SWPs days increases significantly at a rate of 3.2 days/year,faster than the increases in the ozone exceedance days(3.0 days/year).The interannual variability between the occurrence of ozone-favorable SWPs and ozone exceedance days are generally consistent with a temporal correlation coefficient of 0.6.In particular,the significant increase in ozone-favorable SWPs in 2022,especially the Subtropical High type which typically occurs in September,is consistent with a long-lasting ozone pollution episode in Guangzhou during September 2022.Our results thus reveal that enhanced frequency of ozone-favorable SWPs plays an important role in the observed 2015-2022 ozone increase in Guangzhou.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
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
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
Coral reef limestone(CRL)constitutes a distinctive marine carbonate formation with complex mechanical properties.This study investigates the multiscale damage and fracture mechanisms of CRL through integrated experime...Coral reef limestone(CRL)constitutes a distinctive marine carbonate formation with complex mechanical properties.This study investigates the multiscale damage and fracture mechanisms of CRL through integrated experimental testing,digital core technology,and theoretical modelling.Two CRL types with contrasting mesostructures were characterized across three scales.Macroscopically,CRL-I and CRL-II exhibited mean compressive strengths of 8.46 and 5.17 MPa,respectively.Mesoscopically,CRL-I featured small-scale highly interconnected pores,whilst CRL-II developed larger stratified pores with diminished connectivity.Microscopically,both CRL matrices demonstrated remarkable similarity in mineral composition and mechanical properties.A novel voxel average-based digital core scaling methodology was developed to facilitate numerical simulation of cross-scale damage processes,revealing network-progressive failure in CRL-I versus directional-brittle failure in CRL-II.Furthermore,a damage statistical constitutive model based on digital core technology and mesoscopic homogenisation theory established quantitative relationships between microelement strength distribution and macroscopic mechanical behavior.These findings illuminate the fundamental mechanisms through which mesoscopic structure governs the macroscopic mechanical properties of CRL.展开更多
The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method f...The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method for infrared and visible image fusion is proposed.The encoder designed according to the optimization objective consists of a base encoder and a detail encoder,which is used to extract low-frequency and high-frequency information from the image.This extraction may lead to some information not being captured,so a compensation encoder is proposed to supplement the missing information.Multi-scale decomposition is also employed to extract image features more comprehensively.The decoder combines low-frequency,high-frequency and supplementary information to obtain multi-scale features.Subsequently,the attention strategy and fusion module are introduced to perform multi-scale fusion for image reconstruction.Experimental results on three datasets show that the fused images generated by this network effectively retain salient targets while being more consistent with human visual perception.展开更多
A nonlinear multi-scale interaction(NMI)model was proposed and developed by the first author for nearly 30 years to represent the evolution of atmospheric blocking.In this review paper,we first review the creation and...A nonlinear multi-scale interaction(NMI)model was proposed and developed by the first author for nearly 30 years to represent the evolution of atmospheric blocking.In this review paper,we first review the creation and development of the NMI model and then emphasize that the NMI model represents a new tool for identifying the basic physics of how climate change influences mid-to-high latitude weather extremes.The building of the NMI model took place over three main periods.In the 1990s,a nonlinear Schr?dinger(NLS)equation model was presented to describe atmospheric blocking as a wave packet;however,it could not depict the lifetime(10-20 days)of atmospheric blocking.In the 2000s,we proposed an NMI model of atmospheric blocking in a uniform basic flow by making a scale-separation assumption and deriving an eddyforced NLS equation.This model succeeded in describing the life cycle of atmospheric blocking.In the 2020s,the NMI model was extended to include the impact of a changing climate mainly by altering the basic zonal winds and the magnitude of the meridional background potential vorticity gradient(PVy).Model results show that when PVy is smaller,blocking has a weaker dispersion and a stronger nonlinearity,so blocking can be more persistent and have a larger zonal scale and weaker eastward movement,thus favoring stronger weather extremes.However,when PVy is much smaller and below a critical threshold under much stronger winter Arctic warming of global warming,atmospheric blocking becomes locally less persistent and shows a much stronger westward movement,which acts to inhibit local cold extremes.Such a case does not happen in summer under global warming because PVy fails to fall below the critical threshold.Thus,our theory indicates that global warming can render summer-blocking anticyclones and mid-to-high latitude heatwaves more persistent,intense,and widespread.展开更多
Mêdog,located at the entrance of the water vapour channel of the Yarlung Zangbo Grand Canyon,and it has the highest rainfall and lowest elevation on the Tibetan Plateau(TP).The droplet size distribution(DSD)and m...Mêdog,located at the entrance of the water vapour channel of the Yarlung Zangbo Grand Canyon,and it has the highest rainfall and lowest elevation on the Tibetan Plateau(TP).The droplet size distribution(DSD)and microphysical processes associated with rainfall usually exhibit different characteristics under different synoptic patterns.In this study,an objective classification method is used to categorize the synoptic patterns that affect heavy rainfall(daily rainfall amounts>10 mm)in Mêdog into four patterns:southwest airflow(SWA),southern-branch trough(SBT),intense baroclinicity(IBC),and terrain-forced precipitation(TFP).SWA occurs most frequently(approximately 70%)with a mean daily rainfall of~22 mm,while TFP has the lowest occurrence frequency(7.7%)but the highest mean daily rainfall(29 mm).Both SBT and IBC exhibit occurrence frequencies around 12%.Among these patterns,the SWA pattern predominantly occurs during the monsoon season with abundant moisture and the lowest concentration of small raindrops.In contrast,the TFP pattern exhibits the highest concentration of large raindrops and the widest DSD spectrum,which can be attributed to the frequent convective activities in this area.As a result,compared with those of the other three synoptic patterns,the TFP pattern exhibits a larger mass-weighted mean diameter(D_(m))and higher rain rate(R).For stratiform rainfall,the difference in D_(m)among the four synoptic patterns can be neglected.The largest(smallest)average lgNW-value is observed in the SWA(IBC)pattern.Regarding convective rainfall,IBC dominated by northerly cold air exhibits mixed-phase processes characterized by larger raindrops and lower concentrations,resembling continental-like rainfall.In contrast,SWA occurring in monsoon season shows high concentrations of small raindrops,deeming it similar to maritime-like rainfall.In terms of the derived relationships,there are significant differences in the D_(m)-R andμ-Λrelationships among the four synoptic patterns.In addition,the diurnal variation in the DSD is analyzed in terms of the four synoptic patterns.These findings can improve the understanding of the microphysical processes of heavy rainfall events under different synoptic patterns and provide a reference for microphysical parameterizations of numerical models.展开更多
By applying the convolution-based Hilbert transform in the zonal direction on six-hourly streamfunction fields at200 h Pa, we present the climatology and trends of the local wave period, and zonal and meridional phase...By applying the convolution-based Hilbert transform in the zonal direction on six-hourly streamfunction fields at200 h Pa, we present the climatology and trends of the local wave period, and zonal and meridional phase speeds, of Rossby waves over the globe during the solstice seasons of 1979–2023. While partly similar to and inspired by Fragkoulidis and Wirth(2020), our method differs in its ability to cover both planetary-scale and synoptic-scale waves over not only the extratropics, but also the tropics and subtropics. Based on a physically reasonable global distribution of wave periods, our key new finding is a robust prolonging of wave periods over most regions of the tropics and subtropics during both solstice seasons of 1979–2023, except for the tropical Atlantic, which experiences a shortened wave period during June–July–August of 1979–2022. Both the prolonging and shortening of wave periods are mainly associated with the changes in planetary-scale waves. Regionally varying trends of the zonal phase speed(Cpx) of synoptic waves are consistent in sign with, but smaller in magnitude than, the trends of local zonal wind, confirming the conclusion of Wu and Lu(2023)on the opposite effects of zonal wind and the meridional gradient of potential vorticity on Cpx. Meanwhile, the Cpx trends of planetary-scale waves are relatively weak, and do not exhibit a robust relation with the trend of zonal wind. These new results are helpful toward better understanding the changes in atmospheric waves and extreme events under global warming.展开更多
In July 2021,a catastrophic extreme precipitation(EP)event occurred in Henan Province,China,resulting in considerable human and economic losses.The synoptic pattern during this event is distinctive,characterized by th...In July 2021,a catastrophic extreme precipitation(EP)event occurred in Henan Province,China,resulting in considerable human and economic losses.The synoptic pattern during this event is distinctive,characterized by the presence of two typhoons and substantial water transport into Henan.However,a favorable synoptic pattern only does not guarantee the occurrence of heavy precipitation in Henan.This study investigates the key environmental features critical for EP under similar synoptic patterns to the 2021 Henan extreme event.It is found that cold clouds are better aggregated on EP days,accompanied by beneficial environment features like enhanced moisture conditions,stronger updrafts,and greater atmospheric instability.The temporal evolution of these environmental features shows a leading signal by one to three days.These results suggest the importance of combining the synoptic pattern and environmental features in the forecasting of heavy precipitation events.展开更多
Urbanization’s impact on pre-monsoon extreme rainfall in the Greater Bay Area(GBA),coastal South China(SC),and its relation to different synoptic systems remains understudied.This research investigates urbanization e...Urbanization’s impact on pre-monsoon extreme rainfall in the Greater Bay Area(GBA),coastal South China(SC),and its relation to different synoptic systems remains understudied.This research investigates urbanization effects on premonsoon rainfall using hourly station observations and Weather Research and Forecasting model with the Single Layer Urban Canopy Model(WRF-SLUCM)simulations.Observations show stronger pre-monsoon extreme rainfall in GBA cities than surrounding rural areas,with the urban heat island(UHI)intensifying the urban rainfall intensity and probability.Extreme cases were classified into frontal and shear-line warm-sector types.Enhanced urban rainfall due to UHI was more pronounced under shear-line and warm-sector systems.Four frontal and four shear-line cases were dynamically downscaled using WRF-SLUCM,and four parallel experiments were conducted:“Nourban”(urban areas replaced by cropland),“AH0”,“AH100”,and“AH300”[normal land use,with the diurnal maximum anthropogenic heat(AH)set to 0,100,and 300 W m^(−2)in SLUCM,respectively].In frontal cases,significantly reduced urban rainfall in AH0 is due to decreased(enhanced)surface evaporation(wind divergence)in cities compared to cropland.Strong northerly winds and cold-air intrusion suppress the UHI in AH0 and AH100 during the rainfall process;enhanced urban rainfall occurs only in AH300.In contrast,for shear-line cases,urban friction and UHI promote local convection and wind convergence,increasing urban rainfall significantly in all urban experiments compared to Nourban.Overall,urbanization’s influence on SC’s premonsoon extreme rainfall is highly sensitive to the type of synoptic systems,necessitating further investigation of urban rainfall in this season.展开更多
Mental health problems and potential psychological crises affect the healthy growth and learning performance of college students.Effective and suitable prevention of psychological crises among college students is a co...Mental health problems and potential psychological crises affect the healthy growth and learning performance of college students.Effective and suitable prevention of psychological crises among college students is a continuous challenge university managers face.To explore a method of preventing psychological crises among college students,we measured 38661 students by using SCL-90(symptom check list-90)and screened out 5790 students with positive results.Then,we measured 33188 students by using PHQ-9(patient health questionnaire-9)and screened out 603 students with suicidal ideation or behavior;we interviewed 392 students by using GAQ(growth adversity questionnaire).The number of students who had positive results at both phases is 155.As a result,we obtained a data set(N=76)by integrating the students who tested positive on the PHQ-9(i.e.total score≥20)with those who completed the PHQ-9 and GAQ.In addition,we obtained a data set(N=50)by excluding the cases in which the GAQ score is 0.With regard to QCA(qualitative comparative analysis)results,the data set(N=76)exhibits 5 constellations of solutions with a coverage rate greater than 0.7,and the first eight indicators of the PHQ-9 constitute the explanatory variables in the combined solutions.About the data set(N=50),the combined solutions are extremely complicated and the explanatory variables encompass indicators from both the PHQ-9 and GAQ.All these mean that the multi-scale could more comprehensively reflect mental health states of college students,thus enhance the accuracy and effectiveness of the corresponding hierarchical intervention,and finally provide support for preventing psychological crises in universities.展开更多
The Sichuan Basin(SCB),China has a high incidence of extremely persistent heavy rainfall(EPHR)events.The EPHR events from 2009 to 2019 in the SCB were mainly concentrated over the northern and northwestern windward sl...The Sichuan Basin(SCB),China has a high incidence of extremely persistent heavy rainfall(EPHR)events.The EPHR events from 2009 to 2019 in the SCB were mainly concentrated over the northern and northwestern windward slopes and the central basin.They occurred from June to September,but especially in July,and peaked at 0300 LST.ERA5 reanalysis data and objective classification were used to investigate the synoptic patterns and their effects.There were three synoptic patterns during EPHR events,all accompanied by a Southwest Vortex.The location and intensity of the Southwest Vortex,thermal forcing of the Tibetan Plateau(TP),and low-level winds can greatly affect the intensity and spatial distribution of EPHR.When the Southwest Vortex was located in the western SCB and there were southerly low-level jets(LLJs),convergence and upslope wind would lead to EPHR over the northwestern or northern windward slopes.If there was no LLJ and the whole SCB was under the center of the Southwest Vortex,nocturnal EPHR was controlled by the internal circulation of the Southwest Vortex and the updraft generated by the thermal forcing of the TP,and the rainfall was weaker.The southeastern entrance of the SCB was a key area where the low-level wind dominated the nocturnal peak of EPHR.The nocturnal strengthened southeasterly wind in the key area is attributable to inertial oscillation,and the topographic friction plays an essential role in transporting momentum and moisture into the basin by generating easterly and northeasterly ageostrophic winds.展开更多
Water electrolyzers play a crucial role in green hydrogen production.However,their efficiency and scalability are often compromised by bubble dynamics across various scales,from nanoscale to macroscale components.This...Water electrolyzers play a crucial role in green hydrogen production.However,their efficiency and scalability are often compromised by bubble dynamics across various scales,from nanoscale to macroscale components.This review explores multi-scale modeling as a tool to visualize multi-phase flow and improve mass transport in water electrolyzers.At the nanoscale,molecular dynamics(MD)simulations reveal how electrode surface features and wettability influence nanobubble nucleation and stability.Moving to the mesoscale,models such as volume of fluid(VOF)and lattice Boltzmann method(LBM)shed light on bubble transport in porous transport layers(PTLs).These insights inform innovative designs,including gradient porosity and hydrophilic-hydrophobic patterning,aimed at minimizing gas saturation.At the macroscale,VOF simulations elucidate two-phase flow regimes within channels,showing how flow field geometry and wettability affect bubble discharging.Moreover,artificial intelligence(AI)-driven surrogate models expedite the optimization process,allowing for rapid exploration of structural parameters in channel-rib flow fields and porous flow field designs.By integrating these approaches,we can bridge theoretical insights with experimental validation,ultimately enhancing water electrolyzer performance,reducing costs,and advancing affordable,high-efficiency hydrogen production.展开更多
Segmenting skin lesions is critical for early skin cancer detection.Existing CNN and Transformer-based methods face challenges such as high computational complexity and limited adaptability to variations in lesion siz...Segmenting skin lesions is critical for early skin cancer detection.Existing CNN and Transformer-based methods face challenges such as high computational complexity and limited adaptability to variations in lesion sizes.To overcome these limitations,we introduce MSAMamba-UNet,a lightweight model that integrates two novel architectures:Multi-Scale Mamba(MSMamba)and Adaptive Dynamic Gating Block(ADGB).MSMamba utilizes multi-scale decomposition and a parallel hierarchical structure to enhance the delineation of irregular lesion boundaries and sensitivity to small targets.ADGB dynamically selects convolutional kernels with varying receptive fields based on input features,improving the model’s capacity to accommodate diverse lesion textures and scales.Additionally,we introduce a Mix Attention Fusion Block(MAF)to enhance shallow feature representation by integrating parallel channel and pixel attention mechanisms.Extensive evaluation of MSAMamba-UNet on the ISIC 2016,ISIC 2017,and ISIC 2018 datasets demonstrates competitive segmentation accuracy with only 0.056 M parameters and 0.069 GFLOPs.Our experiments revealed that MSAMamba-UNet achieved IoU scores of 85.53%,85.47%,and 82.22%,as well as DSC scores of 92.20%,92.17%,and 90.24%,respectively.These results underscore the lightweight design and effectiveness of MSAMamba-UNet.展开更多
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.
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supported by the Guangdong Basic and Applied Basic Research project (No.2020B0301030004)the Key-Area Research and Development Program of Guangdong Province (No.2020B1111360003)+1 种基金the National Natural Science Foundation of China (No.42105103)the Guangdong Basic and Applied Basic Research Foundation (No.2022A1515011554).
文摘Objective weather classification methods have been extensively applied to identify dominant ozone-favorable synoptic weather patterns(SWPs),however,the consistency of different classification methods is rarely examined.In this study,we apply two widely-used objective methods,the self-organizing map(SOM)and K-means clustering analysis,to derive ozone-favorable SWPs at four Chinese megacities in 2015-2022.We find that the two algorithms are largely consistent in recognizing dominant ozone-favorable SWPs for four Chinese megacities.In the case of classifying six SWPs,the derived circulation fields are highly similar with a spatial correlation of 0.99 between the two methods,and the difference in themean frequency of each SWP is less than 7%.The six dominant ozone-favorable SWPs in Guangzhou are all characterized by anomaly higher radiation and temperature,lower cloud cover,relative humidity,and wind speed,and stronger subsidence compared to climatology mean.We find that during 2015-2022,the occurrence of ozone-favorable SWPs days increases significantly at a rate of 3.2 days/year,faster than the increases in the ozone exceedance days(3.0 days/year).The interannual variability between the occurrence of ozone-favorable SWPs and ozone exceedance days are generally consistent with a temporal correlation coefficient of 0.6.In particular,the significant increase in ozone-favorable SWPs in 2022,especially the Subtropical High type which typically occurs in September,is consistent with a long-lasting ozone pollution episode in Guangzhou during September 2022.Our results thus reveal that enhanced frequency of ozone-favorable SWPs plays an important role in the observed 2015-2022 ozone increase in Guangzhou.
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National Key Research and Development Program of China (No.2021YFC3100800)the National Natural Science Foundation of China (Nos.42407235 and 42271026)+1 种基金the Project of Sanya Yazhou Bay Science and Technology City (No.SCKJ-JYRC-2023-54)supported by the Hefei advanced computing center
文摘Coral reef limestone(CRL)constitutes a distinctive marine carbonate formation with complex mechanical properties.This study investigates the multiscale damage and fracture mechanisms of CRL through integrated experimental testing,digital core technology,and theoretical modelling.Two CRL types with contrasting mesostructures were characterized across three scales.Macroscopically,CRL-I and CRL-II exhibited mean compressive strengths of 8.46 and 5.17 MPa,respectively.Mesoscopically,CRL-I featured small-scale highly interconnected pores,whilst CRL-II developed larger stratified pores with diminished connectivity.Microscopically,both CRL matrices demonstrated remarkable similarity in mineral composition and mechanical properties.A novel voxel average-based digital core scaling methodology was developed to facilitate numerical simulation of cross-scale damage processes,revealing network-progressive failure in CRL-I versus directional-brittle failure in CRL-II.Furthermore,a damage statistical constitutive model based on digital core technology and mesoscopic homogenisation theory established quantitative relationships between microelement strength distribution and macroscopic mechanical behavior.These findings illuminate the fundamental mechanisms through which mesoscopic structure governs the macroscopic mechanical properties of CRL.
基金Supported by the Henan Province Key Research and Development Project(231111211300)the Central Government of Henan Province Guides Local Science and Technology Development Funds(Z20231811005)+2 种基金Henan Province Key Research and Development Project(231111110100)Henan Provincial Outstanding Foreign Scientist Studio(GZS2024006)Henan Provincial Joint Fund for Scientific and Technological Research and Development Plan(Application and Overcoming Technical Barriers)(242103810028)。
文摘The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method for infrared and visible image fusion is proposed.The encoder designed according to the optimization objective consists of a base encoder and a detail encoder,which is used to extract low-frequency and high-frequency information from the image.This extraction may lead to some information not being captured,so a compensation encoder is proposed to supplement the missing information.Multi-scale decomposition is also employed to extract image features more comprehensively.The decoder combines low-frequency,high-frequency and supplementary information to obtain multi-scale features.Subsequently,the attention strategy and fusion module are introduced to perform multi-scale fusion for image reconstruction.Experimental results on three datasets show that the fused images generated by this network effectively retain salient targets while being more consistent with human visual perception.
基金supported by the National Natural Science Foundation of China(Grant Nos.42150204 and 2288101)supported by the China National Postdoctoral Program for Innovative Talents(BX20230045)the China Postdoctoral Science Foundation(2023M730279)。
文摘A nonlinear multi-scale interaction(NMI)model was proposed and developed by the first author for nearly 30 years to represent the evolution of atmospheric blocking.In this review paper,we first review the creation and development of the NMI model and then emphasize that the NMI model represents a new tool for identifying the basic physics of how climate change influences mid-to-high latitude weather extremes.The building of the NMI model took place over three main periods.In the 1990s,a nonlinear Schr?dinger(NLS)equation model was presented to describe atmospheric blocking as a wave packet;however,it could not depict the lifetime(10-20 days)of atmospheric blocking.In the 2000s,we proposed an NMI model of atmospheric blocking in a uniform basic flow by making a scale-separation assumption and deriving an eddyforced NLS equation.This model succeeded in describing the life cycle of atmospheric blocking.In the 2020s,the NMI model was extended to include the impact of a changing climate mainly by altering the basic zonal winds and the magnitude of the meridional background potential vorticity gradient(PVy).Model results show that when PVy is smaller,blocking has a weaker dispersion and a stronger nonlinearity,so blocking can be more persistent and have a larger zonal scale and weaker eastward movement,thus favoring stronger weather extremes.However,when PVy is much smaller and below a critical threshold under much stronger winter Arctic warming of global warming,atmospheric blocking becomes locally less persistent and shows a much stronger westward movement,which acts to inhibit local cold extremes.Such a case does not happen in summer under global warming because PVy fails to fall below the critical threshold.Thus,our theory indicates that global warming can render summer-blocking anticyclones and mid-to-high latitude heatwaves more persistent,intense,and widespread.
基金funded by the Second Tibetan Plateau Scientific Expedition and Research(STEP)program,Grant No.2019QZKK0105Basic Research Fund of CAMS(2023Z009)Science and Technology Development Fund of Chinese Academy of Meteorological Sciences(2023KJ041).
文摘Mêdog,located at the entrance of the water vapour channel of the Yarlung Zangbo Grand Canyon,and it has the highest rainfall and lowest elevation on the Tibetan Plateau(TP).The droplet size distribution(DSD)and microphysical processes associated with rainfall usually exhibit different characteristics under different synoptic patterns.In this study,an objective classification method is used to categorize the synoptic patterns that affect heavy rainfall(daily rainfall amounts>10 mm)in Mêdog into four patterns:southwest airflow(SWA),southern-branch trough(SBT),intense baroclinicity(IBC),and terrain-forced precipitation(TFP).SWA occurs most frequently(approximately 70%)with a mean daily rainfall of~22 mm,while TFP has the lowest occurrence frequency(7.7%)but the highest mean daily rainfall(29 mm).Both SBT and IBC exhibit occurrence frequencies around 12%.Among these patterns,the SWA pattern predominantly occurs during the monsoon season with abundant moisture and the lowest concentration of small raindrops.In contrast,the TFP pattern exhibits the highest concentration of large raindrops and the widest DSD spectrum,which can be attributed to the frequent convective activities in this area.As a result,compared with those of the other three synoptic patterns,the TFP pattern exhibits a larger mass-weighted mean diameter(D_(m))and higher rain rate(R).For stratiform rainfall,the difference in D_(m)among the four synoptic patterns can be neglected.The largest(smallest)average lgNW-value is observed in the SWA(IBC)pattern.Regarding convective rainfall,IBC dominated by northerly cold air exhibits mixed-phase processes characterized by larger raindrops and lower concentrations,resembling continental-like rainfall.In contrast,SWA occurring in monsoon season shows high concentrations of small raindrops,deeming it similar to maritime-like rainfall.In terms of the derived relationships,there are significant differences in the D_(m)-R andμ-Λrelationships among the four synoptic patterns.In addition,the diurnal variation in the DSD is analyzed in terms of the four synoptic patterns.These findings can improve the understanding of the microphysical processes of heavy rainfall events under different synoptic patterns and provide a reference for microphysical parameterizations of numerical models.
基金the support from the National Natural Science Foundation of China (Grant No. 42175070)supported by the National Natural Science Foundation of China (Grant No. 42288101)supported by the National Key Scientific and Technological Infrastructure project “Earth System Numerical Simulation Facility” (Earth Lab)。
文摘By applying the convolution-based Hilbert transform in the zonal direction on six-hourly streamfunction fields at200 h Pa, we present the climatology and trends of the local wave period, and zonal and meridional phase speeds, of Rossby waves over the globe during the solstice seasons of 1979–2023. While partly similar to and inspired by Fragkoulidis and Wirth(2020), our method differs in its ability to cover both planetary-scale and synoptic-scale waves over not only the extratropics, but also the tropics and subtropics. Based on a physically reasonable global distribution of wave periods, our key new finding is a robust prolonging of wave periods over most regions of the tropics and subtropics during both solstice seasons of 1979–2023, except for the tropical Atlantic, which experiences a shortened wave period during June–July–August of 1979–2022. Both the prolonging and shortening of wave periods are mainly associated with the changes in planetary-scale waves. Regionally varying trends of the zonal phase speed(Cpx) of synoptic waves are consistent in sign with, but smaller in magnitude than, the trends of local zonal wind, confirming the conclusion of Wu and Lu(2023)on the opposite effects of zonal wind and the meridional gradient of potential vorticity on Cpx. Meanwhile, the Cpx trends of planetary-scale waves are relatively weak, and do not exhibit a robust relation with the trend of zonal wind. These new results are helpful toward better understanding the changes in atmospheric waves and extreme events under global warming.
基金supported by the National Key Research and Development Pro-gram of China(Grant No.2022YFC3003902)the National Natu-ral Science Foundation of China(Grant Nos.42075146 and 42275006).
文摘In July 2021,a catastrophic extreme precipitation(EP)event occurred in Henan Province,China,resulting in considerable human and economic losses.The synoptic pattern during this event is distinctive,characterized by the presence of two typhoons and substantial water transport into Henan.However,a favorable synoptic pattern only does not guarantee the occurrence of heavy precipitation in Henan.This study investigates the key environmental features critical for EP under similar synoptic patterns to the 2021 Henan extreme event.It is found that cold clouds are better aggregated on EP days,accompanied by beneficial environment features like enhanced moisture conditions,stronger updrafts,and greater atmospheric instability.The temporal evolution of these environmental features shows a leading signal by one to three days.These results suggest the importance of combining the synoptic pattern and environmental features in the forecasting of heavy precipitation events.
基金supported by CUHK Strategic Impact Enhancement Fund(project no.3135536)Guangdong Basic and Applied Basic Research Foundation(2023B1515020029).
文摘Urbanization’s impact on pre-monsoon extreme rainfall in the Greater Bay Area(GBA),coastal South China(SC),and its relation to different synoptic systems remains understudied.This research investigates urbanization effects on premonsoon rainfall using hourly station observations and Weather Research and Forecasting model with the Single Layer Urban Canopy Model(WRF-SLUCM)simulations.Observations show stronger pre-monsoon extreme rainfall in GBA cities than surrounding rural areas,with the urban heat island(UHI)intensifying the urban rainfall intensity and probability.Extreme cases were classified into frontal and shear-line warm-sector types.Enhanced urban rainfall due to UHI was more pronounced under shear-line and warm-sector systems.Four frontal and four shear-line cases were dynamically downscaled using WRF-SLUCM,and four parallel experiments were conducted:“Nourban”(urban areas replaced by cropland),“AH0”,“AH100”,and“AH300”[normal land use,with the diurnal maximum anthropogenic heat(AH)set to 0,100,and 300 W m^(−2)in SLUCM,respectively].In frontal cases,significantly reduced urban rainfall in AH0 is due to decreased(enhanced)surface evaporation(wind divergence)in cities compared to cropland.Strong northerly winds and cold-air intrusion suppress the UHI in AH0 and AH100 during the rainfall process;enhanced urban rainfall occurs only in AH300.In contrast,for shear-line cases,urban friction and UHI promote local convection and wind convergence,increasing urban rainfall significantly in all urban experiments compared to Nourban.Overall,urbanization’s influence on SC’s premonsoon extreme rainfall is highly sensitive to the type of synoptic systems,necessitating further investigation of urban rainfall in this season.
文摘Mental health problems and potential psychological crises affect the healthy growth and learning performance of college students.Effective and suitable prevention of psychological crises among college students is a continuous challenge university managers face.To explore a method of preventing psychological crises among college students,we measured 38661 students by using SCL-90(symptom check list-90)and screened out 5790 students with positive results.Then,we measured 33188 students by using PHQ-9(patient health questionnaire-9)and screened out 603 students with suicidal ideation or behavior;we interviewed 392 students by using GAQ(growth adversity questionnaire).The number of students who had positive results at both phases is 155.As a result,we obtained a data set(N=76)by integrating the students who tested positive on the PHQ-9(i.e.total score≥20)with those who completed the PHQ-9 and GAQ.In addition,we obtained a data set(N=50)by excluding the cases in which the GAQ score is 0.With regard to QCA(qualitative comparative analysis)results,the data set(N=76)exhibits 5 constellations of solutions with a coverage rate greater than 0.7,and the first eight indicators of the PHQ-9 constitute the explanatory variables in the combined solutions.About the data set(N=50),the combined solutions are extremely complicated and the explanatory variables encompass indicators from both the PHQ-9 and GAQ.All these mean that the multi-scale could more comprehensively reflect mental health states of college students,thus enhance the accuracy and effectiveness of the corresponding hierarchical intervention,and finally provide support for preventing psychological crises in universities.
基金supported by the National Natural Science Foundation of China(Grant Nos.42330610 and 42075010)。
文摘The Sichuan Basin(SCB),China has a high incidence of extremely persistent heavy rainfall(EPHR)events.The EPHR events from 2009 to 2019 in the SCB were mainly concentrated over the northern and northwestern windward slopes and the central basin.They occurred from June to September,but especially in July,and peaked at 0300 LST.ERA5 reanalysis data and objective classification were used to investigate the synoptic patterns and their effects.There were three synoptic patterns during EPHR events,all accompanied by a Southwest Vortex.The location and intensity of the Southwest Vortex,thermal forcing of the Tibetan Plateau(TP),and low-level winds can greatly affect the intensity and spatial distribution of EPHR.When the Southwest Vortex was located in the western SCB and there were southerly low-level jets(LLJs),convergence and upslope wind would lead to EPHR over the northwestern or northern windward slopes.If there was no LLJ and the whole SCB was under the center of the Southwest Vortex,nocturnal EPHR was controlled by the internal circulation of the Southwest Vortex and the updraft generated by the thermal forcing of the TP,and the rainfall was weaker.The southeastern entrance of the SCB was a key area where the low-level wind dominated the nocturnal peak of EPHR.The nocturnal strengthened southeasterly wind in the key area is attributable to inertial oscillation,and the topographic friction plays an essential role in transporting momentum and moisture into the basin by generating easterly and northeasterly ageostrophic winds.
基金supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region,China(Project No.15308024)a grant from Research Centre for Carbon-Strategic Catalysis,The Hong Kong Polytechnic University(CE2X).
文摘Water electrolyzers play a crucial role in green hydrogen production.However,their efficiency and scalability are often compromised by bubble dynamics across various scales,from nanoscale to macroscale components.This review explores multi-scale modeling as a tool to visualize multi-phase flow and improve mass transport in water electrolyzers.At the nanoscale,molecular dynamics(MD)simulations reveal how electrode surface features and wettability influence nanobubble nucleation and stability.Moving to the mesoscale,models such as volume of fluid(VOF)and lattice Boltzmann method(LBM)shed light on bubble transport in porous transport layers(PTLs).These insights inform innovative designs,including gradient porosity and hydrophilic-hydrophobic patterning,aimed at minimizing gas saturation.At the macroscale,VOF simulations elucidate two-phase flow regimes within channels,showing how flow field geometry and wettability affect bubble discharging.Moreover,artificial intelligence(AI)-driven surrogate models expedite the optimization process,allowing for rapid exploration of structural parameters in channel-rib flow fields and porous flow field designs.By integrating these approaches,we can bridge theoretical insights with experimental validation,ultimately enhancing water electrolyzer performance,reducing costs,and advancing affordable,high-efficiency hydrogen production.
基金supported in part by the National Natural Science Foundation of China under Grant 62201201the Foundation of Henan Educational Committee under Grant 242102211042.
文摘Segmenting skin lesions is critical for early skin cancer detection.Existing CNN and Transformer-based methods face challenges such as high computational complexity and limited adaptability to variations in lesion sizes.To overcome these limitations,we introduce MSAMamba-UNet,a lightweight model that integrates two novel architectures:Multi-Scale Mamba(MSMamba)and Adaptive Dynamic Gating Block(ADGB).MSMamba utilizes multi-scale decomposition and a parallel hierarchical structure to enhance the delineation of irregular lesion boundaries and sensitivity to small targets.ADGB dynamically selects convolutional kernels with varying receptive fields based on input features,improving the model’s capacity to accommodate diverse lesion textures and scales.Additionally,we introduce a Mix Attention Fusion Block(MAF)to enhance shallow feature representation by integrating parallel channel and pixel attention mechanisms.Extensive evaluation of MSAMamba-UNet on the ISIC 2016,ISIC 2017,and ISIC 2018 datasets demonstrates competitive segmentation accuracy with only 0.056 M parameters and 0.069 GFLOPs.Our experiments revealed that MSAMamba-UNet achieved IoU scores of 85.53%,85.47%,and 82.22%,as well as DSC scores of 92.20%,92.17%,and 90.24%,respectively.These results underscore the lightweight design and effectiveness of MSAMamba-UNet.
