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Multi-scale research on coal chemical wastewater treatment:Pollutant molecules,technologies,and process integration 认领 引用
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作者 Yirong Feng Jianxin Shi +4 位作者 Qingya Wang Bingxiao Feng Yongqing Wei Wei He Hengjun Gai 《Green Energy & Environment》 SCIE EI CAS CSCD 2026年第5期1233-1254,共22页
The coal chemical industry serves as an indispensable element in the fabric of the global energy system.However,the wastewater generated from its production processes exhibits high chemical oxygen demand,high toxicity... The coal chemical industry serves as an indispensable element in the fabric of the global energy system.However,the wastewater generated from its production processes exhibits high chemical oxygen demand,high toxicity,and poor biodegradability,posing severe challenges to the ecological sustainability of the coal chemical industry.This review systematically summarizes recent advances in treatment technologies for coal chemical wastewater(CCW)through a“pollutant molecules-technology-process integration”framework analyzed from micro-to macro-scale perspectives.It begins by delineating the complex chemical composition of CCW and identifying key toxic substances,thereby clarifying the current challenges in treatment processes and establishing a micro-scale foundation for technological development.The review then highlights solvent extraction,grounded in intermolecular interactions,as a core method for recovering phenolic compounds.This is followed by an in-depth analysis of the performance and mechanisms of biological treatment and advanced oxidation processes(AOPs)for the deep removal of refractory organic pollutants.Finally,from a macro-scale perspective,the integration of pretreatment,biological treatment,and AOPs into systematic frameworks is discussed.A thorough understanding of the molecular characteristics of pollutants is crucial for developing efficient treatment technologies.Moreover,system-level integration via process intensification and technological synergy is considered essential for achieving efficient purification and resource recovery from CCW. 展开更多
关键词 Coal chemical wastewater Phenol recovery Biochemical treatment Advanced oxidation Process integration
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Multi-scale intelligent fusion and dynamic validation for high-resolution seismic data processing in drilling 认领 引用 被引量：1
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作者 YUAN Sanyi XU Yanwu +2 位作者 XIE Renjun CHEN Shuai YUAN Junliang 《Petroleum Exploration and Development》 SCIE 2025年第3期680-691,共12页
During drilling operations,the low resolution of seismic data often limits the accurate characterization of small-scale geological bodies near the borehole and ahead of the drill bit.This study investigates high-resol... During drilling operations,the low resolution of seismic data often limits the accurate characterization of small-scale geological bodies near the borehole and ahead of the drill bit.This study investigates high-resolution seismic data processing technologies and methods tailored for drilling scenarios.The high-resolution processing of seismic data is divided into three stages:pre-drilling processing,post-drilling correction,and while-drilling updating.By integrating seismic data from different stages,spatial ranges,and frequencies,together with information from drilled wells and while-drilling data,and applying artificial intelligence modeling techniques,a progressive high-resolution processing technology of seismic data based on multi-source information fusion is developed,which performs simple and efficient seismic information updates during drilling.Case studies show that,with the gradual integration of multi-source information,the resolution and accuracy of seismic data are significantly improved,and thin-bed weak reflections are more clearly imaged.The updated seismic information while-drilling demonstrates high value in predicting geological bodies ahead of the drill bit.Validation using logging,mud logging,and drilling engineering data ensures the fidelity of the processing results of high-resolution seismic data.This provides clearer and more accurate stratigraphic information for drilling operations,enhancing both drilling safety and efficiency. 展开更多
关键词 high-resolution seismic data processing while-drilling update while-drilling logging multi-source information fusion thin-bed weak reflection artificial intelligence modeling
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Response of wind turbine loads to multi-scale turbulent structures:a study based on turbulence signals observed in the field 认领 引用 被引量：2
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作者 Yongfen Chai Yan Wang +2 位作者 Haolin Li Jingjing Zhang Jian Zheng 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第2期593-609,共17页
Accurately assessing the impact of turbulence structures on load fluctuation is crucial for the long-term stable operation of wind turbines.Based on turbulence signals observed at the Qingtu Lake Observed Array in Chi... Accurately assessing the impact of turbulence structures on load fluctuation is crucial for the long-term stable operation of wind turbines.Based on turbulence signals observed at the Qingtu Lake Observed Array in China,the aerodynamic load responses of the wind turbine to different turbulence scales are quantitatively analyzed in this study.The results indicate that very large-scale motions(VLSMs)are associated with significant load fluctuations due to its low frequency and high energy characteristics,increasing the risk of extreme loads.Large-scale motions coupled with the natural frequency of wind turbines in the medium frequency range,result in resonance phenomena.Small-scale motions,due to their high-frequency rapid vibration characteristics,cause instantaneous oscillations in wind turbine loads.Furthermore,correlation analysis indicates that the flapwise moment and thrust are most sensitive to VLSMs,while the edgewise moment is less affected by the scale characteristics.It is worth noting that this study is the first to explore the modulation effects of different scales of turbulent structures on the amplitude of wind turbine load fluctuation.It was found that turbulent structures exceeding a scale of 3δ have the most significant impact on modulating the load amplitudes,where δ is the boundary layer thickness,which is 99% of the flow velocity outside the boundary layer.These findings contribute to the enhancement of understanding regarding the load response of wind turbines in multi-scale turbulent environments and provide important references for the optimization of wind turbine design and load control. 展开更多
关键词 Multi-scale turbulent structures Wind turbine load response Field observation experiment Wavelet analyze
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Multi-scale simulation for atomic oxygen erosion in low Earth orbit on polymer matrix by bridging reactive molecular dynamics and finite element analysis 认领 引用 被引量：1
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作者 Jiwon JUNG Jongkyung AN +2 位作者 Seunghwan KWON Byeong-Joo KIM Gun Jin YUN 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第4期654-667,共14页
Materials constituting satellites in the Low Earth Orbit(LEO)environment undergo degradation during missions due to harsh conditions such as cyclic temperature variations in high-vacuum,exposure to UV radiation,and co... Materials constituting satellites in the Low Earth Orbit(LEO)environment undergo degradation during missions due to harsh conditions such as cyclic temperature variations in high-vacuum,exposure to UV radiation,and collisions with highly reactive Atomic Oxygens(AO).Especially among those,AO collisions oxidize the surface and induce mass loss by generating volatile gases,leading to component failure.Reactive Force Field(Reax FF)molecular dynamics simulations,capable of describing chemical reactions,have been continuously performed to evaluate the AO erosion resistance of surface materials in LEO.Previous molecular simulation-based studies,however,evaluated AO resistance qualitatively by utilizing constant particle Number,Volume,Energy(NVE)ensemble simulations,where temperatures rise to several thousand kelvins over tens of picoseconds,and such extreme temperature conditions were not directly compatible with physical conditions in LEO.Therefore,we aimed to develop a multi-scale AO erosion analysis bridging thermal Finite Element Analysis(FEA)with mass loss rate determined from the Reax FF MD simulations.The overall thermal analysis was conducted over solar heat flux and surface radiation,while the ABAQUS Umeshmotion and Arbitrary Lagrangian-Eulerian(ALE)algorithm was adopted to analyze the surface recession of the model.The relation between erosion yields in given temperature conditions was calculated using constant particle Number,Volume,Temperature(NVT)ensemble,fitted as the Arrhenius equation form,and implemented to the FEA simulations. 展开更多
关键词 Atomic oxygen Low Earth orbit Multi-scale ReaxFF Surface erosion
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Multi-scale quantitative study on cemented tailings and waste-rock backfill under different loading rates 认领 引用 被引量：1
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作者 YIN Sheng-hua CHEN Jun-wei +4 位作者 YAN Ze-peng ZENG Jia-lu ZHOU Yun YANG Jian ZHANG Fu-shun 《Journal of Central South University》 SCIE EI CAS CSCD 2026年第1期357-374,共18页
The development of metallic mineral resources generates a significant amount of solid waste,such as tailings and waste rock.Cemented tailings and waste-rock backfill(CTWB)is an effective method for managing and dispos... The development of metallic mineral resources generates a significant amount of solid waste,such as tailings and waste rock.Cemented tailings and waste-rock backfill(CTWB)is an effective method for managing and disposing of this mining waste.This study employs a macro-meso-micro testing method to investigate the effects of the waste rock grading index(WGI)and loading rate(LR)on the uniaxial compressive strength(UCS),pore structure,and micromorphology of CTWB materials.Pore structures were analyzed using scanning electron microscopy(SEM)and mercury intrusion porosimetry(MIP).The particles(pores)and cracks analysis system(PCAS)software was used to quantitatively characterize the multi-scale micropores in the SEM images.The key findings indicate that the macroscopic results(UCS)of CTWB materials correspond to the microscopic results(pore structure and micromorphology).Changes in porosity largely depend on the conditions of waste rock grading index and loading rate.The inclusion of waste rock initially increases and then decreases the UCS,while porosity first decreases and then increases,with a critical waste rock grading index of 0.6.As the loading rate increases,UCS initially rises and then falls,while porosity gradually increases.Based on MIP and SEM results,at waste rock grading index 0.6,the most probable pore diameters,total pore area(TPA),pore number(PN),maximum pore area(MPA),and area probability distribution index(APDI)are minimized,while average pore form factor(APF)and fractal dimension of pore porosity distribution(FDPD)are maximized,indicating the most compact pore structure.At a loading rate of 12.0 mm/min,the most probable pore diameters,TPA,PN,MPA,APF,and APDI reach their maximum values,while FDPD reaches its minimum value.Finally,the mechanism of CTWB materials during compression is analyzed,based on the quantitative results of UCS and porosity.The research findings play a crucial role in ensuring the successful application of CTWB materials in deep metal mines. 展开更多
关键词 cemented backfill waste rock loading rate multi-scale analysis mercury intrusion porosimetry pore structure micromorphology
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Multi-scale modeling of ultra-thin commercially pure titanium sheet for fuel cell bipolar plates:Plastic anisotropy and distortional strain hardening 认领 引用 被引量：1
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作者 Kyung Mun Min Seonghwan Choi +2 位作者 Xiaohua Hu Jinwoo Lee Hyuk Jong Bong 《International Journal of Minerals,Metallurgy and Materials》 SCIE EI CAS CSCD 2026年第5期1637-1651,共15页
This study presents a multi-scale modeling framework to describe the mechanical behavior of a 0.1 mm-thick commercially pure titanium(CP-Ti)sheet developed for fuel cell bipolar plates.Since standardized methods for c... This study presents a multi-scale modeling framework to describe the mechanical behavior of a 0.1 mm-thick commercially pure titanium(CP-Ti)sheet developed for fuel cell bipolar plates.Since standardized methods for characterizing ultra-thin sheets under complex stress states are lacking,a virtual modeling approach was employed.At the grain scale,a crystal plasticity finite element(CPFE)model was constructed to incorporate the relevant slip and twinning systems,enabling prediction of responses under diverse loading conditions.Extending to the continuum scale,the CPFE results,combined with tensile data,were used to calibrate an advanced constitutive model based on the evolutionary Yld2000-2d yield function,capable of capturing anisotropic behavior.Validation against independent limiting dome height tests confirmed the predictive accuracy of the framework.The proposed approach provides a basis for simulating the forming behavior of ultra-thin CP-Ti sheets and supports precise manufacturing of bipolar plates in fuel cell systems. 展开更多
关键词 commercially pure titanium sheet crystal plasticity plastic anisotropy distortional strain hardening multi-scale modeling
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Atomic-scale ultrafast laser processing of nanocrystal with plasmonic modulation for machine learning combined flexible sensor devices 认领 引用 被引量：1
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作者 Ma Luo Jiaqun Li +6 位作者 Jiawang Xie Haoze Han Jianfeng Yan Yuzhi Zhao Yaoyao Li Huhu Cheng Liangti Qu 《International Journal of Extreme Manufacturing》 SCIE EI CAS CSCD 2026年第2期620-638,共19页
Nanometallic materials have attracted wide research attention in the fabrication of functional devices,including flexible electronics circuits and high-sensitive sensors.Sintering of nanometallic materials is generall... Nanometallic materials have attracted wide research attention in the fabrication of functional devices,including flexible electronics circuits and high-sensitive sensors.Sintering of nanometallic materials is generally thought as an effective technology for the functional manufacturing,and the controllable sintering of nanometallic materials and its major mechanisms have long been a challenge.Here,an ultrafast laser processing strategy for Ag nanoparticles(NPs)is achieved by modulating plasmonic.The excitation mode of plasmon can be designed by laser parameters,including polarization with a specific crystal size.The atomic-scale ultrafast dynamics are revealed for understanding the sintering process and design of the sintered structures.The non-equilibrium energy transfer between electron and lattice and dynamic evolution of pressure are proved to be the foremost driving forces on the motion of atomic structures.Through research of plasmonic-induced electric field enhancement and non-uniform deposition of heat and in-situ observation of relative transmittance,mapping from atomic-scale structure to micro behavior is established.Based on plasmonic modulation and processing of Ag NPs,a machine learning combined flexible gesture sensor with high recognition accuracy is displayed.This work expands the knowledge of interactions between lasers and nanometallic materials and provides a method for designing functional devices for a wide range of applications. 展开更多
关键词 ultrafast laser fabrication plasmonic modulation atomic scale processing flexible devices
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Microseismic signal processing and rockburst disaster identification:A multi-task deep learning and machine learning approach 认领 引用 被引量：1
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作者 Chunchi Ma Weihao Xu +3 位作者 Xuefeng Ran Tianbin Li Hang Zhang Dongwei Xing 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第1期441-456,共16页
Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely id... Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely identification of rockbursts.However,conventional processing encompasses multi-step workflows,including classification,denoising,picking,locating,and computational analysis,coupled with manual intervention,which collectively compromise the reliability of early warnings.To address these challenges,this study innovatively proposes the“microseismic stethoscope"-a multi-task machine learning and deep learning model designed for the automated processing of massive microseismic signals.This model efficiently extracts three key parameters that are necessary for recognizing rockburst disasters:rupture location,microseismic energy,and moment magnitude.Specifically,the model extracts raw waveform features from three dedicated sub-networks:a classifier for source zone classification,and two regressors for microseismic energy and moment magnitude estimation.This model demonstrates superior efficiency compared to traditional processing and semi-automated processing,reducing per-event processing time from 0.71 s to 0.49 s to merely 0.036 s.It concurrently achieves 98%accuracy in source zone classification,with microseismic energy and moment magnitude estimation errors of 0.13 and 0.05,respectively.This model has been well applied and validated in the Daxiagu Tunnel case in Sichuan,China.The application results indicate that the model is as accurate as traditional methods in determining source parameters,and thus can be used to identify potential geomechanical processes of rockburst disasters.By enhancing the signal processing reliability of microseismic events,the proposed model in this study presents a significant advancement in the identification of rockburst disasters. 展开更多
关键词 Underground engineering Microseismic signal processing Deep learning Multi-task Rockburst identification
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Evaluation of the multi-scale variability of ocean bottom pressure in a global ocean general circulation model 认领 引用
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作者 Jiahui Bai Jingwei Xie +7 位作者 Zipeng Yu Jiangfeng Yu Hailong Liu Fan Yang Pengfei Lin Tao Zhang Chunxiang Shi Yun Xiao 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2026年第2期87-101,共15页
Ocean bottom pressure(OBP)reflects ocean dynamics,thermodynamics,and Earth’s gravity field,playing a key role in physical oceanography and in reducing aliasing errors in satellite gravimetry.Due to limited observatio... Ocean bottom pressure(OBP)reflects ocean dynamics,thermodynamics,and Earth’s gravity field,playing a key role in physical oceanography and in reducing aliasing errors in satellite gravimetry.Due to limited observations,highfrequency global OBP studies rely on numerical models,which inherently contain uncertainties.This study employs the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics/Institute of Atmospheric Physics(LASG/IAP)Climate System Ocean Model version 3.0(LICOM3.0)to simulate global OBP from 2002 to 2018,driven by two different atmospheric reanalysis datasets.Validation against in situ observations shows that LICOM3.0 effectively captures sub-seasonal OBP variability(1–30 d)at the available stations,which are located in the Pacific and along the Atlantic coast.Compared to another ocean model,LICOM3.0 reproduces similar OBP patterns for periods longer than 1 d and spatial scales greater than 500 km,demonstrating its capability for large-scale OBP analysis and assessing inter-model uncertainty.However,the model underestimates OBP amplitudes,and exhibits marked discrepancies at sub-daily periods,in marginal seas,and on spatial scales below 500 km with reference data.These issues are consistent across both experiments,indicating that model configuration contributes to the limitations.Potential sources of error are discussed to support future model improvements.Overall,LICOM3.0 can serve as an effective tool for oceanic scientific applications and for de-aliasing in satellite gravimetry applications. 展开更多
关键词 ocean bottom pressure ocean model multi-scale
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Multi-scale keypoints detection and motion features extraction in dairy cows using ResNet101-ASPP network 认领 引用
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作者 Saisai Wu Shuqing Han +5 位作者 Jing Zhang Guodong Cheng Yali Wang Kai Zhang Mingming Han Jianzhai Wu 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2026年第5期2028-2040,共13页
Detecting keypoints in dairy cows aims to locate and track the motion trajectories of the body's joints,which plays a crucial role in behavior analysis and lameness detection.However,real farming scenarios,charact... Detecting keypoints in dairy cows aims to locate and track the motion trajectories of the body's joints,which plays a crucial role in behavior analysis and lameness detection.However,real farming scenarios,characterized by occlusions and large variations in object scale may result in poor detection results.Therefore,we introduce the atrous spatial pyramid pooling(ASPP) module into the shallow layers network of ResNet101,designed to improve the multi-scale feature extraction capability of the model.The ASPP module enhances the robustness of recognition for different dimensional sizes and occluded keypoints using different dilatation rates in the parallel atrous convolutional layers to expand the model's receptive field.Furthermore,seven types of motion features,including tracking up,gait symmetry,step height balance,motion speed variability,head swing amplitude,head-neck slope and back curvature are extracted simultaneously by monitoring and tracking the motion trajectory of distinct keypoints.Several of these features represent innovative extraction models and attributes,first proposed in this study.Multiple models are trained and tested on datasets containing 2,385 frames for ablation experiments.The experiments show that,in comparison with the ResNet50,MobileNet_v2_1.0,and EfficientNet-b0backbone networks,the training error and test error of ResNet101 are reduced by 4.04-30.12 pixels and 3.81-28.14 pixels.Therefore,ResNet101 is used as the benchmark for subsequent model improvement by adding the ASPP module.The training error and test error of the ResNet101-ASPP network are reduced by 0.27 and 0.24 pixels,respectively,compared to the benchmark network.The prediction confidence improves by 1.65-2.50% at three different dairy cow object scales.In addition,the keypoints under different occlusion conditions improve considerably,especially for small-scale keypoints,demonstrating the capability of the ASPP module for multi-scale feature extraction.By analyzing the distribution of the seven features and health,mild lameness,and severe lameness in dairy cows,it is shown that all the different features play an important role in distinguishing between different levels of lameness. 展开更多
关键词 dairy cows multi-scale keypoints detection ResNet101-ASPP network motion features
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MewCDNet: A Wavelet-Based Multi-Scale Interaction Network for Efficient Remote Sensing Building Change Detection 认领 引用
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作者 Jia Liu Hao Chen +5 位作者 Hang Gu Yushan Pan Haoran Chen Erlin Tian Min Huang Zuhe Li 《Computers, Materials & Continua》 SCIE EI 2026年第1期687-710,共24页
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
关键词 Remote sensing change detection deep learning wavelet transform multi-scale
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Efficient Video Emotion Recognition via Multi-Scale Region-Aware Convolution and Temporal Interaction Sampling 认领 引用
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作者 Xiaorui Zhang Chunlin Yuan +1 位作者 Wei Sun Ting Wang 《Computers, Materials & Continua》 SCIE EI 2026年第2期2036-2054,共19页
Video emotion recognition is widely used due to its alignment with the temporal characteristics of human emotional expression,but existingmodels have significant shortcomings.On the one hand,Transformermultihead self-... Video emotion recognition is widely used due to its alignment with the temporal characteristics of human emotional expression,but existingmodels have significant shortcomings.On the one hand,Transformermultihead self-attention modeling of global temporal dependency has problems of high computational overhead and feature similarity.On the other hand,fixed-size convolution kernels are often used,which have weak perception ability for emotional regions of different scales.Therefore,this paper proposes a video emotion recognition model that combines multi-scale region-aware convolution with temporal interactive sampling.In terms of space,multi-branch large-kernel stripe convolution is used to perceive emotional region features at different scales,and attention weights are generated for each scale feature.In terms of time,multi-layer odd-even down-sampling is performed on the time series,and oddeven sub-sequence interaction is performed to solve the problem of feature similarity,while reducing computational costs due to the linear relationship between sampling and convolution overhead.This paper was tested on CMU-MOSI,CMU-MOSEI,and Hume Reaction.The Acc-2 reached 83.4%,85.2%,and 81.2%,respectively.The experimental results show that the model can significantly improve the accuracy of emotion recognition. 展开更多
关键词 Multi-scale region-aware convolution temporal interaction sampling video emotion recognition
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A multi-scale fracture prediction method based on improved deep embedded clustering 认领 引用
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作者 Yu-jia Lu Chao Chen +2 位作者 Zhu-jiang Liu Fu-bin Wei Zhe-ge Liu 《Applied Geophysics》 SCIE CSCD 2026年第2期571-590,867,共20页
Accurately characterizing the distribution and scale characteristics of fractures in subsurface media is a crucial step for quantitatively evaluating shale gas sweet spots and guiding reservoir fracturing stimulation.... Accurately characterizing the distribution and scale characteristics of fractures in subsurface media is a crucial step for quantitatively evaluating shale gas sweet spots and guiding reservoir fracturing stimulation.However,a single seismic attribute is often used to identify fracture features of a specifi c scale,making it diffi cult to achieve detailed characterization of fractures across multiple scales simultaneously.Multi-attribute fusion algorithms often focus on statistical correlations,lacking in-depth exploration of the spatial topological relationships and intrinsic physical connections among fractures of diff erent scales,resulting in reduced accuracy in complex structural areas.To address this challenge,we propose a multi-scale integrated fracture prediction method based on an improved deep embedded clustering(DEC)framework,using the marine shale reservoir of the Wufeng–Longmaxi Formation in southeastern Sichuan Basin as a case study.Specifically,(1)an improved DEC objective function integrating fracture topology constraints and cluster-balancing mechanisms is developed to enhance the model’s adaptability to complex geological structures;(2)an“expand–then–contract”stacked autoencoder architecture is designed to better capture nonlinear relationships among multi-attribute data and decouple multi-scale fracture features;and(3)an integrated workfl ow from multi-attribute optimization,intelligent fusion clustering to geological interpretation is established,enabling diff erentiated and high-precision characterization of multi-scale fractures.Furthermore,based on the geological characteristics of the study area,we systematically analyze the spatial mapping relationships of the autoencoder’s multi-layer features and elucidate their implicit geophysical signifi cance.This analysis reveals the intrinsic processes through which the proposed model performs fracture attribute optimization,noise separation,and multi-scale feature extraction.Finally,by integrating intelligent fault identifi cation,micro-fracture amplitude variation with azimuth(AVAZ)inversion,and conventional geometric attributes,high-precision spatial characterization of the fracture system is achieved,spanning from large-scale faults to micro-fractures.The prediction results show strong agreement with geological understanding. 展开更多
关键词 Multi-scale fractures Deep embedded clustering Deep learning Autoencoding Marine shale
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M2ATNet: Multi-Scale Multi-Attention Denoising and Feature Fusion Transformer for Low-Light Image Enhancement 认领 引用
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作者 Zhongliang Wei Jianlong An Chang Su 《Computers, Materials & Continua》 SCIE EI 2026年第1期1819-1838,共20页
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
关键词 Low-light image enhancement multi-scale multi-attention transformer
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Highly biased DNA sequence reconstruction in DNA storage with multi-scale attention mechanism and contrast learning 认领 引用
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作者 Xue Li Yanfen Zheng +6 位作者 Qi Shao Jiadong Wang Wei Li Bin Wang Shihua Zhou Ben Cao Pan Zheng 《Synthetic and Systems Biotechnology》 SCIE CSCD 2026年第2期422-432,共11页
Base substitution,insertion,and deletion errors due to inherent technical constraints inducing unavoidable sequencing inaccuracies,limiting access to high-quality raw data and biological knowledge.To address this,we p... Base substitution,insertion,and deletion errors due to inherent technical constraints inducing unavoidable sequencing inaccuracies,limiting access to high-quality raw data and biological knowledge.To address this,we propose a deep sequence reconstruction model based on the multi-scale attention mechanism and contrastive learning(MACL),designed to enhance DNA sequence reconstruction under highly error rate conditions.The multi-scale attention mechanism includes base scale,inter-sequence and intra-sequence scale.First,the MSA Transformer fully extracts both global and local features of the base scale from the dimensions of the rows and columns.Furthermore,for the errors between sequences and the substitution errors within sequences,MACL proposes Inter-Sequence and Intra-Sequence Multi-Head Attention Mechanisms,respectively,and handles the insertion and deletion errors through the convolution module.In order to maximize the consistency of positive sample pairs in the representation space,we introduce contrastive learning and design a negative sample construction method and data augmentation that are more suitable for substitution errors in sequencing channels.Experiments on real-world DNA storage and viral genome datasets demonstrate that MACL significantly outperforms existing methods in reconstructing the DNA sequence.In particular,when combined with RS codes,MACL can losslessly reconstruct medical images in highly biased sequence(base error rate=5%)in DNA storage.In summary,the MACL introduces a novel approach to DNA sequence reconstruction in highly error rate conditions,laying a solid foundation for practical applications in DNA storage and genomics research. 展开更多
关键词 DNA sequence reconstruction DNA storage Noisy sequencing channels Multi-scale attention Contrast learning
MSA-DETR:Multi-scale attention enhanced DETR for object detection in oilfield surveillance 认领 引用
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作者 Qian-Wen Cao Jin-Rong Ma Lai-Bin Zhang 《Petroleum Science》 SCIE EI CAS CSCD 2026年第5期2686-2697,共12页
In modern petroleum engineering,ensuring operational safety at drilling sites is of critical importance.Visual object detection plays a key role inintelligent safety monitoring systems by enabling real-time supervisio... In modern petroleum engineering,ensuring operational safety at drilling sites is of critical importance.Visual object detection plays a key role inintelligent safety monitoring systems by enabling real-time supervision of personnel and equipment.However,safety-critical targets in drilling scenes are often small,partially occluded,and embedded in cluttered environments,leading to decreased detection accuracy and potential safety risks.Existing convolutional neural networks(CNN)-based detectors,although effective in natural scenes,often exhibit limited robustness under such complex industrial conditions.To address these challenges,this paper proposes MSA-DETR,a Transformer-based detection framework designed to enhance multi-scale perception in drilling monitoring scenarios.By improving the ability to capture both global contextualinformation andfine-grained visual cues,the proposed approach enhances sensitivity to safety-relevant objects.Extensive experiments conducted on two realworld drilling monitoring datasets demonstrate that MSA-DETR consistently outperforms state-of-theart detection methods,providing more reliable visual perception for petroleum safety management and accident prevention. 展开更多
关键词 Object detection Petroleum drilling safety Multi-scale perception Drilling engineering Artificial intelligence
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Multi-scale modeling:Analysis and design of thermal–mechanical coupling behavior of integrated thermal protection systems 认领 引用
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作者 Yang LIU Haitao ZHAO +3 位作者 Kai LIU Zhongjie ZHAO Min FENG Ji'an CHEN 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第3期370-383,共14页
In this study,an integrated thermal protection system was formed by bonding the Carbon/Carbon(C/C) composite thermal insulation layer and carbon foam thermal insulation tile on an aluminum honeycomb sandwich panel acc... In this study,an integrated thermal protection system was formed by bonding the Carbon/Carbon(C/C) composite thermal insulation layer and carbon foam thermal insulation tile on an aluminum honeycomb sandwich panel according to the functions of each layer of materials,and the thermal–mechanical response was analyzed by experimental tests and numerical simulations.First,infrared lamp facility and arcjet wind tunnel tests were used to check the accuracy of the model and calculate the heat-shielding index.Then,using the aerodynamic heat flow and pressure of the vehicles re-entry process,the temperature field and thermal deformation of the thermal protection system were analyzed according to the thermal–mechanical coupling analysis,and its performance requirements as a vehicles shell were evaluated.Analysis show that the thermomechanical properties of each layer were mismatched due to thermal deformation,resulting in debonding at the interlayer interface,which was also observed in the experiment.In addition,a 1 mm gap in the insulation tile promotes the release of thermal stress and reduces interlayer disbonding.According to the multi-scale model,10 thermal cycles(corresponding to the flight process) were analyzed,and the failure and damage evolution process of C/C composites at the microscopic level were revealed.The results of thermal cycling show that the microscopic damage started from the interfacial debonding of the fiber/matrix and ended with the connection of the pores through crack propagation in the matrix.This study provides a solution for analyzing the thermal–mechanical response of a thermal protection system and a design solution for improving reusability. 展开更多
关键词 Thermal protection system Multi-scale models C/C composites Thermal-mechanical coupling Thermal cycle
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Multi-scale simplified residual convolutional neural network model for predicting compositions of binary magnesium alloys 认领 引用
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作者 Xu Qin Qinghang Wang +6 位作者 Xinqian Zhao Shouxin Xia Li Wang Jiabao Long Yuhui Zhang Yanfu Chai Daolun Chen 《Journal of Magnesium and Alloys》 SCIE EI CAS CSCD 2026年第1期117-123,共7页
This study proposes a multi-scale simplified residual convolutional neural network(MS-SRCNN)for the precise prediction of Mg-Nd binary alloy compositions from scanning electron microscope(SEM)images.A multi-scale data... This study proposes a multi-scale simplified residual convolutional neural network(MS-SRCNN)for the precise prediction of Mg-Nd binary alloy compositions from scanning electron microscope(SEM)images.A multi-scale data structure is established by spatially aligning and stacking SEM images at different magnifications.The MS-SRCNN significantly reduces computational runtime by over 90%compared to traditional architectures like ResNet50,VGG16,and VGG19,without compromising prediction accuracy.The model demonstrates more excellent predictive performance,achieving a>5%increase in R2 compared to single-scale models.Furthermore,the MS-SRCNN exhibits robust composition prediction capability across other Mg-based binary alloys,including Mg-La,Mg-Sn,Mg-Ce,Mg-Sm,Mg-Ag,and Mg-Y,thereby emphasizing its generalization and extrapolation potential.This research establishes a non-destructive,microstructure-informed composition analysis framework,reduces characterization time compared to traditional experiment methods and provides insights into the composition-microstructure relationship in diverse material systems. 展开更多
关键词 Magnesium alloys Composition prediction Scanning electron microscope images Multi-scale simplified residual convolutional neural network
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Identification of small impact craters in Chang’e-4 landing areas using a new multi-scale fusion crater detection algorithm 认领 引用
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作者 FangChao Liu HuiWen Liu +7 位作者 Li Zhang Jian Chen DiJun Guo Bo Li ChangQing Liu ZongCheng Ling Ying-Bo Lu JunSheng Yao 《Earth and Planetary Physics》 EI CSCD 2026年第1期92-104,共13页
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 of140 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
关键词 impact craters Chang’e-4 landing area multi-scale automatic detection YOLO11 Fusion algorithm
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Revisiting starch degradability in dairy ruminants:from feed processing to gut microbiota and metabolic regulation 认领 引用
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作者 Qingyan Yin Shengru Wu +4 位作者 Xinjian Lei Jun Zhang Dangdang Wang Xinwei Li Junhu Yao 《Journal of Animal Science and Biotechnology》 SCIE CAS CSCD 2026年第4期1802-1819,共18页
Starch serves as the primary energy source for high-producing dairy ruminants,which include both dairy cows and dairy goats.Optimizing starch digestion is crucial for ensuring high milk production and maintaining anim... Starch serves as the primary energy source for high-producing dairy ruminants,which include both dairy cows and dairy goats.Optimizing starch digestion is crucial for ensuring high milk production and maintaining animal health.This narrative review summarizes and discusses recent findings concerning the degradability of starch in these species.Dietary starch is classified into three distinct types based on the basis of its degradation characteristics:rumen degradable starch(RDS),which ferments in the rumen;rumen escape starch(RES),which is subsequently digested in the small intestine;and resistant starch(RS),which resists complete digestion and enters the large intestine.This review systematically links feed processing methods,which directly influence starch structure,to their subsequent effects on the gut microbiota composition and host metabolic regulation.Three key insights emerge from this synthesis of literature.First,processing techniques such as steam-flaking critically alter the ratio among the three starch types,thereby shifting the effective site of digestion.Second,the optimal application of RDS differs significantly between dairy cows and dairy goats,primarily because these species exhibit distinct digestive physiologies.Nutritionists must carefully account for these species-specific differences to effectively prevent metabolic disorders.Third,the primary site of starch digestion significantly reshaped the gut microbiota profile.While a proper balance supports beneficial bacteria,excessive RS reduces energy efficiency,whereas an overload of RDS can readily lead to severe rumen acidosis.Therefore,balancing the proportions of RDS,RES,and RS is vital for helping animals effectively manage the elevated energy demands experienced during peak lactation.Future research must focus on developing precise starch management strategies tailored to the specific needs of various ruminant species. 展开更多
关键词 Degradation sites Energy efficiency Feed processing Ruminants Starch
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