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Revealing the mechanism of Zr coating modification on Ni-rich cathode materials through solid-phase diffusion 认领 引用
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作者 Lingjun Li Yanjie Huang +6 位作者 Zhenxuan Liu Niu Zhao Huazhao Liang Minzheng Zhou Kangyu Zou Lei Tan Tianxiang Ning 《Chinese Chemical Letters》 SCIE CAS CSCD 2026年第7期766-772,共7页
Ni-rich layered oxides are regarded as one of the most reliable cathode materials for lithium-ion batteries.Modifying the crystal structure through doping with foreign elements and constructing surface coating layers ... Ni-rich layered oxides are regarded as one of the most reliable cathode materials for lithium-ion batteries.Modifying the crystal structure through doping with foreign elements and constructing surface coating layers are common modification methods for Ni-rich cathode materials.However,the relationship between the diffusion depth and distribution behavior of foreign elements within the cathode material and the composition of the cathode material has rarely been studied in depth.In this work,by exploring the relationship between element concentration and position in a specially prepared two-substances diffusion couple,the diffusion coefficients between Zr4+and transition metal elements TMM+(TM=Ni,Co,Mn;n=3,4)were obtained.It was found that the magnitude relationship of their diffusion coefficients is:Zr4+/Mn4+>Zr4+/Mn3+>Zr4+/Co3+>Zr4+/Ni3+.Moreover,through the Arrhenius equation,it was determined that the Zr4+/Mn4+diffusion couple has the smallest diffusion activation energy of only 0.43eV,while the Zr4+/Ni3+diffusion couple has the largest diffusion activation energy,which is 0.63 eV.In addition,this study designed a specific core-shell structure model based on the microscopic morphology of the prepared cathode precursor particles,accurately predicting the distribution differences of Zr4+in cathode materials with different compositions during the actual sintering process.This work explains the reason for the formation of a Li2ZrO3 secondary phase coating layer on the surface of Ni-rich cathode material particles from the perspective of diffusion kinetics,providing a strong theoretical basis for the future design of high-performance element-modified Ni-rich cathode materials. 展开更多
关键词 Ni-rich cathode Diffusion coefficients Diffusion activation energy Li2ZrO3coating layer Diffusion kinetics
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Expressive Diffusion Network:a Novel Approach to Grayscale Image Colorization Using Diffusion Models 认领 引用
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作者 WANG Xingshuo WANG Tong 《Journal of Donghua University(English Edition)》 CAS 2026年第2期103-111,共9页
Image colorization has attracted considerable research interest over the past few decades.However,current methodologies frequently struggle with limited local colorization flexibility and produce unnatural color outpu... Image colorization has attracted considerable research interest over the past few decades.However,current methodologies frequently struggle with limited local colorization flexibility and produce unnatural color outputs,primarily due to the absence of comprehensive understanding of color perception.In this work,we propose an expressive diffusion network(EDN)that leverages a robust diffusion network to significantly enhance both colorization accuracy and diversity.The EDN consists of two main components:a pre-trained latent diffusion model and a perceptual luminance model based on VQ-Diffusion.These components work together to generate rich and vibrant colors while maintaining high fidelity to the structural features of the original grayscale image.The EDN incorporates controllable creative diffusion(CCD)to direct the color generation process toward more realistic outcomes.Extensive experiments demonstrate that the EDN outperforms existing methods in perceptual quality,offering notable improvements in visual realism and vibrancy across various scenes.The proposed EDN showcases significant improvements over ChromaGAN and InstColor,confirming its robustness in both simple and complex scenarios. 展开更多
关键词 diffusion model image colorization expressive diffusion network(EDN) controllable creative diffusion(CCD) guided model
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Application research of a hybrid data-and knowledge-driven artificial intelligence scientific computing model in neutron diffusion calculation for nuclear reactors 认领 引用 被引量：1
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作者 Fu-Lin Zeng Xiao-Long Zhang +1 位作者 Peng-Cheng Zhao Zi-Jing Liu 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2026年第2期223-244,共22页
Amidst the growing global emphasis on nuclear safety,the integrity of nuclear reactor systems has garnered attention in the aftermath of consequential events.Moreover,the rapid development of artificial intelligence t... Amidst the growing global emphasis on nuclear safety,the integrity of nuclear reactor systems has garnered attention in the aftermath of consequential events.Moreover,the rapid development of artificial intelligence technology has provided immense opportunities to enhance the safety and economy of nuclear energy.However,data-driven deep learning techniques often lack interpretability,which hinders their applicability in the nuclear energy sector.To address this problem,this study proposes a hybrid data-driven and knowledge-driven artificial intelligence model based on physics-informed neural networks to accurately compute the neutron flux distribution inside a nuclear reactor core.Innovative techniques,such as regional decomposition,intelligent keff(effective multiplication factor)search,and keffinversion,have been introduced for the calculation.Furthermore,hyperparameters of the model are automatically optimized using a whale optimization algorithm.A series of computational examples are used to validate the proposed model,demonstrating its applicability,generality,and high accuracy in calculating the neutron flux within the nuclear reactor.The model offers a dependable strategy for computing the neutron flux distribution in nuclear reactors for advanced simulation techniques in the future,including reactor digital twinning.This approach is data-light,requires little to no training data,and still delivers remarkably precise output data. 展开更多
关键词 Neutron diffusion equation Physics informed neural network Effective multiplication factor Whale optimization algorithm
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Interfacial microstructure and mechanical properties of diffusion-bonded Mg-4Y-3RE magnesium alloy 认领 引用
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作者 Dexi Xu Huiping Wu +3 位作者 Dayong An Qi Hu Xifeng Li Jun Chen 《Journal of Magnesium and Alloys》 SCIE EI CAS CSCD 2026年第5期312-326,共15页
The Mg-4Y-3RE(WE43)magnesium alloy possesses high specific strength,excellent shock absorption,strong electromagnetic shielding,and recyclability.However,the oxidation and defects often happen during conventional weld... The Mg-4Y-3RE(WE43)magnesium alloy possesses high specific strength,excellent shock absorption,strong electromagnetic shielding,and recyclability.However,the oxidation and defects often happen during conventional welding.Solid-state diffusion bonding in a nearvacuum environment enables high-reliability joints by minimizing these issues.It is difficult to obtain high bonding joint strength due to the limitation of various factors.This work systematically investigates the effects of temperature,time,pressure,and surface roughness on the diffusion-bonded joint quality of WE43 magnesium alloy through a phased optimization strategy.The optimal parameter combination is optimized.The results demonstrate that the joint interface achieves a shear strength of 179.9±3.9 MPa and a bonding ratio of 94.14%when the minimal plastic deformation is ensured.Microstructural characterization reveals that recrystallization,precipitates evolution and elemental diffusion effects collectively promote metallurgical bonding at the interface.Subsequent solution treatment at 525℃ for 8 h and aging at 250℃ for 16 h,the shear strength significantly increases to 229.5±5.2 MPa,which represents the highest value in comparable reported studies.This research provides theoretical foundations and technical references for solid-state bonding processes of high-strength magnesium alloys. 展开更多
关键词 WE43 magnesium alloy Diffusion bonding Precipitates evolution Elemental diffusion T6 treatment
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Detection of white matter microstructural changes in patients with systemic lupus erythematosus based on multiple diffusion models and related diffusion metrics 认领 引用
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作者 Zhenxing Li Huanhuan Li +5 位作者 Bailing Tian Huiyang Liu Yueluan Jiang Pingting Yang Guoguang Fan Hu Liu 《Neural Regeneration Research》 SCIE CAS CSCD 2026年第6期2467-2474,共8页
Some patients with systemic lupus erythematosus experience neuropsychiatric symptoms.Although magnetic resonance imaging can detect abnormal signals in the white matter of the brain,conventional methods often struggle... Some patients with systemic lupus erythematosus experience neuropsychiatric symptoms.Although magnetic resonance imaging can detect abnormal signals in the white matter of the brain,conventional methods often struggle to accurately capture microstructural changes.Various diffusion models have been used to study white matter in systemic lupus erythematosus;however,comparative analyses of their sensitivity and specificity for detecting microstructural changes remain insufficient.To address this,our team designed a diagnostic trial that used multimodal diffusion imaging techniques to observe white matter microstructural changes in patients with systemic lupus erythematosus who had neuropsychiatric symptoms,with an aim to identify key diagnostic biomarkers for these patients.Patients with active lupus who received treatment at the Department of Rheumatology and Immunology,The First Affiliated Hospital of China Medical University,from September 2023 to March 2024 were recruited.According to the standards of the American College of Rheumatology,patients with systemic lupus erythematosus who had neuropsychiatric symptoms were assigned to the systemic lupus erythematosus group,whereas those without neuropsychiatric symptoms were assigned to the non-systemic lupus erythematosus group.Additionally,healthy volunteers matched by region,sex,and age were recruited as controls.All three groups underwent the same diffusion magnetic resonance imaging examination protocol to compare differences in diffusion parameters.Advanced diffusion imaging models were able to sensitively detect microstructural changes in the white matter fibers of patients with systemic lupus erythematosus who had neuropsychiatric symptoms,with specific diffusion parameters showing significant abnormalities in key brain regions.In the left superior longitudinal fasciculus subregion and the right thalamic radiations of patients with systemic lupus erythematosus who had neuropsychiatric symptoms,we also identified abnormal diffusion characteristics that were clearly correlated with disease activity,suggesting that microstructural changes in these areas may reflect the dynamic process of neuroinflammatory damage.The present study addresses critical challenges in the diagnosis of systemic lupus erythematosus by identifying specific white matter imaging biomarkers and elucidating the association between microstructural damage and clinical manifestations.The main contributions of our study include:1)establishing axial regression probability parameters from mean apparent propagator magnetic resonance imaging as sensitive biomarkers for systemic lupus erythematosus,particularly in the third subregion of the left superior longitudinal fasciculus;2)demonstrating that multimodal diffusion imaging may be superior to conventional diffusion tensor imaging for detecting white matter microstructural abnormalities in patients with systemic lupus erythematosus;and 3)integrating tract-based spatial statistics with clinically relevant analyses to link imaging findings to pathological mechanisms. 展开更多
关键词 diffusion kurtosis imaging diffusion tensor imaging mean apparent propagator neurite orientation dispersion and density imaging neuropsychiatric systemic lupus erythematosus return to axis probability return to origin probability superior longitudinal fasciculus-3 superior thalamic radiation tract-based spatial statistics white matter microstructure
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Role of diffusion-weighted magnetic resonance imaging and apparent diffusion coefficient in subtyping renal cell carcinoma 认领 引用
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作者 Hira Lal Pinky Jowel +9 位作者 Pragati Verma Athar Shamim Raghunandan Prasad Anuradha Singh Uday P Singh Sanjoy K Sureka Farhana Siddiqui Manoj Jain Aneesh Srivastava Priyank Yadav 《World Journal of Radiology》 2026年第5期67-76,共10页
BACKGROUND Diffusion-weighted magnetic resonance imaging(DWI)has emerged as a noncontrast functional imaging technique for renal mass characterization.Its role in differentiating histopathological subtypes of renal ce... BACKGROUND Diffusion-weighted magnetic resonance imaging(DWI)has emerged as a noncontrast functional imaging technique for renal mass characterization.Its role in differentiating histopathological subtypes of renal cell carcinoma(RCC)remains an area of active investigation.AIM To evaluate the role of DWI and apparent diffusion coefficient(ADC)values in differentiating histopathological subtypes of RCC.METHODS In this prospective observational study,127 patients with histopathologically proven RCC who underwent preoperative magnetic resonance imaging(MRI)including DWI were analyzed.Diffusion-weighted imaging was performed using b values of 0 second/mm2 and 1000 seconds/mm2,and ADC maps were generated.ADC values were measured from solid tumor components and compared among RCC subtypes.Statistical analysis included subgroup comparisons and receiver operating characteristic curve analysis to assess the ability of ADC values to differentiate clear cell RCC(ccRCC)from non-ccRCC.RESULTS Of the 127 RCCs,97(76.4%)were ccRCC,24(18.9%)papillary RCC,and 6(4.7%)chromophobe RCC.The mean ADC value of ccRCC[(1.391±0.271)×10-3mm2/second]was significantly higher than that of papillary RCC[(0.876±0.293)×10-3mm2/second;P<0.001]and chromophobe RCC[(1.059±0.369)×10-3mm2/second;P=0.04].No significant difference was observed between papillary and chromophobe RCC(P=0.396).When grouped,ccRCC demonstrated a significantly higher mean ADC value compared with non-ccRCC[(1.391±0.271)×10-3mm2/second vs(0.915±0.312)×10-3mm2/second;P<0.001].Receiver operating characteristic analysis yielded an area under the curve of 0.889(95%confidence interval:0.804-0.975).An ADC threshold of 1.08×10-3mm2/second achieved 90%sensitivity and 83%specificity for identifying ccRCC.CONCLUSION DWI with quantitative ADC analysis reliably differentiates clear cell from non-ccRCC and demonstrates significant correlation with RCC subtype and tumor grade.DWI serves as a valuable adjunct to conventional MRI,particularly in patients with contraindications to contrast administration. 展开更多
关键词 Renal cell carcinoma Diffusion-weighted imaging Apparent diffusion coefficient Magnetic resonance imaging Tumor subtyping
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Boosting Li+Diffusion in Lithium‑Rich Oxides through Intrinsic Structural Design:Insights and Design Principles 认领 引用
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作者 Lifeng Xu Min Hong +6 位作者 Jingjing Guo Fangming Shen Da Xu Jinjian Zhang Ying Zhang Jianhui Zheng Jun Lu 《Nano-Micro Letters》 SCIE EI CAS CSCD 2026年第8期442-476,共35页
Lithium-rich oxide cathodes present high specific capacities(>250 mAh g−1)and wide operating voltage windows(2.0-4.8 V),making them promising candidates for nextgeneration high-energy batteries.Their practical d... Lithium-rich oxide cathodes present high specific capacities(>250 mAh g−1)and wide operating voltage windows(2.0-4.8 V),making them promising candidates for nextgeneration high-energy batteries.Their practical deployment,however,is limited by sluggish ion transport kinetics that arise from inherent structural constraints,including confined twodimensional diffusion channels,transition metal migration,and local lattice distortions.These structural perturbations narrow Li+pathways,intensify cation mixing,and generate localized strain fields,collectively increasing the Li+migration energy barrier.To facilitate the rational design of fast-kinetic lithium-rich oxides through intrinsic structural optimization,a comprehensive elucidation of the structure-diffusion interplay is presented,with emphasis on the roles of lattice distortion and oxygen redox chemistry in modulating Li+pathways and associated energy barriers.Structural design strategies that aim to improve ionic diffusivity are systematically evaluated,including interface engineering,morphology-directed design,and the modulation of redox chemistry.Advanced operando characterization techniques that capture dynamic structural and chemical evolution are also described as essential tools for guiding precise structure-performance analysis.The mechanistic insights and integrated analytical approaches summarized in this review establish a robust conceptual foundation for engineering lithium-rich oxides with enhanced ion transport kinetics,thereby supporting the advancement of next-generation high-power battery technologies. 展开更多
关键词 Li-rich oxides Li+diffusion Structural modification In situ characterization
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LesionDiff:Synthetic data via lesion information transfer diffusion model facilitates plant disease diagnosis 认领 引用
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作者 Xingcai Wu Qiaoling Wan +6 位作者 Ya Yu Yujiao Dan Hanying Xie G.M.A.D.Sirishantha Qi Wang Gefei Hao Yongjin Liu 《The Crop Journal》 SCIE CSCD 2026年第3期1051-1063,共13页
Training software models for crop disease diagnosis requires large image datasets to achieve high accuracy.We describe a lesion information transfer diffusion model,LesionDiff,for generating image data that augments a... Training software models for crop disease diagnosis requires large image datasets to achieve high accuracy.We describe a lesion information transfer diffusion model,LesionDiff,for generating image data that augments a real-world disease lesion image dataset.An information preprocessing module identifies lesion areas on leaves,an enhancement module captures diverse visual and semantic lesion features,and a generation module fills missing regions in masked disease images by synthesizing lesion phenotypes.This augmentation increased the average diagnostic accuracy of a test dataset by more than 3%. 展开更多
关键词 Plant disease Lesion synthesis Diffusion model Data enhancement
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Diffusion of polymer permeation grouting in water-rich sand layers:Mathematical model and experimental validation 认领 引用
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作者 Jiasen Liang Xueming Du +5 位作者 Lei Wang Hongyuan Fang Xiaohua Zhao Bin Li Kejie Zhai Shanyong Wang 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第5期3703-3721,共19页
Water-rich sand layers are frequently encountered as adverse geological conditions during underground construction.Polymer slurry grouting has been widely recognized as an effective technique for reducing permeability... Water-rich sand layers are frequently encountered as adverse geological conditions during underground construction.Polymer slurry grouting has been widely recognized as an effective technique for reducing permeability and enhancing the stability of such strata.In this study,a mathematical model is established to describe the diffusion behavior of polymer slurry in porous media under dynamic water conditions and is further validated through laboratory experiments.The theoretical formulation of the slurry permeation process is developed based on Darcy's law,the Hagen–Poiseuille flow principle,and the physicochemical characteristics of the slurry.The derivation primarily focuses on analyzing the dynamic response of the slurry under the influence of water flow,considering the effects of flow velocity,grouting pressure,and sand-layer porosity on diffusion behavior.To verify the proposed model,a visualized grouting simulation system was designed to observe the diffusion process of polymer slurry in water-rich sand layers.The results demonstrate that slurry diffusion is significantly affected by grouting pressure,porosity,and water flow velocity.The observed staged diffusion characteristics,dynamic evolution patterns,and directional effects are in good agreement with theoretical predictions.Furthermore,the average relative deviations between the theoretical and experimental results for diffusion pressure and diffusion distance are both less than 25%,confirming the reliability of the proposed model.Additionally,this study identifies distinct differences in slurry diffusion between porous and void media.In porous media,slurry propagation encounters greater hydraulic resistance,leading to rapid pressure attenuation and a limited diffusion range.Conversely,diffusion in void media occurs more smoothly due to the continuous cavity structure,resulting in slower pressure decay and a substantially larger diffusion radius.These findings elucidate the mechanisms governing slurry diffusion under dynamic water conditions and provide a theoretical basis for optimizing grouting parameters and improving construction efficiency in water-bearing strata. 展开更多
关键词 Water-rich sand layer Model derivation Permeation grouting Polymer slurry Diffusion mechanism
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Residual resampling-based physics-informed neural network for neutron diffusion equations 认领 引用
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作者 Heng Zhang Yun-Ling He +3 位作者 Dong Liu Qin Hang He-Min Yao Di Xiang 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2026年第2期16-41,共26页
The neutron diffusion equation plays a pivotal role in nuclear reactor analysis.Nevertheless,employing the physics-informed neural network(PINN)method for its solution entails certain limitations.Conventional PINN app... The neutron diffusion equation plays a pivotal role in nuclear reactor analysis.Nevertheless,employing the physics-informed neural network(PINN)method for its solution entails certain limitations.Conventional PINN approaches generally utilize a fully connected network(FCN)architecture that is susceptible to overfitting,training instability,and gradient vanishing as the network depth increases.These challenges result in accuracy bottlenecks in the solution.In response to these issues,the residual-based resample physics-informed neural network(R2-PINN)is proposed.It is an improved PINN architecture that replaces the FCN with a convolutional neural network with a shortcut(S-CNN).It incorporates skip connections to facilitate gradient propagation between network layers.Additionally,the incorporation of the residual adaptive resampling(RAR)mechanism dynamically increases the number of sampling points.This,in turn,enhances the spatial representation capabilities and overall predictive accuracy of the model.The experimental results illustrate that our approach significantly improves the convergence capability of the model and achieves high-precision predictions of the physical fields.Compared with conventional FCN-based PINN methods,R 2-PINN effectively overcomes the limitations inherent in current methods.Thus,it provides more accurate and robust solutions for neutron diffusion equations. 展开更多
关键词 Neutron diffusion equation Physics-informed neural network CNN with shortcut Residual adaptive resampling
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The diffusion and distribution of CO in confined mine spaces:A scaled experimental model approach 认领 引用
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作者 Gaofeng Shi Sheng Xue +1 位作者 Qingyi Tu Zuheng Zhao 《International Journal of Coal Science & Technology》 SCIE EI CAS CSCD 2026年第1期84-97,共14页
Diesel vehicles are the primary mode of transportation in underground coal mines,widely used in mining regions such as Inner Mongolia and Shaanxi Province,China.However,their extensive use has led to growing concerns ... Diesel vehicles are the primary mode of transportation in underground coal mines,widely used in mining regions such as Inner Mongolia and Shaanxi Province,China.However,their extensive use has led to growing concerns over diesel exhaust pollution in confined mine spaces.Carbon monoxide,a major pollutant in diesel exhaust,often results in localized concentrations exceeding 24 ppm,posing significant health risks to coal miners.This study utilizes a self-developed diesel exhaust experimental platform,with air speed as the variable,to investigate the characteristics of exhaust distribution under varying conditions.It also explores the diffusion and transport mechanisms of exhaust from diesel vehicles.The results demonstrate that CO concentration decreases with increasing distance from the exhaust source,following a"three-region"pattern.In Region I,molecular motion and concentration gradients cause a rapid reduction in CO.In Region II,convection and diffusion further dilute the CO,while in Region III,the concentration stabilizes and becomes more evenly distributed.These changes are attributed to the unique operational conditions of diesel vehicles and the fluid dynamics of exhaust diffusion.High concentrations of CO accumulate near the exhaust pipe,where dilution is slow.However,increased air speed accelerate CO reduction,with concentrations continuing to decrease as the distance from the exhaust outlet increases.The CO concentration was observed to decrease from 95.1 ppm to 9.5 ppm,a reduction of 90.01%.Comparisons of field and experimental data confirm the reliability of the experimental platform.These findings highlight the diffusion and jetting effects of CO concentration and offer engineering guidance for CO management in vehicle exhaust in coal mines. 展开更多
关键词 CO diffusion Diesel engine exhaust Confined spaces Experimental model Mine ventilation
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Motion In-Betweening via Frequency-Domain Diffusion Model 认领 引用
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作者 Qiang Zhang Shuo Feng +2 位作者 Shanxiong Chen Teng Wan Ying Qi 《Computers, Materials & Continua》 SCIE EI 2026年第1期275-296,共22页
Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frame... Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frames remains a fundamental yet unresolved challenge.Existing methods typically rely on dense keyframe inputs or complex prior structures,making it difficult to balance motion quality and plausibility under conditions such as sparse constraints,long-term dependencies,and diverse motion styles.To address this,we propose a motion generation framework based on a frequency-domain diffusion model,which aims to better model complex motion distributions and enhance generation stability under sparse conditions.Our method maps motion sequences to the frequency domain via the Discrete Cosine Transform(DCT),enabling more effective modeling of low-frequency motion structures while suppressing high-frequency noise.A denoising network based on self-attention is introduced to capture long-range temporal dependencies and improve global structural awareness.Additionally,a multi-objective loss function is employed to jointly optimize motion smoothness,pose diversity,and anatomical consistency,enhancing the realism and physical plausibility of the generated sequences.Comparative experiments on the Human3.6M and LaFAN1 datasets demonstrate that our method outperforms state-of-the-art approaches across multiple performance metrics,showing stronger capabilities in generating intermediate motion frames.This research offers a new perspective and methodology for human motion generation and holds promise for applications in character animation,game development,and virtual interaction. 展开更多
关键词 Motion generation diffusion model frequency domain human motion synthesis self-attention network 3D motion interpolation
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Negative-One-Day Malware Detection with Generative AI:A Stable Diffusion-Based Proactive Defense Framework 认领 引用
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作者 Sohail Khan Toqeer Ali Syed +3 位作者 Mohammad Nauman Salman Jan It Ee Lee Qamar Wali 《Computers, Materials & Continua》 SCIE EI 2026年第7期1505-1522,共18页
The detection of zero-day malware represents one of the most significant challenges in contemporary cybersecurity.In this paper,we introduce a novel concept called“Negative-One-Day Malware Detection”,which aims to i... The detection of zero-day malware represents one of the most significant challenges in contemporary cybersecurity.In this paper,we introduce a novel concept called“Negative-One-Day Malware Detection”,which aims to identify potentially malicious software before it is actually created by threat actors.Our approach leverages recent advancements in generative AI,specifically diffusion-based generative models,to generate and analyze potential future malware variants.By doing so,we can train detection systems to recognize these variants before they emerge in the wild,thereby closing the critical protection gap that currently exists between malware creation and detection.We demonstrate the effectiveness of our approach through extensive experimentation,showing that our framework can generate executable malware samples that combine characteristics from different families while exhibiting novel behaviors.These synthetically generated samples significantly improve the detection capabilities of security systems when incorporated into training data,providing a proactive rather than reactive approach to cybersecurity. 展开更多
关键词 Adversarial machine learning Generative AI stable diffusion models zero-day malware detection negative-one-day malware detection proactive cyber defense
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Apparent diffusion coefficient of kidneys with non-contrast magnetic resonance angiography for functional and anatomical assessment in renal artery stenosis 认领 引用
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作者 Hira Lal Surabhi Agarwal +7 位作者 Kaushik Ponmalai Raghunandan Prasad Dharmendra S Bhadauria Sanjay Gambhir Swagata Mandal Sandeep Kumar Priyank Yadav Pinky Jowel 《World Journal of Methodology》 2026年第1期198-210,共13页
BACKGROUND Renal artery stenosis(RAS)is a vascular disorder linked to secondary hypertension,chronic kidney disease,and renal failure due to interstitial fibrosis.Early diagnosis is crucial as RAS-induced hypertension... BACKGROUND Renal artery stenosis(RAS)is a vascular disorder linked to secondary hypertension,chronic kidney disease,and renal failure due to interstitial fibrosis.Early diagnosis is crucial as RAS-induced hypertension responds well to angioplasty.Non-invasive imaging techniques,including non-contrast magnetic resonance angiography(NC-MRA),help assess RAS without contrast-related risks.Diffusion-weighted MR imaging(DW-MRI)has emerged as a promising method for evaluating kidney function by measuring the apparent diffusion coefficient(ADC),which correlates with renal pathology.AIM To compare ADC values in hypertensive,RAS,and healthy kidneys,assess the correlation between ADC and stenosis severity,and evaluate its relationship with split glomerular filtration rate(GFR).METHODS This prospective observational study which included 86 patients with suspected RAS and twenty normal healthy controls underwent NC-MRA on a 3T-MR-Scanner followed by DW-MRI at b values of 0 and 1000 seconds/mm2 in the transverse plane.ADC maps were created using Functool.ADC values were measured in the cortex and medulla of each kidney's upper,middle,and lower pole,and the average ADC(ADCavg)for cortex and medulla calculated.In patients with RAS,degree of stenosis(DOS)was calculated on NC-MRA.The ADC of 212 kidneys was compared,and the relationship between DOS and ADC was established.In addition,split GFR was calculated in 30 kidneys using 99mTc-DTPA,and correlated with ADC value.The ADC values of kidneys with and without RAS were compared using the Student’s t-test.The correlation between ADC and stenosis severity was assessed by Spearman’s test,while the relationship between ADC and split GFR was evaluated using Pearson’s test.A P value<0.05 was considered statistically significant.RESULTS RAS was detected in 58 of 86(67.44%)hypertensive patients(81 of 172 kidneys),and the ADCavg(P=0.044)was significantly lower in RAS kidneys than in kidneys with normal arteries and essential hypertension and healthy controls.CONCLUSION DW-MRI can be a useful non-invasive technique to estimate the kidney’s functional status in RAS patients.It can be used as a complementary assessment tool with NC-MRA to triage patients in need of interventional management. 展开更多
关键词 Renal artery stenosis Diffusion weighted imaging Apparent diffusion coefficient Magnetic resonance imaging Non-contrast renal magnetic resonance angiography Hypertension
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Trajectory servo scanning 3D electro discharge machining for diffusion shaped film cooling holes 认领 引用
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作者 Yao YAO Yingjie CUI +1 位作者 Hao TONG Yong LI 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第3期616-630,共15页
Diffusion shaped film cooling holes with compound-angle diffuser structures exhibit superior cooling performance,which have gradually been applied in turbine blades of the advanced aeroengines.In our previous research... Diffusion shaped film cooling holes with compound-angle diffuser structures exhibit superior cooling performance,which have gradually been applied in turbine blades of the advanced aeroengines.In our previous research,the method of Servo Scanning three-dimensional Electrical Discharge Machining(SS-3D EDM)has been proven effective for high-precision machining of complex 3D cavities,offering notable advantages such as low tool cost,automatic compensation of electrode wear,and high machining flexibility.However,using tubular electrodes in SS-3D EDM,challenges persist under the conditions of the large layer depth.The lateral discharge phenomenon of tubular electrodes causes significant deformation at the electrode tip,increases the risk of lateral collisions,and complicates the accurate calculation of electrode wear length.To address these limitations,this research proposes a Trajectory Servo Scanning three-dimensional Electrical Discharge Machining(TSS-3D EDM)process.Axial servo motion of tubular electrode is used to maintain the discharge gap of electrode bottom,and an innovative trajectory servo motion along the tangential orientation is introduced to stabilize the lateral discharge gap,enabling automatic compensation for tool wear at the rotating electrode tip.The effect of servo control parameters on machining depth accuracy is analyzed.Furthermore,a method for electrode wear length calculation is proposed based on the voltage signals of discharge gaps.An estimation method for the electrode wear coefficient is presented.Machining experiments on superalloys validate the effectiveness and capabilities of the TSS-3D EDM method by fabricating fan-shaped and conical diffusion shaped film cooling holes.The results show that the calculation error of tubular electrode wear length<5%,the dimensional error of hole profile dimensions as 4%–6%,the repeatability error<±4μm,and the material removal rate up to 0.664 mm3/min using tubular electrodes with an outer diameter of 0.4 mm. 展开更多
关键词 Aeroengine Diffusion shaped film cooling hole Electrical discharge machining(EDM) Film cooling Trajectory servo scanning Turbine blade Electrode wear compensation
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How to generate a museum floor plan:LoRA for diffusion models,or GANs? 认领 引用
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作者 Zhou Li Ziyu Song Shaoming Lu 《Frontiers of Architectural Research》 EI CSCD 2026年第3期1040-1058,共19页
This study utilized a comparative methodology to explore the generative design of museum floor plans,addressing the complexity of meeting curatorial,designer,and visitor de-mands.A dataset of 263 museum plans was cura... This study utilized a comparative methodology to explore the generative design of museum floor plans,addressing the complexity of meeting curatorial,designer,and visitor de-mands.A dataset of 263 museum plans was curated,and four generative methods―LoRA for diffusion models,Pix2Pix,CycleGAN,and the generative segmentation model―were trained.The results were evaluated by FID,SSIM,and MAE metrics for image fidelity and diversity.The pixel-based space syntax method was applied to evaluate the connectivity of the functional space,and expert scoring was combined to assess whether the generated plans met the layout characteristics of museums.Findings are:First,Pix2Pix led in terms of the image structural similarity metric SSIM and diversity metric FID but scored lowest in the image detail recon-struction metric MAE.Second,space syntax analysis revealed that the generated layouts had greater exhibition space connectivity but lower public space connectivity than the original images.Third,the expert grading results indicate that the outputs of LoRA better align with the professional expectations of designers regarding functional composition and spatial orga-nization.These findings highlight the potential of generative models in museum design,partic-ularly when enhanced by space syntax evaluations to improve spatial intelligibility and meet diverse stakeholder needs. 展开更多
关键词 Museum floor plan design Intelligent generative design Generative adversarial network(GAN) Diffusion models Space syntax
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Adaptive Denoising Diffusion Null-Space Models for Semantic Communications over MIMO Channels 认领 引用
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作者 Tao Meixia Chen Zhiyong +2 位作者 Duan Yiheng Wu Tong Zhang Hongwei 《China Communications》 SCIE EI CSCD 2026年第4期312-329,共18页
Multiple-input multiple-output(MIMO)systems are essential for improving capacity and reliability in semantic communications.Existing methods mainly design the channel-aware neural networks but neglect the underlying s... Multiple-input multiple-output(MIMO)systems are essential for improving capacity and reliability in semantic communications.Existing methods mainly design the channel-aware neural networks but neglect the underlying signal distribution.In this paper,we develop a denoising diffusion null-space model-based module over MIMO channels(DDNM-MIMO),which is a plug-in module deployed at the receiver.By modeling the MIMO channel,precoding,and equalization as a linear transformation with additive noise,we design corresponding linear and scaling matrices to construct a sampling process for denoising the received signal.The DDNM-MIMO integrates channel state information(CSI)embedding,supporting both closed-loop MIMO with CSI at the transmitter and open-loop MIMO with CSI at the receiver,thereby improving channel adaptability across various noise levels.As a plug-in,the DDNM-MIMO module operates independently of the joint source-channel coding(JSCC)coder structure,offering flexible integration into diverse systems.Experimental results show that DDNM-MIMO effectively reduces the mean square errors(MSE)between the encoded and equalized signals.Consequently,the proposed DDNM-MIMO semantic communication system achieves superior image reconstruction performance compared to existing JSCC-based semantic communication method. 展开更多
关键词 channel state information(CSI) diffusion model(DM) multiple-input multiple-output(MIMO) semantic communications
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Graph Guide Diffusion Solvers with Noises for Travelling Salesman Problem 认领 引用
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作者 Yan Kong Xinpeng Guo Chih-Hsien Hsia 《Computers, Materials & Continua》 SCIE EI 2026年第3期689-707,共19页
With the development of technology,diffusion model-based solvers have shown significant promise in solving Combinatorial Optimization(CO)problems,particularly in tackling Non-deterministic Polynomial-time hard(NP-hard... With the development of technology,diffusion model-based solvers have shown significant promise in solving Combinatorial Optimization(CO)problems,particularly in tackling Non-deterministic Polynomial-time hard(NP-hard)problems such as the Traveling Salesman Problem(TSP).However,existing diffusion model-based solvers typically employ a fixed,uniform noise schedule(e.g.,linear or cosine annealing)across all training instances,failing to fully account for the unique characteristics of each problem instance.To address this challenge,we present GraphGuided Diffusion Solvers(GGDS),an enhanced method for improving graph-based diffusion models.GGDS leverages Graph Neural Networks(GNNs)to capture graph structural information embedded in node coordinates and adjacency matrices,dynamically adjusting the noise levels in the diffusion model.This study investigates the TSP by examining two distinct time-step noise generation strategies:cosine annealing and a Neural Network(NN)-based approach.We evaluate their performance across different problem scales,particularly after integrating graph structural information.Experimental results indicate that GGDS outperforms previous methods with average performance improvements of 18.7%,6.3%,and 88.7%on TSP-500,TSP-100,and TSP-50,respectively.Specifically,GGDS demonstrates superior performance on TSP-500 and TSP-50,while its performance on TSP-100 is either comparable to or slightly better than that of previous methods,depending on the chosen noise schedule and decoding strategy. 展开更多
关键词 Combinatorial optimization problem diffusion model noise schedule traveling salesman problem
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Diffusion-Driven Generation of Synthetic Complex Concrete Crack Images for Segmentation Tasks 认领 引用
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作者 Pengwei Guo Xiao Tan Yiming Liu 《Structural Durability & Health Monitoring》 EI 2026年第1期47-69,共23页
Crack detection accuracy in computer vision is often constrained by limited annotated datasets.Although Generative Adversarial Networks(GANs)have been applied for data augmentation,they frequently introduce blurs and ... Crack detection accuracy in computer vision is often constrained by limited annotated datasets.Although Generative Adversarial Networks(GANs)have been applied for data augmentation,they frequently introduce blurs and artifacts.To address this challenge,this study leverages Denoising Diffusion Probabilistic Models(DDPMs)to generate high-quality synthetic crack images,enriching the training set with diverse and structurally consistent samples that enhance the crack segmentation.The proposed framework involves a two-stage pipeline:first,DDPMs are used to synthesize high-fidelity crack images that capture fine structural details.Second,these generated samples are combined with real data to train segmentation networks,thereby improving accuracy and robustness in crack detection.Compared with GAN-based approaches,DDPM achieved the best fidelity,with the highest Structural Similarity Index(SSIM)(0.302)and lowest Learned Perceptual Image Patch Similarity(LPIPS)(0.461),producing artifact-free images that preserve fine crack details.To validate its effectiveness,six segmentation models were tested,among which LinkNet consistently achieved the best performance,excelling in both region-level accuracy and structural continuity.Incorporating DDPM-augmented data further enhanced segmentation outcomes,increasing F1 scores by up to 1.1%and IoU by 1.7%,while also improving boundary alignment and skeleton continuity compared with models trained on real images alone.Experiments with varying augmentation ratios showed consistent improvements,with F1 rising from 0.946(no augmentation)to 0.957 and IoU from 0.897 to 0.913 at the highest ratio.These findings demonstrate the effectiveness of diffusion-based augmentation for complex crack detection in structural health monitoring. 展开更多
关键词 Crack monitoring complex cracks denoising diffusion models generative artificial intelligence synthetic data augmentation
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基于生成式大模型Stable Diffusion的产品设计课程教学改革与实践研究 认领 引用
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作者 包光旋 袁添佳 +2 位作者 耿冉冉 杨天鹏 万慧雯 《中国现代教育装备》 2026年第13期28-31,共4页
当前产品设计课程教学存在技能与产业需求脱节、方案迭代低效、个性化教学不足等问题。本文探索生成式大模型Stable Diffusion在课程中的应用路径,构建“技术赋能—创意主导—实践落地”教学体系,设计适配的课程模块、实践案例与评价标... 当前产品设计课程教学存在技能与产业需求脱节、方案迭代低效、个性化教学不足等问题。本文探索生成式大模型Stable Diffusion在课程中的应用路径,构建“技术赋能—创意主导—实践落地”教学体系,设计适配的课程模块、实践案例与评价标准,实现Stable Diffusion与产品设计课程的深度融合,推动产品设计课程向数字化、智能化转型。实践表明,该融合模式可有效提升学生的创意转化能力与跨学科应用水平,培养复合型设计人才,为课程数字化转型提供参考。 展开更多
关键词 生成式大模型 Stable Diffusion 产品设计 教学改革
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