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Local magnetic moment oscillation around an Anderson impurity on graphene

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 192-201页

作者： Shuai Li Zhen Ma Jin-Hua Gao School of Science Hebei University of Science and Technology School of electronics and information Zhengzhou University of Light Industry School of Physics and Wuhan National High Magnetic Field Center Huazhong University of Science and Technology

We theoretically investigate the spin resolved Friedel oscillation （FO） and quasiparticle interference （QPI） in graphene induced by an Anderson *** the impurity becomes magnetic,the resulted FO becomes spin dependent,which gives rise to a local magnetic moment oscillation with an envelop decaying as r-2in real space in the doping ***,at half filling,the electron density and local magnetic moment will not oscillate but decay as *** spin resolved FO has both sublattice and spin ***,the local magnetic moment decay at half filling only occurs at one sublattice of graphene,which is quite like the phenomenon observed in the STM experiment (***ález-Herrero et al.,Science 352,437 （2016）).We further give an analytic formula about such spin dependent FO based on the stationary phase ***,we study the interference of quasiparticles around the magnetic impurity by calculating the spin dependent Fourier-transformed local density of states （FT-LDOS）.Our work gives a comprehensive understanding about the local magnetic moment oscillation around an Anderson impurity on graphene.

关键词： Friedel oscillation local magnetic moment graphene Anderson impurity

Semantic Segmentation of Lumbar Vertebrae Using Meijering U-Net(MU-Net)on Spine Magnetic Resonance Images

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 733-757页

作者： Lakshmi S V V Shiloah Elizabeth Darmanayagam Sunil Retmin Raj Cyril department of Computer Science and Engineering College of Engineering GuindyAnna UniversityChennai600025India department of information technology Madras Institute of TechnologyAnna UniversityChrompetChennai600044India

Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people *** to its ability to produce a detailed view of the soft tissues,including the spinal cord,nerves,intervertebral discs,and vertebrae,Magnetic Resonance Imaging is thought to be the most effective method for imaging the *** semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar *** is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of tissues,including muscles,ligaments,and intervertebral discs.U-Net is a powerful deep-learning architecture to handle the challenges of medical image analysis tasks and achieves high segmentation *** work proposes a modified U-Net architecture namely MU-Net,consisting of the Meijering convolutional layer that incorporates the Meijering filter to perform the semantic segmentation of lumbar vertebrae L1 to L5 and sacral vertebra ***-colour mask images were generated and used as ground truth for training the *** work has been carried out on 1312 images expanded from T1-weighted mid-sagittal MRI images of 515 patients in the Lumbar Spine MRI Dataset publicly available from Mendeley *** proposed MU-Net model for the semantic segmentation of the lumbar vertebrae gives better performance with 98.79%of pixel accuracy(PA),98.66%of dice similarity coefficient(DSC),97.36%of Jaccard coefficient,and 92.55%mean Intersection over Union(mean IoU)metrics using the mentioned dataset.

关键词： Computer aided diagnosis(CAD) magnetic resonance imaging(MRI) semantic segmentation lumbar vertebrae deep learning U-Net model

Three-Stage Transfer Learning with AlexNet50 for MRI Image Multi-Class Classification with Optimal Learning Rate

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 155-183页

作者： Suganya Athisayamani A.Robert Singh Gyanendra Prasad Joshi Woong Cho School of Computing Sastra Deemed to be UniversityThanjavur613401India department of Computational Intelligence SRM Institute of Science and TechnologyKattankulathur603203India department of AI and Software Engineering Kangwon National UniversitySamcheok25913Republic of Korea department of electronics Information and Communication EngineeringKangwon National UniversitySamcheok25913Republic of Korea

In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological *** is particularly effective for detecting soft tissue ***,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of *** address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI *** manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning *** are three stages for learning;in the first stage,the whole dataset is used to learn the *** the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented *** method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical Engineering of Southern Medical University(SMU-dataset),and The National Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for *** hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning ***-dataset registers maximum classification *** evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.

关键词： MRI tumors classification AlexNet50 transfer learning hyperparameter tuning optimizer

Morphology engineering of ZnO micro/nanostructures under mild conditions for optoelectronic application

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International Journal of Minerals,Metallurgy and Materials 2025年第2期32卷 498-503页

作者： Liang Chu Haoyu Shen Hudie Wei Hongyu Chen Guoqiang Ma Wensheng Yan Institute of Carbon Neutrality and New Energy&School of electronics and information Hangzhou Dianzi UniversityHangzhou 310018China School of Applied Physics and Materials Wuyi UniversityJiangmen 529020China

Zinc oxide(ZnO)serves as a crucial functional semiconductor with a wide direct bandgap of approximately 3.37 *** reaction is commonly used in the synthesis of ZnO micro/nanostructures,given its low cost,simplicity,and easy ***,ZnO morphology engineering has become desirable through the alteration of minor conditions in the reaction process,particularly at room *** this work,ZnO micro/nanostructures were synthesized in a solution by varying the amounts of the ammonia added at low temperatures(including room temperature).The formation of Zn^(2+)complexes by ammonia in the precursor regulated the reaction rate of the morphology engineering of ZnO,which resulted in various structures,such as nanoparticles,nanosheets,microflowers,and single ***,the obtained ZnO was used in the optoelectronic application of ultraviolet detectors.

关键词： morphology engineering low temperature ZnO nanosheets microflowers ultraviolet detector

Steel Surface Defect Recognition in Smart Manufacturing Using Deep Ensemble Transfer Learning-Based Techniques

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 231-250页

作者： Tajmal Hussain Jongwon Seok department of information and Communication Engineering Changwon National UniversityChangwon51140Republic of Korea

Smart manufacturing and Industry 4.0 are transforming traditional manufacturing processes by utilizing innovative technologies such as the artificial intelligence(AI)and internet of things(IoT)to enhance efficiency,reduce costs,and ensure product *** light of the recent advancement of Industry 4.0,identifying defects has become important for ensuring the quality of products during the manufacturing *** this research,we present an ensemble methodology for accurately classifying hot rolled steel surface defects by combining the strengths of four pre-trained convolutional neural network(CNN)architectures:VGG16,VGG19,Xception,and Mobile-Net V2,compensating for their individual *** evaluated our methodology on the Xsteel surface defect dataset(XSDD),which comprises seven different *** ensemble methodology integrated the predictions of individual models through two methods:model averaging and weighted *** evaluation showed that the model averaging ensemble achieved an accuracy of 98.89%,a recall of 98.92%,a precision of 99.05%,and an F1-score of 98.97%,while the weighted averaging ensemble reached an accuracy of 99.72%,a recall of 99.74%,a precision of 99.67%,and an F1-score of 99.70%.The proposed weighted averaging ensemble model outperformed the model averaging method and the individual models in detecting defects in terms of accuracy,recall,precision,and *** analysis with recent studies also showed the superior performance of our methodology.

关键词： Smart manufacturing CNN steel defects ensemble models

Data-Driven Healthcare:The Role of Computational Methods in Medical Innovation

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 1-48页

作者： Hariharasakthisudhan Ponnarengan Sivakumar Rajendran Vikas Khalkar Gunapriya Devarajan Logesh Kamaraj department of Mechanical Engineering Dr.Mahalingam College of Engineering and TechnologyPollachiCoimbatoreTamil Nadu642003India department of Mechanical Engineering JKKN College of Engineering and TechnologyKumarapalayamNamakkalTamil Nadu637209India department of Mechanical Engineering Gharda Institute of TechnologyLavelMaharashtra415708India department of Electrical and electronics Engineering Sri Eshwar College of EngineeringCoimbatoreTamilnadu641202India department of Mechanical Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai600062India

The purpose of this review is to explore the intersection of computational engineering and biomedical science,highlighting the transformative potential this convergence holds for innovation in healthcare and medical *** review covers key topics such as computational modelling,bioinformatics,machine learning in medical diagnostics,and the integration of wearable technology for real-time health *** findings indicate that computational models have significantly enhanced the understanding of complex biological systems,while machine learning algorithms have improved the accuracy of disease prediction and *** synergy between bioinformatics and computational techniques has led to breakthroughs in personalized medicine,enabling more precise treatment ***,the integration of wearable devices with advanced computational methods has opened new avenues for continuous health monitoring and early disease *** review emphasizes the need for interdisciplinary collaboration to further advance this *** research should focus on developing more robust and scalable computational models,enhancing data integration techniques,and addressing ethical considerations related to data privacy and *** fostering innovation at the intersection of these disciplines,the potential to revolutionize healthcare delivery and outcomes becomes increasingly attainable.

关键词： Computational models biomedical engineering bioinformatics machine learning wearable technology

Zinc-based fiber-shaped rechargeable batteries:Insights into structures,electrodes,and electrolytes

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Nano Research 2025年第1期18卷 278-293页

作者： Xuan Zhang Hongcheng Zhang Mengxiang Chen Jingqi Lu Evgeny Zhuravlev Jing Jiang Pin Liu Shengyang Dong Du Yuan Guoyin Zhu Lianbo Ma Yizhou Zhang Institute of Advanced Materials and Flexible electronics(IAMFE) School of Chemistry and Materials ScienceNanjing University of Information Science and TechnologyNanjing 210044China School of Materials Science and Engineering Anhui University of TechnologyMaanshan 243002China College of Materials Science and Engineering Changsha University of Science and TechnologyChangsha 410004China

The rapid evolution of flexible wearable electronics has spurred a growing demand for energy storage devices,characterized by low-cost manufacturing processes,high safety standards,exceptional electrochemical performance and robust mechanical *** novel flexible devices,fiber-shaped batteries(FSBs)have emerged as prominent solutions exceptionally suited to future applications,owing to their unique one-dimensional(1D)architecture,remarkable flexibility,potential for miniaturization,adaptability to deformation and compatibility with the conventional textile *** the forefront research on fiber-shaped batteries,zincbased FSBs(ZFSBs)have garnered significant attentions,featured by the promising electrochemical properties of metallic *** enthusiasm is driven by the impressive capacity of Zn(820 mAh·g^(-1))and its low redox potential(Zn/Zn^(2+):-0.76 V *** hydrogen electrode).This review aims to consolidate recent achievements in the structural design,fabrication processes and electrode materials of flexible ***,we highlight three representative structural configurations:parallel type,twisted type and coaxial *** also place special emphasis on electrode modifications and electrolyte ***,we delve into the promising development opportunities and anticipate future challenges associated with ZFSBs,emphasizing their potential roles in powering the next generation of wearable electronics.

关键词： zinc-based batteries fiber-shape parallel type twisted type coaxial type

An Enhanced Lung Cancer Detection Approach Using Dual-Model Deep Learning Technique

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 835-867页

作者： Sumaia Mohamed Elhassan Saad Mohamed Darwish Saleh Mesbah Elkaffas department of information technology Institute of Graduate Studies and ResearchAlexandria UniversityAlexandria21526Egypt College of Computing and information technology Arab Academy for ScienceTechnology and Maritime TransportAlexandriaP.O.Box 1029Egypt

Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic *** detection of lung tumors significantly increases the chances of successful treatment and ***,current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung ***-model deep learning technologies for lung cancer detection,while beneficial,cannot capture the full range of features present in medical imaging data,leading to incomplete or inaccurate ***,it may not be robust enough to handle the wide variability in medical images due to different imaging conditions,patient anatomy,and tumor *** overcome these disadvantages,dual-model or multi-model approaches can be *** research focuses on enhancing the detection of lung cancer by utilizing a combination of two learning models:a Convolutional Neural Network(CNN)for categorization and the You Only Look Once(YOLOv8)architecture for real-time identification and pinpointing of *** automatically learn to extract hierarchical features from raw image data,capturing patterns such as edges,textures,and complex structures that are crucial for identifying lung ***8 incorporates multiscale feature extraction,enabling the detection of tumors of varying sizes and scales within a single *** is particularly beneficial for identifying small or irregularly shaped tumors that may be challenging to ***,through the utilization of cutting-edge data augmentation methods,such as Deep Convolutional Generative Adversarial Networks(DCGAN),the suggested approach can handle the issue of limited data and boost the models’ability to learn from diverse and comprehensive *** combined method not only improved accuracy and localization but also ensured efficient real-time processing,which is crucial for practical clinical applicati

关键词： Lung cancer detection dual-model deep learning technique data augmentation CNN YOLOv8

Multi-Scale Dilated Convolution Network for SPECT-MPI Cardiovascular Disease Classification with Adaptive Denoising and Attenuation Correction

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 299-327页

作者： A.Robert Singh Suganya Athisayamani Gyanendra Prasad Joshi Bhanu Shrestha department of Computational Intelligence SRM Institute of Science and TechnologyKattankulathur603203Tamil NaduIndia School of Computing Sastra Deemed to be UniversityThanjavur613401Tamil NaduIndia department of Artificial Intelligence&Software Kangwon National UniversitySamcheok25913Republic of Korea department of information Convergence System Graduate School of Smart ConvergenceKwangwoon UniversitySeoul01897Republic of Korea

Myocardial perfusion imaging(MPI),which uses single-photon emission computed tomography(SPECT),is a well-known estimating tool for medical diagnosis,employing the classification of images to show situations in coronary artery disease(CAD).The automatic classification of SPECT images for different techniques has achieved near-optimal accuracy when using convolutional neural networks(CNNs).This paper uses a SPECT classification framework with three steps:1)Image denoising,2)Attenuation correction,and 3)Image *** denoising is done by a U-Net architecture that ensures effective image *** correction is implemented by a convolution neural network model that can remove the attenuation that affects the feature extraction process of ***,a novel multi-scale diluted convolution(MSDC)network is *** merges the features extracted in different scales and makes the model learn the features more *** scales of filters with size 3×3 are used to extract *** three steps are compared with state-of-the-art *** proposed denoising architecture ensures a high-quality image with the highest peak signal-to-noise ratio(PSNR)value of *** proposed classification method is compared with the five different CNN models,and the proposed method ensures better classification with an accuracy of 96%,precision of 87%,sensitivity of 87%,specificity of 89%,and F1-score of 87%.To demonstrate the importance of preprocessing,the classification model was analyzed without denoising and attenuation correction.

关键词： SPECT-MPI CAD MSDC denoising attenuation correction classification