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Coarse to Fine: The role of Severe Plastic Deformation in Advancing Titanium-Based Medical Implants - A Comprehensive review

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Journal of Wuhan University of Technology(Materials Science) 2025年第1期40卷 295-306页

作者： Dragana r Mihajlović Bojan I Međo Ivana Lj Cvijović-Alagić Veljko r Djokić Faculty of Technology and Metallurgy University of BelgradeKarnegijeva 411120 BelgradeSerbia Vinča Institute of Nuclear Sci-ences-National Institute of the republic of Serbia University of Belgrade11001BeogradSerbia Innovation Centre of the Faculty of Technology and Metallurgy Karnegijeva 411120 BelgradeSerbia

This paper describes severe plastic deformation(SPD)procedures,which are utilized to form an ultrafine-grained structure in metallic *** the SPD process,a solid material sample is subjected to very high loads without a significant change in sample *** the present work,the high-pressure torsion(HPT)process,as one of the SPD techniques,which achieves a high degree of deformation and ensures refinement of the microstructure,will be discussed in more *** that grain size control is accepted as a method to obtain materials with desired characteristics,an overview of the properties of ultrafine-grained titanium-based biomaterials to be used in medicine is ***,particular attention is dedicated to the influences of HPT process parameters,primarily hydrostatic pressure,and number of revolutions during torsion,on the grain size and physical and mechanical characteristics(modulus of elasticity,microhardness,and tensile properties),corrosion resistance,and biocompatibility of the titanium-based biomaterials.A review of the literature indicates that titanium-based materials obtained by the SPD process show improved mechanical and physical properties without losing biocompatibility and corrosion resistance,which suggests that these methods of obtaining implants are something that should be further developed in the future.

关键词： high-pressure torsion process ultrafine-grained titanium-based materials electrochemical anodization physical properties mechanical properties

A Novel Fuzzy Inference System-Based Endmember Extraction in Hyperspectral Images

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Intelligent Automation & Soft Computing 2023年第8期37卷 2459-2476页

作者： M.r.vimala Devi S.Kalaivani School of Electronics Engineering Vellore Institute of TechnologyVellore632051India

Spectral unmixing helps to identify different components present in the spectral mixtures which occur in the uppermost layer of the area owing to the low spatial resolution of hyperspectral *** spectral unmixing methods are globally based and do not consider the spectral variability among its endmembers that occur due to illumination,atmospheric,and environmental ***,endmember bundle extraction plays a major role in overcoming the above-mentioned limitations leading to more accurate abundance ***,a two-stage approach is proposed to extract endmembers through endmember bundles in hyperspectral *** divide and conquer method is applied as the first step in subset images with only the non-redundant bands to extract endmembers using the Vertex Component Analysis(VCA)and N-FINDr algorithms.A fuzzy rule-based inference system utilizing spectral matching parameters is proposed in the second step to categorize *** endmember with the minimum error is chosen as the final endmember in each specific *** proposed method is simple and automatically considers endmember variability in hyperspectral *** efficiency of the proposed method is evaluated using two real hyperspectral *** average spectral angle and abundance angle are used to analyze the performance measures.

关键词： Hyperspectral image spectral unmixing spectral matching endmember bundles fuzzy inference system

A study on distributive and modular lattice ordered fuzzy soft group and its duality

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Applied Mathematics(A Journal of Chinese Universities) 2016年第4期31卷 491-502页

作者： J Arockia reeta J vimala Department of Mathematics Alagappa University

Soft set theory has a rich potential application in several fields. A soft group is a parameterized family of subgroups and a fuzzy soft group is a parameterized family of fuzzy subgroups. The concept of fuzzy soft group is the generalization of soft group. Abdulkadir Aygunoglu and Halis Aygun introduced the notion of fuzzy soft groups in 2009[1]. In this paper, the concept of lattice ordered fuzzy soft groups and its duality has been introduced. Then distributive and modular lattice ordered fuzzy soft groups are analysed. The objective of this paper is to study the lattice theory over the collection of fuzzy soft group in a parametric manner. Some pertinent properties have been analysed and hence established duality principle.

关键词： Fuzzy soft set fuzzy soft group lattice ordered fuzzy soft groups distributive lattice orderedfuzzy soft groups modular lattice ordered fuzzy soft groups.

维普期刊数据库

Emerging potential of progranulin-dependent SorCS2 signaling in healthy and diseased nervous systems

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Neural regeneration research 2025年第9期20卷 2591-2593页

作者： Alena Salasova Anders Nykjær Danish research Institute of Translational Neuroscience DANDrITE-Nordic EMBL Partnership for Molecular Medicine and Centre of Excellence PROMEMODepartment of BiomedicineAarhus UniversityAarhusDenmark

The formation of the mammalian nervous system and its maturation into sensory,motor,cognitive,and behavioral circuits is a complex process that begins during early embryogenesis and lasts until young *** neurodevelopm... 详细信息

关键词： progr jointly CS2

维普期刊数据库

An improved evaporation rate water cycle algorithm for energy-efficient routing protocol in WSNs

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International Journal of Intelligent Computing and Cybernetics 2023年第1期16卷 30-45页

作者： vimala Dayalan Manikandan Kuppusamy School of Computer Science and Engineering Vellore Institute of TechnologyVelloreIndia

Purpose-The paper aims to introduce an efficient routing algorithm for wireless sensor networks(WSNs).It proposes an improved evaporation rate water cycle(improved Er-WC)algorithm and outlining the systems performance in improving the energy efficiency of *** proposed technique mainly analyzes the clustering problem of WSNs when huge tasks are ***/methodology/approach-This proposed improved Er-WC algorithm is used for analyzing various factors such as network cluster-head(CH)energy,CH location and CH density in improved *** proposed study will solve the energy efficiency and improve network throughput in ***-This proposed work provides optimal clustering method for Fuzzy C-means(FCM)where efficiency is improved in *** evaluations are conducted to find network lifespan,network throughput,total network residual energy and network *** limitations/implications-The proposed improved Er-WC algorithm has some implications when different energy levels of node are used in *** implications-This research work analyzes the nodes’energy and throughput by selecting correct CHs in intra-cluster *** can possibly analyze the factors such as CH location,network CH energy and CH ***/value-This proposed research work proves to be performing better for improving the network throughput and increases energy efficiency for WSNs.

关键词： WSNs Improved Er-WCA Energy efficiency routing protocol PCM Fuzzy C-Means Paper type research paper

Security Strategy of Digital Medical Contents Based on Blockchain in Generative AI Model

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Computers, Materials & Continua 2025年第1期82卷 259-278页

作者： Hoon Ko Marek r.Ogiela Division of Computer Science and Engineering Sunmoon UniversityAsan31460Republic of Korea AGH University of Krakow Cryptography and Cognitive Informatics LaboratoryKrakow30-059Poland

This study presents an innovative approach to enhancing the security of visual medical data in the generative AI environment through the integration of blockchain *** combining the strengths of blockchain and generative AI,the research team aimed to address the timely challenge of safeguarding visual medical *** participating researchers conducted a comprehensive analysis,examining the vulnerabilities of medical AI services,personal information protection issues,and overall security *** multi faceted exploration led to an indepth evaluation of the model’s performance and ***,the correlation between accuracy,detection rate,and error rate was *** analysis revealed insights into the model’s strengths and limitations,while the consideration of standard deviation shed light on the model’s stability and performance *** study proposed practical improvements,emphasizing the reduction of false negatives to enhance detection rate and leveraging blockchain technology to ensure visual data integrity in medical *** blockchain to generative AI-created medical content addresses key personal information protection *** utilizing the distributed ledger system of blockchain,the research team aimed to protect the privacy and integrity of medical data especially medical *** approach not only enhances security but also enables transparent and tamperproof ***,the use of generative AI models ensures the creation of novel medical content without compromising personal information,further safeguarding patient *** conclusion,this study showcases the potential of blockchain-based solutions in the medical field,particularly in securing sensitive medical data and protecting patient *** proposed approach,combining blockchain and generative AI,offers a promising direction toward more robust and secure medical content *** research and advancements

关键词： Digitalmedical content medical diagnostic visualization security analysis generativeAI blockchain vulnerability pattern recognition

Spectral Analysis and Validation of Parietal Signals for Different Arm Movements

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Intelligent Automation & Soft Computing 2023年第6期36卷 2849-2863页

作者： Umashankar Ganesan A.vimala Juliet r.Amala Jenith Joshi School of Electrical and Electronics Engineering Sathyabama Institute of Science and TechnologyChennai600119India School of Electronics and Instrumentation Engineering SRM Institute of Science and TechnologyChennai630203India Department of Biomedical and Mechanical Engineering School of EngineeringNational University of IrelandGalwayH91 CF50Ireland

Brain signal analysis plays a significant role in attaining data related to motor *** parietal region of the brain plays a vital role in muscular *** approach aims to demonstrate a unique technique to identify an ideal region of the human brain that generates signals responsible for muscular movements;perform statistical analysis to provide an absolute characterization of the signal and validate the obtained results using a prototype *** can enhance the practical implementation of these frequency extractions for future neuro-prosthetic applications and the characterization of neurological diseases like Parkinson’s disease(PD).To play out this handling method,electroencepha-logram(EEG)signals are gained while the subject is performing different wrist and elbow ***,the frontal brain signals and just the parietal signals are separated from the obtained EEG signal by utilizing a band pass ***,feature extraction is carried out using Fast Fourier Transform(FFT).Subse-quently,the extraction process is done by Daubechies(db4)and Haar wavelet(db1)in MATLAB and classified using the Levenberg-Marquardt *** results of the frequency changes that occurred during various wrist move-ments in the parietal region are compared with the frequency changes that occurred in frontal EEG *** proposed algorithm also uses the deep learn-ing pattern analysis network to evaluate the matching sequence for each action that takes *** accuracy of 97.2%and maximum error range of 0.6684%are achieved during the *** of this research confirm that the Levenberg-Marquardt algorithm,along with the newly developed deep learn-ing hybrid PatternNet,provides a more accurate range of frequency changes than any other classifier used in previous works of *** on the analysis,the peak-to-peak value is used to define the threshold for the prototype arm,which performs all the intended degrees of freedom(DOF),verifying

关键词： Parietal EEG signals fast fourier transform Levenberg-Marquardt algorithm haar wavelet daubechies wavelet statistical analysis

A comprehensive investigation on nonlinear vibration and bending characteristics of bio-inspired helicoidal laminated composite structures

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Applied Mathematics and Mechanics(English Edition) 2025年第1期46卷 81-100页

作者： S.SAUrABH r.KIrAN D.SINGH r.VAISH V.S.CHAUHAN School of Mechanical and Materials Engineering Indian Institute of Technology Mandi

Bio-inspired helicoidal composite laminates, inspired by the intricate helical structures found in nature, present a promising frontier for enhancing the mechanical properties of structural designs. Hence, this study provides a comprehensive investigation into the nonlinear free vibration and nonlinear bending behavior of bio-inspired composite plates. The inverse hyperbolic shear deformation theory(IHSDT) of plates is employed to characterize the displacement field, with the incorporation of Green-Lagrange nonlinearity. The problem is modeled using the C0finite element method(FEM), and an in-house code is developed in the MATLAB environment to solve it numerically. Various helicoidal layup configurations including helicoidal recursive(Hr), helicoidal exponential(HE), helicoidal semi-circular(HS), linear helicoidal(LH), and Fibonacci helicoidal(FH)with different layup sequences and quasi-isotropic configurations are studied. The model is validated, and parametric studies are conducted. These studies investigate the effects of layup configurations, side-to-thickness ratio, modulus ratios, boundary conditions, and loading conditions at different load amplitudes on the nonlinear vibration and nonlinear bending behaviors of bio-inspired composite plates. The results show that the laminate sequence exerts a substantial impact on both nonlinear natural frequencies and nonlinear bending behaviors. Moreover, this influence varies across different side-to-thickness ratios and boundary conditions of the bio-inspired composite plate.

关键词： Inverse problems

Machine Learning Techniques in Predicting Hot Deformation Behavior of Metallic Materials

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 713-732页

作者： Petr Opela Josef Walek Jaromír Kopecek Department of Metallurgical Technologies Faculty of Materials Science and TechnologyVSB–Technical University of OstravaOstrava70800Czech Republic FZU—Institute of Physics of the Czech Academy of Sciences Prague18200Czech Republic

In engineering practice,it is often necessary to determine functional relationships between dependent and independent *** relationships can be highly nonlinear,and classical regression approaches cannot always provide sufficiently reliable ***,Machine Learning(ML)techniques,which offer advanced regression tools to address complicated engineering issues,have been developed and widely *** study investigates the selected ML techniques to evaluate their suitability for application in the hot deformation behavior of metallic *** ML-based regression methods of Artificial Neural Networks(ANNs),Support Vector Machine(SVM),Decision Tree regression(DTr),and Gaussian Process regression(GPr)are applied to mathematically describe hot flow stress curve datasets acquired experimentally for a medium-carbon *** the GPr method has not been used for such a regression task before,the results showed that its performance is the most favorable and practically unrivaled;neither the ANN method nor the other studied ML techniques provide such precise results of the solved regression analysis.

关键词： Machine learning Gaussian process regression artificial neural networks support vector machine hot deformation behavior