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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

Electrochemical sensor for dopamine detection based on multiwalled carbon nanotube/molybdenum disulfide quantum dots with machine learning integration and anti-interference capability

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Tungsten 2025年第1期7卷 137-151页

作者： Geethukrishnan Onkar Apte kiran Kumar Tadi Energy Electrodics and Electrocatalysis (EEE) Lab Department of Chemistry School of Advanced Sciences Vellore Institute of Technology School of Computer Science and Engineering Vellore Institute of Technology

The precise detection and measurement of dopamine（DA）,a crucial neurotransmitter in the human body,plays a significant role in diagnosing,preventing,and treating neurological diseases associated with its levels.A highly sensitive DA electrochemical sensor was constructed by combining molybdenum disulfide quantum dots（MSQDs） with multiwalled carbon nanotubes（MWCNTs）.The MSQDs were synthesized using the shear exfoliation *** sensors consist of MSQDs with Mo-S edge catalytic centers for the DA redox reaction,and MWCNTs amplify the sensor *** linearity of the sensor for the detection of DA was tested in the presence of ascorbic acid(AA,50 μmol·L-1) and uric acid(UA,200 μmol·L-1),and exhibited linearity from 2 to 966 μmol·L-1of DA with 0.097 μA(mol·L-1)-1sensitivity and a low limit of detection of0.6 μmol·L-1（the ratio between signal and noise,S/N=3）.Moreover,the sensitivity and selectivity of the sensor were also studied using *** is no increase in amperometric current after adding the most potentially interfering *** sensor was successfully applied to recover DA in human blood sera ***,machine learning algorithms were operated to aid in the near-precise detection of DA in the heterogeneous mixture containing AA and *** algorithms facilitate the identification and quantification of DA amidst coexisting interferents,including AA,that are commonly present in biological matrices.

关键词： Biosensor Complex mixture Dopamine Machine learning MoS2 Multiwalled carbon nanotubes

Ultra‑Transparent and Multifunctional IZVO Mesh Electrodes for Next‑Generation Flexible Optoelectronics

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Nano-Micro Letters 2025年第1期17卷 293-309页

作者： kiran A.Nirmal Tukaram D.Dongale Atul C.Khot Chenjie Yao Nahyun Kim Tae Geun Kim School of Electrical Engineering Korea UniversityAnam‑ro 145Seongbuk‑guSeoulRepublic of Korea Computational Electronics and Nanoscience research Laboratory School of Nanoscience and BiotechnologyShivaji UniversityKolhapur 416004India

Mechanically durable transparent electrodes are essential for achieving long-term stability in flexible optoelectronic ***,they are crucial for applications in the fields of energy,display,healthcare,and soft *** meshes represent a promising alternative to traditional,brittle,metal oxide conductors due to their high electrical conductivity,optical transparency,and enhanced mechanical *** this paper,we present a simple method for fabricating an ultra-transparent conducting metal oxide mesh electrode using selfcracking-assisted *** this method,we produced an electrode with ultra-transparency(97.39%),high conductance(rs=21.24Ωsq^(−1)),elevated work function(5.16 eV),and good mechanical *** also evaluated the effectiveness of the fabricated electrodes by integrating them into organic photovoltaics,organic light-emitting diodes,and flexible transparent memristor devices for neuromorphic computing,resulting in exceptional device *** addition,the unique porous structure of the vanadium-doped indium zinc oxide mesh electrodes provided excellent flexibility,rendering them a promising option for application in flexible optoelectronics.

关键词： Self-cracking template Vanadium-doped indium zinc oxide mesh Organic solar cells Organic light-emitting diodes Flexible transparent memory

Machine learning empowers efficient design of ternary organic solar cells with PM6 donor

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Journal of Energy Chemistry 2025年第1期100卷 337-347页

作者： kiran A.Nirmal Tukaram D.Dongale Santosh S.Sutar Atul C.Khot Tae Geun Kim School of Electrical Engineering Korea University Computational Electronics and Nanoscience research Laboratory School of Nanoscience and Biotechnology Shivaji University Yashwantrao Chavan School of rural Development Shivaji University

Organic solar cells(OSCs) hold great potential as a photovoltaic technology for practical ***, the traditional experimental trial-and-error method for designing and engineering OSCs can be complex, expensive, and time-consuming. Machine learning(ML) techniques enable the proficient extraction of information from datasets, allowing the development of realistic models that are capable of predicting the efficacy of materials with commendable accuracy. The PM6 donor has great potential for high-performance OSCs. However, it is crucial for the rational design of a ternary blend to accurately forecast the power conversion efficiency(PCE) of ternary OSCs(TOSCs) based on a PM6 ***, we collected the device parameters of PM6-based TOSCs and evaluated the feature importance of their molecule descriptors to develop predictive models. In this study, we used five different ML algorithms for analysis and prediction. For the analysis, the classification and regression tree provided different rules, heuristics, and patterns from the heterogeneous dataset. The random forest algorithm outperforms other prediction ML algorithms in predicting the output performance of PM6-based TOSCs. Finally, we validated the ML outcomes by fabricating PM6-based TOSCs. Our study presents a rapid strategy for assessing a high PCE while elucidating the substantial influence of diverse descriptors.

关键词： random forests

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

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

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

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

Antisense oligonucleotides provide optimism to the therapeutic landscape for tauopathies

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Neural regeneration research 2025年第3期20卷 803-804页

作者： Glenn A.Harris Lauren r.Hirschfeld rainwater Charitable Foundation Fort WorthTXUSA

Tauopathies are a group of neurodegenerative diseases characterized by abnormal metabolism of the misfolded tau protein. Tau is encoded by the microtubule-associated protein tau gene(MAPT) and can be classified as either 3-repeat(3r) or 4-repeat(4r) based on the number of repeat domains from alternative splicing of exon 10 of the MAPT gene.

关键词： tau metabolism diseases

Throughflow and g-jitter effects on binary fluid saturated porous medium

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Applied Mathematics and Mechanics(English Edition) 2015年第10期36卷 1285-1304页

作者： P.kiran Department of Applied Mathematics School for Physical Sciences Babasaheb Bhimrao Ambedkar University

A non-autonomous complex Ginzburg-Landau equation （CGLE） for the finite amplitude of convection is derived, and a method is presented here to determine the amplitude of this convection with a weakly nonlinear thermal instability for an oscillatory mode under throughflow and gravity modulation. Only infinitesimal disturbances are considered. The disturbances in velocity, temperature, and solutal fields are treated by a perturbation expansion in powers of the amplitude of the applied gravity field. Throughfiow can stabilize or destabilize the system for stress free and isothermal boundary conditions. The Nusselt and Sherwood numbers are obtained numerically to present the results of heat and mass transfer. It is found that throughfiow and gravity modulation can be used alternately to heat and mass transfer. Further, oscillatory flow, rather than stationary flow, enhances heat and mass transfer.

关键词： weakly nonlinear theory throughflow complex Ginzburg-Landau equation(CGLE) gravity modulation