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Understanding the corrosion and bio-corrosion behaviour of Magnesium composites – a critical review

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Journal of Magnesium and Alloys 2024年第3期12卷 890-939页

作者： Prithivirajan sekar s.k.Panigrahi Department of Mechanical Engineering Indian Institute of Technology MadrasChennai600036India Applied Magnesium Research Center Center of Excellence in Materials and Manufacturing for Futuristic MobilityIndian Institute of Technology MadrasChennai600036India

Realising the potential of Magnesium(Mg),several globally leading ventures have invested in the Mg industry,but their relatively poor corrosion resistance is a never ending saga till *** corrosion and bio-corrosion behaviour of Mg has gained research attention and still remains a hot topic in the application of automobile,aerospace and biomedical *** intrinsic high electrochemical nature of Mg limits their utilization in diverse *** scenario has prompted the development of Mg composites with an aim to achieve superior corrosion and bio-corrosion *** present review enlightens the influence of grain size(Gs),secondary phase,texture,type of matrix and reinforcement on the corrosion and bio-corrosion behaviour of Mg ***,the corrosion and bio-corrosion behaviour of Mg composites manufactured by primary and secondary processing routes are ***,the comprehensive corrosion and bio-corrosion mechanisms of these Mg composites are ***,the individual role of Gs,texture and corrosive medium on corrosion and bio-corrosion behaviour of Mg composites are clarified and *** challenges encountered,unanswered issues in this field are explained in detail and accordingly the scope for future research is *** review is presented from basic concrete background to advanced corrosion mechanisms with an aim of creating interest among the readers like students,researchers and industry experts from various research ***,the corrosion and bio-corrosion behaviour of Mg composites are critically reviewed for the first time to:(i)contribute to the body of knowledge,(ii)foster research and development,(iii)make breakthrough,and(iv)create life changing innovations in the field of Mg composite corrosion.

关键词： Corrosion Bio corrosion Magnesium alloys Magnesium composites Magnesium implants.

A new route to weak force sensing beyond sQL:squeezed magnons in cavity magnomechanics

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science China(Physics,Mechanics & Astronomy) 2024年第10期67卷 166-167页

作者： s.k.Özdemir Department of Engineering science and Mechanics and Materials Research Institute(MRI)The Pennsylvania State UniversityUniversity ParkPA 16802USA

Quantum mechanics imposes limits on measurement accuracy through quantum noise which typically has two components:First is the shot noise originating from vacuum fluctuations and referred to as the imprecision *** is ... 详细信息

关键词： quantum typically vacuum

Enhancing Multicriteria-Based Recommendations by Alleviating scalability and sparsity Issues Using Collaborative Denoising Autoencoder

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Computers, Materials & Continua 2024年第2期78卷 2269-2286页

作者： s.Abinaya k.Uttej kumar school of Computer science&Engineering Vellore Institute of TechnologyChennaiTamilnadu600127India

A Recommender system(Rs)is a crucial part of several firms,particularly those involved in *** conventional Rs,a user may only offer a single rating for an item-that is insufficient to perceive consumer ***,businesses in industries like e-learning and tourism enable customers to rate a product using a variety of factors to comprehend customers’*** the other hand,the collaborative filtering(CF)algorithm utilizing AutoEncoder(AE)is seen to be effective in identifying user-interested ***,the cost of these computations increases nonlinearly as the number of items and users *** triumph over the issues,a novel expanded stacked autoencoder(EsAE)with kernel Fuzzy C-Means Clustering(kFCM)technique is proposed with two *** the first phase of offline,the sparse multicriteria rating matrix is smoothened to a complete matrix by predicting the users’intact rating by the EsAE approach and users are clustered using the kFCM *** the next phase of online,the top-N recommendation prediction is made by the EsAE approach involving only the most similar user from multiple *** the EsAE_kFCM model upgrades the prediction accuracy of 98.2%in Top-N recommendation with a minimized recommendation generation *** experimental check on the Yahoo!Movies(YM)movie dataset and TripAdvisor(TA)travel dataset confirmed that the EsAE_kFCM model constantly outperforms conventional Rs algorithms on a variety of assessment measures.

关键词： Recommender systems multicriteria rating collaborative filtering sparsity issue scalability issue stacked-autoencoder kernel Fuzzy C-Means Clustering

Emission colour tuning in kLnF_(4):Yb^(3+),Er^(3+)/Ho^(3+)phosphor

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Journal of Rare Earths 2024年第1期42卷 57-65,I0002页

作者： Janani k Gayathri E Vijayatha V Ramasubramanian s Thiyagarajan P Department of science and Humanities Nehru Institute of TechnologyKaliapuramThirumalayampalayamCoimbatoreIndia Department of sciences Amrita School of Physical SciencesAmrita Vishwa VidyapeethamCoimbatoreIndia Department of Physics Anna UniversityChennaiIndia

kLnF_(4):Yb^(3+),Er^(3+)/Ho^(3+)(Ln=Y,Yb,Gd)phosphors were prepared by solid-state reaction *** each of the Yb^(3+)/Er^(3+)and Yb^(3+)/Ho^(3+)co-doped phosphors independently,phase confirmation and morphological examination were accomplished using the X-ray diffraction technique and scanning electron microscope analysis.X-ray photoelectron spectroscopy analysis shows the charge state and existence of lanthanides and *** luminescence emissions of green and red colour were detected for the synthesized *** changes in the upconverted emission intensities and colour output pertaining to the change of the lanthanide host matrix along with dopants were investigated for the prepared *** the upconversion luminescence,erbium-doped samples exhibit a predominance of red emission while holmium-doped samples display a prevalence of green *** rare-earth Er/Ho doped kYbF4shows strong red emission compared to other ***_(4):Yb,Er/Ho exhibits relatively significant green upconve rsion emissions,while Yb,Er-doped kGdF_(4)host displays a 10-fold increase in green/red emission ratio with an increase in the input power of 20-780 *** photoluminescence studies and decay curve analysis were carried out using UV light excitation for the developed phosphors.A higher lifetime of 2.2 ms was measured for the downshifting red emission in the Ho doped kYbF_(4)*** study suggests a potential for colour tunability in the upconversion and downshifting emissions through host modifications to the fluoride phosphor.

关键词： Rare earths Energy transfer upconversion Downshifting Colour coordinates

sTING agonist-conjugated metal-organic framework induces artificial leukocytoid structures and immune hotspots for systemic antitumor responses

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National science Review 2024年第07期 189-204页

作者： Taokun Luo Xiaomin Jiang Yingjie Fan Eric Yuan Jinhong Li Langston tillman Wenbin Lin Department of Chemistry University of Chicago Department of Radiation and Cellular Oncology and the Ludwig Center for Metastasis Research University of Chicago

Radiotherapy is widely used for cancer treatment, but its clinical utility is limited by radioresistance and its inability to target metastases. Nanoscale metal-organic frameworks(MOFs) have shown promise as high-Z nanoradiosensitizers to enhance radiotherapy and induce immunostimulatory regulation of the tumor microenvironment. We hypothesized that MOFs could deliver small-molecule therapeutics to synergize with radiotherapy for enhanced antitumor efficacy. Herein, we develop a robust nanoradiosensitizer,GA-MOF, by conjugating a sTING agonist, 2,3-cyclic guanosine monophosphate–adenosine monophosphate(GA), on MOFs for synergistic radiosensitization and sTING activation. GA-MOF demonstrated strong anticancer efficacy by forming immune-cell-rich nodules(artificial leukocytoid structures) and transforming them into immunostimulatory hotspots with radiotherapy. Further combination with an immune checkpoint blockade suppressed distant tumors through systemic immune activation. Our work not only demonstrates the potent radiosensitization of GA-MOF, but also provides detailed mechanisms regarding MOF distribution, immune regulatory pathways and long-term immune effects.

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High-precision inverse potentials for neutron-proton scattering using piece-wise smooth Morse functions

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Chinese Physics C 2024年第10期48卷 201-213页

作者： Ayushi Awasthi Arushi sharma Ishwar kant O.s.k.s.sastri Department of Physics and Astronomical science Central University of Himachal PradeshDharmshala176215India

The aim of this study is to construct inverse potentials for various ℓ-channels of neutron-proton scattering using a piece-wise smooth Morse function as a *** phase equations for single-channel states and the coupled equations of multi-channel scattering are solved numerically using the 5^(th) order Runge-kutta *** employ a piece-wise smooth reference potential comprising three Morse functions as the initial *** a machine learning-based genetic algorithm,we optimize the model parameters to minimize the mean-squared error between simulated and anticipated phase *** approach yields inverse potentials for both single and multichannel scattering,achieving convergence to a mean-squared error≤10^(-3).The resulting scattering lengths"a_(0)"and effective ranges"r"for ^(3)s_(1) and ^(1)s_(0) states,expressed as[a_(0),r],are found to be[5.445(5.424),1.770(1.760)]and[–23.741(–23.749),2.63(2.81)],respectively;these values are in excellent agreement with experimental ***,the calculated total scattering cross-sections are highly consistent with their experimental counterparts,having a percentage error of less than 1%.This computational approach can be easily extended to obtain interaction potentials for charged particle scattering.

关键词： inverse potentials neutron-proton scattering piece-wise smooth Morse function phase function method reference potential approach genetic algorithm

Juxtapose of system Performance for Food Calorie Estimation Using IoT and Web-based Indagation

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Journal of Harbin Institute of Technology(New series) 2024年第1期31卷 54-63页

作者： Anusuya s sharmila k Department of Computer science VISTASChennai 600117India Department of Computer science Dr.MGR Janaki College for WomenChennai 600028India

A rudimentary aspect of human life is the health of an individual,and most commonly the wellbeing is impacted in a colossal manner through the consumption of food. The intake of calories therefore is a crucial aspect that must be meticulously monitored. Various health gremlins can be largely circumvented when there is a substantial balance in the number of calories ingested versus the quantity of calories *** food calorie estimation is a popular domain of research in recent times and is meticulously analyzed through various image processing and machine learning techniques. However,the need to scrutinize and evaluate the calorie estimation through various platforms and algorithmic approaches aids in providing a deeper insight on the bottlenecks involved,and in improvising the bariatric health of an individual. This paper pivots on comprehending a juxtaposed approach of food calorie estimation through the use of employing Convolution Neural Network(CNN)incorporated in Internet of Things(IoT),and using the Django framework in Python,along with query rule-based training to analyze the subsequent actions to be followed post the consumption of food calories in the constructed webpage. The comparative analysis of the food calorie estimate implemented in both platforms is analyzed for the swiftness of identification,error rate and classification accuracy to appropriately determine the optimal method of use. The simulation results for IoT are carried out using the Raspberry Pi4B model,while the Anaconda prompt is used to run the server holding the web page.

关键词： food calorie estimation convolution neural network Django framework internet of things raspberry Pi

Radiative heat transfer analysis of a concave porous fin under the local thermal non-equilibrium condition:application of the clique polynomial method and physics-informed neural networks

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Applied Mathematics and Mechanics(English Edition) 2024年第9期45卷 1613-1632页

作者： k.CHANDAN k.kARTHIk k.V.NAGARAJA B.C.PRAsANNAkUMARA R.s.VARUN kUMAR T.MUHAMMAD Amrita school of Artificial Intelligence Amrita Vishwa VidyapeethamBengaluru 560035KarnatakaIndia Department of studies in Mathematics Davangere UniversityDavangere 577002KarnatakaIndia Computational science Lab Amrita School of EngineeringAmrita Vishwa VidyapeethamBengaluru 560035KarnatakaIndia Department of Pure and Applied Mathematics School of Mathematical SciencesSunway UniversityPetaling Jaya 47500Malaysia Department of Mathematics College of ScienceKing Khalid UniversityAbha 61413Saudi Arabia

The heat transfer through a concave permeable fin is analyzed by the local thermal non-equilibrium(LTNE)*** governing dimensional temperature equations for the solid and fluid phases of the porous extended surface are modeled,and then are nondimensionalized by suitable dimensionless ***,the obtained nondimensional equations are solved by the clique polynomial method(CPM).The effects of several dimensionless parameters on the fin's thermal profiles are shown by graphical ***,the current study implements deep neural structures to solve physics-governed coupled equations,and the best-suited hyperparameters are attained by comparison with various network *** results of the CPM and physicsinformed neural network(PINN)exhibit good agreement,signifying that both methods effectively solve the thermal modeling problem.

关键词： heat transfer fin porous fin local thermal non-equilibrium(LTNE)model physics-informed neural network(PINN)

5G Resource Allocation Using Feature selection and Greylag Goose Optimization Algorithm

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Computers, Materials & Continua 2024年第7期80卷 1179-1201页

作者： Amel Ali Alhussan s.k.Towfek Department of Computer sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer science and Intelligent systems Research Center BlacksburgVA 24060USA

In the contemporary world of highly efficient technological development,fifth-generation technology(5G)is seen as a vital step forward with theoretical maximum download speeds of up to twenty gigabits per second(Gbps).As far as the current implementations are concerned,they are at the level of slightly below 1 Gbps,but this allowed a great leap forward from fourth generation technology(4G),as well as enabling significantly reduced latency,making 5G an absolute necessity for applications such as gaming,virtual conferencing,and other interactive electronic *** of this change are not limited to connectivity alone;it urges operators to refine their business strategies and offers users better and improved digital *** essential factor is optimization and the application of artificial intelligence throughout the general arrangement of intricate and detailed 5G *** Binary Greylag Goose Optimization(bGGO)to achieve a significant reduction in the feature set while maintaining or improving model performance,leading to more efficient and effective 5G network management,and Greylag Goose Optimization(GGO)increases the efficiency of the machine ***,the model performs and yields more accurate *** work proposes a new method to schedule the resources in the next generation,5G,based on a feature selection using GGO and a regression model that is an ensemble of k-Nearest Neighbors(kNN),Gradient Boosting,and Extra Trees *** ensemble model shows better prediction performance with the coefficient of determination R squared value equal *** proposed framework is supported by several statistical analyses,such as theWilcoxon signed-rank *** of the benefits of this study are the introduction of new efficient optimization algorithms,the selection of features and more reliable ensemble models which improve the efficiency of 5G technology.

关键词： Optimization ensemble learning 5G technology artificial intelligence greylag goose optimization ANOVA test