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SR-CLD: spatially-resolved chord length distributions for statistical description, visualization, and alignment of non-uniform microstructures

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arXiv 2024年

作者： Whitman, Sheila E. Latypov, Marat I. Graduate Interdisciplinary Program in Applied Mathematics University of Arizona TucsonAZ85721 United States Department of Materials Science and Engineering University of Arizona TucsonAZ85721 United States

This study introduces the calculation of spatially-resolved chord length distribution (SR-CLD) as an efficient approach for quantifying and visualizing non-uniform microstructures in heterogeneous materials. SR-CLD enables detailed analysis of spatial variation of microstructure constituent sizes in different directions that can be overlooked with traditional descriptions. We present the calculation of SR-CLD using efficient scan-line algorithm that counts pixels in constituents along pixel rows or columns of microstructure images for detailed, high-resolution SR-CLD maps. We demonstrate the application of SR-CLD in two case studies: one on synthetic polycrystalline microstructures with known and intentionally created uniform and gradient spatial distributions of grain size;and one on experimental images of two-phase microstructures of additively manufactured Ti alloys with significant spatially non-uniform distributions of laths of one of the phases. Additionally, we present how SR-CLDs can enable automated, computationally efficient, and robust alignment of large sets of images for merging into accurate composite images of large microstructure areas. © 2024, CC BY.

关键词： Titanium alloys

AnisoGNN: graph neural networks generalizing to anisotropic properties of polycrystals

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arXiv 2024年

作者： Hu, Guangyu Latypov, Marat I. Department of Materials Science and Engineering University of Arizona TucsonAZ85721 United States Graduate Interdisciplinary Program in Applied Mathematics University of Arizona TucsonAZ85721 United States

We present AnisoGNNs - graph neural networks (GNNs) that generalize predictions of anisotropic properties of polycrystals in arbitrary testing directions without the need in excessive training data. To this end, we develop GNNs with a physics-inspired combination of node attributes and aggregation function. We demonstrate the excellent generalization capabilities of AnisoGNNs in predicting anisotropic elastic and inelastic properties of two alloys. © 2024, CC BY.

关键词： Anisotropy

Effective Dimension Aware Fractional-Order Stochastic Gradient Descent for Convex Optimization Problems

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arXiv 2025年

作者： Partohaghighi, Mohammad Marcia, Roummel Chen, YangQuan Electrical Engineering and Computer Science graduate program University of California Merced United States Department of Applied Mathematics University of California Merced MercedCA United States Lab Department of Mechanical Engineering School of Engineering University of California MercedCA95343 United States

Fractional-order stochastic gradient descent (FOSGD) leverages a fractional exponent to capture long-memory effects in optimization, yet its practical impact is often constrained by the difficulty of tuning and stabilizing this exponent. In this work, we introduce 2SED Fractional-Order Stochastic Gradient Descent (2SEDFOSGD), a novel method that synergistically combines the Two-Scale Effective Dimension (2SED) algorithm with FOSGD to automatically calibrate the fractional exponent in a data-driven manner. By continuously gauging model sensitivity and effective dimensionality, 2SED dynamically adjusts the exponent to curb erratic oscillations and enhance convergence rates. Theoretically, we demonstrate how this dimension-aware adaptation retains the benefits of fractional memory while averting the sluggish or unstable behaviors frequently observed in naive fractional SGD. Empirical evaluations across multiple benchmarks confirm that our 2SED-driven fractional exponent approach not only converges faster but also achieves more robust final performance, suggesting broad applicability for fractional-order methodologies in large-scale machine learning and related domains. © 2025, CC BY.

关键词： Convex optimization

Identifying Systems with Symmetries using Equivariant Autoregressive Reservoir Computers

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arXiv 2023年

作者： Vides, Fredy Nogueira, Idelfonso B.R. Banegas, Lendy Flores, Evelyn Department of Statistics and Research CNBS Honduras Tegucigalpa Honduras Department of Chemical Engineering Norwegian University of Science and Technology Norway Department of Applied Mathematics UNAH Honduras Tegucigalpa Honduras

The investigation reported in this document focuses on identifying systems with symmetries using equivariant autoregressive reservoir computers. General results in structured matrix approximation theory are presented, exploring a two-fold approach. Firstly, a comprehensive examination of generic symmetry-preserving nonlinear time delay embedding is conducted. This involves analyzing time series data sampled from an equivariant system under study. Secondly, sparse least-squares methods are applied to discern approximate representations of the output coupling matrices. These matrices play a pivotal role in determining the nonlinear autoregressive representation of an equivariant system. The structural characteristics of these matrices are dictated by the set of symmetries inherent in the system. The document outlines prototypical algorithms derived from the described techniques, offering insight into their practical applications. Emphasis is placed on their effectiveness in the identification and predictive simulation of equivariant nonlinear systems, regardless of whether such systems exhibit chaotic behavior. Copyright © 2023, The Authors. All rights reserved.

关键词： Least squares approximations

New Hexafunctional Epoxy Prepolymer: Innovation Structure for Corrosion Inhibition

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Journal of Bio- and Tribo-Corrosion 2024年第3期10卷 1-18页

作者： El-Aouni, Naoual Dagdag, Omar El Amri, Abdeslam Kim, Hansang Dkhireche, Nadia Elbachiri, Abderrahim Berdimurodov, Elyor Berisha, Avni Rafik, Mohamed Aliev, Nizomiddin Laboratory of Organic Chemistry Catalysis and Environment Department of Chemistry Faculty of Sciences University Ibn Tofail BP 242 Kenitra14000 Morocco Department of Mechanical Engineering Gachon University Seongnam13120 Korea Republic of Laboratory of Advanced Materials and Process Engineering Faculty of Sciences Ibn Tofail University BP 242 Kenitra14000 Morocco Royal Naval School University Department—Boulevard Sour-Jdid Casablanca Morocco Chemical and Materials Engineering New Uzbekistan University 54 Mustaqillik Ave. Tashkent100007 Uzbekistan University of Tashkent for Applied Sciences Str. Gavhar 1 Tashkent100149 Uzbekistan Faculty of Chemistry National University of Uzbekistan Tashkent100034 Uzbekistan Department of Chemistry Faculty of Natural and Mathematics Science University of Prishtina Prishtina10000 Tashkent State University of Economics Tashkent100066 Uzbekistan

The corrosion of metals is a pervasive issue across various industries, leading to significant economic losses. The development of new protective agents, particularly organic inhibitors, is a promising approach for mitigating corrosion. This study introduces a novel hexafunctional phosphorus epoxy resin, HGTMDAP, as an innovative corrosion inhibitor for metal surfaces in acidic environments. HGTMDAP was synthesized via a two-step reaction involving methylene dianiline and phosphorus trichloride, followed by epichlorohydrin addition. Its structure was confirmed using FTIR, 1H, and 13C NMR techniques. The corrosion inhibition performance was assessed through electrochemical evaluations (EIS, PDP), SEM/EDX analysis for surface characterization, and theoretical simulations (DFT, MC, and MD) to elucidate the adsorption mechanism and interaction with metal surfaces. The HGTMDAP resin demonstrated exceptional corrosion inhibition efficiency, reaching 96.3% at an optimal concentration of 10–3 M. Electrochemical studies validated its role as a mixed-type inhibitor, forming a protective film on the metal surface. Theoretical simulations supported the experimental findings, revealing strong adsorption and interaction mechanisms at the molecular level. This comprehensive approach underscores the potential of HGTMDAP as a highly effective corrosion inhibitor under acidic conditions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Corrosion inhibitors

The Smart Analysis of Performing Scalable Inference for Big Data Analytics

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The Smart Analysis of Performing Scalable Inference for Big ...

2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Bhandarkar, Vishakha D. Rongali, Aruna Sri Shamout, Mohamed Dawood Pund, Sachin Nama, Gigih Forda Patil, Shrishailappa Yeshwantrao Chavan College of Engineering Applied Mathematics and Humanities India University of the Cumberlands Graduate Doctoral Information Technology Information Technology Emphasis/Information Technology United States College of Business Administration University of Sharjah Department of Management Sharjah United Arab Emirates Shri Ramdeobaba College of Engineering and Management Department of Industrial Engineering Maharashtra Nagpur India Doctoral Program of Environtmental Science University of Lampung Department of Informatics Bandar Lampung Indonesia Vishwakarma Institute of Technology Computer Engineering Pune India

ISBN: (纸本)9798350357769

This paper examines the reproducibility of massive information analytics under particular factors. The paper proposes the 'performing Scalable Inference' technique to cope with scalability troubles and to exploit current big statistics platforms for efficient computing and statistics garage of the statistics. In particular, the paper describes how to perform leak-free, parallelizable visible analytics over massive datasets using present extensive records analytics frameworks such as Apache Flink. This method presents an automated manner to execute analytics that preserves reproducibility and the ability to make adjustments without re-running the entire technique. The paper also demonstrates how these analytics may help several real-world use instances, explore affected person cohorts for studies, and develop stratified patient cohorts for hospital therapy. In the end, the paper observes how the proposed method may be exercised within the real world. Actively scalable inference for massive information analytics is pivotal in optimizing decision-making and allocation of assets. Typically, such inferences are made based on information accumulated from numerous sources, databases, unstructured data, and different digital sources. So one can ensure scalability, a complete cloud-primarily based platform has to be hired. This solution will permit the ***, deploying the essential records series and evaluation algorithms are prime here. It could permit the platform to recognize the styles inside the statistics and discover any ability correlations or traits. Additionally, predictive analytics and system mastering strategies may be incorporated to provide insights into the results of the information. In the long run, by leveraging those techniques, the platform can draw efficient inferences and appropriately compare situations in an agile and green way.. © 2023 IEEE.

关键词： adjustments an a lytics automated preserves statistics

Deep potentials for materials science

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Materials Futures 2022年第2期1卷 89-115页

作者： Tongqi Wen Linfeng Zhang Han Wang Weinan E David J Srolovitz Department of Mechanical Engineering The University of Hong KongHong KongHong Kong Special Administrative Region of China DP Technology BeijingPeople’s Republic of China AI for Science Institute BeijingPeople’s Republic of China Laboratory of Computational Physics Institute of Applied Physics and Computational MathematicsBeijingPeople’s Republic of China HEDPS CAPTCollege of EngineeringPeking UniversityBeijingPeople’s Republic of China School of Mathematical Sciences Peking UniversityBeijingPeople’s Republic of China Department of Mathematics and Program in Applied and Computational Mathematics Princeton UniversityPrincetonNJUnited States of America International Digital Economy Academy(IDEA) ShenzhenPeople’s Republic of China

To fill the gap between accurate(and expensive)ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials,a new class of descriptions of atomic interactions has emerged and been widely applied;*** learning potentials(MLPs).One recently developed type of MLP is the deep potential(DP)*** this review,we provide an introduction to DP methods in computational materials *** theory underlying the DP method is presented along with a step-by-step introduction to their development and *** also review materials applications of DPs in a wide range of materials *** DP Library provides a platform for the development of DPs and a database of extant *** discuss the accuracy and efficiency of DPs compared with ab initio methods and empirical potentials.

关键词： deep potential atomistic simulation machine learning potential neural network

Bibliometric Analysis of Trends and Directions in Machine Learning applied to FinTech

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Bibliometric Analysis of Trends and Directions in Machine Le...

Electronic and Electrical engineering and Intelligent System (ICE3IS), International Conference on

作者： Erman Arif Suherman Suherman Aris Puji Widodo Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Chemical Engineering Faculty of Engineering Diponegoro University Semarang Indonesia Department of Informatics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia

ISBN: (数字)9798350378368

ISBN: (纸本)9798350378375

Our article presents a comprehensive analysis of trends and directions in the application of machine learning in the FinTech sector. By utilizing data from academic databases such as Scopus, our objective is to showcase the advancements in this interdisciplinary research area. The study identifies key trends, influential authors, leading journals, and significant collaborative networks that shape the field. The methodology employed involves quantitative bibliometric analysis to evaluate publications from the past five years. The results indicate a substantial increase in the number of studies during this period, highlighting the importance of machine learning in addressing critical financial challenges, including fraud detection, algorithmic trading, credit scoring, and risk management. Additionally, we delve into the main themes, countries, and institutions that dominate the current research agenda. This analysis provides valuable insights into the present state and future direction of machine learning in FinTech, serving as a resource for researchers and practitioners seeking to comprehend and contribute to this dynamic and rapidly evolving field. We also discuss implications for future research, emphasizing the necessity for continuous innovation and interdisciplinary collaboration to maximize the potential of machine learning in transforming financial services.

关键词： Symbiosis Technological innovation Machine learning algorithms Bibliometrics Collaboration Machine learning Market research Dynamic scheduling Fraud Risk management

Graphene-functionalized textile composites for wearable Joule heating applications

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Advanced Nanocomposites

Advanced Nanocomposites 2025年 2卷 108-123页

作者： Omar Faruk Abbas Ahmed Ashfaqul Hoque Khadem Lu Jia Luyi Sun Department of Materials Science and Engineering Binghamton University State University of New York at Binghamton NY 13902 USA Polymer Program Institute of Materials Science and Department of Chemical & Biomolecular Engineering University of Connecticut Storrs CT 06269 USA Department Mechanical Engineering College of Engineering Computing and Applied Science Clemson University Clemson SC 29634 USA College of Textile Engineering Taiyuan University of Technology Jinzhong Shanxi 030600 China

Thermal comfort is essential for maintaining physiological well-being, with textile materials traditionally serving as the primary medium for regulating heat exchange between the body and its environment. However, conventional textiles often fall short of maintaining optimal thermal balance. So, there is an increasing demand for advanced thermoregulation systems that can effectively reduce heat loss and enhance warmth, ensuring consistent comfort. Recent research has highlighted the promise of advanced functional materials, especially graphene and its composites, for modifying textiles to improve thermal properties. This article comprehensively reviews recent advancements in graphene-functionalized textile composites, focusing on their applications in wearable Joule heaters designed for personalized thermal comfort or thermal therapy. Various graphene-functionalized textile composites are reviewed from the perspectives of material properties, processing strategies and device fabrication methods. Key challenges and future opportunities are summarized for graphene-functionalized textile-based Joule heaters as innovative solutions in wearable thermal regulation.

关键词： Graphene Textile Composite Wearable electronics Joule heater