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Semi-analytical finite element method applied for characterizing micropolar fibrous composites

Applied mathematics and Mechanics(English Edition) 2024年第12期45卷 2147-2164页

作者： J.A.OTERO Y.ESPINOSA-ALMEYDA R.RODRIGUEZ-RAMOS J.MERODIO School of Engineering and Sciences Monterrey Institute of TechnologyEstado de Mexico 52926Mexico Institute of Engineering and Technology Autonomous University of Ciudad JuarezCiudad JuarezChihuahua 32310Mexico Postgraduate Program in Computational Modeling in Science and Technology Federal Fluminense UniversityRıo de Janeiro 27255-125Brazil Faculty of Mathematics and Computer Science University of HavanaLa Habana 10400Cuba Department of Mathematics Applied to ICT School of Informatics Systems EngineeringTechnical University of MadridMadrid 28031Spain

A semi-analytical finite element method(SAFEM),based on the two-scale asymptotic homogenization method(AHM)and the finite element method(FEM),is implemented to obtain the effective properties of two-phase fiber-reinforced composites(FRCs).The fibers are periodically distributed and unidirectionally aligned in a homogeneous *** framework addresses the static linear elastic micropolar problem through partial differential equations,subject to boundary conditions and perfect interface contact *** mathematical formulation of the local problems and the effective coefficients are presented by the *** local problems obtained from the AHM are solved by the FEM,which is denoted as the *** numerical results are provided,and the accuracy of the solutions is analyzed,indicating that the formulas and results obtained with the SAFEM may serve as the reference points for validating the outcomes of experimental and numerical computations.

关键词： semi-analytical approach fiber-reinforced composite(FRC) effective property finite element method(FEM) asymptotic homogenization method(AHM) micropolar elasticity

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Optimizing Preacher Assignments Using AHC-PCA Algorithm in Kampar Regency 2

Optimizing Preacher Assignments Using AHC-PCA Algorithm in K...

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2nd IEEE International Conference on Electrical Engineering, computer and Information Technology, ICEECIT 2024

作者： Kurniawan, Rahmad Suhada, Nuraina Sukamto Melia, Tisha Zailani Risanto, Joko Department of Computer Science Faculty of Mathematics and Natural Sciences Universitas Riau Pekanbaru Indonesia Graduate Program Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia

ISBN: (纸本)9798331504373

Inefficiencies in assigning preachers for Islamic preaching activities often arise due to distant placements, leading to delays and logistical challenges. In Kampar Regency, geographical diversity intensifies this issue, resulting in suboptimal preacher placements. This study aims to optimize preacher assignments by considering distance and mosque profiles. The dataset, which includes 102 mosques and 148 preachers was obtained from the Indonesian Ulema Council in Kampar. The Agglomerative Hierarchical Clustering (AHC) algorithm was selected for its capacity to cluster data across multiple characteristics. Principal Component Analysis (PCA) was employed to reduce data dimensionality, thereby enhancing clustering outcomes. The optimization of the AHC-PCA algorithm generated an optimal clustering solution with a silhouette score of 0.625524 at three clusters. Furthermore, the elbow method also indicated that three clusters are optimal with a value of 390.234457, while the lowest Davies-Bouldin Index (DBI) score of 0.468573 was achieved with three clusters. Business Intelligence techniques visualized these clusters, providing a spatial distribution of preachers and mosques for detailed analysis. This study improves preacher assignments by reducing travel time and aligning preacher placements with mosque proximity and needs, thus enhancing the efficiency of Islamic preaching in Kampar Regency. © 2024 IEEE.

关键词： Efficiency

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Physics-informed neural networks for modeling water flows in a river channel

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1001-1015页

作者： Nazari, Luis Fernando Camponogara, Eduardo Seman, Laio Oriel Department of Mathematics Federal Institute of Education Science and Technology Catarinense Rio do Sul89160-202 Brazil Department of Automation and Systems Engineering Federal University of Santa Catarina Florianopolis88040-900 Brazil Program in Applied Computer Science Universidade Do Vale Do Itajai Itajai88302-901 Brazil

The impacts incurred by floods regularly affect the planets population, inflicting social and economic problems. Optimal control strategies based on reservoir management may aid in controlling floods and mitigating the resulting damage. To this end, an accurate dynamic representation of water systems is needed. In practice, flood control strategies rely on hydrological forecasting models obtained fromconceptual or data-drivenmethods. Encouraged by recent works, this research proposes a novel surrogate model for water flow in a river channel based on physics-informed neural networks (PINNs). This approach achieved promising results regarding the assimilation of real-data measurements and the parameter identification of differential equations that govern the underlying dynamics. This article investigates PINN performance in a simulated environment built directly from a configuration of the Saint-Venant equations. The objective is to create a suitable model with high prediction accuracy and scientifically consistent behavior for use in real-Time applications. The experiments revealed promising results for hydrological modeling and presented alternatives to solve the main challenges found in conventional methods while assisting in synthesizing real-world representations. Impact Statement-The research seeks to contribute to the hydrological modeling area with a surrogate model based on physicsinformed neural networks (PINNs) to water flow in a watershed. In practice, thesemodels use conceptual or *** models to reach the precision provided by themethodology use large numbers of physical parameters. These parameters can demand deep knowledge about the environment and are possibly hard to identify in a complex basin. On the other hand, while data-driven methods do not require such knowledge about the dynamic system, they depend on a reliable and useful database to guarantee the accuracy of system *** introduce PINNs as a viable solution for

关键词： Floods

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Domain Adaptation in Color-Shifted Datasets Through Adversarial Learning Balancing 4

Domain Adaptation in Color-Shifted Datasets Through Adversar...

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4th International Conference on Artificial Intelligence and Data sciences, AiDAS 2023

作者： Hidayaturrahman Trisetyarso, Agung Kartowisastro, Iman Herwidiana Budiharto, Widodo School of Computer Science Bina Nusatara University Mathematics Department Jakarta11480 Indonesia Bina Nusatara University Binus Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Computer Engineering Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350318432

Due to the disparity in the levels of difficulty presented by the several tasks, doing domain adaptation in an adversarial way may result in an imbalanced learning process. In the MNIST dataset, this phenomenon also manifests itself in the form of domain adaptation for color-shifted distribution. In this particular situation, the domain classifier has a higher tendency to fit more quickly, but the category classifier fits quite poorly in the learning process. In order to address this problem, a new hyper-parameter has been added to the loss function in order to strike a compromise between the learning speed of the domain and the categorical classifier. By using this technique, the categorical classifier may better match the data while still maintaining the same level of performance as the domain classifier. In order to determine whether or not making use of this hyper-parameter is useful, the phenomena in question is examined using three distinct color-shifted settings. Following the evaluations, it was discovered that the newly introduced hyper-parameter is capable of coping with imbalanced learning while simultaneously engaging in domain adaptation. © 2023 IEEE.

关键词： Color

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Retinal Blood Vessel Tracking and Diameter Estimation via Gaussian Process With Rider Optimization Algorithm

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IEEE Journal of Biomedical and Health Informatics 2024年第3期28卷 1173-1184页

作者： Ahmad, Nehal Lai, Kuan-Ting Tanveer, M. Indian Institute of Technology Indore Simrol Department of Mathematics Indore453552 India National Taipei University of Technology Department of International Program of Electrical Engineering and Computer Science Taipei106 Taiwan National Taipei University of Technology Department of Electronic Engineering Taipei106 Taiwan

Retinal blood vessels structure analysis is an important step in the detection of ocular diseases such as diabetic retinopathy and retinopathy of prematurity. Accurate tracking and estimation of retinal blood vessels in terms of their diameter remains a major challenge in retinal structure analysis. In this research, we develop a rider-based Gaussian approach for accurate tracking and diameter estimation of retinal blood vessels. The diameter and curvature of the blood vessel are assumed as the Gaussian processes. The features are determined for training the Gaussian process using Radon transform. The kernel hyperparameter of Gaussian processes is optimized using Rider Optimization Algorithm for evaluating the direction of the vessel. Multiple Gaussian processes are used for detecting the bifurcations and the difference in the prediction direction is quantified. The performance of the proposed Rider-based Gaussian process is evaluated with mean and standard deviation. Our method achieved high performance with the standard deviation of 0.2499 and mean average of 0.0147, which outperformed the state-of-the-art method by 6.32%. Although the proposed model outperformed the state-of-the-art method in normal blood vessels, in future research, one can include tortuous blood vessels of different retinopathy patients, which would be more challenging due to large angle variations. We used Rider-based Gaussian process for tracking blood vessels to obtain the diameter of retinal blood vessels, and the method performed well on the 'STrutred Analysis of the REtina (STARE) Database' accessed on Oct. 2020 (https://***/~ahoover/stare/). To the best of our knowledge, this experiment is one of the most recent analysis using this type of algorithm. © 2013 IEEE.

关键词： Blood vessels

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Fast Cauchy Sum Algorithms for Polynomial Zeros and Matrix Eigenvalues 13th

Fast Cauchy Sum Algorithms for Polynomial Zeros and Matrix...

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13th International Symposium on Algorithms and Complexity, CIAC 2023

作者： Pan, Victor Y. Go, Soo Luan, Qi Zhao, Liang Department of Computer Science Lehman College of CUNY BronxNY United States Program in Computer Science The Graduate Center of the City University of New York New York United States Program in Mathematics The Graduate Center of the City University of New York New York United States

ISBN: (纸本)9783031304477

Given a black box oracle that evaluates a univariate polynomial p(x) of a degree d, we seek its zeros, aka the roots of the equation. At FOCS 2016, Louis and Vempala approximated within an absolutely largest zero of such a real-rooted polynomial at the cost of the evaluation of Newton’s ratio (formula presented) points x and then extended this algorithm to approximation of an absolutely largest eigenvalue of a symmetric matrix at a record Boolean cost. By applying distinct approach and techniques we obtain much more general results at the same computational cost. Our use of Cauchy integrals and randomization is non-trivial and pioneering in this field. Somewhat surprisingly, the Boolean complexity of the accelerated versions of our algorithms in [25, 26] reached below the known lower bounds on the Boolean complexity of polynomial root-finding. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Approximation algorithms

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Expert System Research for Prostate Cancer : A Literature Review

Expert System Research for Prostate Cancer : A Literature Re...

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2023 International Conference on Networking, Electrical Engineering, computer science, and Technology, IConNECT 2023

作者： Rusliyawati, Rusliyawati Sutyarso, Sutyarso Wantoro, Agus University of Lampung Doctoral Program of Mathematics and Natural Science Faculty Bandar Lampung Indonesia Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandar Lampung Indonesia University of Lampung Faculty of Mathematics and Natural Sciences Bandar Lampung Indonesia

ISBN: (纸本)9798350331172

In the field of computer science, expert systems have attracted many computer scientists. In recent years, expert systems have been widely used in the medical field, particularly for diagnosing and treating various diseases, especially prostate cancer. Reality has shown that expert systems hold great promise for accurate diagnosis and treatment of prostate cancer. This research summarizes several studies related to the application of artificial intelligence in the field of prostate cancer through bibliometric analysis and explores potential future research. Articles and reviews on the application of expert systems in prostate cancer from 2015 to 2022 were selected from the PubMed, Embase, and Web of science databases. Microsoft Excel 2019 and GraphPad Prism 8 were used for target variable analysis. A total of 37 articles were selected for this research. Research on artificial intelligence in prostate cancer has grown exponentially in recent years, particularly in Indonesia. Image data usage is more predominant (62%) than numerical data types in prostate cancer research. Artificial intelligence research in prostate cancer, especially in the field of treatment cases, and the use of Deep Neural Network (DNN), Human Neural Network (HNN), Fully Convolutional Network (FCN), QT Environment for C++, Artificial Neural Network (ANN) algorithms, are highly feasible for further research. © 2023 IEEE.

关键词： Fuzzy logic

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Utilizing Semi-supervised Method in Predicting BRCA1 Pathogenicity Variants 8

Utilizing Semi-supervised Method in Predicting BRCA1 Pathoge...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Hidayat, Alam Ahmad Trinugroho, Joko Pebrianto Nirwantono, Rudi Sudigyo, Digdo Pardamean, Bens Department of Mathematics School of Computer Science Bina Nusantara University 11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

Quantifying the effect of mutations in the BRCA1 gene is useful for understanding their clinical consequences on breast cancer. Machine learning models can be applied to predict the landscape of protein variant effects that might not be always accessible by experiments. In this work, we propose a simple semi-supervised learning method using a Gaussian mixture model to predict ~90% of the unlabeled missense variants of the BRCA1 gene collected from the ClinVar database. High-quality embeddings are used as a feature of the protein sequences, extracted using the latest pre-trained transformer-based language protein model. A statistical test show that the protein embeddings are effective and robust for predicting pathogenicity. Further, the lower representations of the features are then fed into the semi-supervised model. The prediction performance of the model only for the labeled testing data achieves an AUC score and an accuracy of 79.27% and 71.58%, respectively. Using our defined pathogenic probability score, we find that ~94% of variants in our unlabeled dataset are well-separated into either benign or pathogenic classes according to that scoring. Our scores obtain a moderate Spearman rank correlation with the results of established unsupervised variant effect models. Finally, our approach can potentially be developed for more accurate and biologically reliable predictions of the variant effects. © 2023 The Authors. Published by Elsevier B.V.

关键词： embeddings language model mutation effect protein sequence semi-supervised learning

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Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images

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computers, Materials & Continua 2023年第6期75卷 5255-5270页

作者： JoséEscorcia-Gutierrez Margarita Gamarra Roosvel Soto-Diaz Safa Alsafari Ayman Yafoz Romany F.Mansour Departament of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080002Colombia Biomedical Engineering Program Universidad Simón BolívarBarranquilla080002Colombia Department of Computer Science and AI Faculty of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the *** X-ray(CXR)gained much interest after the COVID-19 outbreak thanks to its rapid imaging time,widespread availability,low cost,and *** radiological investigations,computer-aided diagnostic tools are implemented to reduce intra-and inter-observer *** lately industrialized Artificial Intelligence(AI)algorithms and radiological techniques to diagnose and classify disease is *** current study develops an automatic identification and classification model for CXR pictures using Gaussian Fil-tering based Optimized Synergic Deep Learning using Remora Optimization Algorithm(GF-OSDL-ROA).This method is inclusive of preprocessing and classification based on *** data is preprocessed using Gaussian filtering(GF)to remove any extraneous noise from the image’s ***,the OSDL model is applied to classify the CXRs under different severity levels based on CXR *** learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the *** model,applied in this study,was validated using the COVID-19 *** experiments were conducted upon the proposed OSDL model,which achieved a classification accuracy of 99.83%,while the current Convolutional Neural Network achieved less classification accuracy,i.e.,98.14%.

关键词： Artificial intelligence chest X-ray COVID-19 optimized synergic deep learning preprocessing public health

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Lung Cancer Mortality Pattern in Thailand, 2019

Lung Cancer Mortality Pattern in Thailand, 2019

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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Chealong, Nurainee Saelim, Rattikan Lim, Apiradee Sangkhathat, Surasak Bundhamcharoen, Kanitta Makka, Nuttapat Prince of Songkla University Faculty of Science and Technology Department of Mathematics and Computer Science Pattani Thailand Prince of Songkla University Faculty of Medicine Department of Surgery Songkhla Thailand International Health Policy Program Ministry of Public Health Nonthaburi Thailand

ISBN: (纸本)9798350372229

Worldwide, lung cancer is a serious public health issue. The aim of this paper was to investigate the patterns of lung cancer mortality in Thailand, 2019. Two datasets provided by the Ministry of Public Health, Thailand, one is Verbal Autopsy (VA) and the other is Vital Registration (VR) were utilized. The VA study was conducted in the selected nine provinces in 2019 with a total number of 8,848 deaths, whereas 377 (4.3%) cases were accounted for deaths from lung cancer. On the other hand, the VR data reporting the total number of 488,503 deaths for the entire country in 2019 with 15,675 (3.2%) cases were accounted for deaths by lung cancer. A logistic regression model was applied to obtain the estimated proportion of deaths by lung cancer. After that, a triangulation method was utilized, and then the logistic regression model was applied to project the number of deaths by lung cancer in 2019 for the entire nation. It was discovered that males aged 50-79 and females aged 50-69 have statistically significant proportions of death by lung cancer higher than the overall mean (3.14%). Moreover, geographically, the proportion of deaths by lung cancer was the highest in the north and the lowest in the south. Additionally, the proportion of deaths by lung cancer for both genders aged younger than 50 and older than 80 years was statistically significantly lower than the overall mean. However, among the patients who are older, they might die from other comorbidities. © 2024 IEEE.

关键词： Logistic Regression Model Lung cancer Mortality Thailand Triangulation method

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