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Stability analysis and modeling for the three-dimensional Darcy-Forchheimer stagnation point nanofluid flow towards a moving surface

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applied mathematics and Mechanics(English Edition) 2021年第3期42卷 357-370页

作者： Yuming CHU M.I.KHAN M.I.U.REHMAN S.KADRY S.QAYYUM M.WAQAS Department of Mathematics Huzhou UniversityHuzhou 313000Zhejiang ProvinceChina Hunan Provincial Key Laboratory of Mathematical Modeling and Analysis in Engineering Changsha University of Science&TechnologyChangsha 410114China Department of Mathematics Riphah International UniversityFaisalabad CampusFaisalabad 38000Pakistan Department of Mathematics Quaid-i-Azam UniversityIslamabad 44000Pakistan Department of Mathematics and Computer Science Beirut Arab UniversityBeirut 11072809Lebanon NUTECH School of Applied Sciences and Humanities National University of TechnologyIslamabad 44000Pakistan

In this research,the three-dimensional(3D)steady and incompressible laminar Homann stagnation point nanofluid flow over a porous moving surface is *** disturbance in the porous medium has been characterized by the Darcy-Forchheimer *** slip for viscous fluid is *** energy equation is organized in view of radiative heat flux which plays an important role in the heat transfer *** governing flow expressions are first altered into first-order ordinary ones and then solved numerically by the shooting *** solutions are obtained for the velocity,skin friction coefficient,temperature,and Nusselt number subject to sundry flow parameters,magnetic parameter,Darcy-Forchheimer number,thermal radiation parameter,suction parameter,and dimensionless slip *** this research,the main consideration is given to the engineering interest like skin friction coefficient(velocity gradient or surface drag force)and Nusselt number(temperature gradient or heat transfer rate)and discussed numerically through *** conclusion,it is noticed from the stability results that the upper branch solution(UBS)is more reliable and physically stable than the lower branch solution(LBS).

关键词： viscous slip Darcy-Forchheimer relation thermal radiation stagnation point flow dual solution heat generation/absorption stability result

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An Effective Over-sampling Method for Imbalanced Data Sets Classification

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Chinese Journal of Electronics 2023年第3期20卷 489-494页

作者： Yun ZHAI Nan MA Da RUAN Bing AN School of Information Engineering University of Science and Technology Beijing Beijing China School of Computer Science Liaocheng University Liaocheng China Information College Beijing Union University Beijing China Department of Applied Mathematics and Computer Science Ghent University Ghent Belgium Belgian Nuclear Research Centre (SCK*CEN) Mol Belgium

Imbalanced data sets in real-world applications have a majority class with normal instances and a minority class with abnormal or important instances. Learning from such data sets usually generates biased classifiers that have a higher predictive accuracy over the majority class, but a rather poorer predictive accuracy over the minority class. The Synthetic minority over-sampling technique (SMOTE) is specifically designed for learning from imbalanced data sets. This paper presents a novel approach for learning from imbalanced data sets, based on an improved SMOTE algorithm. The approach deals with noise data by a hierarchical filtering mechanism, employs a selection strategy of the minority instances and makes full use of dynamic distribution density of the minority followed by the SMOTE process. This empirical analysis of the approach showed quantitatively competitive with SMOTE and series of its improved algorithm in terms of the receiver operating characteristic curve when applied to several highly and moderately imbalanced data sets.

关键词： Electric potential Filtering Heuristic algorithms Decision making Receivers Machine learning Knowledge discovery

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PirateNets: physics-informed deep learning with residual adaptive networks

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 19707-19757页

作者： Sifan Wang Bowen Li Yuhan Chen Paris Perdikaris Institution for Foundation of Data Science Yale University New Haven CT Department of Mathematics City University of Hong Kong Hong Kong SAR Department of Electrical and Computer Engineering North Carolina State University Raleigh NC Department of Mechanical Engineering and Applied Mechanics University of Pennsylvania Philadelphia PA

While physics-informed neural networks (PINNs) have become a popular deep learning framework for tackling forward and inverse problems governed by partial differential equations (PDEs), their performance is known to degrade when larger and deeper neural network architectures are employed. Our study identifies that the root of this counterintuitive behavior lies in the use of multi-layer perceptron (MLP) architectures with nonsuitable initialization schemes, which result in poor trainablity for the network derivatives, and ultimately lead to an unstable minimization of the PDE residual loss. To address this, we introduce Physics-Informed Residual Adaptive Networks (PirateNets), a novel architecture that is designed to facilitate stable and efficient training of deep PINN models. PirateNets leverage a novel adaptive residual connection, which allows the networks to be initialized as shallow networks that progressively deepen during training. We also show that the proposed initialization scheme allows us to encode appropriate inductive biases corresponding to a given PDE system into the network architecture. We provide comprehensive empirical evidence showing that PirateNets are easier to optimize and can gain accuracy from considerably increased depth, ultimately achieving state-of-the-art results across various benchmarks. All code and data accompanying this manuscript will be made publicly available at https://***/PredictiveIntelligenceLab/jaxpi/tree/pirate.

关键词： deep learning physics-informed neural networks partial differential equations computational physics neural solvers

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Mechanically Laser-Induced Acoustic Cavitation in Viscoelastic Tissue: Theoretical Study

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Bulletin of the Russian Academy of sciences: Physics 2023年第Suppl 3期87卷 S348-S355页

作者： Zain Abu Shaeer Amerah M. Hakami Abu-Bakr, Ali F. Ahmed K. Abu-Nab Department of Basic Science Higher Institute of Engineering and Technology Kafrelsheikh33511 Egypt Department of Mathematics Faculty of Science Jazan University Jazan82723 Saudi Arabia Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom32511 Egypt Phystech School of Applied Mathematics and Informatics Moscow Institute of Physics and Technology Moscow oblast Dolgoprudny141700 Russia

Abstract: Laser-induced acoustic cavitation microbubble dynamics have exhibited significant development in biological systems such as lithotripsy. Investigating the mechanically laser-induced acoustic cavitation microbubble in viscoelastic tissue is the goal of this work. Based on surface tension and viscous forces, the laser-induced cavitation model (also known as the modified Rayleigh–Plesset model), the theoretical model is developed and numerically solved using exponential cubic B-spline Collocation Method. In addition, the energy released in viscoelastic tissue by laser-induced acoustic cavitation as well as mechanically induced cavitation is investigated. The findings of our work provide valuable insight into the application of laser-induced acoustic cavitation microbubbles such as kidney stones and lithotripsy. © Pleiades Publishing, Ltd. 2023. ISSN 1062-8738, Bulletin of the Russian Academy of sciences: Physics, 2023, Vol. 87, Suppl. 3, pp. S348–S355. Pleiades Publishing, Ltd., 2023. ISSN 1062-8738, Bulletin of the Russian Academy of sciences: Physics, 2023. Pleiades Publishing, Ltd., 2023.

关键词： Tissue

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Noncrossing Longest Paths and Cycles

arXiv

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arXiv 2024年

作者： Aloupis, Greg Biniaz, Ahmad Bose, Prosenjit De Carufel, Jean-Lou Eppstein, David Maheshwari, Anil Odak, Saeed Smid, Michiel Tóth, Csaba D. Valtr, Pavel Khoury College of Computer Sciences Northeastern University BostonMA United States School of Computer Science University of Windsor WindsorON Canada School of Computer Science Carleton University OttawaON Canada School of Electrical Engineering and Computer Science University of Ottawa OttawaON Canada Computer Science Department University of California IrvineCA United States Department of Mathematics California State University Northridge Los AngelesCA United States Department of Computer Science Tufts University MedfordMA United States Department of Applied Mathematics Charles University Czech Republic

Edge crossings in geometric graphs are sometimes undesirable as they could lead to unwanted situations such as collisions in motion planning and inconsistency in VLSI layout. Short geometric structures such as shortest perfect matchings, shortest spanning trees, shortest spanning paths, and shortest spanning cycles on a given point set are inherently noncrossing. However, the longest such structures need not be noncrossing. In fact, it is intuitive to expect many edge crossings in various geometric graphs that are longest. Recently, Álvarez-Rebollar, Cravioto-Lagos, Marín, Solé-Pi, and Urrutia (Graphs and Combinatorics, 2024) constructed a set of points for which the longest perfect matching is noncrossing. They raised several challenging questions in this direction. In particular, they asked whether the longest spanning path, on any finite set of points in the plane, must have a pair of crossing edges. They also conjectured that the longest spanning cycle must have a pair of crossing edges. In this paper, we give a negative answer to the question and also refute the conjecture. We present a framework for constructing arbitrarily large point sets for which the longest perfect matchings, the longest spanning paths, and the longest spanning cycles are noncrossing. © 2024, CC BY.

关键词： Trees (mathematics)

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Negative differential resistance in Josephson junctions coupled to a cavity

arXiv

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arXiv 2023年

作者： Pedersen, N.F. Filatrella, G. Pierro, V. Sørensen, M.P. Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs. Lyngby 2800 Denmark Department of Science and Technology University of Sannio Italy Department of Engineering University of Sannio Italy

Regions with negative differential resistance can arise in the IV curve of Josephson junctions and this phenomenon plays an essential role for applications, in particular for THz radiation emission. For the measurement of high frequency radiation from Josephson junctions, a cavity – either internal or external – is often used. A cavity may also induce a negative differential resistance region at the lower side of the resonance frequency. We investigate the dynamics of Josephson junctions with a negative differential resistance in the quasi particle tunnel current, i.e. in the McCumber curve. We find that very complicated and unexpected interactions take place. This may be useful for the interpretation of experimental measurements of THz radiation from intrinsic Josephson junctions. Copyright © 2023, The Authors. All rights reserved.

关键词： Nonlinear optics

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Evaluating Gaussianity of heterogeneous fractional Brownian motion

arXiv

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arXiv 2025年

作者： Balcerek, Michal Pacheco-Pozo, Adrian Wylomańska, Agnieszka Krapf, Diego Department of Electrical and Computer Engineering School of Biomedical Engineering Colorado State University Fort CollinsCO80523 United States Faculty of Pure and Applied Mathematics Hugo Steinhaus Center Wroclaw University of Science and Technology Wroclaw50-370 Poland

Heterogeneous diffusion processes are prevalent in various fields, including the motion of proteins in living cells, the migratory movement of birds and mammals, and finance. These processes are often characterized by time-varying dynamics, where interactions with the environment evolve, and the system undergoes fluctuations in diffusivity. Moreover, in many complex systems anomalous diffusion is observed, where the mean square displacement (MSD) exhibits non-linear scaling with time. Among the models used to describe this phenomenon, fractional Brownian motion (FBM) is a widely applied stochastic process, particularly for systems exhibiting long-range temporal correlations. Although FBM is characterized by Gaussian increments, heterogeneous processes with FBM-like characteristics may deviate from Gaussianity. In this article, we study the non-Gaussian behavior of switching fractional Brownian motion (SFBM), a model in which the diffusivity of the FBM process varies while temporal correlations are maintained. To characterize non-Gaussianity, we evaluate the kurtosis, a common tool used to quantify deviations from the normal distribution. We derive exact expressions for the kurtosis of the considered heterogeneous anomalous diffusion process and investigate how it can identify non-Gaussian behavior. We also compare the kurtosis results with those obtained using the Hellinger distance, a classical measure of divergence between probability density functions. Through both analytical and numerical methods, we demonstrate the potential of kurtosis as a metric for detecting non-Gaussianity in heterogeneous anomalous diffusion processes. © 2025, CC BY.

关键词： Brownian movement

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ARGUABLY @ AI Debater-NLPCC 2021 Task 3: Argument Pair Extraction from Peer Review and Rebuttals 10th

ARGUABLY @ AI Debater-NLPCC 2021 Task 3: Argument Pair Extra...

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10th CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2021

作者： Kohli, Guneet Singh Kaur, Prabsimran Singh, Muskaan Ghosal, Tirthankar Rana, Prashant Singh Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India Institute of Formal and Applied Linguistics Faculty of Mathematics and Physics Charles University Prague Czech Republic

ISBN: (纸本)9783030884826

This paper describes our participating system run to the argumentative text understanding shared task for AI Debater at NLPCC 2021 (http://***/sharedtask/AIDebater21/*** ). The tasks are motivated towards developing an autonomous debating system. We make an initial attempt with Track-3, namely, argument pair extraction from peer review and rebuttal where we extract arguments from peer reviews and their corresponding rebuttals from author responses. Compared to the multi-task baseline by the organizers, we introduce two significant changes: (i) we use ERNIE 2.0 token embedding, which can better capture lexical, syntactic, and semantic aspects of information in the training data, (ii) we perform double attention learning to capture long-term dependencies. Our proposed model achieves the state-of-the-art results with a relative improvement of 8.81% in terms of F1 score over the baseline model. We make our code available publicly at https://***/guneetsk99/ArgumentMining_SharedTask. Our team ARGUABLY is one of the third prize-winning teams in Track 3 of the shared task. © 2021, Springer Nature Switzerland AG.

关键词： Extraction

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CFD-DRIVEN TOPOLOGY OPTIMIZATION FOR PERSONALIZED INTRACRANIAL ANEURYSM IMPLANT DESIGN

CFD-DRIVEN TOPOLOGY OPTIMIZATION FOR PERSONALIZED INTRACRANI...

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ASME 2024 International Design engineering Technical Conferences and computers and Information in engineering Conference, IDETC-CIE 2024

作者： Lei, Weijun Sadasivan, Chander Gu, Xianfeng David Chen, Shikui Department of Mechanical Engineering State University of New York at Stony Brook Stony BrookNY11794 United States Department of Neurological Surgery Stony Brook University Medical Center Stony BrookNY11794 United States Department of Computer Science Department of Applied Mathematics and Statistics State University of New York at Stony Brook Stony BrookNY11794 United States

ISBN: (纸本)9780791888377

Intracranial aneurysm rupture causes life-threatening subarachnoid hemorrhage. Current endovascular devices like coils, flow diverters, and intravascular implants aim to thrombose the aneurysm but have limitations and varying success rates depending on aneurysm characteristics. We propose a new computational framework integrating CFD and topology optimization to design personalized aneurysm implants. The optimization problem aims to reduce blood flow velocity within the aneurysm while ensuring adequate structural integrity of the implant. The fluid dynamics are governed by the Navier-Stokes equations, while the solid mechanics are described by the linear elasticity equations. A Darcy-Brinkman model is employed to simulate flow through the porous implant in the fluid domain, while the Solid Isotropic Material with Penalization (SIMP) method is used to interpolate between solid and void regions in the structural domain during topology optimization. The objective combines fluid energy dissipation ratio and solid strain energy with spatially varying weights. Global and local volume constraints generate personalized implants with porosity and flow-diverting architectures. The approach is demonstrated on patient-specific aneurysm geometries from rotational angiography. This CFD-driven topology optimization method enables personalized aneurysm implant design to potentially improve occlusion rates and reduce complications compared to current devices. Further studies will validate the optimized designs and investigate their efficacy in vitro and in vivo. Copyright © 2024 by ASME.

关键词： Navier Stokes equations

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Enhancing Transfer Learning for Medical Image Classification with SMOTE: A Comparative Study

arXiv

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arXiv 2024年

作者： Alam, Md. Zehan Roy, Tonmoy Kawsar, H.M. Nahid Rimi, Iffat Department of Mathematics University of Dhaka Dhaka Bangladesh Department of Data Analytics and Information Systems Utah State University LoganUT United States Department of Applied Mathematics Noakhali Science and Technology University Noakhali Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh

This paper explores and enhances the application of Transfer Learning (TL) for multilabel image classification in medical imaging, focusing on brain tumor class and diabetic retinopathy stage detection. The effectiveness of TL—using pre-trained models on the ImageNet dataset—varies due to domain-specific challenges. We evaluate five pre-trained models—MobileNet, Xception, InceptionV3, ResNet50, and DenseNet201—on two datasets: Brain Tumor MRI and APTOS 2019. Our results show that TL models excel in brain tumor classification, achieving near-optimal metrics. However, performance in diabetic retinopathy detection is hindered by class imbalance. To mitigate this, we integrate the Synthetic Minority Over-sampling Technique (SMOTE) with TL and traditional machine learning(ML) methods, which improves accuracy by 1.97%, recall (sensitivity) by 5.43%, and specificity by 0.72%. These findings underscore the need for combining TL with resampling techniques and ML methods to address data imbalance and enhance classification performance, offering a pathway to more accurate and reliable medical image analysis and improved patient outcomes with minimal extra computation powers. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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