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SSRN 2024年

作者： Jeptoo, Mercy Okello, Veronica Achieng Maghanga, Justin Kambale Alunda, Bernard Ouma Department of Mining & Mineral Processing Engineering Mines & Engineering Taita Taveta University Kenya Department of Physical Sciences Machakos University Kenya Department of Mathematics Statistics & Physical Sciences Taita Taveta University Kenya

Various studies carried out in Kenya over the last 40 years, indicated very high levels of fluoride in drinking water, with a prevalence in dental fluorosis observed in children and adults living in Rift valley and central regions due to basaltic and volcanic rocks. Recently, this trend has been observed in regions such as Nairobi and Machakos which were initially presumed to contain low fluoride levels. This study sought to analyze the presence of fluoride ions in drinking water samples collected from twenty-nine (29) groundwater and tap water samples in Machakos and Kajiado Counties. Physicochemical properties such as pH, turbidity, conductivity, total hardness, fluoride (F-), Chloride (Cl-), Sulphate (SO42-), and nitrates (NO3-) were analyzed. The maximum value for fluorides in Kajiado and Machakos Counties were 7.5 mg/L and 4.25 mg/L respectively. These values are very high in comparison to the limits of 1.5 mg/L for fluorides stipulated by World Health Organization (WHO) and Kenya Bureau of Standards (KEBS). The results indicated that the sampled waters were mostly polluted with fluorides, chlorides and nitrates. Based on the findings of this study, continuous monitoring of groundwater resources should be done in order to obtain sufficient data for water quality control and improvement. © 2024, The Authors. All rights reserved.

关键词： Volcanic rocks

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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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Learning a Sparse Neural Network using IHT

Learning a Sparse Neural Network using IHT

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International Joint Conference on Neural Networks (IJCNN)

作者： Saeed Damadi Soroush Zolfaghari Mahdi Rezaie Jinglai Shen Department of Mathematics and Statistics University of Maryland Baltimore County (UMBC) Baltimore USA Department of Computer Engineering Isfahan University of Technology Isfahan Iran Department of Computer Engineering Amirkabir University of Technology Tehran Iran

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The core of a good model is in its ability to focus only on important information that reflects the basic patterns and consistencies, thus pulling out a clear, noise-free signal from the dataset. This necessitates using a simplified model defined by fewer parameters. The importance of theoretical foundations becomes clear in this context, as this paper relies on established results from the domain of advanced sparse optimization, particularly those addressing nonlinear differentiable functions. The need for such theoretical foundations is further highlighted by the trend that as computational power for training NNs increases, so does the complexity of the models in terms of a higher number of parameters. In practical scenarios, these large models are often simplified to more manageable versions with fewer *** why these simplified models with less number of parameters remain effective raises a crucial question. Understanding why these simplified models with fewer parameters remain effective raises an important question. This leads to the broader question of whether there is a theoretical framework that can clearly explain these empirical observations. Recent developments, such as establishing necessary conditions for the convergence of iterative hard thresholding (IHT) to a sparse local minimum—a sparse method analogous to gradient descent, are promising. The IHT algorithm’s remarkable capacity to accurately identify and learn the locations of nonzero parameters underscores its practical effectiveness and *** paper aims to investigate whether the theoretical prerequisites for such convergence are applicable in the realm of neural network (NN) training by providing justification for all the necessary conditions for convergence. Then, these conditions are validated by experiments on a single-layer NN, using the IRIS dataset as a testbed. Our empirical results demonstrate that convergence conditions can be reliably ensured during the traini

关键词： Training Iris Computational modeling Artificial neural networks Reliability theory Market research Iterative algorithms

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Sensory Perception of Haptic Rendering in Surgical Simulation 14

Sensory Perception of Haptic Rendering in Surgical Simulatio...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Kaushik, Rohit Sachdeva, Rachit Vashisht, Chirag Choudhary, Krishan Kant University of Illinois Data Analytics Program Cs and Mathematics Department Springfield United States Dr. Akhilesh das Gupta Institute of Technology and Management Electronics and Communication Engineering Department New Delhi India Dr. Akhilesh das Gupta Institute of Technology and Management Information Technology and Engineering Department New Delhi India

ISBN: (纸本)9798350335095

The essential ideas behind haptic technology, including haptic interface and haptic rendering methods, as well as its application to surgical simulation, medical education, and other diverse topics, are presented in this paper. In empirical psychology and physiology, this refers to the sense of contact. The process of analyzing and developing capabilities in response to user interactions with virtual entities is elaborated as haptic rendering. Haptic may indicate haptic transmission, haptic perception, and haptic technology. Humans and other animals can respond to one another by using their feelings through haptic technology. Haptic perception is the process of recognizing objects through *** implies tactile response, which means it delivers feedback in the state of sensation as we may sense the material properties and motions of virtual objects devised by a computer. The use of haptic technology in the medical profession has proven extremely beneficial, and it has a wide range of applications in many different industries. Providing effective exchange via virtual reality helps to ease the surgeon's obligations. Amplify a person's experience in the virtual environment. The haptics ability ranges from computer graphics to psychophysics, cognitive science, and neurosciences. © 2023 IEEE.

关键词： Virtual reality

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Generated admissible orders for intervals by matrices and continuous functions

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Information Sciences 2024年 659卷

作者： Wu, Xinxing Chen, Shyi-Ming Zhang, Xu School of Mathematics and Statistics Guizhou University of Finance and Economics Guizhou Guiyang550025 China Zhuhai College of Science and Technology Guangdong Zhuhai519041 China Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Mathematics Shandong University Weihai264209 China

In this paper, we systematically study the algebraic structures of the spaces L([0,1]), encompassing all closed subintervals of [0,1], under the generated admissible orders. We first prove that the admissible order on L([0,1]) generated by a non-degenerate matrix must be the form of two weighted averaging operators. As a corollary, we deduce that each admissible order on L([0,1]) generated by a non-degenerate matrix and the standard order ≤ on [0,1] are not isomorphic. Furthermore, we show that each admissible order on L([0,1]) derived from two continuous mappings and the standard order ≤ on [0,1] are not isomorphic, partially answering a conjecture proposed by Santana et al. (2020) [38]. Besides, we prove that L([0,1]) is a complete lattice under the admissible order generated by two continuous mappings. This is the first result regarding the completeness of L([0,1]). Finally, we apply the admissible orders to solve a minimal path problem within the context of interval-valued fuzzy weighted graph. The above results theoretically refine the study of the classification, non-isomorphism, and completeness of admissible orders, while expanding the scope of interval-valued fuzzy sets in practical applications. © 2023 Elsevier Inc.

关键词： Matrix algebra

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An investigation of machine learning algorithms and data augmentation techniques for diabetes diagnosis using class imbalanced BRFSS dataset

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Healthcare Analytics 2024年 5卷 100297-100297页

作者： Chowdhury, Mohammad Mihrab Ayon, Ragib Shahariar Hossain, Md Sakhawat Department of Mathematics and Statistics Texas Tech University Lubbock TX United States Department of Electronics and Telecommunication Engineering Rajshahi University of Engineering and Technology Rajshahi Bangladesh

Diabetes is a prevalent chronic condition that poses significant challenges to early diagnosis and identifying at-risk individuals. Machine learning plays a crucial role in diabetes detection by leveraging its ability to process large volumes of data and identify complex patterns. However, imbalanced data, where the number of diabetic cases is substantially smaller than non-diabetic cases, complicates the identification of individuals with diabetes using machine learning algorithms. This study focuses on predicting whether a person is at risk of diabetes, considering the individual's health and socio-economic conditions while mitigating the challenges posed by imbalanced data. We employ several data augmentation techniques, such as oversampling (Synthetic Minority Over Sampling for Nominal Data, ***-N), undersampling (Edited Nearest Neighbor, i.e. ENN), and hybrid sampling techniques (SMOTE-Tomek and SMOTE-ENN) on training data before applying machine learning algorithms to minimize the impact of imbalanced data. Our study sheds light on the significance of carefully utilizing data augmentation techniques without any data leakage to enhance the effectiveness of machine learning algorithms. Moreover, it offers a complete machine learning structure for healthcare practitioners, from data obtaining to machine learning prediction, enabling them to make informed decisions. © 2023 The Author(s)

关键词： Behavioral Risk Factor Surveillance System (BRFSS) Classification Diabetes Imbalanced data Machine learning Nominal data Sampling

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DIFFUSION-BASED MODELS FOR UNPAIRED SUPER-RESOLTUON IN FLUID DYNAMICS

arXiv

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arXiv 2025年

作者： Xu, Wuzhe Lu, Yulong Shen, Lian Xuan, Anqing Barzegari, Ali Department of Mathematics and Statistics University of Massachusetts Amherst AmherstMA01003 United States School of Mathematics University of Minnesota MinneapolisMN55455 United States Department of Mechanical Engineering Saint Anthony Falls Laboratory University of Minnesota MinneapolisMN55455 United States

High-fidelity, high-resolution numerical simulations are crucial for studying complex multiscale phenomena in fluid dynamics, such as turbulent flows and ocean waves. However, direct numerical simulations with high-resolution solvers are computationally prohibitive. As an alternative, super-resolution techniques enable the enhancement of low-fidelity, low-resolution simulations. However, traditional super-resolution approaches rely on paired low-fidelity, low-resolution and high-fidelity, high-resolution datasets for training, which are often impossible to acquire in complex flow systems. To address this challenge, we propose a novel two-step approach that eliminates the need for paired datasets. First, we perform unpaired domain translation at the low-resolution level using an Enhanced Denoising Diffusion Implicit Bridge. This process transforms low-fidelity, low-resolution inputs into high-fidelity, low-resolution outputs, and we provide a theoretical analysis to highlight the advantages of this enhanced diffusion-based approach. Second, we employ the cascaded Super-Resolution via Repeated Refinement model to upscale the high-fidelity, low-resolution prediction to the high-resolution result. We demonstrate the effectiveness of our approach across three fluid dynamics problems. Moreover, by incorporating a neural operator to learn system dynamics, our method can be extended to improve evolutionary simulations of low-fidelity, low-resolution *** Codes 65C60, 65M22, 65M50, 68T07, 76F55 Copyright © 2025, The Authors. All rights reserved.

关键词： Flow interactions

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Spatial Regression With Multiplicative Errors, and Its Application With Lidar Measurements

arXiv

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arXiv 2023年

作者： You, Hojun Wu, Wei-Ying Lim, Chae Young Yoon, Kyubaek Choi, Jongeun Department of Mathematics University of Houston United States Department of Applied Mathematics National Dong Hwa University Taiwan Department of Statistics Seoul National University Korea Republic of Department of Mechanical Engineering Yonsei University Korea Republic of

Multiplicative errors in addition to spatially referenced observations often arise in geodetic applications, particularly in surface estimation with light detection and ranging (LiDAR) measurements. However, spatial regression involving multiplicative errors remains relatively unexplored in such applications. In this regard, we present a penalized modified least squares estimator to handle the complexities of a multiplicative error structure while identifying significant variables in spatially dependent observations for surface estimation. The proposed estimator can be also applied to classical additive error spatial regression. By establishing asymptotic properties of the proposed estimator under increasing domain asymptotics with stochastic sampling design, we provide a rigorous foundation for its effectiveness. A comprehensive simulation study confirms the superior performance of our proposed estimator in accurately estimating and selecting parameters, outperforming existing approaches. To demonstrate its real-world applicability, we employ our proposed method, along with other alternative techniques, to estimate a rotational landslide surface using LiDAR measurements. The results highlight the efficacy and potential of our approach in tackling complex spatial regression problems involving multiplicative errors. © 2023, CC BY.

关键词： Stochastic systems

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Error Analysis for the Implicit Boundary Integral Method

arXiv

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arXiv 2023年

作者： Zhong, Yimin Ren, Kui Runborg, Olof Tsai, Richard Department of Mathematics and Statistics Auburn University AuburnAL United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY United States Department of Mathematics KTH Royal Institute of Technology Stockholm Sweden Department of Mathematics Oden Institute for Computational Engineering and Sciences The University of Texas at Austin AustinTX United States

The implicit boundary integral method (IBIM) provides a framework to construct quadrature rules on regular lattices for integrals over irregular domain boundaries. This work provides a systematic error analysis for IBIMs on uniform Cartesian grids for boundaries with different degree of regularities. We first show that the quadrature error gains an addition order of d-1/2 from the curvature for a strongly convex smooth boundary due to the "randomness" in the signed distances. This gain is discounted for degenerated convex surfaces. We then extend the error estimate to general boundaries under some special circumstances, including how quadrature error depends on the boundary's local geometry relative to the underlying grid. Bounds on the variance of the quadrature error under random shifts and rotations of the lattices are also *** Codes 65D32, 41A55 © 2023, CC BY.

关键词： Error analysis

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Spatio-Temporal Graph Network-Based Traffic Forecasting Method 4

Spatio-Temporal Graph Network-Based Traffic Forecasting Meth...

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4th International Conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Zhang, Ning Li, Kaimeng Zhang, Xiangyue Wang, Wenjing Li, Chao Kang, Yuyun School of Logistics Linyi University LYU Linyi China School of Information Science and Engineering Linyi University LYU Linyi China School of Mathematics and Statistics Linyi University LYU Linyi China Top Information Technology Co. LTD. TITC Technology R&D Department Jinan China

ISBN: (纸本)9798331531225

Traffic forecasting is widely recognized as a fundamental component of smart city development;however, classic models struggle to manage long-term time series data effectively. In other words, existing models rely on multiple algorithm modules, which significantly increase computational costs and, in turn, reduce model accuracy. Furthermore, any new input dataset requires retraining the model from scratch. To address these challenges, this study proposes a novel approach, where a long-sequence embedding module and an encoder-decoder structure integrating a temporal attention layer and a graph convolution operator are introduced during the pretraining section. In the fine-tuning section, the learned parameters are frozen, and the decoder is replaced with a prediction header that incorporates a meta-learning module. This approach enables the integration of both long-term and short-term time series data while facilitating training tailored to specific target tasks. The method has demonstrated strong performance on real-world datasets, providing clear evidence of its effectiveness in addressing traffic forecasting challenges. © 2024 IEEE.

关键词： Decoding

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