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A whole-body multi-scale mathematical model for dynamic simulation of the metabolism in man

IFAC-PapersOnLine 2022年第23期55卷 58-63页

作者： Peter Emil Carstensen Jacob Bendsen Asbjørn Thode Reenberg Tobias K.S. Ritschel John Bagterp Jørgensen Department of Applied Mathematics and Computer Science Technical University of Denmark DK-2800 Kgs. Lyngby Denmark

We propose a whole-body model of the metabolism in man as well as a generalized approach for modeling metabolic networks. Using this approach, we are able to write a large metabolic network in a systematic and compact way. We demonstrate the approach using a whole-body model of the metabolism of the three macronutrients, carbohydrates, proteins and lipids. The model contains 7 organs, 16 metabolites and 31 enzymatic reactions. All reaction rates are described by Michaelis-Menten kinetics with an addition of a hormonal regulator based on the two hormones insulin and glucagon. We incorporate ingestion of food in order to simulate metabolite concentrations during the feed-fast cycle. The model can simulate several days due to the inclusion of storage forms (glycogen, muscle protein and lipid droplets), that can be depleted if food is not ingested regularly. A physiological model incorporating complex cellular metabolism and whole-body mass dynamics can be used in virtual clinical trials. Such trials can be used to improve the development of medicine, treatment strategies such as control algorithms, and increase the likelihood of a successful clinical trial.

关键词： Mathematical modeling metabolism systems biology cyber-medical systems multi-scale modeling quantitative systems pharmacology

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Pain Classification Using Statistical Feature Extraction Using Machine Learning Approach: A Pilot Study

Pain Classification Using Statistical Feature Extraction Usi...

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IEEE International Conference on Control System, Computing and Engineering (ICCSCE)

作者： Sudimanto Agung Trisetyarso Iman Herwidiana Kartowisastro Widodo Budiharto Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Computer Engineering Department Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jl. K. H. Syahdan No. 9 Kemanggisan Palmerah Jakarta Indonesia

Electrodermal activity (EDA) is a general term for all electrical phenomena occurring on the skin, both passive and active. EDA measurements are used by researchers to measure levels of stress, emotion, mental strain, and so on. Measuring human stress levels, emotions, and mental strain are generally associated with the skin conductance response. The function GSR sensor is not only used to read people’s psychology but also can be used as a pain sensor used to read the degree of pain in the skin. This pilot study uses sample data from ***. The *** data is galvanic skin response sensor data. The output of this sensor is the conductivity value that occurs in the skin. The data obtained from *** will be extracted using the mean, standard deviation, maximum, minimum, RMS, skewness, and peak-to-peak characteristics. The extracted functions are selected using the forward selection method. The results of the feature selection are three features with an accuracy percentage greater than 50%, namely the mean feature, the RMS feature, and the skewness feature. The machine learning models used are bagged tree, SVM, and K-NN models. Of the three models used, the bagged tree model has the highest accuracy rate, at 98.05%, with an F1 score is 0.9807. The KNN model with k=10 has the lowest level of accuracy compared to other models, at 96.75%.

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Physics-informed machine learning with smoothed particle hydrodynamics: Hierarchy of reduced Lagrangian models of turbulence

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Physical Review Fluids 2023年第5期8卷 054602-054602页

作者： Michael Woodward Yifeng Tian Criston Hyett Chris Fryer Mikhail Stepanov Daniel Livescu Michael Chertkov Graduate Interdisciplinary Program in Applied Mathematics University of Arizona Tucson Arizona 85721 USA Department of Mathematics University of Arizona Tucson Arizona 85721 USA Computer Computational and Statistical Sciences Division LANL Los Alamos New Mexico 87544 USA

Building efficient, accurate, and generalizable reduced-order models of developed turbulence remains a major challenge. This manuscript approaches this problem by developing a hierarchy of parameterized reduced Lagrangian models for turbulent flows, and it investigates the effects of enforcing physical structure through smoothed particle hydrodynamics (SPH) versus relying on neural networks (NNs) as universal function approximators. Starting from NN parametrizations of a Lagrangian acceleration operator, this hierarchy of models gradually incorporates a weakly compressible and parameterized SPH framework, which enforces physical symmetries, such as Galilean, rotational, and translational invariances. Within this hierarchy, two new parameterized smoothing kernels are developed to increase the flexibility of the learn-able SPH simulators. For each model we experiment with different loss functions which are minimized using gradient based optimization, where efficient computations of gradients are obtained by using automatic differentiation and sensitivity analysis. Each model within the hierarchy is trained on two data sets associated with weakly compressible homogeneous isotropic turbulence: (1) a validation set using weakly compressible SPH; and (2) a high-fidelity set from direct numerical simulations. Numerical evidence shows that encoding more SPH structure improves generalizability to different turbulent Mach numbers and time shifts, and that including the novel parameterized smoothing kernels improves the accuracy of SPH at the resolved scales.

关键词： Sensitivity analysis

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Binarization and Classification of RGB Images

Binarization and Classification of RGB Images

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International Symposium on Multiple-Valued Logic

作者： Tagir Nukenov Kamila Abdiyeva Oliver Keszocze Shinobu Nagayama Martin Lukac Graduate School of Information Sciences Hiroshima City University Hiroshima Japan Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign IL USA Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark

ISBN: (数字)9798331507442

ISBN: (纸本)9798331507459

Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in various real-world information processing tasks, such as object classification. However, CNNs tend to be computationally and power intensive, making them less suitable for implementation on wearable and embedded systems. One potential solution is using binary models, which have been proven to significantly reduce memory and computational requirements. Nevertheless, this reduction in resources often leads to a decrease in accuracy and raises questions about the most effective way to handle non-binary inputs such as color images. While a direct mapping from binarization to accuracy loss is almost impossible to predict analytically, we empirically study the problem of input binarization and its impact on the classification task. In particular, we explore how multiple-valued binarization, as opposed to single value binarization, will affect the neural model classification ability. We conducted a study in which we explored three different color channel feature fusion methods and found that extracting features from each color channel separately and then fusing those features at later stages is the most effective approach for preserving classification accuracy in the binary network with multiple-valued thresholding inputs. We conducted experiments on two color image datasets in classification tasks with increasing difficulty. The results demonstrate that while the accuracy for the GTSRB datasets remain comparable to its full-precision counterpart, the binarization process noticeably affects the representational power and accuracy of CNNs when applied to the more complex in textures CIFAR-10 dataset.

关键词： Accuracy Filters Image color analysis Memory management Color Information processing Feature extraction Logic Wearable devices Mutual information

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Clinically adaptable machine learning model to identify early appreciable features of diabetes

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Intelligent Medicine 2024年第1期4卷 22-32页

作者： Nurjahan Nipa Mahmudul Hasan Riyad Shahriare Satu Walliullah Koushik Chandra Howlader Mohammad Ali Moni Department of Information and Communication Technology Bangabandhu Sheikh Mujibur Rahman Digital UniversityBangladeshKaliakairGazipur1750 Bangladesh Department of Applied Mathematics Noakhali Science and Technology UniversityNoakhali3814Bangladesh Department of Management Information Systems Noakhali Science and Technology UniversityNoakhali3814Bangladesh Department of Computer Science and Telecommunication Engineering Noakhali Science and Technology UniversityNoakhali3814Bangladesh Department of Computer Science North Dakota State UniversityFargo58105NDUnited States School of Health and Rehabilitation Sciences Faculty of Health and Behavioural SciencesThe University of QueenslandSt LuciaQLD 4072Australia

Objective Diabetes mellitus is a serious disease where the body of affected patients are failed to produce enough insulin that causes an abnormality of blood *** disease happens for a number of reasons including modern lifestyle,lethargic attitude,unhealthy food consumption,family history,age,overweight,*** aim of this study was to propose a machine learning based prediction model that detected diabetes at the *** In this work,we collected 520 patients records from the University of California,Irvine(UCI)machine learning repository of Sylhet Diabetes Hospital,***,a similar questionnaire of that hospital was followed and assembled 558 patients records from all over Bangladesh through this ***,we accumulated patient records of these two *** the next step,these datasets were cleaned and applied thirty five state-of-arts classifiers such as logistic regression(LR),K nearest neighbors(KNN),support vector classifier(SVC),Nave Byes(NB),decision tree(DT),random forest(RF),stochastic gradient descent(SGD),Perceptron,AdaBoost,XGBoost,passive aggressive classifier(PAC),ridge classifier(RC),Nu-support vector classifier(NuSVC),linear support vector classifier(LSVC),calibrated classifier CV(CCCV),nearest centroid(NC),Gaussian process classifier(GPC),multinomial NB(MNB),complement NB,Bernoulli NB(BNB),categorical NB,Bagging,extra tree(ET),gradiant boosting classifier(GBC),Hist gradiant boosting classifier(HGBC),one vs rest classifier(OVsRC),multi-layer perceptron(MLP),label propagation(LP),label spreading(LS),stacking,ridge classifier CV(RCCV),logistic regression CV(LRCV),linear discriminant analysis(LDA),quadratic discriminant analysis(QDA),and light gradient boosting machine(LGBM)to explore best stable predictive *** performance of the classifiers has been measured using five metrics such as accuracy,precision,recall,F1-score,and area under the receiver operating ***,these outcomes were interpret

关键词： Diabetes Early features Machine learning Classification

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An Efficient Three-Factor Authenticated Key Agreement Technique Using FCM Under HC-IoT Architectures

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computers, Materials & Continua 2022年第7期72卷 1373-1389页

作者： Chandrashekhar Meshram Agbotiname Lucky Imoize Sajjad Shaukat Jamal Parkash Tambare Adel R.Alharbi Iqtadar Hussain Department of Post Graduate Studies and Research in Mathematics Jayawanti Haksar Government Post-Graduation CollegeCollege of Chhindwara UniversityBetul460001M.P.India Department of Electrical and Electronics Engineering Faculty of EngineeringUniversity of LagosAkokaLagos100213Nigeria Department of Electrical Engineering and Information Technology Institute of Digital CommunicationRuhr University44801BochumGermany Department of Mathematics College of ScienceKing Khalid UniversityAbhaSaudi Arabia Water Resources&Applied Mathematics Research Lab Nagpur440027India College of Computing and Information Technology University of TabukTabuk71491Saudi Arabia Mathematics Program Department of MathematicsStatistics and PhysicsCollege of Arts and SciencesQatar University2713DohaQatar

The Human-Centered Internet of Things(HC-IoT)is fast becoming a hotbed of security and privacy *** users can establish a common session key through a trusted server over an open communication channel using a three-party authenticated key *** of the early authenticated key agreement systems relied on pairing,hashing,or modular exponentiation processes that are computationally intensive and *** order to address this problem,this paper offers a new three-party authenticated key agreement technique based on fractional chaotic *** new scheme uses fractional chaotic maps and supports the dynamic sensing of HC-IoT devices in the network architecture without a password *** projected security scheme utilized a hash function,which works well for the resource-limited HC-IoT *** results show that our new technique is resistant to password guessing attacks since it does not use a ***,our approach provides users with comprehensive privacy protection,ensuring that a user forgery attack causes no ***,our new technique offers better security features than the techniques currently available in the literature.

关键词： Three-party authenticated key agreement anonymity fractional chaotic maps Chebyshev polynomial password table human-centered internet of things(HC-IoT)

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AN END DEGREE FOR DIGRAPHS

arXiv

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arXiv 2024年

作者： Hamann, Matthias Heuer, Karl University of Hamburg Department of Mathematics Bundesstr. 55 Hamburg20146 Germany Technical University of Denmark Department of Applied Mathematics and Computer Science Richard Petersens Plads Building 322 Kongens Lyngby2800 Denmark

In this paper we define a degree for ends of infinite digraphs. The welldefinedness of our definition in particular resolves a problem by Zuther. Furthermore, we extend our notion of end degree to also respect, among others, the vertices dominating the end, which we denote as combined end degree. Our main result is a characterisation of the combined end degree in terms of certain sequences of vertices, which we call end-exhausting sequences. This establishes a similar, although more complex relationship as known for the combined end degree and end-defining sequences in undirected *** Codes 05C63, 05C20 © 2024, CC BY.

关键词： Undirected graphs

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Identifying Critical Tokens for Accurate Predictions in Transformer-based Medical Imaging Models

arXiv

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arXiv 2025年

作者： Kang, Solha Vankerschaver, Joris Ozbulak, Utku Center for Biosystems and Biotech Data Science Ghent University Global Campus Korea Republic of Department of Applied Mathematics Computer Science and Statistics Ghent University Belgium Department of Electronics and Information Systems Ghent University Belgium

With the advancements in self-supervised learning (SSL), transformer-based computer vision models have recently demonstrated superior results compared to convolutional neural networks (CNNs) and are poised to dominate the field of artificial intelligence (AI)-based medical imaging in the upcoming years. Nevertheless, similar to CNNs, unveiling the decision-making process of transformer-based models remains a challenge. In this work, we take a step towards demystifying the decision-making process of transformer-based medical imaging models and propose "Token Insight", a novel method that identifies the critical tokens that contribute to the prediction made by the model. Our method relies on the principled approach of token discarding native to transformer-based models, requires no additional module, and can be applied to any transformer model. Using the proposed approach, we quantify the importance of each token based on its contribution to the prediction and enable a more nuanced understanding of the model’s decisions. Our experimental results which are showcased on the problem of colonic polyp identification using both supervised and self-supervised pretrained vision transformers indicate that Token Insight contributes to a more transparent and interpretable transformer-based medical imaging model, fostering trust and facilitating broader adoption in clinical settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Self-supervised learning

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Multirate Time-Integration Based on Dynamic Ode Partitioning Through Adaptively Refined Meshes for Compressible Fluid Dynamics

SSRN

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SSRN 2024年

作者： Doehring, Daniel Schlottke-Lakemper, Michael Gassner, Gregor J. Torrilhon, Manuel Applied and Computational Mathematics RWTH Aachen University Schinkelstrasse 2 North Rhine-Westphalia Aachen52062 Germany University of Stuttgart Germany Department of Mathematics and Computer Science Center for Data and Simulation Science University of Cologne Germany

In this paper, we apply the Paired-Explicit Runge-Kutta (P-ERK) schemes by Vermeire et. al. (2019, 2022) to dynamically partitioned systems arising from adaptive mesh refinement. The P-ERK schemes enable multirate time-integration with no changes in the spatial discretization methodology, making them readily implementable in existing codes that employ a method-of- lines *** show that speedup compared to a range of state of the art Runge-Kutta methods can be realized, despite additional overhead due to the dynamic re-assignment of flagging variables and restricting nonlinear stability properties. The effectiveness of the approach is demonstrated for a range of simulation setups for viscous and inviscid convection-dominated compressible flows for which we provide a reproducibility *** addition, we perform a thorough investigation of the nonlinear stability properties of the Paired-Explicit Runge-Kutta schemes regarding limitations due to the violation of monotonicity properties of the underlying spatial discretization. Furthermore, we present a novel approach for estimating the relevant eigenvalues of large Jacobians required for the optimization of stability polynomials . © 2024, The Authors. All rights reserved.

关键词： Runge Kutta methods

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Identifying Student Learning Styles using Support Vector Machine in The Felder-Silverman Learning Style Model

Identifying Student Learning Styles using Support Vector Mac...

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Advanced applied Informatics Winter (IIAI-AAI-Winter), International Congress on

作者： Jeffri Prayitno Bangkit Saputra Harjanto Prabowo Ford Lumban Gaol Gatot Fatwanto Hertono Doctor Of Computer Science Program Bina Nusantara University Jakarta Indonesia Department of Mathematics Indonesia University Jakarta Indonesia

This research explores the intricate relationship between questionnaire structures and the accuracy of learning style predictions among students. Focusing on the balance between core and secondary questions, the study meticulously analyzes various ratios, emphasizing their impact on the learning style classification system. Through rigorous testing and integration of the Felder-Silverman Learning Style Model (FSLSM) with the Support Vector Machine (SVM) algorithm, the research uncovers crucial insights. Results showcase that a careful balance and distribution of questions significantly enhance the accuracy of predicting student learning styles, with accuracy rates reaching up to 89%. This study not only emphasizes the importance of personalized education but also demonstrates the practical applicability of advanced machine learning techniques. The findings provide a roadmap for the future of education, emphasizing a nuanced, question-specific approach for a more accurate, tailored, and responsive learning experience.

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