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Centroidal-Polygon: Solving First Order Ordinary Differential Equation using Centroidal mean to improve Euler Method

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Journal of Physics: Conference Series 2021年第1期1874卷

作者： Z Nur Shahirah S Nooraida W A Wan Farah Hanan R A Suzanna M A Iliana Section of Instrumentation Technology Faculty of Computer Media and Technology Management Faculty of Engineering TATI University College Teluk Kalong Kemaman Terengganu

Euler method is one of the oldest and simplest numerical techniques to solve mathematical problems. However, the Euler method lacks accuracy compared to other methods such as the Runge-Kutta and the Adam Bashforth. This research proposed a new scheme by enhancing the Euler method using centroidal mean, i.e. combining the Euler method and the mean concept. In this research, the proposed scheme would be used to improve the accuracy of the first order Ordinary Differential Equation (ODE). The enhanced Euler method was used based on the improved Euler method, which is a midpoint method. To analyse the accuracy of the proposed scheme and the existing schemes, SCILAB 6.0 software was used to compare the results of different step sizes. This research compared the proposed scheme with the Polygon scheme by Zul Zamri and the Harmonic-Polygon scheme by Nurhafizah. By using Centroidal-Polygon as the proposed scheme, more accurate results were yielded. However, the scheme was limited to some of the linear and non-linear equations only.

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Numerical treatment based on artificial neural network to Soret and Dufour effects on MHD squeezing flow of Jeffrey fluid in horizontal channel with thermal radiation

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International Journal of Thermofluids 2024年 23卷

作者： Ullah, Hakeem Alqahtani, Aisha M. Raja, Muhammad Asif Zahoor Fiza, Mehreen Ullah, Kashif Omer, Abdoalrahman S.A. Khan, Ilyas Shoaib, Muhmmad Department of Mathematics Abdul Wali Khan University KP Mardan23200 Pakistan Department of Mathematical Science Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section.3Yunlin Douliou64002 Taiwan Department of Mathematics College of Education Al Fashir University Sudan Department of Information System College of Computer and Information Sciences Majmaah University Al-Majmaah 11952 Saudi Arabia Department of Mathematics College of Science Al-Zulfi Majmaah University Al-Majmaah 11952 Saudi Arabia Yuan Ze University AI centre Taoyun320 Taiwan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Department of Mathematics Saveetha School of Engineering SIMATS Tamil Nadu Chennai India Al-Ayen research center Al-Ayen University Iraq

The current investigation formulated an efficient computing technique by incorporating the algorithm of Artificial Neural Network Levenberg Marquardt Method (ANNLMM) to approximate Soret and Dufour effects on Magnetohydrodynamic squeezing flow of Jeffrey fluid (MHDSFJF) in MATLAB environment. Due to its efficiency and flexibility, the applied neural network model can accurately predict the output results. In the ANNLMM, 80% of the data is used for training and 10% is set for both the testing and validation processes. The flow under consideration is mathematically formulated with the help of Navier Stokes Equations. The partial differential equations (PDEs) are converted into ordinary differential equations (ODEs) by similarity variables. The effects of the physical parameters are discussed briefly. When the Hartmann number and Jeffrey fluid parameter increase, the concentration, temperature, and velocity profiles decreases. The temperature profile and heat transfer rate are improved by the effects of joule dissipation, Dufour numbers, and heat generation/absorption. On the other hand, as thermal radiation rises, the temperature profile decreases and the heat transfer rate increases. With an augment in Soret number, the mass transfer rate increases and the concentration profile slows down. Additionally, increasing chemical reactions, the mass transfer rate increases, but in convective chemical reactions, the contradiction is observed. © 2024

关键词： Magnetohydrodynamics

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Corrigendum to “Modeling PROTAC degradation activity with machine learning” [Artificial Intelligence in the Life Sciences 6 (2024) 100104]

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Artificial Intelligence in the Life Sciences 2024年 6卷

作者： Stefano Ribes Eva Nittinger Christian Tyrchan Rocío Mercado Department of Computer Science and Engineering Section for Data Science and AI Chalmers University of Technology Chalmersplatsen 4 Gothenburg 412 96 Sweden Medicinal Chemistry Research and Early Development Respiratory and Immunology (R&I) BioPharmaceuticals R&D AstraZeneca Pepparedsleden 1 Mölndal 431 83 Sweden

PROTACs are a promising therapeutic modality that harnesses the cell’s built-in degradation machinery to degrade specific proteins. Despite their potential, developing new PROTACs is challenging and requires significant domain expertise, time, and cost. Meanwhile, machine learning has transformed drug design and development. In this work, we present a strategy for curating open-source PROTAC data and an open-source deep learning tool for predicting the degradation activity of novel PROTAC molecules. The curated dataset incorporates important information such as p D C 50 , D m a x , E3 ligase type, POI amino acid sequence, and experimental cell type. Our model architecture leverages learned embeddings from pretrained machine learning models, in particular for encoding protein sequences and cell type information. We assessed the quality of the curated data and the generalization ability of our model architecture against new PROTACs and targets via three tailored studies, which we recommend other researchers to use in evaluating their degradation activity models. In each study, three models predict protein degradation in a majority vote setting, reaching a top test accuracy of 80.8% and 0.865 ROC-AUC, and a test accuracy of 62.3% and 0.604 ROC-AUC when generalizing to novel protein targets. Our results are not only comparable to state-of-the-art models for protein degradation prediction, but also part of an open-source implementation which is easily reproducible and less computationally complex than existing approaches.

关键词： PROTAC Machine learning Drug discovery Protein degradation

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Advanced Linear Machines and Drive Systems 1

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2019年

作者： Wei Xu Md. Rabiul Islam Marcello Pucci

ISBN: (数字)9789811396168

ISBN: (纸本)9789811396151;9789811396182

This book collects the latest theoretical and technological concepts in the design and control of various linear machines and drive systems. Discussing advances in the new linear machine topologies, integrated modeling, multi-objective optimization techniques, and high-performance control strategies, it focuses on emerging applications of linear machines in transportation and energy systems.;The book presents both theoretical and practical/experimental results, providing a consistent compilation of fundamental theories, a compendium of current research and development activities as well as new directions to overcome critical limitations.

关键词： Electronic Circuits and Devices Power Electronics, Electrical Machines and Networks Control and Systems Theory Optimization

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AB1556 MACHINE LEARNING APPROACH FOR CLASSIFICATION OF ARTHRITIS ACTIVITY STATE, USING DATA FROM A SINGLE ACCELEROMETER

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Annals of the Rheumatic Diseases 2023年 82卷 2012-2012页

作者： P. Mielnik A. Myhre Hjelle A. Traseth K. Tokarz B. Pollen M. Fojcik Helse Forde Section for Rheumatology； Department for Neurology Rheumatology and Physical Medicine Forde Norway Institute of Informatics Silesian University of Technology Department of Graphics Computer Vision and Digital Systems Gliwice Poland Western Norway University of Applied Sciences Faculty of Engineering and Science Forde Norway

Background Machine learning (ML) potential is not fully exploited in diagnostics and follow up of autoimmune inflammatory rheumatic disease (AIIRD). It is despite the broader use of ML in, e.g. imaging diagnostics. The specific tools for AIIRD are lacking. Objectives This is an interim analysis of data from the first checkpoint of the proof-of-concept study on using accelerometer (ACC) data in follow up of patients with AIIRD. The main goal of the study was to investigate the value of single ACC data in the classification of arthritis activity status. Methods Subjects with AIIRD are enrolled in the study when they start of new treatment due to the disease activity. Several comorbidities, such as severe neurological and cardiological disorders, as wells as impaired mobility, are part of the protocol exclusion criteria. Volunteers without AIIRD who fulfil other inclusion/exclusion criteria are included as controls. The study was approved by local competent authorities and informed consent was given by all participants. We used data collected up till the first study checkpoint. This analysis covers nine patients with AIIRD (5 rheumatoid and four psoriasis arthritis) and 13 controls. Five patients had 3 study visits, 1 had 2 visits and 3 only one. Controls have only one visit per protocol. We analysed ACC data from 3 minutes of clapping using a home-brewed device based on Arduino nano 33 BLE with 6-axis MTU by Nordic Semiconductor. Data was divided into 6-second chunks that were found optimal in our prior study. We conducted binary classification between any/ no arthritis in any of the upper extremities. We used accuracy and area under curve (AUC) as an efficacy function derived from the receiver operating characteristic curve (ROC). We extracted 54 features from 3 ACC axis signal. The features encompassed, among others, Fourier's components, autoregression coefficients, median absolute deviation (MAD), variance, Fourier's entropy, etc. We built linear discriminant anal

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A comparative study of eight human auditory models of monaural processing

arXiv

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arXiv 2021年

作者： Vecchi, Alejandro Osses Varnet, Léo Carney, Laurel H. Dau, Torsten Bruce, Ian C. Verhulst, Sarah Majdak, Piotr Laboratoire des systèmes perceptifs Département d'études cognitives École Normale Supérieure PSL University CNRS Paris France Departments of Biomedical Engineering and Neuroscience University of Rochester RochesterNY United States Hearing Systems Section Department of Health Technology Technical University of Denmark Lyngby Denmark Department of Electrical and Computer Engineering McMaster University HamiltonON Canada Hearing Technology group WAVES Department of Information Technology Ghent University Ghent Belgium Acoustics Research Institute Austrian Academy of Sciences Vienna Austria

A number of auditory models have been developed using diverging approaches, either physiological or perceptual, but they share comparable stages of signal processing, as they are inspired by the same constitutive parts of the auditory system. We compare eight monaural models that are openly accessible in the Auditory Modelling Toolbox. We discuss the considerations required to make the model outputs comparable to each other, as well as the results for the following model processing stages or their equivalents: Outer and middle ear, cochlear filter bank, inner hair cell, auditory nerve synapse, cochlear nucleus, and inferior colliculus. The discussion includes a list of recommendations for future applications of auditory models. © 2021, CC BY-NC-SA.

关键词： Physiological models

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Scientific Workflow Scheduling in Mobile Edge Computing Based on a Discrete Butterfly Optimization Algorithm

Research Square

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Research Square 2021年

作者： Lansky, Jan Mohammadi, Mokhtar Mohammed, Adil Hussein Taher Karim, Sarkhel H. Rashidi, Shima Rahmani, Amir Masoud Hosseinzadeh, Mehdi Department of Computer Science and Mathematics Faculty of Economic Studies University of Finance and Administration Prague Czech Republic Department of Information Technology Lebanese French University Kurdistan Region Erbil Iraq Department of Communication and computer engineering Cihan university-Erbil Kurdistan Region Erbil Iraq Computer Department College of Science University of Halabja Halabja Iraq Sulaimani Polytechnic University Technical College of Informatics Computer Networks Department Sulaymaniyah Iraq Department of Computer Science University of Human Development Sulaymaniyah Iraq Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan Department of computer science Khazar University Baku Azerbaijan Institute of Research and Development Duy Tan University Da Nang550000 Viet Nam Mental Health Research Center Psychosocial Health Research Institute Iran University of Medical Sciences Tehran Iran

Mobile Edge Computing (MEC) is an interesting technology aimed at providing various processing and storage resources at the edge of the Internet of things (IoT) networks. However, MECs contain limited resources, and they should be managed effectively to improve resource utilization. Workflow scheduling is a process that tries to map the workflow tasks to the most proper set of computing resources regarding some objectives. For this purpose, this paper presents DBOA, a discrete version of the Butterfly Optimization Algorithm (BOA) that applies the Levy flight to improve its convergence speed and prevent the local optima problem. Then, DBOA is applied for DVFS-based data-intensive workflow scheduling and data placement in MEC environments. This scheme also employs the HEFT algorithm's task prioritization method to find the task execution order in the scientific workflows. For evaluating the performance of the proposed scheduling scheme, extensive simulations are conducted on various well-known scientific workflows with different sizes. The obtained experimental results indicate that this method can outperform other algorithms regarding energy consumption, data access overheads, etc. © 2021, CC BY.

关键词： Optimization

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Different solitary wave solutions and bilinear form for modified mixed-KDV equation

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Optik 2023年第1期287卷

作者： Zil-E-Huma Butt, Asma Rashid Raza, Nauman Ahmad, Hijaz Ozsahin, Dilber Uzun Tchier, Fairouz Department of Mathematics University of Engineering and Technology Lahore Pakistan Department of Mathematics University of the Punjab Quaid-e-Azam Campus Lahore Pakistan Near East University Operational Research Center in Healthcare TRNC Mersin 10 Nicosia99138 Turkey Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Section of Mathematics International Telematic University Uninettuno Corso Vittorio Emanuele II 39 Roma00186 Italy Department of Medical Diagnostic Imaging College of Health Science University of Sharjah Sharjah United Arab Emirates Research Institute for Medical and Health Sciences University of Sharjah Sharjah United Arab Emirates Department of mathematics College of science King Saud University P.O. Box 2455 Riyadh11451 Saudi Arabia

This study we handled (1+1)-dimensional modified-mixed KDV (mmKdV) equation for unique soliton solutions. For this purpose Firstly, auto-Bäcklund and Cole-Hopf transformations are created through the homogeneous balance method. Various soliton-like solutions are given by the trigonometric, hyperbolic, and exponential function waves and that leads to the extraction of novel periodic, dark, and bright soliton solutions. Moreover, the bilinear form of the model is found by utilizing the logarithmic transformation. Density, 3-D, and 2-D graphics are displayed to illustrate the dynamics of the acquired solutions. It is determined that the approach employed to obtain analytic solutions to nonlinear partial differential equation is efficient and powerful. © 2023 Elsevier GmbH

关键词： Hyperbolic functions

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New multi-wave solutions of the conformable LPD model with nonlinear wave phenomena arise in mathematical physics

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Journal of Ocean engineering and Science 2022年

作者： Tripathy, Ananya Sahoo, Subhadarshan Rezazadeh, Hadi Ahmad, Hijaz Inc, Mustafa Kalinga Institute of Industrial Technology Deemed to be University Odisha Bhubaneswar 751024 India Faculty of Modern Technologies Engineering Amol University of Special Modern Technologies Amol Iran Section of Mathematics International Telematic University Uninettuno Corso Vittorio Emanuele II Roma 3900186 Italy Department of Computer Engineering Biruni University Istanbul Turkey Firat University Science Faculty Department of Mathematics Elazig 23119 Turkey Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan

In this paper, the conformable Lakshmanan–Porsezian–Daniel equation with parabolic law nonlinearity is solved and the newly acquired optical solutions are analysed. To achieve this, two robust analytical techniques are implemented namely, the three-wave method and the interaction phenomena form. The newly obtained solutions are in the form of dark, w-shaped, periodic, peakon, bright and three-soliton solutions and their interaction solution in a kinky base. These characteristics are analysed by the graphical representation which demonstrates the reliability and virtue of the proposed methods. The most fascinating part of the reported solutions is their use in science and engineering. The novel aspect of this study is the obtained solutions, which were not before achieved and demonstrate a good balance between the nonlinear physical components. Furthermore, the solutions can facilitate estimating the performance of wave patterns in fibers when needed in a design. © 2022

关键词： 02.30.Hq 02.30.Jr 42.65.Wi 42.81.Dp Fractional derivative Lakshmanan–Porsezian–Daniel equation Optical fibers Optical solutions Three-wave method

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Classifier performance comparison on American sign language gesture recognition

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AIP Conference Proceedings 2024年第1期3069卷

作者： Asep Nugroho Latif Rozaqi Yukhi Mustaqim Kusuma Sya'ban Sharfiden Hassen Yusuf Rio Prasetyo Lukodono Research Center for Smart Mechatronics National Research and Innovation Agency (BRIN) Kawasan Sains dan Teknologi (KST) Samaun Samadikun Jalan Sangkuriang Bandung 40135 Indonesia School of Electrical and Computer Engineering Dire Dawa Institute of Technology Dire Dawa 1487 Ethiopia School of Industrial Management National Taiwan University of Science and Technology No. 43 Section 4 Keelung Rd Da'an District Taipei City 10607 Taiwan

Non-verbal communication usually uses for human computer interactions. One of non-verbal communication uses sign language such as hand gestures. To understand the command through hand gestures, the machine should recognize every movement so it needs classifier algorithms. Common classifiers algorithms such as k-nearest neighbor, decision tree, and support vector machine are used to recognize American sign language gestures. This study provides comparison performance among three common classifiers. Based on performance assessment, the support vector machine has the best accuracy and precision performance compared to two other algorithms. It can achieve 81.11% accuracy and 83.54% precision. In another hand, the k-nearest neighbor gives 74.44% accuracy and 74.40% precision while decision trees deliver 74.44% accuracy and 75.30%. Overall algorithms that are assessed giving accuracy below 90%. The features extraction that is based only on the time domain is not enough to spot distinction especially for signal that has a quite similar pattern, although it is a different class. In order to achieve more than 90% accuracy deep convolution neural network could be proposed as a future works.

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