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Development the Numerical Method to Solve the Inverse Initial Value Problem for the Thermal Conductivity Equation of Composite Materials

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Journal of Physics: Conference Series 2021年第3期1879卷

作者： H K Al-Mahdawi Department of System Programming School of Electronic Engineering and Computer Science South Ural State University Lenina 76 Chelyabinsk 454080 Russia

In this paper, the heat conduction equation for composite materials posed and solved. This problem is known as an inverse initial value problem for the heat conduction equation. In order to solve and formulate this inverse problem, the function spaces must be defined and represented. By studying and solving the direct problem for the heat equation in composite materials, it is possible to determine the function spaces and solve the inverse initial value problem. Scientific methods used: the separation of variables method used to solve the direct problem for the heat equation. It found that method separation of variables does not completely lead to the solution of the inverse initial value problem, since this method leads to a divergent series of solutions and rather massive errors. The heat conduction problem can be formulated as Fredholm integral first kind equations. The discretization algorithm applied to reformulated the problem as linear operator problem as matrix and vectors form. Then, Tikhonov's regularization inversion method has been used to find an approximation solution. Finally, as shown in the numerical example the regularized approximate solutions obtained.

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Hyper-parameter tuning for the long short-term memory algorithm

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AIP Conference Proceedings 2023年第1期2977卷

作者： Tatiana Makarovskikh Mostafa Abotaleb Zainalabideen Albadran Ali J. Ramadhan System Programming Department South Ural State University Chelyabinsk Russia Department of Computer Techniques Engineering College of Technical Engineering University of Alkafeel Najaf Iraq

This research focuses on hyperparameter optimization for LSTM to forecast SARS-CoV-2 infection cases in the Russian Federation, aiming to determine the best combination of parameters for a well-fitting model. Using LSTM’s capability to analyze relationships within time series data, a bidirectional LSTM-based method is introduced for predicting daily infection cases. The study evaluates nearly 10 unique forecasting models and conducts a comprehensive analysis and comparison of their results. The Bidirectional LSTM model proves to be a reliable approach for forecasting daily SARS-CoV-2 infection cases in Russia, displaying the highest prediction accuracy among the tested models.

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The Relationship between Diagnostic Coverage and Proof Test Interval for 1oo2 and 1oo2D Architectures

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IFAC Proceedings Volumes 2012年第24期45卷 181-184页

作者： İlker üstoğlu özgür T. Kaymakçı Josef Börcsök Yıldız Technical University Davutpaşa Campus A209 34220 34220 Esenler İstanbul TURKEY (Tel: +90-535-418-22-64 ) Yıldız Technical University Davutpaşa Campus A210 34220 34220 Esenler İstanbul TURKEY University Kassel Faculty of Electrical Engineering/Computer Science Department of Computer Architecture and System Programming Wilhelmshöher Allee 71 34121 Kassel GERMANY

In order to measure the effectiveness of safety protection systems there are several design parameters. Diagnostic coverage factor is one of the most important parameter which influences all architectures. In this short paper the relationship between PFD avg , DC and T I are presented. 1oo2 and 1oo2D architectures are considered as examples.

关键词： Functional safety 1002 architecture

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Quality of life during the Pandemic: A cross sectional study about Attitude, Individual perspective and behavior change affecting general population in daily life 6

Quality of life during the Pandemic: A cross sectional study...

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6th Smart Cities Symposium, SCS 2022

作者： Khatoon, Fahmida Kumar, Manish Khalid, Ayesha Akbar Alshammari, Amal Daher Khan, Farida Alshammari, Rashid D. Balouch, Zahid Verma, Deepmala Mishra, Pradeep Abotaleb, Mostafa Makarovskikh, Tatiana El-Kenawy, El-Sayed M. Dutta, P.K. Marques, Joao Alexandre Biochemistry Department College of Medicine University of Hail Saudi Arabia Department of Zoology Narain College Shikohabad India Queen Elizabeth the Queen Mother Hospital East Kent NHS Foundation Trust Margate United Kingdom Dept. of Public Health Community Medicine in College of Medicine Princess Nourah Bint Abdul Rahman University Saudi Arabia Sulaiman Al-Rajhi University Saudi Arabia Department of Community Medicine Community Medicine Hail University Saudi Arabia United Medical College Jinnah University Pakistan Department of Zoology SS Jain Subodh Autonomous College Raj Jaipur India J.N.K.V.V. College of Agriculture M.P. Powarkheda India Department of System Programming South Ural State University Chelyabinsk454080 Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt School of Engineering and Technology Amity University West Bengal Kolkata India Laboratory of Applied Neurosciences - LAN/USJ University of Saint Joseph China

ISBN: (纸本)9781839538544

Quality of life in general population before and during pandemic is topic need to be address by researcher in terms of mobility, self-care, usual activities, pain/discomfort and anxiety/depression. The study was carried out among Saudi population. Data were collected from general population using questionnaire during the period from 22 August 2021 to 10th January 2022. As a result, total 214 participants have included in this study. Among them prevalent age group include 40 years (n= 63, 29.4%) shadowed by the age group 25-35 (n= 61, 28.5%) while above 60 years group were least frequent (n= 1, 0.5%). On questioning the applicants whether they were satisfied with their health and how would they rate their quality of life, their answers were as follows: yes, or satisfied (n= 86, 40.2%), very Satisfied (n= 102, 47.7%) Dissatisfied (n= 11, 5.1%) and neither satisfied nor dissatisfied (n= 15, 7%). Due to pandemic, they were rate quality of life very good (n= 94, 43.9%), good (n= 63, 29.4 %) poor (n= 5, 2.3 %) and neither good and nor poor (n= 52, 24.3 %). During pandemic 96 participants feel no change in their weight but 110 participants respond that there is increase in coffee intake during the pandemic. Similarly increased in smoking habits and decrease rate in social activities (n=119,41.4%). The psychosomatic well-being of people has been interrupted by disturbing their social activities during pandemic. © 2022 IET Conference Proceedings. All rights reserved.

关键词： SARS

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Develop an unsupervised attention-based LSTM network algorithm for forecasting infectious disease

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AIP Conference Proceedings 2023年第1期2977卷

作者： Mostafa Abotaleb Tatiana Makarovskikh Zainalabideen Albadran Ali J. Ramadhan System Programming Department South Ural State University Chelyabinsk Russia Department of Computer Techniques Engineering College of Technical Engineering University of Alkafeel Najaf Iraq

time series Due to better algorithms, more accessible data, and higher computing power over the past ten years, forecasting has become more popular. It is used in a variety of industries, including as financial time series, weather forecasting, and medical diagnostics. In this study, we provide a model of the mechanism governing attention, which enables attended input to be provided to the model in place of actual input. In order for the model to produce more precise predictions, it seeks to demonstrate a fresh perspective on the data. The experiments were conducted with the (encoder-decoder) LSTM model as well to demonstrate the usefulness and superiority of the suggested strategy. The obtained results demonstrate that, when compared to the (encoder-decoder) LSTM base model, the proposed approach could reduce the mean square error (RMSE=9819.05), relative root mean square error (RRMSE=99.09), and coefficient of determination (R Square=0.96). The obtained results support the suggested approach’s efficacy, superiority, and importance in predicting SARS-CoV-2 infection cases.

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AbotalebNet: A Novel Deep Learning Architecture for Time Series Forecasting with Convolutional and LSTM Layers Enhanced by Multi-Head Attention

AbotalebNet: A Novel Deep Learning Architecture for Time Ser...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： Mostafa Abotaleb Tatiana Makarovskikh Maad M. Mijwil Indu Bala Kamal Kant Hiran Ruchi Doshi Dep. of System Programming South Ural State University Chelyabinsk Russia Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Electrical and Electronics Engineering Lovely Professional University Punjab India Dep. of Computer Science Sir Padampat Singhania University Udaipur India Dep. of Computer Science and Engineering Universidad Azteca Chalco Mexico

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

This paper introduces AbotalebNet, a novel deep learning architecture optimized for time series forecasting, with a particular focus on the complexities of COVID-19 data. AbotalebNet’s architecture is mathematically represented as:\begin{equation*}\hat Y = {\text{Attention}}(\operatorname{LSTM} ({\text{CNN}}(X)))\tag{1}\end{equation*}Here, X represents the input time series data, and Ŷ is the forecasted output. AbotalebNet integrates the feature extraction capabilities of Convolutional Neural Networks (CNNs) with the sequential data processing power of Long Short-Term Memory (LSTM) networks, further enhanced by a Multi-Head Attention *** address overfitting, a key challenge in deep learning, the model incorporates regularization strategies, dropout mechanisms, and batch normalization:\begin{equation*}\mathop {\min }\limits_\Theta ( Loss (X,\hat Y;\Theta ) + \lambda R(\Theta ))\tag{2}\end{equation*}In this equation, Θ denotes the model parameters, R(Θ) represents regularization terms, and λ is the regularization coefficient. These additions aid in preventing the model from overfitting to the training data, ensuring robust performance on unseen *** evaluation on COVID-19 time series data demonstrates AbotalebNet’s enhanced predictive accuracy over traditional models, solidifying its potential for advanced non-linear time series analysis.

关键词： COVID-19 Analytical models Solid modeling Time series analysis Training data Predictive models Data models

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Location–allocation problem for green efficient two-stage vehicle-based logistics system: A type-2 neutrosophic multi-objective modeling approach

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Expert systems with Applications 2024年 238卷

作者： Das, Soumen Kumar Yu, Vincent F. Roy, Sankar Kumar Weber, Gerhard Wilhelm Department of Pure and Applied Sciences Midnapore City College Kuturia West Bengal Bhadutala721129 India Department of Applied Mathematics with Oceanology and Computer Programming Vidyasagar University West Bengal Midnapore721102 India Department of Industrial Management National Taiwan University of Science and Technology Taipei Taiwan Center for Cyber-Physical System Innovation National Taiwan University of Science and Technology Taipei Taiwan Faculty of Engineering Management Poznan University of Technology Poznan Poland IAM METU Ankara Turkey

In recent decades, global climate change has become one of the most critical environmental issues, leading to increased environmental and social concerns about the sustainability of logistics networks. This study proposes a novel multi-objective, multi-facility location–allocation model for a two-stage solid logistics network that supports green practices. To account for uncertainty in real-world problems, some parameters of the model are represented as triangular type-2 neutrosophic numbers to deal with hesitant situations. The fuzzy model is then converted into a deterministic one using a new ranking approach. Three conflicting objective functions – minimizing total financial costs, maximizing customers’ satisfaction levels, and ensuring sustainable and effective conveyances – are simultaneously optimized using the ϵ-constraint approach. The proposed model and solution approach are tested with two numerical examples. A comparative study is also presented, comparing the proposed approach with other Pareto-based multi-objective approaches, such as the global criterion approach, multi-objective fuzzy programming, and weighted Chebychev programming. Finally, the paper concludes with some remarks. © 2023

关键词： Sustainable development

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Using the inverse Cauchy problem of the Laplace equation for wave propagation to implement a numerical regularization homotopy method

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AIP Conference Proceedings 2023年第1期2977卷

作者： H. K. Al-Mahdawi Zainalabideen Albadran Hussein Alkattan Mostafa Abotaleb Khder Alakkari Ali J. Ramadhan Electronic Computer Centre University of Diyala Diyala Iraq Department of Computer Techniques Engineering College of Technical Engineering University of Alkafeel Najaf Iraq Department of System Programming South Ural State University South Ural State Russia Department of Statistics and Programming Faculty of Economics University of Tishreen Latakia Syria

In this paper, we introduce an approach via regularization and Homotopy way for resolving the inverse Cauchy problem of the Laplace of system partial differential equation which appears in the wave propagation for communication networks. We considered the method of Homotopy Perturbation Metheod (HPM) for solving the integral equations of the first kind named Fredholm. In order to formulate the Laplace equation into the first-kind integral equation (Fredholm) the Fourier series used. Then the discretization method used to reduce the integral equation into a linear operator equation for the first kind. It is clear that this kind of problem is callsified as an ill-posed and the direct way to solve it unacceptably. Tikhonov’s regularization method with Homotopy Perturbation algorithm used for obtaing the approximation solution for the Laplace differential equation. Finally, the numerical example is proposed.

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Design and Implementation of a Smart Human Posture Monitoring Device Using a Wearable Vest

Design and Implementation of a Smart Human Posture Monitorin...

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International Conference on Advances in Biomedical Engineering (ICABME)

作者： Roy Abi Zeid Daou Elio Geha Youssef Aoun Ali Hayek Josef Boercsoek Jose Javier Serrano Olmedo Biomedical Engineering department Furn El Chebbak Université La Sagesse Beirut Lebanon Education and Research Center MART Learning Chananir Lebanon Biomedical Technologies Department Faculty of Public Health Lebanese German University Jounieh Lebanon Department Lippstadt 2 Hardware Engineering Marker Allee 76-78 University of Applied Science Hamm-Lippstadt Hamm Department of Computer Architecture and System Programming Faculty of Electrical Engineering /Computer Science University of Kassel Germany Lab of Bioinstrumentation and Nanomedicine Centro de Tecnologia Biomedica Universidad Politecnica de Madrid Spain

Chronic back pain can present a serious health concern, with symptoms that can significantly affect an individual's well-being, mobility, and overall quality of life over an extended period. While chronic back pain may manifest suddenly in some cases, it often develops gradually and persists for weeks, and in untreated cases, it can linger for years. Hence, the utilization of assistive devices such as wearable posture-monitoring vests can offer valuable assistance and guidance to users. This research paper is dedicated to the development of a system for detecting, diagnosing, and correcting poor posture, specifically leaning posture. The vest is designed to provide users with visual, auditory, and tactile cues to help them address this issue, thereby reducing the risk associated with leaning. Additionally, an integrated electrical box has been designed to consolidate all components directly onto the main board in a secure enclosure. This box also displays the daily count of instances where the user has leaned. This system is characterized by its electrical safety, portability, compactness, comfort, and affordability. A comprehensive analysis of the system's performance has been conducted with a meticulous evaluation of accuracy. Each component of the system has undergone successful testing, and the system as a whole is currently in the testing phase. The results of these tests have indicated a lack of faulty errors and have demonstrated outstanding accuracy and detection rates. Over 100 individuals of varying ages, genders, and BMI categories were involved in testing, with each person wearing the device for an average of six hours. The accuracy rate achieved was 98.85%, with an average of 54.35 instances of poor posture detected per participant.

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Data transmission via erasure type channels protected by linear codes

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Journal of Physics: Conference Series 2012年第1期364卷

作者： H D Wacker P Pendli J Börcsök To whom any correspondence should be addressed University Kassel Faculty of Electrical Engineering/Computer Science Department of Computer Architecture and System Programming Wilhelmshöher Allee 71 34121 Kassel

The paper is concerned with data transmission via channels composed of a memoryless binary symmetric channel and the erasure channel of Peter Elias. Channels of this type play an important role in modelling different types of networks especially wireless networks, and have been investigated using, amongst others, the theory of Markov chains. Channel Capacities and network flows have been determined. The authors focus their interest on some aspects of coding theory. They assume the data transmission to be protected by a linear code, a CRC for example, and determine the probability of undetected error of the code. They then consider redundant transmission via two or more channels with bit inversion, and calculate the probability of undetected error. They prove some inequalities that are useful instruments to estimate the rate of transmission errors and to determine safety integrity levels according to the standards. Finally the authors apply their results to Bluetooth channels suffering from different types of noise.

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