Advances in Systems Science and Applications <p><strong><em>Advances in Systems Science and Applications </em></strong><strong>(<em>ASSA</em>) </strong>is an international peer-reviewed open-source online academic journal. Its&nbsp;scope covers all major aspects of systems (and processes) analysis, modeling, simulation, and control, ranging from theoretical and methodological developments to a large variety of application areas. Survey articles and innovative results are also welcome.</p> <p>ASSA is aimed at the audience of scientists, engineers and researchers working in the framework of these problems. ASSA should be a platform on which researchers will be able to communicate and discuss both their specialized issues and interdisciplinary problems of systems analysis and its applications in science and industry, including data science, artificial intelligence, material science, manufacturing, transportation, power and energy, ecology, corporate management, public&nbsp;governance, finance, and many others.</p> en-US (Natalya Pavlova) (Alexander Kotyukov, Technical Editor) Mon, 15 Jan 2024 11:24:07 +0000 OJS 60 An Exact Solution of the Hunter–Saxton–Calogero Equation by Contact Linearization Method <p>In this paper we consider a class of generalized nonlinear hyperbolic partial differential equations of the Hunter–Saxton–Calogero type, which arise in the theory of control of liquid crystals and in the control of unsteady gas flows. We found such conditions that the original equation can be reduced to linear one by contact transformations. The general exact multivalued solutions of the Hunter–Saxton–Calogero equation are found. The obtained solutions are visualized.</p> Svetlana Mukhina ##submission.copyrightStatement## Sun, 31 Dec 2023 00:00:00 +0000 A State Space Filtering-Based Approach for Price Prediction <p>We present a method of the forecasting and the data filtering of a linear dynamic system based on the dimension reduction of the space of unobservable states. The method relies on the singular value decomposition of the Hankel matrix. The decomposition is used to calculate unknown parameters of the model. The elements of the singular value decomposition are separated into blocks enabling to estimate the initial state and the system matrices and predict the system dynamics and the data filtering by identifying exponential trends and periods of seasonal fluctuations.</p> <p>To illustrate the quality of fitting and the determined periods of an oscillatory system with trends and the white noise, we conducted numerical simulations of such systems. The parameter estimates were obtained with high precision. Then, daily electricity price data from the NordPool system from 2016 to 2020 were used to generate in-sample and out-of-sample forecasts.</p> <p>The advantages of the proposed method include the ability to handle ill-conditioned matrices and to determine the periods of oscillatory systems. This is significant due to the presence of seasonality in many economic indicators. In the analyzed daily electricity price data, the method identified the presence of biweekly and monthly seasonality.</p> Anton Belyakov, Aleksei Kurbatskii, Artur Sidorenko ##submission.copyrightStatement## Sun, 31 Dec 2023 00:00:00 +0000 Optimizing Frequency Stability in Distributed Power Grids through Advanced Power Flow Control with 25MW Photovoltaic Integration <p class="ASSAAbstract">This study addresses the challenges associated with integrating photovoltaic (PV) systems into the Western Algerian power grid, primarily focusing on mitigating frequency fluctuations induced by variable electrical load profiles. These fluctuations can have adverse effects on power quality and grid stability. To address this challenge, the study introduces the benefits of frequency stability analysis and Optimal Power Flow (OPF) control within distributed and adaptable power networks. The primary goal is to enhance the integration of PV systems by ensuring the dynamic stability of the network, optimizing solar energy utilization through the Maximum Power Point Tracking (MPPT) technique, and employing var compensation methods. These strategies are designed to facilitate reliable and economically viable PV integration into the power grid, particularly in the SAIDA-NAAMA region. Through the implementation of OPF control, var compensation techniques, and PV integration, this research achieves a notable reduction in power losses, ranging from 10% to 25% within a single day. Furthermore, the utilization of reactive power control, employing NAAMA's Static Var Compensator (SVC) for the transmission network, along with localized compensatory measures for the distribution network, effectively maintains voltage levels within acceptable parameters. These combined efforts provide critical support for the advancement of PV integration, the transition towards Variable Renewable Energy (VRE) sources, and the promotion of sustainable power generation practices within the region.</p> Ali Abderrazak Tadjeddine, Mohammed Sofiane Bendelhoum, Iliace Arbaoui, Ridha Iliace Bendjillali, Mohamed Alami ##submission.copyrightStatement## Sun, 31 Dec 2023 00:00:00 +0000 Study of a Redundant Residue Number System for Single Error Correction <p>One approach to reliability is to use a redundant residue number system. In general, two redundant moduli are required to detect and correct a single error. This paper considers an approach to error correction using a single redundant modulo, which allows a significant reduction in the hardware used, but at a significant cost in computational speed. The use of an approximate method based on the Chinese remainder theorem allows the speed of computation to be increased by eliminating the computationally complex operation of taking the remainder from the division by the range of the residue number system. A method based on the approximate method with one redundant modulo is proposed. Modelling of the considered methods on ASICs in RTL and physical synthesis environment Cadence Genus Synthesis Solution is carried out.</p> Viktor Kuchukov, Mikhail Babenko, Safwat Al-Galda ##submission.copyrightStatement## Sun, 31 Dec 2023 00:00:00 +0000 Balancing Accuracy, Fairness and Privacy in Machine Learning through Adversarial Learning <p>This paper investigates balancing accuracy, fairness and privacy in machine learning through adversarial learning. Differential privacy (DP) provides strong guarantees for protecting individual privacy in datasets. However, DP can impact model accuracy and fairness of decisions. This paper explores the effect of integrating DP into the adversarial learning framework called LAFTR (Learning Adversarially Fair and Transferable Representations) on fairness and accuracy metrics. Experiments were conducted using the Adult income dataset to classify individuals into high vs low income groups based on features like age, education etc. Gender was considered a sensitive attribute. Models were trained with different levels of DP noise (controlled by the epsilon hyperparameter) added to different modules like the encoder, classifier and adversary. Results show that adding DP consistently improves fairness metrics like demographic parity and equalized odds by 3-5% compared to an unfair classifier, albeit at a cost of 1-3% reduction in accuracy. Stronger adversary models further improve fairness but require careful tuning to avoid instability during training. Overall, with proper configuration, DP models can achieve high fairness with minimal sacrifice of accuracy compared to an unfair classifier. The study provides insights into balancing competing objectives of privacy, fairness and accuracy in machine learning models.</p> Alexander Eponeshnikov, Rustem Sabitov, Gulnara Smirnova, Shamil Sabitov ##submission.copyrightStatement## Sun, 31 Dec 2023 00:00:00 +0000 Bayesian Estimation and Prediction from a Mixture of Weibull and Gompertz Distributions <pre style="-qt-block-indent: 0; text-indent: 0px; margin: 0px;">We study different methods for estimation the parameters of a mixture of Weibull and Gompertz distributions as a lifetime model, based on a complete sample. Maximum likelihood estimation and Bayes estimation under informative and non-informative priors have been obtained using the symmetric squared error (SE) loss function, the asymmetric Linear exponential (LINEX) loss function and general entropy (GE) loss function. Also, we discuss two-sample Bayesian prediction intervals of the proposed model. For the illustration of the developing results, some computation results for the proposed model is presented.</pre> Abdulqader Al-Dugin, Mostafa Mohie El-Din, Amr Sadek ##submission.copyrightStatement## Sun, 31 Dec 2023 00:00:00 +0000 Dynamic Approach to the Energy Functioning of a Built Space and Associated Carbon Footprint: Application to a School Complex Located in Ouagadougou <p>We propose in this study a dynamic simulation of the energy functioning of a built space in order to deduce an evaluation of the associated carbon footprint. A simulation and modeling tool is implemented based on several methodological references. We first use a systemic approach to describe the structural and functional aspects of the systems that make up the built space and the energy flux that we observe. This simulation is done in particular in a hierarchical way to allow a relevant analysis of flux from adequate data processing. The tool is also based on a typological approach around the notions of typical days, typical structural and functional configurations at different scales and angles of observation. The main purpose of this study is to present the correlation or weighting functions that will enable us to move from typical days to months of the year. We present results at different scales of observation in time and space. These results are explained in terms of energy consumption and in terms of carbon footprint based on emission factors of the energy mix of the WAEMU territory, more specifically Burkina Faso.</p> Daouda Sawadogo, Ousmane Coulibaly, Xavier Chesneau, Belkacem Zeghmati ##submission.copyrightStatement## Sun, 31 Dec 2023 00:00:00 +0000 Global Stability and Sensitivity Analysis of Basic Reproduction Number of a Malaria Model <p>This paper explores a mathematical model of malaria, focusing on the basic reproduction number <em>R</em><sub>0</sub> and employing Lyapunov functions to assess the global stability of disease-free and endemic equilibria. Sensitivity analysis of key parameters is conducted to evaluate their impact on disease control. The results indicate an active malaria outbreak with decreasing human classes signifying disease progression and increasing mosquito classes suggesting heightened transmission risk. Effective control measures, including mosquito control and treatment of infected individuals, are essential to mitigate the outbreak.</p> Adesoye I. Abioye, Olumuyiwa J. Peter, Festus A. Oguntolu, Tawakalt A. Ayoola, Asimiyu O. Oladapo ##submission.copyrightStatement## Sun, 31 Dec 2023 00:00:00 +0000 A Fractal Array Antenna: A Review of the State of the Art <p>Possible array antennas with programmable multibeam, broadband, high end of coverage, high gain, less side-lobe level with wider side-lobe level angles, improved signal-to-noise ratio, and small size are required for modern astronomical and other advanced wireless communication systems. This has sparked numerous schools of thought on the subject of array antennas, one of which employs fractal array antennas. This paper provides an in-depth analysis of current developments in fractal array antenna design. To better understand how fractal antennas function, a primer on the theory behind them is presented. In addition, comparative research of the present state-of-the-art in antenna miniaturisation, gain, and Bandwidth augmentation with fractal array are performed.</p> Anuj Kumar Sharma, Vipul Sharma, Sanjay Singh ##submission.copyrightStatement## Sun, 31 Dec 2023 00:00:00 +0000 Identifiability of a Family of Dynamical Systems Application to Crops Identification <p>In this paper we consider the problem of identifying a system among a family of given systems. Thus, from measurements collected on an unidentified system but that is part of a family of known model systems, we seek to determine this unidentified system. This differs from identifying the parameters of a given system through experimental observations. The determination (identification) in a given family not always being possible, we refer to the identifiable family as any family for which this identification is possible. We thus introduce the concept of identifiability of a family of systems through a given measurement function. For localized linear systems we give algebraic characterizations that use the notion of system observability. We then propose algorithms which, in case of identifiability of the family and by a process of elimination, identify the system to which the collected measurements correspond. We have given some examples to illustrate these algorithms. We have also added an exemplified extension to discrete localized systems.</p> Abdes Bernoussi, Edyta Wozniak, Abdelaâziz Belfekih ##submission.copyrightStatement## Sun, 31 Dec 2023 00:00:00 +0000 Comparison to the Proposed Hybrid Model and Machine Learning Techniques for Survival Prediction of Corona, Infected Patients <p class="ASSAAbstract"><span lang="EN-US">SARS-CoV-2, a novel coronavirus discovered in Wuhan, China is spreading quickly and has a high incidence rate around the globe. As a result, everyone on the planet is having difficulty adjusting to the effects of Corona and is unable to foresee the devastation and disaster caused by COVID-19. In this work, we predict the survival status of patients infected with coronavirus using three distinct machine learning (ML) techniques: Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR). We also assess the classification performances of these algorithms. Here, we put out a hybrid model and evaluated it against the three previously discussed machine learning techniques. The outcomes demonstrated the 97.85% prediction accuracy of our suggested hybrid model. Aside from our suggested hybrid model, the random forest machine learning method demonstrated the highest accuracy of 94.62% among the three. Nonetheless, the prediction accuracy of the hybrid model outperforms that of the random forest and is significantly better than that of the other three ML techniques. The classification performances were assessed using the F-score, sensitivity, specificity, and precision metrics. Using 10-fold cross-validation, ROC assessments and confusion matrices produced by these machine learning algorithms were provided and examined. to assess the effectiveness of the classification. These machine learning algorithms' ROC assessments and confusion matrices are shown and examined by 10-fold cross-validation.</span></p> Md. Asadullah, Md. Murad Hossain, Md. Matiur Rahman Molla, Md. Matiur Rahaman ##submission.copyrightStatement## Sun, 31 Dec 2023 00:00:00 +0000 A Novel Two-Stage Hybrid Multi-Objective Differential Evolution with Opposition Based-Learning <p>Evolutionary algorithms have been shown to be powerful for solving multi-objective optimization problems, where non-dominated sorting is a widely adopted selection method. Differential Evolution (DE) is a simple and efficient population-based EA that has been reported in several studies for its high robustness, fast convergence speed, and good solution quality, making it a very popular EA in the evolutionary computing community. In this paper, a new two stage hybridized multi-objective differential evolution algorithm MODE based on opposition learning OBL is proposed, which balances exploration and exploitation capabilities as found in the original differential evolution DE, as well as OBL that brings higher selection pressure. in this developed approach, two stage are evolved. Firstly, MODE based on ranking mutation is applied using non-dominated sorting and crowding distance. Secondly, jumping probability is used in second stage in order to meet the objective of balancing the precision of the solution and the rate of convergence while maintaining the diversity of the population by opposition-based learning technique. Through the validation of MODE-OBL using a suite of carefully selected test reference problems for continuous multi-objective optimization, it is observed that MODE-OBL achieves overall better performance in terms of convergence and diversity compared to other algorithms of literature. In addition, MODE-OBL can be recommended to solve large portfolio optimization problems as well as problems with complex Pareto sets, as evidenced by its superior optimization performance in these types of problems.</p> Noureddine Boukhari, Mohamed Amine Nemmich, Fatima Debbat, Nicolas Monmarché, Mohamed Slimane ##submission.copyrightStatement## Sun, 31 Dec 2023 00:00:00 +0000 Machine Learning and Geometric Mathematical Models in Kimberlite Well Classification Problems <p class="ASSAAbstract">Nowadays, the application of mathematical models in geology becomes more and more relevant. The steady trend towards the global digitalization has led to the possibility of using the most modern computational methods in the construction of mathematical models. Digitalization, further processing of digital data, their analysis and subsequent modeling contributes to the improvement of production efficiency. The purpose of this paper is the development of various methods of classification of kimberlite wells. The paper presents neural network, statistical and geometric mathematical models for solving the problem of kimberlite well classification. The problem was solved using geological and exploration data from wells drilled in the Süldükar and Ulakhan-Kurung-Yuryakh areas located in Western Yakutia. For the constructed models the estimations of the models' qualities were obtained, the comparative analysis of the models was carried out. The analysis of mathematical models showed that the most accurate models are neural network models and models using geometric methods.</p> Nailia Gabdrakhmanova, Pavel Klimtsev ##submission.copyrightStatement## Sun, 31 Dec 2023 00:00:00 +0000