Journals: Abdelhafid Boussouf University Center of Mila, Algeria
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A comparative study on strategic analysis and forecasting on profit maximization and operational efficiency in manufacturing business through differential equations: A comparative study on strategic analysis and forecasting on profit maximization and operational efficiency in manufacturing business through differential equations
No full textDifferential equations are fundamental mathematical tools with wide-ranging applications in science and economics. This study delves into their role in business, focusing on strategic analysis and forecasting for profit maximization and operational efficiency in manufacturing. It explores various equation types, from ordinary to partial differentials, highlighting their critical role in modeling economic phenomena. Through a comprehensive case study, this research demonstrates the practical application of differential equations in optimizing production, sales, revenue, and profit. The study emphasizes their impact on strategic decision-making and navigating complex market dynamics for sustained growth and profitability
A Bayesian approach for predicting match outcomes: FIFA World Cup 2026
Full text linkOne of the biggest international football competitions, the FIFA World Cup provides teams with an exciting and unpredictable stage on which to display their skills. Predicting match outcomes isn\u27t easy due to the numerous factors involved, like team strategy, player performance, and even unpredictable elements like weather or injuries. Traditional statistical methods in the frequentist framework (such as regression model, machine learning and Monte Carlo simulation) might not fully capture these complexities. This study applied the Bayesian logistics regression and gradient boosting model. to predict possible match outcomes in the forthcoming FIFA World Cup 2026. The Bayesian framework provides a probabilistic and adaptable base that adjusts to tournament dynamics and incorporates prior knowledge, while gradient boosting captures complex non-linear correlations. Key variables include player form, team dynamics, and strategic differences. Data were collected from FIFA\u27s official site and Kaggle, covering historical match data, player statistics and team rankings. Data preprocessing, including median imputation for missing values and feature engineering were carried out. The dataset is split into train-test-validate sets, and the two models evaluated exhibited high predictive accuracy. The study identified top contenders, highlighted offensive and defensive strengths, noted feature importance. The findings emphasize the potential of machine learning in sports analytics. The results identified the leading contenders for the 2026 FIFA World Cup, listing them in order of superiority. Results aim to contribute to the field of sports analytics, offering valuable insights into the complex dynamics influencing success in high-stakes football tournaments. From the literatures, this study on the application of Bayesian logistics regression and gradient boosting model is one of the rare applications to sport analytic
Quasi-exact solvable Dirac equation for the generalized Cornell potential plus a novel angle-dependent potential
Full text linkIn this paper, we present the exact analytical solution of the Dirac equation with equal scalar and vector generalized Cornell potential plus a novel angle-dependent potential in the framework of quasi-exactly solvable problems.By applying the functional Bethe ansatz method, we derive the angular Dirac part solutions and by the biconfluent Heun differential equation, the radial Dirac part solutions are determined.The exact bound states and the corresponding energy eigenvalues are obtained. Overall, this paper is a general reference for many previous scientific researches because it includes many possibilities, both central and non-central, which in turn adds a new addition to theoretical physics as well as modern mathematics
A decomposition analysis of Weyl\u27s curvature tensor via Berwald’s first and second order derivatives in Finsler spaces: A decomposition analysis of Weyl\u27s curvature tensor in Finsler spaces
No full textThis research paper explores the decomposition of Weyl\u27s curvature tensor through the lens of Berwald’s first and second-order derivatives in Finsler spaces. We analyze how Berwald’s differential geometry methods apply to Finsler spaces, which generalize Riemannian geometry and provide a more flexible framework for understanding curvature. The study highlights the importance of these decompositions in advancing both the theoretical aspects of Finsler geometry and their potential applications in physics, particularly in the realm of gravitational theories. Our findings offer a comprehensive understanding of the geometric structures that emerge in Finsler spaces, facilitating further research in high-dimensional and non-Riemannian manifolds
jMetal and MFHS collaboration for task scheduling optimization in heterogeneous distributed system
Full text linkTask scheduling in distributed computing architectures has attracted considerable research interest, leading to the development of numerous algorithms aiming to approach optimal solutions. However, most of these algorithms remain confined to simulation environments and are rarely applied in real-world settings. In a previous study, we introduced the MFHS framework, which facilitates the transition of scheduling algorithms from simulation to practical deployment. Unfortunately, MFHS currently offers a limited selection of scheduling heuristics. In this work, we address this limitation by presenting the MFHS_jMetal framework, integrating the extensive task scheduling algorithms available in the well-established jMetal framework. Our implementation demonstrates the successful expansion of available scheduling algorithms while preserving the core characteristics of MFHS, bridging the gap between theoretical models and real-world deployment
Non-informative Bayesian dispersion particle filter
Full text linkIn this research paper, we attempt to introduce a new algorithm for filtering a state-space model. The observations of this algorithm follow an exponential dispersion model. The paper focuses here on the inclusion of non-informative prior knowledge in parameter estimation on nonlinear state-space models using an improper uniform prior measure. Therefore, a new particle filter is introduced. A conventional particle filter (PF) produces an incorrect sample from a discrete approximation distribution. This new algorithm is a regularized continuous distribution method that is obtained with the exponential dispersion model. A necessary and sufficient condition for the existence and convergence of the non-informative Bayesian estimator of dispersion parameters is established. This methodology extends the classical PF implemented by this new estimation method for the exponential dispersion model framework using a non-informative Bayesian approach. In order to evaluate the performance of the proposed algorithm, a case study with simulations and microscopic image restoration is carried out. The results exhibit a great performance improvement from the proposed approac
Invention and utilization of epoch of Kifilideen sum formula for Kifilideen general matrix progression series of infinite terms in solving word problems: Epoch of Kifilideen’s sum formula for Kifilideen’s general matrix progression series of infinite terms in solving real-world problems
No full textKifilideen’s general matrix progression series of infinite terms is the summation of values of a collection of progressive members of the numbers series system. The number series system or set has endless terms where terms are progressive into levels and steps (within level) with increasing members set in successive levels and just one member in the first level. This kind of series is needed in determining the overall value(s) of the collection in the progressive members of the system which is/are useful for budgeting, analyzing, accounting, allocating and planning the system of arrangement that adopts such series. This study invented and applied the epoch of Kifilideen’s Sum Formula for Kifilideen’s General Matrix Progression Series of infinite terms in solving real-world problems. The mathematical induction of sum formulas of bi–numbers product progression series was formulated and established. These sum formulas obtained were incorporated into inventing Kifilideen’s Sum Formula for Kifilideen’s General Matrix Progression Series of infinite terms. The Kifilideen’s Sum Formula invented in this paper and Kifilideen’s Components Formulas of the Kifilideen’s general matrix progression sequence of infinite terms were used in conjunction to proffer solutions to real-world problems. The established Kifilideen’s Sum Formula for Kifilideen’s General Matrix Progression Series of infinite terms provides an easy and fastening process of finding the summation and evaluation of the overall value(s) of collection of progressive members of the Kifilideen’s General Matrix Progression Series of infinite terms
Formulas of the solutions of a non-autonomous difference equation and two systems of difference equations
Full text linkIn this work, we explicitly solve the following:* A higher-order non-autonomous difference equation:\begin{equation*}x_{n+1} = \alpha_{n} x_{n-k} + \frac{\beta_{n}}{x_{n} x_{n-1} \cdots x_{n-k+1}},\end{equation*}where , , the sequences and are real, and the initial values are nonzero real numbers.* A three-dimensional system of second-order difference equations:\begin{equation*}x_{n+1} = \frac{a_{1} y_{n-1} z_{n-1}}{a x_{n-1} + b y_{n-1} + c z_{n-1}}, \quady_{n+1} = \frac{a_{2} x_{n-1} z_{n-1}}{a x_{n-1} + b y_{n-1} + c z_{n-1}},\end{equation*}\begin{equation*}z_{n+1} = \frac{a_{3} x_{n-1} y_{n-1}}{a x_{n-1} + b y_{n-1} + c z_{n-1}},\end{equation*}where , the parameters are real numbers, and the initial values are nonzero real numbers.* A three-dimensional system of first-order difference equations:\begin{equation*}x_{n+1} = \frac{a_{1} y_{n} z_{n}}{a x_{n} + b y_{n} + c z_{n}}, \quady_{n+1} = \frac{a_{2} x_{n} z_{n}}{a x_{n} + b y_{n} + c z_{n}}, \quadz_{n+1} = \frac{a_{3} x_{n} y_{n}}{a x_{n} + b y_{n} + c z_{n}},\end{equation*}where , the parameters are real numbers, and the initial values are nonzero real numbers.
Adaptive control of a four-dimensional memristor-based Chua\u27s circuit
Full text linkThis paper investigates the behavior of a four-dimensional memristor-based Chua circuit. Specifically, we emphasize its chaotic and hyperchaotic behavior using the phase portrait and the Lyapunov spectrum. As chaos is deemed undesirable in numerous scientific disciplines, particularly in fields like robotics and electronic sciences, where the analyzed circuit holds potential applications in electronic device construction, we aim to alleviate such behaviors. To achieve this, we put forth an adaptive control strategy involving unknown parameters. The effectiveness of the suggested adaptive chaos control is established using the Lyapunov stability theory. To further illustrate and confirm our findings, we present numerical simulations, providing a visual representation of the successful application of the proposed adaptive control in managing the circuit\u27s dynamics.
Discrete-time epidemic modeling with chemo-prophylaxis for controlling multidrug-resistant and extensively drug-resistant Tuberculosis in Russia and India
Full text linkDespite significant progress in preventing and treating Tuberculosis (TB), it continues to be a leading cause of death worldwide. This is largely attributed to the rise of drug-resistant strains, notably Multidrug-Resistant Tuberculosis (MDR-TB) and Extensively Drug-Resistant Tuberculosis (XDR-TB). These resistant forms pose significant challenges, undermining the progress made against TB and necessitating innovative approaches for their management. This study enhances the Discrete VSEIT epidemiological model for TB by incorporating the dynamics of MDR/XDR-TB. The model incorporates distinct compartments for susceptible, exposed, infected, and resistant individuals receiving either first- or second-line treatments, in addition to those actively undergoing treatment. It considers natural population growth, the interactions among these groups, and the impact of treatment by chemoprophylaxis. The fundamental reproduction number () is determined by the mean of the next-generation matrix method. Investigation of the Stability of both the Disease-Free Equilibrium (DFE) and the Epidemic Equilibrium (EE) validate their global asymptotic stability. Model parameters are based on TB case data from India and Russia, two high-burden countries, from 2000 to 2022. The results indicate \mathcal{R}_0 > 1 for India and \mathcal{R}_0 < 1 for Russia. analyzing the sensitivity and numerical simulations show that increasing chemoprophylaxis treatment for exposed individuals decreases the advancement to multidrug-resistant, infectious, and extensively drug-resistant states. Additionally, BCG vaccination of children enhances immunity against TB and reduces disease transmission to healthy individuals, contributing to overall disease reduction. TB remains a more significant issue in India compared to Russia