Search Results (229)

To address the boundary value problem associated with a class of third-order nonlinear differential equations with variable coefficients, this study integrates three key methods: the elastic transformation method (ETM), the similar construction method (SCM), and the elastic inverse transformation method (EITM). Firstly, ETM is employed to transform the original high-order nonlinear differential equations into the Tschebycheff equation, successfully reducing the order of the problem. Subsequently, SCM is applied to determine the general solution of the Tschebycheff equation under boundary conditions, thereby ensuring a structured and systematic approach. Ultimately, the EITM is used to reconstruct the solution of the original third-order nonlinear differential equation. The accuracy of the obtained solution is further validated by analyzing the corresponding solution curves. The synergy of these methods introduces a novel approach to solving nonlinear differential equations and extends the application of Tschebycheff equations in nonlinear systems. Full article

Mathematical modeling plays a crucial role in the advancement of cancer treatments, offering a sophisticated framework for analyzing and optimizing therapeutic strategies. This approach employs mathematical and computational techniques to simulate diverse aspects of cancer therapy, including the effectiveness of various treatment modalities such as chemotherapy, radiation therapy, targeted therapy, and immunotherapy. By incorporating factors such as drug pharmacokinetics, tumor biology, and patient-specific characteristics, these models facilitate predictions of treatment responses and outcomes. Furthermore, mathematical models elucidate the mechanisms behind cancer treatment resistance, including genetic mutations and microenvironmental changes, thereby guiding researchers in designing strategies to mitigate or overcome resistance. The application of optimization techniques allows for the development of personalized treatment regimens that maximize therapeutic efficacy while minimizing adverse effects, taking into account patient-related variables such as tumor size and genetic profiles. This study elaborates on the key applications of mathematical modeling in oncology, encompassing the simulation of various cancer treatment modalities, the elucidation of resistance mechanisms, and the optimization of personalized treatment regimens. By integrating mathematical insights with experimental data and clinical observations, mathematical modeling emerges as a powerful tool in oncology, contributing to the development of more effective and personalized cancer therapies that improve patient outcomes. Full article

The construction industry is inherently marked by high uncertainty levels driven by its complex processes. These relate to the bidding environment, resource availability, and complex project requirements. Accurate bid pricing under such uncertainty remains a critical challenge for contractors seeking a competitive advantage while managing risk exposure. This exploratory study integrates artificial intelligence (AI) into game theory models in an AI-assisted framework for bid pricing in construction. The proposed model addresses uncertainties from external market factors and adversarial behaviours in competitive bidding scenarios by leveraging AI’s predictive capabilities and game theory’s strategic decision-making principles; integrating extreme gradient boosting (XGBOOST) + hyperparameter tuning and Random Forest classifiers. The key findings show an increase of 5–10% in high-inflation periods with a high model accuracy of 87% and precision of 88.4%. AI can classify conservative (70%) and aggressive (30%) bidders through analysis, demonstrating the potential of this integrated approach to improve bid accuracy (cost estimates are generally within 10% of actual bid prices), optimise risk-sharing strategies, and enhance decision making in dynamic and competitive environments. The research extends the current body of knowledge with its potential to reshape bid-pricing strategies in construction in an integrated AI–game-theoretic model under uncertainty. Full article

The main goal of this paper is to study Jarratt-like iterative methods to obtain their order of convergence under weaker conditions. Generally, obtaining the $p^{th}$-order convergence using the Taylor series expansion technique needed at least $p+1$ times differentiability of the involved operator. However, we obtain the fourth- and sixth-order for Jarratt-like methods using up to the third-order derivatives only. An upper bound for the asymptotic error constant (AEC) and a convergence ball are provided. The convergence analysis is developed in the more general setting of Banach spaces and relies on Lipschitz-type conditions, which are required to control the derivative. The results obtained are examined using numerical examples, and some dynamical system concepts are discussed for a better understanding of convergence ideas. Full article

Optimal taxation and profit maximization are two very important problems, naturally related to one another since companies operate under a given tax system. However, in the literature, these two problems are usually considered separately, either by studying optimal taxation or by studying profit maximization. This paper tries to link the two problems together by formulating a bilevel model in which the government acts as a leader and a profit maximizing follower acts as a follower. The exact form of the tax revenue function, as well as optimal tax amount and optimal input levels, are derived in cases when returns to scale take on values 0.5 and 1. Several illustrative numerical examples and accompanying graphical representations are given for decreasing, constant, and increasing returns to scale values. Full article

As crypto assets become more widely adopted, crypto asset markets and traditional financial markets may become increasingly interconnected. The close linkages between these markets have potentially important implications for price formation, contagion, risk management and regulatory frameworks. In this study, we assess the correlation between traditional financial markets and selected crypto assets, study factors that may impact the price of crypto assets and identify potentially significant events that may have an impact on Bitcoin and Ethereum price dynamics. For the latter analyses, we adopt a Bayesian model averaging approach to identify change points in the Bitcoin and Ethereum daily price time series. We then use the dates and probabilities of these change points to link them to specific events, finding that nearly all of the change points can be associated with known historical crypto asset-related events. The events can be classified into broader geopolitical developments, regulatory announcements and idiosyncratic events specific to either Bitcoin or Ethereum. Full article

Following various natural and man-made disasters, a critical challenge in emergency response is establishing an emergency living supplies distribution center that minimizes service costs while ensuring rapid and efficient delivery of essential goods to affected populations, thereby safeguarding their lives and material well-being. The study addresses this challenge by developing an optimization function to minimize the total service cost for locating such distribution centers, using connection points as a foundation. Utilizing a robust optimization approach that incorporates constraint conditions and bounded intervals as the value set for uncertain demand, the optimization function is transformed into a robust equivalent model through the dual principle. The tabu search method, integrated with MATLAB R2015b software, is employed to perform statistical analysis on the data, yielding the optimal solution. Case study analysis demonstrates that the minimum total service cost escalates with increases in robustness level and disturbance parameters. Furthermore, the model incorporating connection points consistently yields better results than the model without connection points, highlighting the efficacy of the proposed approach. Full article

This work presents the modified Lagrange interpolating polynomial (MLIP) method, which aims to provide a straightforward procedure for deriving accurate analytical approximations of a given function. The method introduces an exponential function with several parameters which multiplies one of the terms of a Lagrange interpolating polynomial. These parameters will adjust their values to ensure that the proposed approximation passes through several points of the target function, while also adopting the correct values of its derivative at several points, showing versatility. Lagrange interpolating polynomials (LIPs) present the problem of introducing oscillatory terms and are, therefore, expected to provide poor approximations for the derivative of a given function. We will see that one of the relevant contributions of MLIPs is that their approximations contain fewer oscillatory terms compared to those obtained by LIPs when both approximations pass through the same points of the function to be represented; consequently, better MLIP approximations are expected. A comparison of the results obtained by MLIPs with those from other methods reported in the literature highlights the method’s potential as a useful tool for obtaining accurate analytical approximations when interpolating a set of points. It is expected that this work contributes to break the paradigm that an effective modification of a known method has to be lengthy and complex. Full article

Autonomous drifting is an advanced technique that enhances vehicle maneuverability beyond conventional driving limits. This survey provides a comprehensive, systematic review of autonomous drifting research published between 2005 and early 2025, analyzing approximately 80 peer-reviewed studies. We employed a modified PRISMA approach to categorize and evaluate research across two main methodological frameworks: dynamical model-based approaches and deep learning techniques. Our analysis reveals that while dynamical methods offer precise control when accurately modeled, they often struggle with generalization to unknown environments. In contrast, deep learning approaches demonstrate better adaptability but face challenges in safety verification and sample efficiency. We comprehensively examine experimental platforms used in the field—from high-fidelity simulators to full-scale vehicles—along with their sensor configurations and computational requirements. This review uniquely identifies critical research gaps, including real-time performance limitations, environmental generalization challenges, safety validation concerns, and integration issues with broader autonomous systems. Our findings suggest that hybrid approaches combining model-based knowledge with data-driven learning may offer the most promising path forward for robust autonomous drifting capabilities in diverse applications ranging from motorsports to emergency collision avoidance in production vehicles. Full article

For a graph G, the zero forcing number of G, $Z\left(G\right)$, is defined to be the minimum cardinality of a set S of vertices for which repeated applications of the forcing rule results in all vertices being in S. The forcing rule is as follows: if a vertex v is in S, and exactly one neighbor u of v is not in S, then the vertex u is added to S in the subsequent iteration. Now, the failed zero forcing number of a graph is defined to be the maximum size of a set of vertices which does not force all of the vertices in the graph. A similar type of forcing is called skew zero forcing, which is defined so that if there is exactly one neighbor u of v that is not in S, then the vertex u is added to S in the next iteration. The key difference is that vertices that are not in S can force other vertices. The failed skew zero forcing number of a graph is denoted by $F^{-}\left(G\right)$. At its core, the problem we consider is how to identify the tipping point at which information or infection will spread through a network or a population. The graphs we consider are where computers/routers or people are arranged in a linear or circular formation with varying proximities for contagion. Here, we present new results for failed skew zero forcing numbers of path powers and circulant graphs. Furthermore, we found that the failed skew zero forcing numbers of these families form interesting sequences with increasing n. Full article

Two classes of series involving differences of harmonic numbers and the binomial coefficients $C(3n,n)$ are evaluated in closed form. The classes under consideration are ${\sum }_{k=0}^{\infty }{\displaystyle \frac{H_{3k+1}-H_k}{(3k+1)\left(\genfrac{}{}{0pt}{}{3k}{k}\right)}}\left({\displaystyle \genfrac{}{}{0pt}{}{k}{m}}\right)z^k\mathrm{and}{\sum }_{k=0}^{\infty }{\displaystyle \frac{H_{2k}-H_k}{(3k+1)\left(\genfrac{}{}{0pt}{}{3k}{k}\right)}}\left({\displaystyle \genfrac{}{}{0pt}{}{k}{m}}\right)z^k,$ where z is a complex number and m (a non-negative integer) is an additional parameter. The tool that will be applied is integration in combination with complex analysis and partial fraction decomposition. Several remarkable integral values and difficult series identities are stated as special cases of the main results. Full article

We examine the modified Hill three-body problem by incorporating the oblateness of the primary body and focus on its asymptotic orbits. Specifically, we analyze and characterize homoclinic and heteroclinic connections associated with the collinear equilibrium points. By systematically varying the oblateness parameter, we determine conditions for the existence and location of these orbits. Our results confirm the presence of both homoclinic orbits, where trajectories asymptotically connect an equilibrium point to itself, and heteroclinic orbits, which establish connections between two distinct equilibrium points, via their stable and unstable invariant manifolds, which are computed both analytically and numerically. To achieve precise computations, we employ differential correction techniques and leverage the system’s inherent symmetries. Numerical calculations are carried out for orbit multiplicities up to twelve, ensuring a comprehensive exploration of the dynamical properties. Full article

In the context of holonomic systems, the identification of virtual displacements is clear and consolidated. This provides the possibility, once the class of displacements have been coupled with Newton’s equations, for us to write the correct equations of motion. This method combines the d’Alembert principle with Lagrange formalism. As far as nonholonomic systems are concerned, the conjecture that dates back to $\check{\mathrm{C}}$etaev actually defines a class of virtual displacements through which the d’Alembert–Lagrange method can be applied again. A great deal of literature is dedicated to the $\check{\mathrm{C}}$etaev rule from both the theoretical and experimental points of view. The absence of a rigorous (mathematical) validation of the rule inferable from the constraint equations has been declared to have expired in a recent publication; one of our objectives is to produce a critical comment on this stated result. Finally, we explore the role of the $\check{\mathrm{C}}$etaev condition within the significant class of nonholonomic homogeneous constraints. Full article

This work presents the Leal method for the approximation of integrals without known exact solutions, capable of multi-expanding simultaneously at different points. This method can be coupled with asymptotic approximations and the least squares method to extend the domain of convergence. The complete elliptic integral of the first kind, the Gamma function, and the error function are treated with this new method, resulting in highly accurate and easily computable approximations, exhibiting a wide region of convergence compared to other reported works. Finally, a comparison of computing time using Fortran between our proposals and other approximations from the literature is presented and discussed. Full article

Artificial neural networks have proven to be an important machine learning model that has been widely used in recent decades to tackle a number of difficult classification or data fitting problems within real-world areas. Due to their significance, several techniques have been developed to efficiently identify the parameter vectors for these models. These techniques usually come from the field of optimization and, by minimizing the training error of artificial neural networks, can estimate the vector of their parameters. However, these techniques often either get trapped in the local minima of a training error or lead to overfitting in the artificial neural network, resulting in poor performance when applied to data that were not present during the training process. This paper presents an innovative training technique for artificial neural networks based on the differential evolution optimization method. This new technique creates an initial population of artificial neural networks that evolve, as well as periodically applies a local optimization technique in order to accelerate the training of these networks. The application of the local minimization technique was performed in such a way as to avoid the phenomenon of overfitting. This new method was successfully applied to a series of classification and data fitting problems, and a comparative study was conducted with other training techniques from the relevant literature. Full article