Search Results (42)

Accurate and scalable evaluation in team sports remains challenging, motivating the use of artificial intelligence models to support objective athlete assessment. This study develops and validates a predictive model capable of calibrated, operationally tested classification of team-sport athletes as high- or low-performance using a synthetic, literature-informed dataset (n = 400). Labels were defined a priori by simulated group membership, while a composite score was retained for post hoc checks to avoid circularity. LightGBM served as the primary classifier and was contrasted with Logistic Regression (L2), Random Forest, and XGBoost (v3.0.5). Performance was evaluated with stratified, nested 5 × 5 cross-validation. Calibrated, deployment-ready probabilities were obtained by selecting a monotonic mapping (Platt or isotonic) in the inner CV, with two pre-specified operating points: screening (recall-oriented; precision ≥ 0.70) and shortlisting (F1-optimized). Under this protocol, the model achieved 89.5% accuracy and ROC-AUC 0.93. SHAP analyses indicated VO₂max, decision latency, maximal strength, and reaction time as leading contributors with domain-consistent directions. These results represent a proof-of-concept and an upper bound on synthetic data and require external validation. Taken together, the pipeline offers a transparent, reproducible, and ethically neutral template for athlete selection and targeted training in team sports; calibration and pre-specified thresholds align the approach with real-world decision-making. Full article

This paper proposes a new accelerated fixed-point algorithm based on a double-inertial extrapolation technique for solving structured variational inclusion and convex bilevel optimization problems. The underlying framework leverages fixed-point theory and operator splitting methods to address inclusion problems of the form $0\in (A+B)(x)$, where A is a cocoercive operator and B is a maximally monotone operator defined on a real Hilbert space. The algorithm incorporates two inertial terms and a relaxation step via a contractive mapping, resulting in improved convergence properties and numerical stability. Under mild conditions of step sizes and inertial parameters, we establish strong convergence of the proposed algorithm to a point in the solution set that satisfies a variational inequality with respect to a contractive mapping. Beyond theoretical development, we demonstrate the practical effectiveness of the proposed algorithm by applying it to data classification tasks using Deep Extreme Learning Machines (DELMs). In particular, the training processes of Two-Hidden-Layer ELM (TELM) models is reformulated as convex regularized optimization problems, enabling robust learning without requiring direct matrix inversions. Experimental results on benchmark and real-world medical datasets, including breast cancer and hypertension prediction, confirm the superior performance of our approach in terms of evaluation metrics and convergence. This work unifies and extends existing inertial-type forward–backward schemes, offering a versatile and theoretically grounded optimization tool for both fundamental research and practical applications in machine learning and data science. Full article

In the present paper we use the proximal point method in order to find an approximate common zero of an infinite collection of maximal monotone maps in a real Hilbert space under the presence of computational errors. We prove that the inexact proximal point method generates an approximate solution if these errors are sufficiently small. Full article

With the deepening of degradation, the stability and reliability of the degrading system usually becomes poor, which may lead to random jumps occurring in the degradation path. A non-homogeneous jump diffusion process model is introduced to more accurately capture this type of degradation. In this paper, the proposed degradation model is translated into a state–space model, and then the Monte Carlo simulation of the state dynamic model based on particle filtering is employed for predicting the degradation evolution and estimating the remaining useful life (RUL). In addition, a general model identification approach is presented based on maximization likelihood estimation (MLE), and an iterative model identification approach is provided based on the expectation maximization (EM) algorithm. Finally, the practical value and effectiveness of the proposed method are validated using real-world degradation data from temperature sensors on a blast furnace wall. The results demonstrate that our approach provides a more accurate and robust RUL estimation compared to CNN and LSTM methods, offering a significant contribution to enhancing predictive maintenance strategies and operational safety for systems with complex, non-monotonic degradation patterns. Full article

In the present paper, we use the proximal point method with remotest set control for find an approximate common zero of a finite collection of maximal monotone maps in a real Hilbert space under the presence of computational errors. We prove that the inexact proximal point method generates an approximate solution if these errors are summable. Also, we show that if the computational errors are small enough, then the inexact proximal point method generates approximate solutions Full article

This paper introduces a framework of hypercompositional algebra on fuzzy graphs by defining and analyzing fuzzy path-based hyperoperations. Building on the notion of strongest strong paths (paths that are both strength-optimal and composed exclusively of strong edges, where each edge achieves maximum connection strength between its endpoints), we define two operations: a vertex-based fuzzy path hyperoperation and an edge-based variant. These operations generalize classical graph hyperoperations to the fuzzy setting while maintaining compatibility with the underlying topology. We prove that the vertex fuzzy path hyperoperation is associative, forming a fuzzy hypersemigroup, and establish additional properties such as reflexivity and monotonicity with respect to $\alpha$-cuts. Structural features such as fuzzy strong cut vertices and edges are examined, and a fuzzy distance function is introduced to quantify directional connectivity strength. We define an equivalence relation based on mutual full-strength reachability and construct a quotient fuzzy graph that reflects maximal closed substructures under the vertex fuzzy path hyperoperation. Applications are discussed in domains such as trust networks, biological systems, and uncertainty-aware communications. This work aims to lay the algebraic foundations for further exploration of fuzzy hyperstructures that support modeling, analysis, and decision-making in systems governed by partial and asymmetric relationships. Full article

The Monge–Ampère operator, as a nonlinear operator embedded in parabolic differential equations, complicates the demonstration of maximal regularity for these equations. This research uses the Riesz fractional derivative to connect the Monge–Ampère operator with the fractional Laplacian operator. It is then possible to seek the maximal regularity of the parabolic Monge–Ampère equations by following an approach similar to that used for finding the maximal regularity of the parabolic fractional Laplacian operator. The maximal regularity of nonlocal parabolic Monge–Ampère equations guarantees the existence of solutions in the whole space. Based on these conditions, a modified sliding method, an enhancement of the moving planes method, is employed to establish the monotonicity property of the solutions for the nonlocal parabolic Monge–Ampère equations in the whole space. Full article

We consider an evolutionary inclusion associated with a time-dependent convex in an abstract Hilbert space. We recall a unique solvability result obtained based on arguments of nonlinear equations with maximal monotone operators combined with a penalty method. Then, we state and prove two well-posedness results. Next, we provide three examples of such inclusions that arise in mechanics. The first one concerns an elastic–perfectly plastic constitutive law, while the last two examples are mathematical models that describe the equilibrium of an elastic body and an elastic–perfectly plastic body, respectively, in frictional contact with an obstacle. The contact is bilateral and the friction is modeled with the Tresca friction law. We use our abstract results in the study of these examples to provide the convergence of the solution with respect to the data. Full article

We use the method of moving planes to prove the radial symmetry and monotonicity of solutions of fractional parabolic equations in the unit ball. Since the fractional Laplacian operator is a linear operator, we investigate the maximal regularity of nonlocal parabolic fractional Laplacian equations in the unit ball. The maximal regularity of nonlocal parabolic fractional Laplacian equations guarantees the existence of solutions in the unit ball. Based on these conditions, we first establish a maximum principle in a parabolic cylinder, and the principles provide a starting position to apply the method of moving planes. Then, we consider the fractional parabolic equations and derive the radial symmetry and monotonicity of solutions in the unit ball. Full article

Hydrogen internal combustion engines (H₂-ICEs) are a promising solution for decarbonizing heavy-duty transportation. This study investigates the effects of compression ratio (CR: 9, 11, 13) and excess air ratio (λ: 1–5) on the performance, emissions, and combustion characteristics of a turbocharged direct-injection H₂-ICE under lean-burn conditions. A validated one-dimensional GT-POWER model, calibrated using experimental data (1500 rpm, 0.6 bar intake pressure), was employed to analyze volumetric efficiency (VE), indicated thermal efficiency (ITE), NOx emissions, and combustion stability. Results demonstrate that increasing λ reduces VE and indicated mean effective pressure (IMEP) but enhances ITE, peaking at 41.25% (CR = 13, λ = 2.5). NOx emissions exhibit a non-monotonic trend, reaching 1850 ppm at λ = 1.5 (CR = 13) before declining under leaner conditions. Higher CR extends the lean-burn limit (λ = 5.0 for CR = 13) and advances combustion phasing, though it elevates risks of abnormal combustion. Trade-offs between power, efficiency, and emissions highlight λ = 2.5 as optimal for balancing ITE and NOx control, while λ = 1 maximizes power output. This work provides critical insights into optimizing H₂-ICE operation through CR and λ adjustments, supporting the transition toward sustainable heavy-duty transport systems. Full article

The magnetoelastic effect is known as the dependence between the magnetic properties of the material and applied mechanical stress. The stress might not be applied directly but rather generated by the applied torque. This creates the possibility of developing a torque-sensing device based on the magnetoelastic effect. In this paper, the concept of an axially twisted toroidal magnetic core as a torque-sensing element is considered. Most known works in this field consider the utilization of an amorphous ribbon as the core material. However, Ni-Zn ferrites, exhibiting relatively high magnetostriction, also seem to be promising materials for magnetoelastic torque sensors. This paper introduces a theoretical description of the magnetoelastic effect under torque operation on the basis of total free energy analysis. The methodology of torque application to the toroidal core, utilized previously for coiled cores of amorphous ribbons, was successfully adapted for the bulk ferrite core. For the first time, the influence of torque on the magnetic properties of Ni-Zn ferrite was investigated in a wide range of magnetizing fields. The obtained magnetoelastic characteristics allowed the specification of the magnetoelastic torque sensitivity of the material and the determination of the optimal amplitude of the magnetizing field to maximize this parameter. High sensitivity, in comparison with previously studied amorphous alloys, and monotonic magnetoelastic characteristics indicate that the investigated Ni-Zn ferrite can be utilized in magnetoelastic torque sensors. As such, it can be used in torque-sensing applications required in mechanical engineering or civil engineering, like the evaluation of structural elements exposed to torsion. Full article

In this research paper, we present a novel theoretical technique, referred to as the double Tseng’s algorithm with inertial terms, for finding a common solution to two monotone inclusion problems. Developing the double Tseng’s algorithm in this manner not only comprehensively expands theoretical knowledge in this field but also provides advantages in terms of step-size parameters, which are beneficial for tuning applications and positively impact the numerical results. This new technique can be effectively applied to solve the problem of image deblurring and offers numerical advantages compared to some previously related results. By utilizing certain properties of a Lipschitz monotone operator and a maximally monotone operator, along with the identity associated with the convexity of the quadratic norm in the framework of Hilbert spaces, and by imposing some constraints on the scalar control conditions, we can achieve weak convergence to a common zero point of the sum of two monotone operators. To demonstrate the benefits and advantages of this newly proposed algorithm, we performed numerical experiments to measure the improvement in the signal–to–noise ratio (ISNR) and the structural similarity index measure (SSIM). The results of both numerical experiments (ISNR and SSIM) demonstrate that our new algorithm is more efficient and has a significant advantage over the relevant preceding algorithms. Full article

Suppose each of $A_1,\dots ,A_n$ is a maximal monotone, and $\beta B$ is firmly nonexpansive with $\beta >0$. In this paper, we have two purposes: the first is finding the zeros of ${\sum }_{j=1}^n{A}_j+B$, and the second is finding the zeros of ${\sum }_{j=1}^n{A}_j$. To address the first problem, we produce fixed-point equations on the original Hilbert space as well as on the product space and find that these equations associate with crucial operators which are called generalized forward–backward splitting operators. To tackle the second problem, we point out that it can be reduced to a special instance of $n=2$ by defining new operators on the product space. Iterative schemes are given, which produce convergent sequences and these sequences ultimately lead to solutions for the last two problems. Full article

We study a relaxed inertial forward–backward–half-forward splitting approach with variable step size to solve a monotone inclusion problem involving a maximal monotone operator, a cocoercive operator, and a monotone Lipschitz operator. The convergence of the sequence of iterations generated by the discretisations of a continuous-time dynamical system is established under suitable conditions. Given the challenges associated with computing the resolvent of the composite operator, the proposed method is employed to tackle the composite monotone inclusion problem. Additionally, a convergence analysis is conducted under certain conditions. To demonstrate the effectiveness of the algorithm, numerical experiments are performed on the image deblurring problem. Full article

This paper deals with the research of solutions of bounded variation (BV) to evolution inclusion coupled with a time and state dependent maximal monotone operator. Different problems are studied: existence of solutions, unicity of the solution, existence of periodic and bounded variation right continuous (BVRC) solutions. Second-order evolution inclusions and fractional (Caputo and Riemann–Liouville) differential inclusions are also considered. A result of the Skorohod problem driven by a time- and space-dependent operator under rough signal and a Volterra integral perturbation in the BRC setting is given. The paper finishes with some results for fractional differential inclusions under rough signals and Young integrals. Many of the given results are novel. Full article