Search Results (62)

Waste biomass powders and waste oils are promising sustainable modifiers for asphalt binders, but solid-phase biomass powders and oil-phase modifiers often have competing effects on high-temperature stability and low-temperature relaxation. In this study, peanut shell powder (PSP) and waste cooking oil (WCO) were combined at a fixed mass ratio of 1:1 to modify No. 70 base asphalt binder, and the material characteristics, physical properties, rheological responses, and chemical interactions of unaged PSP/WCO-modified asphalt binders with total modifier dosages of 5%, 10%, and 15% were evaluated. The results showed that PSP had a rough, wrinkled, and locally porous lignocellulosic structure and showed no obvious thermal decomposition near the preparation temperature of approximately 150 °C. As the PSP/WCO dosage increased from 0% to 15%, the softening point increased from 50.2 °C to 53.9 °C, while penetration decreased from 66.2 to 62.6 (0.1 mm) and ductility decreased from 74.0 mm to 69.5 mm, indicating increased binder consistency and improved high-temperature flow resistance. DSR and MSCR results showed enhanced high-temperature deformation resistance; at 15% dosage, Jnr at 3.2 kPa decreased from 2.35 to 1.25 kPa⁻¹, while R increased from 0.51% to 1.36%. However, BBR results showed increased creep stiffness and decreased m-value, indicating reduced low-temperature relaxation capacity. FTIR spectra showed no new strong characteristic absorption peaks, suggesting that the modification was mainly associated with physical blending, compositional regulation, and weak intermolecular interactions. The main novelty of this work is that it demonstrates a fixed-ratio PSP/WCO composite modification strategy that combines biomass-powder reinforcement with oil-phase regulation to improve the unaged high-temperature rheological performance of asphalt binders while promoting the resource utilization of peanut shells and waste cooking oil. Full article

Classical periodic solution theories of undamped Duffing equations are mostly restricted to continuous nonlinearities, while discontinuous models from mechanical impact and switching circuits lack systematic existence criteria for crossing periodic orbits. This work addresses a second-order undamped Duffing system with jump discontinuity of $g\left(x\right)$ on $x=0$, where $g\left(x\right)$ is piecewise continuously differentiable on two half real axes. With the separation condition ruling out sliding trajectories on the discontinuity line, the continuity of the Poincaré map is proved by Filippov theory for discontinuous ODEs. Using the Poincaré–Bohl fixed-point theorem, we derive multiple sufficient conditions ensuring the existence of $2\pi$ crossing periodic solutions via successive relaxation of growth hypotheses, and a numerical test example confirms the practicability of our theoretical findings, extending classical continuous Duffing results to discontinuous dynamical systems. Full article

Wireless links in underground coal mines suffer from severe attenuation, blockage, and limited spatial coverage. To improve link quality under these conditions, we study a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted system with multiple movable antennas (MAs) installed at the base station (BS) panel. Unlike prior models that assume a continuous movement box, we explicitly account for practical panel constraints: mechanical supports and RF feed lines partition the BS panel into non-overlapping irregular feasible subregions. This turns the BS-side antenna-positioning task into a mixed-integer nonlinear program (MINLP). We formulate a joint optimization problem that couples BS beamforming, STAR-RIS transmission/reflection coefficients, BS-side MA positions, and MA-to-subregion assignment with collision-avoidance constraints. To solve it, we adopt a block coordinate descent (BCD) framework: successive convex approximation (SCA) for beamforming, semidefinite relaxation (SDR)-based updates for STAR-RIS coefficients, and a penalty-based continuous relaxation for MINLP handling. The MA solver further integrates Hungarian initialization, cross-region jump updates, and reassignment corrections to escape poor local subregions. Simulation results in coal mine channel settings show that the proposed method yields a 66.7% sum-rate gain over fixed-antenna baselines and reduces required transmit power by 16.8 dB at the target-rate operating point. Compared with a regular-region BS-MA baseline, the irregular-partition design achieves an additional 5.6 dB power saving, demonstrating the practical value of hardware-aware geometry modeling. Full article

This paper introduces a novel class of condensing mappings through the concept of $\alpha$-admissibility. We prove several extensions of Darbo’s fixed point theorem within this framework, including a generalized contractive condition and a coupled fixed point result. Our main results extend the classical condensing condition to a flexible inequality involving $\alpha$-admissibility and an auxiliary function. An example is given to demonstrate the applicability of our theorems where traditional approaches fail. Also, by leveraging the properties of $\alpha$-admissible condensing operators, we proceed to establish a coupled fixed point theorem that relaxes traditional compactness conditions. At the end of the article, we apply our main theorem to prove the existence of solutions for a nonlinear integral equation. Full article

Background: Myocardial ischemia (MI) is a major cause of morbidity and mortality worldwide and requires timely and reliable detection. Although Spin-Exchange Relaxation-Free (SERF) magnetocardiography (MCG) provides femtotesla-level sensitivity for identifying non-linear cardiac repolarization anomalies, its clinical deployment is currently impeded by the computational bottlenecks inherent to portable edge platforms. Methods: We propose a “Sensor-to-Image” Edge-AI framework that links quantum sensing with computer vision. Single-channel SERF-MCG signals from a large cohort of 2118 subjects (1135 Healthy, 983 Ischemia) were transformed into phase-space images using three distinct encoding modalities: Recurrence Plots (RP), Gramian Angular Summation Fields (GASF), and Markov Transition Fields (MTF). These visual representations were subsequently analyzed by a streamlined MobileNetV3-Small architecture, optimized for low-latency inference. To maximize diagnostic precision, an adaptive weighted fusion mechanism was engineered to combine the chaotic specificity captured by RP with the morphological sensitivity of GASF through a validation-optimized fixed global weighting strategy. Results: In our experiments, the fusion model achieved an Area Under the Curve (AUC) of 0.865, which was higher than the 1D-CNN baseline (AUC 0.857) and the single-modality models. Notably, the fusion strategy significantly elevated sensitivity to 88.3% while maintaining a specificity of 66.5%. Although specificity is moderate, this trade-off prioritizes high sensitivity to minimize false negatives in pre-hospital screening scenarios. The average inference time was 4.7 ms per sample on a standard CPU, suggesting suitability for real-time Point-of-Care (PoC) scenarios under further on-device validation. Conclusions: The results suggest that multi-view phase-space fusion can capture subtle spatio-temporal changes associated with ischemia. The proposed lightweight framework may support the development of portable SERF-MCG systems with embedded AI screening. Full article

This paper investigates a novel class of fractional Langevin equations, which introduces a time-varying p(t)-Laplacian operator and parameterized anti-periodic boundary conditions. This approach overcomes the limitations of traditional models characterized by constant diffusion exponents and fixed boundary locations. Under non-compactness conditions, the existence of solutions is established by applying Schaefer’s fixed-point theorem, which significantly relaxes the conventional constraints on the nonlinear term. Moreover, by imposing a Lipschitz condition on the nonlinear term, a Ulam–Hyers-type stability criterion for the coupled system is derived. This work not only extends the relevant stability theory but also provides a rigorous theoretical foundation for error control in practical applications. The effectiveness of the theoretical results is validated through numerical examples. Full article

Recent developments in fixed-point theory have focused on iterative techniques for approximating solutions, yet there remain important questions about whether different methods are equivalent and how well they resist perturbations. In this study, two recently proposed algorithms, referred to as the DF and AR iteration methods, are shown to be connected by proving that they converge similarly when applied to contraction mappings in Banach spaces, provided that their control sequences meet specific, explicit conditions. This work extends previous research on data dependence by removing restrictive assumptions related to both the perturbed operator and the algorithmic parameters, thereby increasing the range of situations where the results are applicable. Utilizing a non-asymptotic analysis, the authors derive improved error bounds for fixed-point deviations under operator perturbations, achieving a tightening of these estimates by a factor of 3–15 compared to earlier results. A key contribution of this study is the demonstration that small approximation errors lead only to proportionally small deviations from equilibrium, which is formalized in bounds of the form $\parallel s^{*}-{\tilde{s}}^{*}\parallel \le O(\epsilon /(1-\lambda ))$. These theoretical findings are validated through applications involving integral equations and examples from function spaces. Overall, this work unifies the convergence analysis of different iterative methods, enhances guarantees regarding stability, and provides practical tools for robust computational methods in areas such as optimization, differential equations, and machine learning. By relaxing structural constraints and offering a detailed sensitivity analysis, this study significantly advances the design and understanding of iterative algorithms in applied mathematics. 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

This paper investigates the existence and uniqueness of bounded nonoscillatory solutions for two classes of m-th-order nonlinear neutral differential equations that incorporate both discrete and distributed delays. By applying Banach’s fixed-point theorem, we establish sufficient conditions under which such solutions exist. The results extend and generalize previous works by relaxing assumptions on the nonlinear terms and accommodating a wider range of feedback structures, including positive, negative, bounded, and unbounded cases. The mathematical framework is unified and applicable to a broad class of problems, providing a comprehensive treatment of neutral equations beyond the first or second order. To demonstrate the practical relevance of the theoretical findings, we analyze a delayed temperature control system as an application and provide numerical simulations to illustrate nonoscillatory behavior. This paper concludes with a discussion of analytical challenges, limitations of the numerical scope, and possible future directions involving stochastic effects and more complex delay structures. Full article

Security checks (SCs) at metro stations are regarded as an effective measure to address the heightened security risks associated with high ridership. Introducing SCs without exacerbating congestion requires a thorough understanding of their impact on passenger flow. Most existing studies were conducted where SCs are mandatory and fixed at certain locations. This study presents a method for advising the scale and placement for SCs under a more relaxed security setting. Using agent-based simulation with heterogeneous profiles for both inbound and outbound passenger flow, existing bottlenecks are first identified. By varying different percentages of passengers for SCs and locations to deploy SCs, we observe the influence on existing bottlenecks and suggest a suitable configuration. In our experiments, key bottlenecks are identified before tap-in fare gantries. When deploying SCs near tap-in fare gantries as seen in current practices, a screening percentage of beyond 10% could exacerbate existing bottlenecks and also create new bottlenecks at SC waiting areas. Relocating the SC to a point beyond the fare gantries helps alleviate congestion. This method provides a reference for station managers and transport authorities for balancing security and congestion. Full article

We develop a symmetry-based variational theory that shows the coarse-grained balance of work inflow to heat outflow in a driven, dissipative system relaxed to the golden ratio. Two order-2 Möbius transformations—a self-dual flip and a self-similar shift—generate a discrete non-abelian subgroup of $\mathrm{PGL}(2,\mathbb{Q}\left(\sqrt{5}\right))$. Requiring any smooth, strictly convex Lyapunov functional to be invariant under both maps enforces a single non-equilibrium fixed point: the golden mean. We confirm this result by (i) a gradient-flow partial-differential equation, (ii) a birth–death Markov chain whose continuum limit is Fokker–Planck, (iii) a Martin–Siggia–Rose field theory, and (iv) exact Ward identities that protect the fixed point against noise. Microscopic kinetics merely set the approach rate; three parameter-free invariants emerge: a $62\%:38\%$ split between entropy production and useful power, an RG-invariant diffusion coefficient linking relaxation time and correlation length ${\mathcal{D}}_{\alpha }={\xi }^z/\tau$, and a $\vartheta ={45}^{\circ }$ eigen-angle that maps to the golden logarithmic spiral. The same dual symmetry underlies scaling laws in rotating turbulence, plant phyllotaxis, cortical avalanches, quantum critical metals, and even de-Sitter cosmology, providing a falsifiable, unifying principle for pattern formation far from equilibrium. Full article

Concrete printing technologies play a key role in the modernization of construction practices. One factor that mitigates their progress is the development of standards and characterization tools for concrete during printing. The aim of this work is to point out correlations between some printability descriptors of mortars and the data obtained from low-field nuclear magnetic resonance (NMR) relaxometry techniques. In this context, the superposed effects of an acrylic-based superplasticizer and calcium nitrate accelerator were investigated. The mortars under study are based on white Portland cement, fine aggregates, and silica fume at fixed ratios. Extrusion tests and visual inspection of the filaments evaluate the extrudability and the printing window. The selected compositions were also investigated via transverse $\left(T_2\right)$ and longitudinal $\left(T_1\right)$ NMR relaxation times. The results indicate that both additives increase the printing window of the mortar, while the accelerator induces a faster increase in specific surface area of capillary pores $\left(S/V\right)$ only after 30–60 min of hydration. Some correlations were found between the printing window and the range where the transverse relaxation rates $\left(1/T_2\right)$ and the pore surface-to-volume ratios $\left(S/V\right)$ increase linearly. This suggests some promising connections between NMR techniques and the study of structural buildup of cementitious materials. Full article

In this paper, the finite-approximate controllability for a class of fractional composite relaxation equations with different nonlocal conditions is discussed. Firstly, under the condition that the nonlocal term is compact, the existence of mild solutions to the equations is obtained by employing resolvent theory, the variational method, and Schauder’s fixed-point theorem. Moreover, under the assumption that the corresponding linear equation is approximately controllable, the fractional composite relaxation equation with the nonlocal condition is derived to be finite-approximately controllable. Furthermore, the existence of mild solutions and the finite-approximate controllability to the equations are considered for the weaker nonlocal problem. Finally, the example of nonlocal problem is provided to verify the feasibility of the results in this paper. Full article

This paper is inspired by cutting-edge advancements in chaos theory, fractional calculus, and fixed point theory, which together provide a powerful framework for examining the dynamics of complex systems. At the heart of our research is the fractional-order Dadras–Momeni chaotic system, a pivotal model in chaos theory celebrated for its intricate, multi-scroll dynamics. Leveraging the Atangana–Baleanu fractional derivative, we extend fractional computation to chaotic systems, offering deeper insights into their behavior. To fortify the mathematical foundation of our analysis, we employ the relaxed $\theta$ rational contractions in the realm of metric spaces, enabling a more precise exploration of the system’s dynamics. A key goal of this work is to simplify the definition of the function class $\mathsf{\Theta }$ while maintaining the existence and uniqueness of fixed points under $\theta$-relaxed contractions, integrating this framework with the established literature on complete metric spaces. We explore the system’s behavior across six distinct cases by varying $\delta$ with a fixed fractional order of $\hslash =0.98$. In the first case, a single scroll forms, while successive cases lead to increased scrolls—reaching up to four by the sixth case. Phase portraits and time series analyses reveal a progression in complexity and chaos, with denser, intertwined scrolls as $\delta$ increases. This behavior highlights the system’s heightened sensitivity to parameter variations, demonstrating how fractional parameters influence the chaotic dynamics. Our results offer meaningful contributions to both the theoretical foundations and practical applications of chaos theory and fractional calculus, advancing the understanding of chaotic systems in new and impacted ways. Full article

Aiming at finding a better way to solve the problem of beer production scheduling, a new collaborative optimization based on the Manhattan Distance and Chameleon Swarm Algorithm is proposed. Firstly, a dynamic relaxation factor is introduced to the constraints at the system level, which combines the changing trend of the inconsistency information and the optimal solution of the discipline level. Additionally, the Manhattan Distance is used to replace the square of the Euclidean Distance at the system level. Thirdly, the Chameleon Swarm Algorithm is used to improve the update rule during the process of iteration. As these improvements are applied to the collaborative optimization, the steps of this new algorithm are given. Through the test case of a multivariate function, it can be found that the algorithm has been improved compared to the original algorithm. Then, a model for beer production scheduling is proposed, and the results of the optimization show that the improved collaborative optimization has better optima effectiveness and fewer iterations and is not sensitive to initial points, which proves that the improved collaborative optimization has a better ability to solve the problem of beer production scheduling than normal collaborative optimization and collaborative optimization with fixed relaxation factors. Full article