Search Results (2,746)

In this scholarly discourse, a rigorous examination is conducted on the boundedness properties of multilinear fractional rough Hardy operators within the structural framework of variable exponent Morrey–Herz spaces. Furthermore, analogous quantitative estimates are meticulously derived for their corresponding commutators, contingent upon the assumption that the governing symbol functions belong to the space of bounded mean oscillation $\left(\mathcal{BMO}\right)$ with variable exponents. Full article

This study concerns the evaluation of adhesive and wettability energetic signatures of a model orthodontic wire exposed to commercial mouthrinses. The surface wetting properties were evaluated from the contact angle hysteresis (CAH) approach applied to dynamic contact angle data derived from the original drop on a vertical filament method. Young, advancing, receding CA apart from adhesive film pressure, surface energy, work of adhesion, etc. were chosen as interfacial interaction indicators, allowing for the optimal concentration and placement of the key component(s) accumulation to be predicted for effective antibacterial activity to eliminate plaque formation on the prosthetic materials. Surfactant compounds when adsorb at interfaces confer rheological properties to the surfaces, leading to surface relaxation, which depends on the timescale of the deformation. The surface dilatational complex modulus E, with compression elasticity E_d and viscosity E_i parts, determined in the stress–relaxation Langmuir trough measurements, exhibited the viscoelastic surface film behavior with the relaxation times (0.41–3.13 s), pointing to the vertically segregated film structure as distinct, stratified layers with the most insoluble compound on the system top (as indicated with the 2D polymer film scaling theory exponent y = 12.9–15.5). Kinetic rheology parameters could affect the wettability, adhesion, and spreading characteristics of mouthrinse liquids. Full article

A fast terminal sliding mode control based on a fixed-time sliding surface is proposed for a permanent magnet arc motor (PMAM), effectively improving speed response, control accuracy, and disturbance rejection capability. Due to its piecewise structure and advanced logarithmic characteristics, a PMAM is subject to high-frequency disturbances. Additionally, it is also influenced by external disturbances. To address this, a sliding mode reaching law that combines terminal terms, linear terms, and switching terms is designed to reduce chattering and enhance robustness. Furthermore, to improve the convergence speed of the sliding mode and disturbance rejection ability, a novel fixed-time converging sliding surface based on a variable exponent terminal term is introduced. Numerical simulations verify the convergence and disturbance rejection capabilities of the proposed sliding surface. Stability based on the Lyapunov theorem is strictly proven. Experimental results validate the effectiveness and superiority of the proposed algorithm. Full article

In 2024, Brazil experienced record-breaking wildfire activity, underscoring the escalating influence of climate change. This study examines the long-range transport of wildfire-generated aerosol plumes to São Paulo, combining multi-platform observations to trace their origin and properties. During August and September—a period marked by intense fire outbreaks in Pará and Mato Grosso do Sul—lidar measurements performed at São Paulo detected pronounced aerosol plumes. To investigate their source and characteristics, we integrated data from the Earth Cloud Aerosol and Radiation Explorer (EarthCARE) satellite, HYSPLIT back-trajectory modeling, and ground-based AERONET and Raman lidar measurements. Aerosol properties were derived from aerosol optical depth (AOD), Ångström exponent, and lidar ratio (LR) retrievals. Back-trajectory analysis identified three transport pathways originating from active fire zones, with coinciding AOD values (0.7–1.1) and elevated LR (60–100 sr), indicative of dense smoke plumes. Compositional analysis revealed a significant black carbon component, implicating wildfires near Corumbá (Mato Grosso do Sul) and São Félix do Xingu (Pará) as probable emission sources. These findings highlight the efficacy of satellite-based lidar systems, such as Atmospheric Lidar (ATLID) onboard EarthCARE, in atmospheric monitoring, particularly in data-sparse regions where ground instrumentation is limited. Full article

This study introduces a novel energy-based inverse method for estimating residual stresses in structures composed of one-dimensional elements undergoing elastic–plastic deformation. The problem is reformulated as a global optimization task governed by the principle of minimum total potential energy. Rather than solving equilibrium equations directly, the internal stress distribution is inferred by minimizing the structure’s total potential energy using a real-coded genetic algorithm. This approach avoids gradient-based solvers, matrix assembly, and incremental loading, making it suitable for nonlinear and history-dependent systems. Plastic deformation is encoded through element-wise stress-free lengths, and a dynamic fitness exponent strategy adaptively controls selection pressure during the evolutionary process. The method is validated on single- and multi-bar truss structures under axial tensile loading, using a bilinear elastoplastic material model. The results are benchmarked against nonlinear finite element simulations and analytical calculations, demonstrating excellent predictive capability with stress errors typically below 1%. In multi-material systems, the technique accurately reconstructs tensile and compressive residual stresses arising from elastic–plastic mismatch using only post-load geometry. These results demonstrate the method’s robustness and accuracy, offering a fully non-incremental, variational alternative to traditional inverse approaches. Its flexibility and computational efficiency make it a promising tool for residual stress estimation in complex structural applications involving plasticity and material heterogeneity. Full article

Aiming at the problems of borehole shrinkage and pipe sticking caused by creep in salt-gypsum rock formations during deep well drilling, multi-field coupling creep experiments on deep salt-bearing gypsum mudstone were carried out. Furthermore, a nonlinear creep constitutive model was constructed based on the Drucker–Prager criterion, and the critical value of liquid column pressure for borehole shrinkage was determined through numerical simulation. Experiments show that at 140 °C, salt-gypsum rock is mainly subjected to brittle failure with single shear fracture, while at 180 °C, multiple sets of cross-cutting shear bands form, shifting to plastic flow-dominated composite failure. The coupling effect of confining pressure and deviatoric stress is temperature-dependent; the critical deviatoric stress is independent of confining pressure at 140 °C, but decreases significantly with increasing confining pressure at 180 °C, revealing that salt-gypsum rock is more prone to plastic flow under high temperatures and confining pressure. The creep constitutive equation was further determined, and fitting parameters show that the stress exponent m = 2–5 and the time exponent n decrease linearly with the increase in deviatoric stress, and the model can accurately describe the characteristics of three-stage creep. The numerical simulation found that there is a nonlinear relationship between the drilling fluid density and borehole shrinkage; the shrinkage rate exceeds 1.47% when the density is ≤2.0 g/cm³, and the expansion amount is >1.0 mm when ≥2.4 g/cm³. The critical safe density range is 2.1–2.3 g/cm³, which is consistent with the field data in the Bayan area. The research results provide an experimental basis and quantitative method for the dynamic regulation of drilling fluid density in deep gypsum rock formations, and have engineering guiding significance for preventing borehole wall instability. Full article

Mine water hazards represent one of the principal threats to safe coal mine operations; therefore, accurately predicting mine water inflow is critical for drainage system design and water hazard mitigation. Because mine water inflow is governed by the combined influence of multiple hydrogeological factors and thus exhibits pronounced non-linear characteristics, conventional approaches are inadequate in terms of forecasting accuracy and medium- to long-term predictive capability. To address this issue, this study proposes a Chaos-Autoformer-based method for predicting mine water inflow. First, the univariate inflow series is mapped into an m-dimensional phase space by means of phase-space reconstruction from chaos theory, thereby fully preserving its non-linear features; the reconstructed vectors are then used to train and forecast inflow with an improved Chaos-Autoformer model. On top of the original Autoformer architecture, the proposed model incorporates a Chaos-Attention mechanism and a Lyap-Dropout scheme, which enhance sensitivity to small perturbations in initial conditions and complex non-linear propagation paths while improving stability in long-horizon forecasting. In addition, the loss function integrates the maximum Lyapunov exponent error and earth mode decomposition (EMD) indices so as to jointly evaluate dynamical consistency and predictive performance. An empirical analysis based on monitoring data from the Liangshuijing Coal Mine for 2022–2025 demonstrates that the trained model delivers high accuracy and stable performance. Ablation experiments further confirm the significant contribution of the chaos-aware components: when these modules are removed, forecasting accuracy declines to only 76.5%. Using the trained model to predict mine water inflow for the period from June 2024 to June 2025 yields a root mean square error (RMSE) of 30.73 m³/h and a coefficient of determination (R²) of 0.895 against observed data, indicating excellent fitting and predictive capability for medium- to long-term tasks. Extending the forecast to July 2025–November 2027 reveals a pronounced annual cyclical pattern in future mine water inflow, with markedly higher inflow in summer than in winter and an overall slowly declining trend. These findings show that the Chaos-Autoformer can achieve high-precision medium- and long-term predictions of mine water inflow, thereby providing technical support for proactive deployment and refined management of mine water hazard prevention. Full article

Magnetic components serve as critical energy conversion elements in power conversion systems, with their performance directly determining overall system efficiency and long-term operational reliability. The development of accurate core loss frameworks and multi-objective optimization strategies has emerged as a pivotal technical bottleneck in power electronics research. This study develops an integrated framework combining physics-informed modeling and multi-objective optimization. Key findings include the following: (1) a square-root temperature correction model (exponent = 0.5) derived via nonlinear least squares outperforms six alternatives for Steinmetz equation enhancement; (2) a hybrid Bi-LSTM-Bayes-ISE model achieves industry-leading predictive accuracy (R² = 96.22%) through Bayesian hyperparameter optimization; and (3) coupled with NSGA-II, the framework optimizes core loss minimization and magnetic energy transmission, yielding Pareto-optimal solutions. Eight decision-making strategies are compared to refine trade-offs, while a crow search algorithm (CSA) improves NSGA-II’s initial population diversity. UFM, as the optimal decision strategy, achieves minimal core loss (659,555 W/m³) and maximal energy transmission (41,201.9 T·Hz) under 90 °C, 489.7 kHz, and 0.0841 T conditions. Experimental results validate the approach’s superiority in balancing performance and multi-objective efficiency under thermal variations. Full article

The combined influence of specimen size and strain rate on the mechanical behaviour of small-diameter bamboo culms remains insufficiently characterised. This study investigates the longitudinal compressive strength of Chimonobambusa utilis through axial compression tests on specimens measuring 15 × 15 × 5 mm, 18 × 18 × 6 mm, and 21 × 21 × 7 mm under strain rates of 10⁻⁴, 10⁻³, and 10⁻² s⁻¹. Coupling experimental data with theoretical analysis, this study develops a size–strain rate interaction model to quantitatively assess the effects of specimen size and strain rate on the compressive strength of small-diameter bamboo. Increasing specimen size reduced strength and shifted failure modes from shear to buckling and splitting. At a strain rate of 10⁻⁴ s⁻¹, strength decreased from 73.35 MPa for the 15 × 15 × 5 mm specimens to 62.84 MPa for the 21 × 21 × 7 mm specimens. Conversely, increasing the strain rate from 10⁻⁴ s⁻¹ to 10⁻² s⁻¹ for the 15 × 15 × 5 mm specimens increased strength from 73.35 MPa to 80.27 MPa, indicating suppressed crack propagation. The Type II Weibull model exhibited higher predictive accuracy and parameter stability than the Type I variant. Coupling the Type II Weibull function with a power-law strain rate term and an interaction exponent developed a predictive equation, achieving relative errors below 5%. The findings demonstrate that specimen size predominantly governs strength, whereas strain rate exerts a secondary but enhancing influence. The proposed coupling model enables reliable axial load prediction for small-diameter bamboo culms, supporting material selection and dimensional optimisation in structural applications. Full article

The crack growth process in metals can be continuously monitored and recorded in real time using highly sensitive acoustic emission technology. To predict this complex behavior, an improved largest Lyapunov exponent method, enhanced by a small data optimization approach named the small data method, is applied. Meanwhile, experimental results reveal that the acoustic emission signals collected during the fatigue crack growth process display distinct chaotic characteristics. By employing the small data method and cross-comparison analysis with the traditional Wolf method, the prediction accuracy and efficiency are significantly enhanced, even when only a limited amount of data is available. Compared to conventional techniques, this method demonstrates higher reliability and robustness, offering a powerful tool for early-stage monitoring and life prediction of metallic materials. Full article

To model the response of neural networks to electromagnetic radiation in real-world environments, this study proposes a memristive dual-wing fractional-order Hopfield neural network (MDW-FOMHNN) model, utilizing a fractional-order memristor to simulate neuronal responses to electromagnetic radiation, thereby achieving complex chaotic dynamics. Analysis reveals that within specific ranges of the coupling strength, the MDW-FOMHNN lacks equilibrium points and exhibits hidden chaotic attractors. Numerical solutions are obtained using the Adomian Decomposition Method (ADM), and the system’s chaotic behavior is confirmed through Lyapunov exponent spectra, bifurcation diagrams, phase portraits, and time series. The study further demonstrates that the coupling strength and fractional order significantly modulate attractor morphologies, revealing diverse attractor structures and their coexistence. The complexity of the MDW-FOMHNN output sequence is quantified using spectral entropy, highlighting the system’s potential for applications in cryptography and related fields. Based on the polynomial form derived from ADM, a field programmable gate array (FPGA) implementation scheme is developed, and the expected chaotic attractors are successfully generated on an oscilloscope, thereby validating the consistency between theoretical analysis and numerical simulations. Finally, to link theory with practice, a simple and efficient MDW-FOMHNN-based encryption/decryption scheme is presented. Full article

This paper investigates the time-varying efficiency of Western European stock markets and examines how macroeconomic events defined as endogenous and exogenous shocks influence the degree of efficiency by either long-range dependence or mean reverting. We apply a rolling-window detrended fluctuation analysis (DFA) with two window sizes, complemented by the Efficiency Index to synthetize multiple measures of market efficiency. The results confirm that efficiency evolves dynamically in response to macroeconomic disruptions. Specifically, endogenous shocks tend to generate anti-persistent behavior, while exogenous shocks are associated with long-memory effect. These shifts in efficiency are also reflected in rolling Kurtosis estimates, suggesting that only the most severe shocks produce spikes in Kurtosis, fat-tailed returns distributions, and structural inefficiencies. This dual approach allows us to classify shocks as major or minor based on their joint impact on both market efficiency and tail behavior. Overall, our findings support the adaptive market hypothesis and extend its implications through the fractal market hypothesis by underlining the role of heterogenous investment horizons during periods of turmoil. The combined use of dynamic DFA and Kurtosis offer a framework to assess how financial markets adapt to different types of macroeconomic shocks. Full article

The highly complex memristive chaotic map provides an excellent alternative for engineering applications. To design a memristive chaotic map with high complexity, this paper proposes a new three-dimensional memristive chaotic map (named MLM) by cascading and coupling a non-locally active memristor with a locally active memristor. The dynamical behaviors of MLM are revealed through phase diagrams, Lyapunov exponent spectra, bifurcation diagrams, and dynamic distribution diagrams. Notably, the internal frequency of MLM exhibits unique LE-controlled behavior and shows an extension of the chaotic parameter range. The high complexity of MLM is validated through the use of Spectral entropy (SE) and C0, and Permutation Entropy (PE) complexity algorithms. Subsequently, a pseudorandom number generator (PRNG) based on MLM is designed. NIST test results validate the high randomness of the PRNG. Finally, the STM32 hardware platform is used to implement MLM, and attractors under different parameters are measured by an oscilloscope, verifying the numerical analysis results. Full article

A natural deep eutectic solvent (NADES)-based electropolymerization strategy was developed to deposit polypyrrole (PPy) and Naproxen-doped PPy films onto a biomedical Ti–20Zr–5Ta–2Ag high-entropy alloy. Using cyclic voltammetry, chronoamperometry, and chronopotentiometry, coatings were grown potentiostatically (1.2–1.6 V) or galvanostatically (0.5–1 mA) to fixed charge values (1.6–2.2 C). Surface morphology and composition were assessed by optical microscopy, SEM and FTIR, while wettability was quantified via static contact-angle measurements in simulated body fluid (SBF). Electrochemical performance in SBF was evaluated through open-circuit potential monitoring, potentiodynamic polarization, and electrochemical impedance spectroscopy. Drug-release kinetics were determined by UV–Vis spectrophotometry and analyzed using mathematical modelling. Compared to uncoated alloy, PPy and PPy–Naproxen coatings increased hydrophilicity (contact angles reduced from ~31° to <10°), and reduced corrosion current densities from 754 µA/cm² to below 5.5 µA/cm², with polarization resistances rising from 0.06 to up to 37.8 kΩ·cm². Naproxen incorporation further enhanced barrier integrity (R_coat up to 1.4 × 10¹¹ Ω·cm²) and enabled sustained drug release (>90% over 8 days), with diffusion exponents indicating Fickian (n ≈ 0.51) and anomalous (n ≈ 0.67) transport for potentiostatic and galvanostatic coatings, respectively. These multifunctional PPy–Naproxen films combine robust corrosion protection with controlled therapeutic delivery, supporting their potential biomimetic role as smart coatings for next-generation implantable devices. Full article