Search Results (1,565)

This study examines the impact that fat-tailed distributions of the spread residuals have on the optimal orders for pairs trading of stocks and cryptocurrencies. Using daily data from selected pairs, the spread dynamics has been modeled through a mean-reverting Ornstein–Uhlenbeck process and investigates how deviations from normality affect strategy design and profitability. Specifically, we compared four fat-tailed distributions—Lévy stable, generalized hyperbolic, Johnson’s S_U, and non-centered Student’s t—and showed how they modify optimal entry and exit thresholds, trading frequency, and performance metrics. The main findings reveal that the proposed pairs trading strategy correctly captures some key stylized facts of residual spreads such as large jumps, skewness, and excess Kurtosis. Interestingly, we considered regime-switching behaviors to account for structural changes in market dynamics, providing empirical evidence that optimal trading rules are regime-dependent and significantly influenced by the residual distribution’s tail behavior. Unlike conventional approaches, we optimized the entry signal and link heavy tails not only to volatility clustering but also to the nonlinearity in switching regimes. These findings suggest the need to account for distributional properties and dynamic regimes when designing robust pairs trading strategies, providing a more realistic and effective framework of these strategies in highly volatile and non-normal markets. Full article

Developing control systems for high aspect ratio aircraft can be challenging due to the flexibility of the structure involved in the control loop design. A model-based approach can be straightforward to tune the control system parameters and, to this aim, a reliable aircraft flexible model is mandatory. This paper aims to present the approach pursued to design a control strategy considering the flexible aircraft simulator in the loop. Once the elastic model for the longitudinal dynamics has been set up, genetic algorithms are used to determine - together with a Linear Quadratic Regulator controller—a logic to improve the dynamic behaviour whilst encountering a gust. A relatively low order elastic model is developed for the dynamics in the longitudinal plane, including both rigid body and elastic degrees of freedom defined in a vehicle-fixed reference frame. The rigid body degrees of freedom and the associated states are the same as those of the rigid vehicle, whilst the additional states represent the elastic degrees of freedom. Modal characteristics are calculated from a finite element model of the aircraft using a commercial code, with the weight distribution added as lumped masses on grid points, while the aerodynamic rigid properties are described with a nonlinear database. Using the 2-D strip theory and neglecting the unsteady effects, the aeroelastic stability derivatives, i.e., elastic influence coefficients, are computed to superimpose the elastic effects on the rigid body degrees of freedom and vice versa. The flexible dynamics is compared to the rigid one in order to highlight the relevant changes in the aircraft modes. Following is herein proposed a control strategy combining genetic algorithms and Linear Quadratic Regulator controller to reduce the load factor, also considering the oscillation amplitude due to a deterministic gust encountered in a predefined flight condition. Full article

This study investigated the combustion profiles and oxidation mechanisms of coke on spent FCC catalysts from two Sinopec refineries and compared the effects of O₂/N₂ and O₂/CO₂ atmospheres. Using the Coats–Redfern method combined with nonlinear regression, the kinetics of coke oxidation were analyzed for the activation energies derived from the modified D3 and F2 models. Both these models yielded results that agreed with the previous reports. Selection of the suitable kinetic model was significantly influenced by specific properties of the coke on spent FCC catalysts. Furthermore, a computational model revealed that on an industrial scale, external mass transfer predominated the intrinsic kinetics; the differences observed in the O₂/N₂ and O₂/CO₂ environments were primarily due to variations in oxygen diffusion. These findings highlight the potential of optimizing FCC catalyst regeneration processes through alternative oxidation environments and the use of catalytic additives. Full article

This study investigates the synergistic effects of pulsed heat load and helium plasma irradiation on the surface damage evolution of high-purity tungsten, a candidate plasma-facing material (PFM) for future fusion reactors. Using a self-developed linear plasma device, tungsten samples were exposed to controlled single-pulse heat loads (32–124 MW·m⁻²) and helium plasma fluxes (7.76 × 10²²–2.40 × 10²³ ions·m⁻²·s⁻¹). SEM and XRD analyses revealed a progressive damage mechanism involving helium bubble formation, pit collapse, coral-like nanostructure evolution, and melting-induced restructuring. These surface changes were accompanied by grain refinement, lattice contraction, and peak shifts in the (110) diffraction plane. Mechanical testing showed a flux-dependent variation in hardness, with initial hardening followed by softening due to crack propagation. Surface reflectivity significantly declined with increasing load, indicating severe optical degradation. This work demonstrates the nonlinear coupling between thermal and irradiation effects in tungsten, offering new insights into damage accumulation under realistic reactor conditions. The findings highlight the dominant role of transient heat loads in driving structural and property changes and emphasize the importance of accounting for synergistic effects in material design. These results provide essential experimental data for optimizing PFMs in divertor and first-wall applications and suggest directions for future research into cyclic loading, long-term exposure, and microstructural recovery mechanisms. Full article

The understanding and application of fluid-type inerters by scholars have been on the rise. However, due to their intricate multiphase mechanical properties, existing models still have considerable room for improvement. This study presents two fractional-order models and conducts parameter identification by integrating them with classical experimental data. The first model is an independent fractional-order model. In comparison with traditional models, it demonstrates significantly higher fitting accuracy in frequency regions beyond the ultra-low frequency range. The second model is a segmented fractional-order model, which determines segments according to critical frequencies. Although this model enhances the overall fitting accuracy, it also leads to increased complexity. To tackle this complexity issue, a rough design strategy is proposed to minimize the critical frequency. Research indicates that under such a strategy, the inertial effect dominates the behavior of the fluid inerter. Even when the independent fractional-order model is used, a high fitting accuracy can be achieved. Consequently, by designing the structural parameters and fluid medium of the fluid inerter based on the rough design strategy, the model can be simplified. Moreover, compared with traditional nonlinear inerter models, the transfer function and eigenvalue analysis methods can be effectively applied. This enables the acquisition of more comprehensive theoretical research results, thereby greatly facilitating theoretical analysis. Full article

Tungsten Inert Gas (TIG) welding is a widespread welding process used in the industry for high-quality joints. However, this welding process suffers from lower productivity. Activated Tungsten Inert Gas (ATIG) is a variant of the TIG that aims to increase the depth penetration capability of conventional TIG welding. This is achieved by applying a thin coating of activating flux material onto the workpiece surface before welding. This work investigates the effect of the thermophysical properties of individual metallic oxide fluxes on 316L stainless steel weld morphology. Four levels of current intensity (120, 150, 180, 200 A) are considered. The weld speed up to 15 cm/min and arc length of 2 mm are maintained constant. Thirteen oxides were tested under various levels of current intensity in addition to multiple thermophysical properties combinations, and the depth penetration (D) and the aspect ratio (R) were recorded. This process has provided 52 combinations (13 oxides * 4 currents). Based on the numerical observations, linear and nonlinear models for describing the effect of the thermophysical parameters on the weld characteristics were tuned using a particle swarm optimization algorithm. While the linear model provided good prediction accuracy, the nonlinear exponential model outperformed the linear one for the depth yielding a mean absolute percentage error of 17%, a coefficient of determination of 0.8266, and a root mean square error of 0.9665 mm. The inverse optimization process, where the depth penetration ranged from 1.5 mm to 12 mm, thus covering a large spectrum of industries, the automotive, power plants, and construction industries, was solved to determine the envelopes’ lower and upper limits of optimal oxide thermophysical properties. The results that allowed the design of the fluxes to be used in advance were promising since they provided the oxide designer with the numerical ranges of the oxide components to achieve the targeted depths. Future directions of this work can be built around investigating additional nonlinear models, including saturation and dead-zone, to efficiently estimate the effect of the thermophysical properties on the welding process of other materials. Full article

To address the growing threat of quantum computing to classical cryptographic primitives, this study introduces NTRU-MCF, a novel lattice-based signature scheme that integrates multidimensional lattice structures with fractional-order chaotic systems. By extending the NTRU framework to multidimensional polynomial rings, NTRU-MCF exponentially expands the private key search space, achieving a key space size $\ge 2^{256}$ for dimensions $m\ge 2$ and rendering brute-force attacks infeasible. By incorporating fractional-order chaotic masks generated via a hyperchaotic Lü system, the scheme introduces nonlinear randomness and robust resistance to physical attacks. Fractional-order chaotic masks, generated via a hyperchaotic Lü system validated through NIST SP 800-22 randomness tests, replace conventional pseudorandom number generators (PRNGs). The sensitivity to initial conditions ensures cryptographic unpredictability, while the use of a fractional-order L hyperchaotic system—instead of conventional pseudorandom number generators (PRNGs)—leverages multiple Lyapunov exponents and initial value sensitivity to embed physically unclonable properties into key generation, effectively mitigating side-channel analysis. Theoretical analysis shows that NTRU-MCF’s security reduces to the Ring Learning with Errors (RLWE) problem, offering superior quantum resistance compared to existing NTRU variants. While its computational and storage complexity suits high-security applications like military and financial systems, it is less suitable for resource-constrained devices. NTRU-MCF provides robust quantum resistance and side-channel defense, advancing PQC for classical computing environments. Full article

This study aims to investigate the effectiveness of two machine learning methods for the prediction of the mechanical and fracture properties of Cenosphere-reinforced lightweight thermoset polyurethane composites. To evaluate the effectiveness of the models, datasets from our experimental study of composites made of five different volume fractions (0% to 40%) of Cenospheres (hollow Aluminum Silicate particles) in increments of 10% are fabricated. Experiments are conducted to determine the effect of the volume fraction of Cenospheres on Young’s modulus (both in tension and compression), percentage elongation at break, tensile strength, specific tensile strength, and fracture toughness of the composites. Two machine learning models, shallow artificial neural network (ANN) and the non-linear deep neural network (DNN), are employed to predict the above properties. A parametric study was performed for each model and optimized parameters were identified and later used to predict the properties beyond 40% volume fraction of Cenospheres. The predictions of non-linear DNN demonstrated less slope than shallow ANN and, for mass density, the non-linear DNN had unexpected predictions of increasing mass density with the addition of lighter Cenospheres. Hence, a double-hidden-layer DNN is used to predict the mass density beyond 40%, which provides the expected behavior. Full article

As critical components in marine engineering fluid transmission systems, fiber-reinforced flexible (FRF) pipes have static mechanical properties that depend on internal fluid pressure. Current analytical approaches predominantly employ uniformly distributed load (UDL) assumptions to simulate unidirectional fluid pressure effects on pipe surfaces. However, existing methodologies neglect fluid–pipe structure coupling effects. This study investigates the rubber-based FRF pipe by establishing a numerical model incorporating fluid–structure interaction effects and material nonlinearity, aiming to explore how different fluid states (closed or constant pressure) and fluid types (incompressible or compressible) influence the mechanical behavior of the FRF pipe under axial loading. Experimental validation of the numerical model demonstrates that UDL assumptions remain valid for gas-filled pipes (both in the closed and constant pressure states) and the liquid-filled pipe in the constant pressure state. The incompressibility of the filled liquid significantly enhances pipe axial stiffness, invalidating the UDL approximation method in liquid-filled closed states. Furthermore, the asymptotic saturation model proposed effectively quantifies the liquid-induced enhancement in axial stiffness. The developed numerical model and derived conclusions provide valuable insights into structural design optimization, experimental protocol development, and practical engineering applications for FRF pipes. Full article

Polyvinyl alcohol cryogels (PVA-C) are promising materials for vascular tissue engineering due to their biocompatibility, hydrophilicity, and tuneable mechanical properties. This study investigates the mechanical performance of multi-layered PVA-C constructs fabricated via sub-zero extrusion-based three-dimensional (3D) bioprinting. Samples with two, four, and six alternating layers were evaluated to assess the effect of layered architecture on elastic and viscoelastic behaviour. Uniaxial tensile testing revealed that increasing the number of layers led to a moderate reduction in stiffness; for instance, at 20% strain, six-layered constructs showed a significantly lower (p < 0.05) Young’s modulus (36.7 ± 2.5 kPa) compared to two-layered ones (47.3 ± 3.1 kPa). Stress–strain curves exhibited nonlinear characteristics, better captured by quadratic (as opposed to linear) fitting, within the tested strain range (≤40%). Dynamic mechanical analysis demonstrated a frequency-independent storage modulus (E′) across 1–10 Hz, with subtle variations in viscoelastic response linked to the number of layers. Visual inspection confirmed improved print fidelity and hydration retention in thicker constructs. These findings demonstrate that a multi-layered design influences the mechanical profile of PVA-C and suggests potential for functionally graded design strategies to enhance compliance matching and mimic the biomechanics of native vessels in small-diameter vascular grafts. Full article

With accelerating climate change and urbanization, regional carbon balance faces increasing uncertainty. Terrestrial carbon sinks play a crucial role in advancing China’s sustainable development under the dual-carbon strategy. This study quantitatively modeled China’s terrestrial carbon sink capacity and analyzed the multidimensional relationships between impact factors and carbon sinks. After preprocessing multi-source raster data, we introduced kernel normalized the difference vegetation index (kNDVI) to the Carnegie–Ames–Stanford approach (CASA) model, together with a heterotrophic respiration (R_h) empirical equation, to simulate pixel-level net ecosystem productivity (NEP) across China. A light gradient-boosting machine (LightGBM) model, optimized via Bayesian algorithms, was trained to regress NEP drivers, categorized into atmospheric components (O₃, NO₂, and SO₂) and subsurface properties (a digital elevation model (DEM), enhanced vegetation index (EVI), soil moisture (SM)), and human activities (land use/cover change (LUCC), POP, gross domestic product (GDP)). Shapley Additive Explanation (SHAP) values were used for model interpretation. The results reveal significant spatial heterogeneity in NEP across geographic and climatic contexts. The pixel-level mean and total NEP in China were 268.588 gC/m²/yr and 2.541 PgC/yr, respectively. The north tropical zone (NRZ) exhibited the highest average NEP (828.631 gC/m²/yr), while the middle subtropical zone (MSZ) and south subtropical zone (SSZ) demonstrated the most stable NEP distributions. LightGBM achieved high simulation accuracy, further enhanced by Bayesian optimization. SHAP analysis identified EVI as the most influential factor, followed by SM, NO₂, DEM, and POP. Additionally, LightGBM effectively captured nonlinear relationships and variable interactions. Full article

In planar linkage mechanisms, due to various influencing factors, the existence of joint clearance becomes an inevitable phenomenon, which substantially diminishes the precision of the system’s movement. Currently, the majority of studies are largely confined to simple mechanisms with a single clearance, whereas investigations into more intricate systems with multiple types of clearances are still lacking. In view of this, this paper proposes an innovative dynamic algorithm for complex multilink mechanisms, aiming to deeply explore the specific impacts of multiple factors on dynamic response and nonlinear rigid-body properties, as well as its reliability analysis. Taking an eight-bar mechanism as an example, a dynamic model with mixed clearances is constructed, based on which the dynamic responses of the mechanism to different types of clearances are studied. Simultaneously, the effects of different variation ranges of clearance values and traveling speeds on the dynamic response, nonlinear characteristics, and dynamic accuracy reliability analysis of the mechanism were investigated. This research not only lays a robust theoretical foundation for the dynamics of multilink mechanisms but also demonstrates significant value and significance in both academic research and engineering application fields. Full article

The present study aims to prepare a composite via pyrolysis, based on sodium alginate (SA) and a natural clay collected from the eastern region of Morocco, specifically the OUJDA area (C.O.R), for use in the disposal process of emerging pharmaceuticals. The strategy of rapid microwave heating followed by nitrogen calcination at 500 °C was successfully applied to produce the pyrolyzed carbonaceous materials. The removal of paracetamol (PCT) by adsorption on the carbonaceous clay (ca-C.O.R) composite was investigated to determine the effect of operating parameters (initial contaminant concentration, contact time, pH, and temperature) on the efficiency of PCT removal. The nanocomposite was analyzed using various techniques, including the nitrogen gas adsorption–desorption isothermal curve, X-ray diffraction, scanning electron microscopy, and Fourier transform infrared spectroscopy. Three models were used to describe the kinetic adsorption, and it was found that the experimental kinetic data fit well with a pseudo-second-order kinetic model with a coefficient of determination R² close to one, a nonlinear chi-square value close to zero, and a reduced root mean square error RMSE (R² → 1, X² → 0 and lower RMSE). The adsorption was best described by the Sips isotherm. The ca-C.O.R composite achieved a PCT removal efficiency of 91% and a maximum adsorption capacity of 122 mg·g⁻¹ improving on the performance of previous work. Furthermore, the variation in enthalpy (∆H°), Gibbs free energy (∆G°), and entropy (∆S°) indicated that the adsorption is exothermic in nature. The composite has shown promising efficiency for the adsorption of PCT as a model of emergent pollutant from aqueous solutions, making it a viable option for industrial wastewater treatment. Using Density Functional Theory (DFT) along with the 6-31G (d) basis set, the geometric structure of the molecule was determined, and the properties were estimated by analyzing its boundary molecular orbitals. The adsorption energy of PCT on MMT and ca-C.O.R studied using the Monte Carlo (MC) simulation method was −120.3 and −292.5 (kcal·mol⁻¹), respectively, which shows the potential of the two adsorbents for the emerging product. Full article

Geometric properties of fractures, such as aperture and width, among others, significantly affect the fluid flow behaviors in fractured media. Previous studies have shown that fractures exhibit fractal properties. In this study, we examine the impact of nonlinear flow regimes and aperture and width fractal distributions on the flow behavior through fractal fractures. Both the aperture and width are treated independently following fractal distribution, but with distinct fractal dimensions. We explicitly examine the flow features without using Darcy’s law concept, which relies on the linear flow assumption with an effective permeability of fractal fractures. We directly consider the flow rate in a fracture with average aperture, average flow rate, and flow rate of linear flow in all the fractures, and nonlinear flow rate in all the fractures, and more realistically, the average flow rate when linear and nonlinear flows may coexist in different fractures and their differences. The results demonstrate that the nonlinear flow regime significantly reduces the flow rate through the fractal fractures, which could be quantified by the ratio of critical aperture to the minimum aperture in the fractal fractures. A large ratio of the maximum over the minimum apertures results in a large average flow rate in the fractal fractures. The increase in the minimum aperture also enhances the average flow rate. When the minimum aperture is close to the critical aperture, however, the flow rate in the fractal fractures starts to turn into nonlinear flow in all the fractures, and the average flow rate decreases. The nonlinear effect is amplified in fractal fractures compared to that in a single fracture. A larger fractal dimension of aperture leads to a lower average flow rate in the fractal fractures, as the average aperture decreases with the fractal dimension. However, the fraction of flow rate from the linear flow portion in the fractal fractures over the pure linear flow in all the fractures increases with the fractal dimension. Full article

Edge computing refers to provision storage and computation resources at the network edge, closer to end users than the remote cloud. In such edge–cloud computing environments, many cloud providers intend to offload cloud services to the edge nodes to offer high-quality services for data-intensive and latency-sensitive applications. The major obstacle is that edge nodes are rarely willing to offer resources voluntarily without any rewards. To this end, this paper proposes an efficient incentive mechanism for edge–cloud computing environments using Stackelberg game theory to motivate more edge nodes to host offloaded cloud services. We analyze the properties of the game model and present a solution to compute the unique Stackelberg Equilibrium (SE) of the nonlinear model. On this basis, we propose an efficient polynomial-time algorithm to find the SE. Moreover, we discuss the adaptation of our incentive mechanism to dynamic node joining or departing. Performance evaluations compare our incentive mechanism with three benchmarks and a state-of-the-art mechanism. The results indicate that our incentive mechanism can effectively motivate both the edge nodes and the remote cloud to participate in the edge–cloud environment, achieving maximum resource utilization with minimal rewards while remaining robust in dynamic situations. Full article