Search Results (179)

Gas nitriding was implemented in the current work at a constant nitrogen potential (K_N) of 2.0 for 8 h to enhance the fatigue properties of SCM 440 steel, and the results were compared with those of the substrate tempered at the nitriding temperature (475 °C). Fine particle peening (FPP) prior to nitriding imposed a refined structure and induced compressive residual stress (CRS) in the near-surface peened zone. The fine-grained structure provided numerous paths to enhance nitrogen diffusion inwards during nitriding. The compound layer formed on the nitrided SCM 440 steel primarily comprised a mixture of Fe₃N and Fe₄N; however, the pre-peened and nitrided (SPN) specimens exhibited a higher proportion of Fe₃N and a thicker compound layer than the non-peened and nitrided (NPN) counterparts. In addition, FPP prior to nitriding increased both the case depth and the magnitude of the CRS field compared with nitriding alone. The fatigue limits of the substrate (SB), NPN, and SPN samples were approximately 750, 1050, and 1400 MPa, respectively. Gas-nitriding at 475 °C significantly improved the fatigue performance of SCM 440 steel. Moreover, pre-peening prior to nitriding further enhanced fatigue strength and life of the treated SCM 440 steel by introducing a deeper case depth and higher CRS field. Multiple cracks initiation at the outer surface of the SB sample accounted for its lowest fatigue limit among the tested samples. Surface microcracks and pits on the surface of the NPN specimen would be crack initiation sites and harmful to its fatigue resistance. These surface dents were considered to be responsible for fatigue crack initiation in the SPN specimens. Therefore, polishing after nitriding to reduce surface roughness and/or microcracks was expected to further increase the fatigue resistance and the reliability of nitrided SCM 440 steel. Full article

Background: Electrochemical impedance spectroscopy (EIS) is indispensable for disentangling charge-transfer, capacitive, and diffusive phenomena, yet reproducible parameter estimation and objective model selection remain unsettled. Methods: We derive closed-form impedances and analytical Jacobians for seven equivalent-circuit models (Randles, constant-phase element (CPE), and Warburg impedance (ZW) variants), enforce physical bounds, and fit synthetic spectra with 2.5% and 5.0% Gaussian noise using hybrid optimization (Differential Evolution (DE) → Levenberg–Marquardt (LM)). Uncertainty is quantified via non-parametric bootstrap; parsimony is assessed with root-mean-square error (RMSE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC); physical consistency is checked by Kramers–Kronig (KK) diagnostics. Results: Solution resistance ($R_s$) and charge-transfer resistance ($R_{ct}$) are consistently identifiable across noise levels. CPE parameters $(Q,n)$ and diffusion amplitude $\left(\sigma \right)$ exhibit expected collinearity unless the frequency window excites both processes. Randles suffices for ideal interfaces; Randles+CPE lowers AIC when non-ideality and/or higher noise dominate; adding Warburg reproduces the ${45}^{\circ }$ tail and improves likelihood when diffusion is present. The $(R_{ct}+Z_W)\Vert \mathrm{CPE}$ architecture offers the best trade-off when heterogeneity and diffusion coexist. Conclusions: The framework unifies analytical derivations, hybrid optimization, and rigorous statistics to deliver traceable, reproducible EIS analysis and clear applicability domains, reducing subjective model choice. All code, data, and settings are released to enable exact reproduction. Full article

The increasing adoption of digital technologies, robotic systems, and IoT applications in sectors such as medicine, agriculture, and industry drives a surge in data generation and necessitates secure and efficient encryption. For resource-constrained systems, lightweight yet robust cryptographic algorithms are critical. This study addresses the security demands of IoRT systems by proposing an enhanced chaos-based encryption method. The approach integrates the lightweight structure of NIST-standardized Ascon-AEAD128 with the randomness of the Zaslavsky map. Ascon-AEAD128 is widely used on many hardware platforms; therefore, it must robustly resist both passive and active attacks. To overcome these challenges and enhance Ascon’s security, we integrate into Ascon the keys and nonces generated by the Zaslavsky chaotic map, which is deterministic, nonperiodic, and highly sensitive to initial conditions and parameter variations.This integration yields a chaos-based Ascon variant with a higher encryption security relative to the standard Ascon. In addition, we introduce exploratory variants that inject non-repeating chaotic values into the initialization vectors (IVs), the round constants (RCs), and the linear diffusion constants (LCs), while preserving the core permutation. Real-time tests are conducted using Raspberry Pi 3B devices and ROS 2–based IoRT robots. The algorithm’s performance is evaluated over 100 encryption runs on 12 grayscale/color images and variable-length text transmitted via MQTT. Statistical and differential analyses—including histogram, entropy, correlation, chi-square, NPCR, UACI, MSE, MAE, PSNR, and NIST SP 800-22 randomness tests—assess the encryption strength. The results indicate that the proposed method delivers consistent improvements in randomness and uniformity over standard Ascon-AEAD128, while remaining comparable to state-of-the-art chaotic encryption schemes across standard security metrics. These findings suggest that the algorithm is a promising option for resource-constrained IoRT applications. Full article

Externally governed hydrogel swelling is a highly convenient yet inherently challenging process, as it requires both responsive materials and appropriately tuned external stimuli. In this work, for the first time, the influence of simultaneous action of external physical fields—ultrasound (US) and microwave heating (MW), combined with cooling—on the isothermal swelling kinetics of poly(methacrylic acid) (PMAA) hydrogel was investigated and compared with swelling under conventional thermal heating (TH) under isothermal conditions. Swelling kinetics were monitored over a temperature range of 248–318 K, under simultaneous cooling with either US, MW, or TH. The well-established Peppas model was used to determine swelling kinetics parameters, revealing a significant acceleration in the swelling process under MW (up to 48.8 times at 313 K), as well as different water penetrating mechanisms (non-Fickian diffusion) compared to TH and US (Super-case II). Additionally, it was demonstrated that the swelling conversion curves could be mathematically described using a “shrinking boundary surfaces” model. Isothermal swelling constants and the corresponding kinetic parameters (activation energy E_a and pre-exponential factor ln A) were calculated. The results confirmed that external physical fields significantly influence the thermal activation and swelling behavior of PMAA xerogels, offering insight into field-responsive transport processes in hydrogel networks. Full article

The article presents a mathematical description of the thermal phenomena occurring both inside and on the surfaces of a homogeneous plate subjected to an external heat flux on one side. Analytical formulae for thermal excitation, with a given duration and constant power, are derived, enabling the determination of temperature increases on both the heated and unheated surfaces of the plate under specific heat transfer conditions to the surroundings. Convective heat transfer, with individual heat transfer coefficients on both sides of the slab, is considered; however, radiative heat loss can also be included. The solution of the problem obtained using two methods is presented: the method of separation of variables (MSV) and the Laplace transform (LT). The advantages and disadvantages of both analytical formulae, as well as the impact of various factors on the accuracy of the solution, are discussed. Among others, the MSV solution works well for a sufficiently long time, whereas the LT solution is better for a sufficiently short time. The theoretical considerations are illustrated with diagrams for several configurations, each representing various heat transfer conditions on both sides of the plate. The presented solution can serve as a starting point for further analysis of more complex geometries or multilayered structures, e.g., in non-destructive testing using active thermography. The developed theoretical model is verified for a determination of the thermal diffusivity of a reference material. The model can be useful for analyzing the method’s sensitivity to various factors occurring during the measurement process, or the method can be adapted to a pulse of known duration and constant power, which is much easier to implement technically than a very short impulse (Dirac) with high energy. Full article

This study presents a three-dimensional numerical analysis of natural convection during the postharvest storage of corn and wheat in a galvanized steel silo with a conical roof and floor, measuring 3 m in radius and 18.7 m in height, located in the Bajío region of Mexico. Simulations were carried out specifically for December, a period characterized by cold ambient temperatures (10–20 °C) and comparatively lower solar radiation than in warmer months, yet still sufficient to induce significant heating of the silo’s metallic surfaces. The governing conservation equations of mass, momentum, energy, and species were solved using the finite volume method under the Boussinesq approximation. The model included grain–air sorption equilibrium via sorption isotherms, as well as metabolic heat generation: for wheat, a constant respiration rate was assumed due to limited biochemical data, whereas for corn, respiration heat was modeled as a function of grain temperature and moisture, thereby more realistically representing metabolic activity. The results, obtained for December storage conditions, reveal distinct thermal and hygroscopic responses between the two grains. Corn, with higher thermal diffusivity, developed a central thermal core reaching 32 °C, whereas wheat, with lower diffusivity, retained heat in the upper region, peaking at 29 °C. Radial temperature profiles showed progressive transitions: the silo core exhibited a delayed response relative to ambient temperature fluctuations, reflecting the insulating effect of grain. In contrast, grain at 1 m from the wall displayed intermediate amplitudes. In contrast, zones adjacent to the wall reached 40–41 °C during solar exposure. In comparison, shaded regions exhibited minimum temperatures close to 15 °C, confirming that wall heating is governed primarily by solar radiation and metal conductivity. Axial gradients further emphasized critical zones, as roof-adjacent grain heated rapidly to 38–40 °C during midday before cooling sharply at night. Relative humidity levels exceeded 70% along roof and wall surfaces, leading to condensation risks, while core moisture remained stable (~14.0% for corn and ~13.9% for wheat). Despite the cold ambient temperatures typical of December, neither temperature nor relative humidity remained within recommended safe storage ranges (10–15 °C; 65–75%). These findings demonstrate that external climatic conditions and solar radiation, even at reduced levels in December, dominate the thermal and hygroscopic behavior of the silo, independent of grain type. The identification of unstable zones near the roof and walls underscores the need for passive conservation strategies, such as grain redistribution and selective ventilation, to mitigate fungal proliferation and storage losses under non-aerated conditions. Full article

A Lotka–Volterra-type system with porous diffusion, which can be used as an alternative model to the classical Lotka–Volterra system, is under study. Multiparameter families of exact solutions of the system in question are constructed and their properties are established. It is shown that the solutions obtained can satisfy the zero Neumann conditions, which are typical conditions for mathematical models describing real-world processes. It is proved that the system possesses two stable steady-state points provided its coefficients are correctly specified. In particular, this occurs when the system models the prey–predator interaction. The exact solutions are used for solving boundary-value problems. The analytical results are compared with numerical solutions of the same boundary-value problems but perturbed initial profiles. It is demonstrated that the numerical solutions coincide with the relevant exact solutions with high exactness in the case of sufficiently small perturbations of the initial profiles. Full article

Copper alloys are critical heat sink materials for fusion reactor divertors due to their high thermal conductivity (TC) and strength, yet their performance under extreme particle bombardment and heat fluxes in future tokamaks requires enhancement. While neutron-induced transmutation helium affects the properties of copper, the atomistic mechanisms linking helium bubble size to thermal transport remain unclear. This study employs non-equilibrium molecular dynamics (NEMD) simulations to isolate the effect of bubble diameter (10, 20, 30, 40 Å) on TC in copper, maintaining a constant He-to-vacancy ratio of 2.5. Results demonstrate that larger bubbles significantly impair TC. This reduction correlates with increased Kapitza thermal resistance and pronounced lattice distortion from outward helium diffusion, intensifying phonon scattering. Phonon density of states (PDOS) analysis reveals diminished low-frequency peaks and an elevated high-frequency peak for bubbles >30 Å, confirming phonon confinement and localized vibrational modes. The PDOS overlap factor decreases with bubble size, directly linking microstructural evolution to thermal resistance. These findings elucidate the size-dependent mechanisms of helium bubble impacts on thermal transport in copper divertor materials. Full article

In this paper, we propose a diffusion-type predator–prey interaction model with harvest and disease in prey, and conduct stability analysis and pattern formation analysis on the model. For the temporal model, the asymptotic stability of each equilibrium is analyzed using the linear stability method, and the conditions for Hopf bifurcation to occur near the positive equilibrium are investigated. The simulation results indicate that an increase in infection force might disrupt the stability of the model, while an increase in harvesting intensity would make the model stable. For the spatiotemporal model, a priori estimate for the positive steady state is obtained for the non-existence of the non-constant positive solution using maximum principle and Harnack inequality. The Leray–Schauder degree theory is used to study the sufficient conditions for the existence of non-constant positive steady states of the model, and pattern formation are achieved through numerical simulations. This indicates that the movement of prey and predators plays an important role in pattern formation, and different diffusions of these species may play essentially different effects. Full article

In this article, we propose a novel nonlinear cross-diffusion framework to model the distribution of susceptible and infected individuals within their habitat using a reduced SIR model that incorporates saturated incidence and treatment rates. The study investigates solution boundedness through the theory of parabolic partial differential equations, thereby validating the proposed spatio-temporal model. Through the implementation of the suggested cross-diffusion mechanism, the model reveals at least one non-constant positive equilibrium state within the susceptible–infected (SI) system. This work demonstrates the potential coexistence of susceptible and infected populations through cross-diffusion and unveils Turing instability within the system. By analyzing codimension-2 Turing–Hopf bifurcation, the study identifies the Turing space within the spatial context. In addition, we explore the results for Turing–Bogdanov–Takens bifurcation. To account for seasonal disease variations, novel perturbations are introduced. Comprehensive numerical simulations illustrate diverse emerging patterns in the Turing space, including holes, strips, and their mixtures. Additionally, the study identifies non-Turing and Turing–Bogdanov–Takens patterns for specific parameter selections. Spatial series and surfaces are graphed to enhance the clarity of the pattern results. This research provides theoretical insights into the implications of cross-diffusion in epidemic modeling, particularly in contexts characterized by localized mobility, clinically evident infections, and community-driven isolation behaviors. Full article

The non-monotonic behavior of amperometric enzyme-based biosensors under uncompetitive and noncompetitive (mixed) substrate inhibition is investigated computationally using a two-compartment model consisting of an enzyme layer and an outer diffusion layer. The model is based on a system of reaction–diffusion equations that includes a nonlinear term associated with non-Michaelis–Menten kinetics of the enzymatic reaction and accounts for the partitioning between layers. In addition to the known effect of substrate inhibition, where the maximum biosensor current differs from the steady-state output, it has been determined that external diffusion limitations can also cause the appearance of a local minimum in the current. At substrate concentrations greater than both the Michaelis–Menten constant and the uncompetitive substrate inhibition constant, and in the presence of external diffusion limitation, the transient response of the biosensor, after immersion in the substrate solution, may follow a five-phase pattern depending on the model parameter values: it starts from zero, reaches a global or local maximum, decreases to a local minimum, increases again, and finally decreases to a steady intermediate value. The biosensor performance is analyzed numerically using the finite difference method. Full article

We derive an infinite-series representation for the transition probability density function (PDF) of the time-inhomogeneous 3/2 model, expressing all coefficients in terms of Bell-polynomial and generalized Laguerre-polynomial formulas. From this series, we obtain explicit expressions for all conditional moments of the variance process, recovering the familiar time-homogeneous formulas when parameters are constant. Numerical experiments illustrate that both the density and moment series converge rapidly, and the resulting distributions agree with high-precision Monte Carlo simulations. Finally, we demonstrate that the same approach extends to a broad family of non-affine, time-varying diffusions, providing a general framework for obtaining transition PDFs and moments in advanced models. Full article

Solar radiation is a first-order essential climate variable like temperature and precipitation. Its significant spatiotemporal variability, mainly due to atmospheric conditions, makes modelling particularly challenging, especially in regions with complex atmospheric dynamics and sparse meteorological stations. This study evaluates 6 solar radiation models (SARAH, PVGIS, Constant Atmospheric Conditions, Physical Solar Model, CAMS Worldwide, and InsolMets) using monthly measurements from 141 ground-based stations across the Iberian Peninsula from 2004–2020. Although all models consistently captured intra-annual variability, discrepancies in absolute values arise due to factors such as the differences in their functional designs and input parameters. InsolMets, which integrates cloud optical thickness, cloud fractional cover, the diffuse radiation component, and enhanced solar illumination geometry, was the most robust model, showing relevant improvements (61.5% in January, 59.7% in November, and 52.0% in December) compared to the worst-performing model (constant atmospheric conditions). Using as a threshold three times the root-mean-square error (RMSE) proposed by the Global Climate Observing System, InsolMets achieved the highest number of months (10) under this limit, also achieving the best overall result, with only 1 month showing non-significant correlations over the same time span. Nevertheless, SARAH and PVGIS matched InsolMets’ performance during March, November, and December. The results provide insights for selecting and improving solar radiation estimations, highlighting the need to incorporate remote sensing atmospheric data to minimize uncertainties. While all models that account for atmospheric effects enhance accuracy, InsolMets stands out as the most accurate model for estimating solar radiation across the Iberian Peninsula throughout the year, achieving the lowest RMSE and normalized RMSE values. Full article

Recent research works have shown the effect of nutrient concentration on cell activity, such as proliferation and differentiation. In 3D cell culture, the impact of scaffold geometry, including pore size, strut diameter, and pore shape, on the concentration gradient within scaffolds under two different loading conditions—constant fluid perfusion and non-fluid perfusion—in a perfusion bioreactor is investigated by developing an in silico model of scaffolds. In this study, both triply periodic minimal surface (TPMS) (with gyroid struts) and non-TPMS (with cubic and spherical pores) scaffolds were investigated. Two types of criteria are applied to the scaffolds: static and perfusion culture conditions. In a static environment, the scaffold in a perfusion bioreactor is loaded with a fluid velocity of $0 \mathrm{m}\mathrm{m}/\mathrm{s}$, whereas in a dynamic environment, perfusion flow with a velocity of 1 mm/s is applied. The results of in silico simulation indicate that the concentration gradient within the scaffold is significantly influenced by pore size, strut diameter, pore shape, and fluid flow, which in turn affects the diffusion rate during drug delivery. Full article

This study investigates magnetohydrodynamic (MHD) heat and mass transport in a water-based ternary hybrid nanofluid flowing past an exponentially accelerated vertical porous plate. Two critical scenarios are analyzed: (i) uniform heat flux with variable mass diffusion and (ii) varying heat source with constant species diffusion. The model integrates thermal radiation, heat sink/source, thermal diffusion, and chemical reaction effects to assess flow stability and thermal performance. Governing equations are non-dimensionalized and solved analytically using the Laplace transform method, with results validated against published data and finite difference method outcomes. Ternary hybrid nanofluids exhibit a significantly higher Nusselt number compared to hybrid and conventional nanofluids, demonstrating superior heat transfer capabilities. Magnetic field intensity reduces fluid velocity, while porosity enhances momentum transfer. Thermal radiation amplifies temperature profiles, critical for energy systems. Concentration boundary layer thickness decreases with higher chemical reaction rates, optimizing species diffusion. These findings contribute to the development of advanced thermal management systems, such as solar energy collectors and nuclear reactors, enhance energy-efficient industrial processes, and support biomedical technologies that require precise heat and mass control. This study positions ternary hybrid nanofluids as a transformative solution for optimizing high-performance thermal systems. Full article