Search Results (930)

Diffusion within porous media, such as biological tissues, often deviates from conventional Fick’s laws that may be described by space-fractional diffusion equations. Microscale tissue heterogeneity can be represented by the space-fractional Riesz Laplacian operator acting on concentration or, alternatively, by fractional a Riesz gradient of the order $\beta$, extending the usual spatial gradient concept. We consider a reaction-diffusion system with two spatial compartments—a proximal one of finite radius having a source, and an outer one extending to infinity where the source is absent but first-order decay takes place. The steady state is derived using Hankel and Mellin transforms, resulting in integral-kernels-containing Bessel functions. We develop and compare three numerical quadrature methods for the Hankel transform: sinc quadrature, Ogata quadrature (based on Bessel zeros), and a hybrid asymptotic–numerical scheme. Numerical results and plots are presented for exponents $\beta =1/2,2/3,3/4$ and 1. The integer-order case ($\beta =1$) is recovered as a limiting case. The hybrid method is about five times faster than the global quadratures for the same accuracy. The novelty of this work lies in the systematic comparison of numerical methods for this specific class of fractional reaction–diffusion problems. Full article

The corrosion of metallic biomaterials under physiological conditions remains a critical challenge due to the risk of ion release affecting biocompatibility. In this study, the time-dependent electrochemical behavior of a poly(vinyl alcohol) (PVA)/chitosan biopolymer coating applied to biomedical-grade steel was evaluated to assess its protective performance. The coating was prepared via dip-coating using a 9.9:0.1 PVA/chitosan formulation and characterized by FTIR and Raman spectroscopy to confirm intermolecular interactions and film formation. Electrochemical impedance spectroscopy was employed over 216 h of immersion in Hanks’ Balanced Salt Solution to monitor coating degradation and corrosion mechanisms. The coated system exhibited a significant increase in the low-frequency impedance modulus from approximately 1200–1400 to 5000–6000 Ω·cm², indicating enhanced barrier properties and interfacial stability compared to uncoated steel. Equivalent circuit analysis revealed a transition from a predominantly capacitive, barrier-controlled response at early immersion stages to diffusion-influenced behavior at longer exposure times, associated with electrolyte penetration and interfacial processes. Overall, the PVA/chitosan coating effectively delays corrosion reactions and maintains high impedance under simulated physiological conditions, demonstrating its potential as a protective and biocompatible surface modification for metallic biomedical applications. Full article

Electrokinetic mechanisms of platinum catalyst degradation in polymer electrolyte membrane fuel cells are represented by platinum ion dissolution and platinum oxide formation. The other primary mechanism of degradation is the diffusion of platinum particles. The governing one-dimensional Holby–Morgan model across the catalyst thickness is described by nonlinear reaction–diffusion equations with Butler–Volmer reaction rates. To approximate properly spatial diffusion, the Chebyshev–Gauss–Lobatto pseudo-spectral collocation method is introduced and tested numerically within implicit–explicit time discretization. The accurate approximation makes it possible to simulate the nonlinear degradation of a platinum catalyst over a long cyclic voltammetry test until it loses its operational capacity. Full article

Accurate network traffic prediction is important for the autonomy, resilience and resource orchestration of 6G and AI-native communication infrastructures, while also supporting green networking and digital twin network applications. However, existing data-driven prediction models face several limitations: over-reliance on massive labeled data, physically implausible predictions, black-box non-interpretability and over-parameterization that impairs edge deployment. To address these issues, this paper proposes a Physics-Informed Network Traffic Prediction (PINTP) framework for graph topology network traffic prediction, which formalizes network traffic evolution as Graph-based Advection–Diffusion–Reaction (ADR) equations and embeds physical regularization into the neural architecture. The framework adopts a hybrid differentiation paradigm unifying automatic differentiation for temporal dynamics and spectral graph theory-derived operators for discrete spatial topologies, and designs a physics-constrained composite loss function with data-driven collocation to balance data fidelity and physical consistency. Experiments are conducted in two complementary settings: a 100-node synthetic random-graph benchmark that evaluates the full graph-topological formulation, and a topology-unavailable real-world telemetry proxy based on Alibaba Cluster Trace v2018 for evaluating sparse-label physics-informed temporal regularization. Comparative analysis with mainstream baselines, including Multilayer Perceptron (MLP), Spatio-Temporal Graph Convolutional Network (STGCN), Graph WaveNet, Transformer, Temporal Convolutional Network (TCN), and XGBoost, shows that the proposed PINTP/PINN implementation achieves a test $R^2$ of 0.898 and MSE of 0.000723 on the 100-node synthetic graph benchmark, close to the strongest Transformer result ($R^2=0.900$, MSE = 0.000710), while using substantially fewer trainable parameters. PINTP/PINN also outperforms Graph WaveNet, STGCN and TCN in this setting, indicating that physics-informed regularization can remain competitive as graph size increases. On the Alibaba proxy task, PINTP/PINN achieves the strongest result among the evaluated models with a test $R^2$ of 0.963. In an independent Alibaba ablation protocol, physical regularization (e.g., $\lambda =10.0$) reduces the mean squared error by 89.15% compared with pure data-driven models and helps mitigate overfitting. This work presents a systematic PINTP framework for graph topology network traffic prediction, achieving competitive prediction accuracy with high parameter efficiency and a degree of physical interpretability. It helps address several limitations of traditional data-driven models, indicates potential for future deployment-oriented studies on real-time network management and resource-constrained edge analytics, and provides an interpretable modeling route for physics-informed network analytics in next-generation communication systems. Full article

This research presents a physics-informed deep learning framework for investigating the magnetohydrodynamic flow of a dissipative non-Newtonian micropolar nanofluid induced by a stretching sheet, incorporating Stefan blowing, internal heat generation, and the zero nanoparticle mass flux condition. The physical model consists of the interplay between the microrotation dynamics, resistance of porosity on the microrotation, Brownian diffusion, and thermophoretic transport phenomenon. The numerical solutions for the nonlinear yielded equations that result from the above interaction are obtained by employing a PINN that considers the laws of physics and boundary conditions. With this technique, the flow behavior, temperature, concentration, and microrotation fields can be predicted accurately without requiring huge datasets. This shows the ability of PINNs to numerically treat highly-coupled nonlinear transport equations in a very efficient manner compared to other traditional methods. The important discoveries from this study include that the porous and magnetic factors increased the skin friction coefficient, but the magnetic effect and viscous dissipation decreased the rate of heat transfer, and the thermophoresis effect decreased the rate of mass transfer while the Brownian effect increased it. The precision of the PINN algorithm is confirmed by comparison of the results with the earlier findings, which proves very high accuracy and hence the robustness of the current computing framework. Results of this research are useful for the development of some thermal management systems, energy converters, cooling methods, chemical reaction processes, fuel cell technology, porous media reactors, and ocean engineering involving the transport of complicated non-Newtonian nanofluids. Full article

This paper presents a stochastic reaction–diffusion framework to analyze the dual role of multiplicative noise in coupled population–economic systems. The model incorporates density-dependent diffusion, realistically representing migration crowding and economic agglomeration. We establish that symmetric noise can suppress Turing instability in the linearized system through an Itô-induced damping, whereas asymmetric noise can induce spatial patterns in otherwise stable regimes. These analytical predictions, supported by numerical simulations, bridge linear stability analysis with nonlinear pattern evolution, providing a new theoretical foundation for understanding stochastic pattern formation in socio-economic dynamics. Full article

Tribological systems involving rolling and sliding contacts generate coupled mechanical interactions that govern friction, wear, and surface degradation. These interactions produce multiaxial residual stresses that influence crack initiation, accelerate wear, and promote environmentally assisted damage. In corrosive environments, tribo-corrosion further intensifies material degradation through the combined action of mechanical wear and electrochemical reactions. Protective organic and metallic coatings are widely used to mitigate these effects; however, their performance depends on adhesion, stress evolution, and resistance to coupled mechanical and chemical degradation. Among the principal failure mechanisms, cathodic blistering is strongly influenced by diffusion, interfacial stresses, and tribological loading. This review therefore links cathodic blister evolution with coating degradation under combined tribological and corrosive conditions. The review critically examines the Khan–Nazir meso-mechanics Models I, II, and III, which integrate stress-assisted diffusion, residual stress development, mixed-mode fracture, and coating–substrate delamination. Recent developments have extended these models through substrate deformation, multilayer coating architectures, and electro-chemo-mechanical phase-field simulations. The models demonstrate how diffusion-induced and residual stresses interact with tribological loading to initiate and propagate interfacial defects. The analysis shows that blister evolution is primarily governed by elastic modulus mismatch and friction-induced stress fields, while stability criteria predict non-axisymmetric blister morphologies associated with buckling and delamination. Overall, this review highlights the significance of the Khan–Nazir models for understanding wear, friction, and coating durability in engineering systems. The unified framework provides valuable guidance for the design and optimisation of advanced multilayer protective coatings for marine, automotive, energy, and manufacturing applications operating under rolling/sliding contact and tribo-corrosion environments. Full article

Dihydrocaffeic acid (DHCA) is a naturally occurring phenolic acid with recognized antioxidant and biological properties. However, its relatively high polarity limits its applicability in lipid-based systems. In this study, myrtenyl dihydrocaffeate was synthesized through lipase-catalyzed esterification of DHCA with myrtenol using immobilized Candida antarctica lipase B. The reaction conditions were optimized using response surface methodology based on a central composite design, yielding an experimental ester yield of 39.94 ± 1.16%. The synthesized ester was characterized by NMR spectroscopy and subsequently evaluated using a combination of experimental and in silico approaches. Antioxidant activity was determined by DPPH^• and ABTS^•+ radical scavenging assays, while oxidative stabilization of rapeseed oil was assessed by pressure differential scanning calorimetry (PDSC). Antimicrobial activity was evaluated using disk diffusion, minimum inhibitory concentration (MIC), and minimum bactericidal concentration (MBC) assays. In silico studies included ADMET profiling, PASS bioactivity prediction, protein target prediction, and molecular docking. These computational analyses were used only as hypothesis-generating tools because the predicted protein targets had low target-probability scores and were not experimentally validated. Myrtenyl dihydrocaffeate retained substantial antioxidant activity and significantly improved the oxidative stability of rapeseed oil, exhibiting protection factors comparable to those of DHCA. The ester also demonstrated mild antimicrobial activity against selected Gram-positive bacteria. Overall, the results indicate that lipophilization of DHCA with myrtenol is an effective strategy for developing lipophilic antioxidant derivatives for lipid-based food, cosmetic, or topical formulations, while the predicted molecular targets require experimental validation. Full article

Diarrheal disease remains a significant public health concern in Bhutan; however, the genomic epidemiology of the circulating diarrhoeagenic Escherichia coli (DEC) strain remains poorly understood. This study characterized the genomic diversity, antimicrobial resistance (AMR), and virulence determinants of DEC isolates using whole-genome sequencing (WGS). DEC isolates recovered from stool samples and collected through Bhutan’s National Diarrheal Disease Surveillance sentinel hospitals during 2023 were identified by a multiplex polymerase chain reaction, tested for antimicrobial susceptibility using the Kirby–Bauer disc diffusion method, and sequenced on the Illumina MiSeq platform. Genomes were analyzed using the Bohra pipeline to determine pathotypes, phylogeny, multilocus sequence types, serotypes, virulence factors, and AMR genes. Of the 29 DEC isolates, 27 were confirmed by WGS and enteropathogenic E. coli (37.0%) and enteroaggregative E. coli (33.3%) were the predominant pathotypes. Isolates exhibited extensive genetic diversity, representing phylogroups A and B1 and 22 serotypes. Phenotypic resistance to β-lactams was common, with 25.9% of isolates carrying bla_CTX-M-15. Virulence profiling identified diverse adhesins, toxins, iron acquisition systems, and type III secretion system components. DEC isolates in Bhutan comprise a genetically diverse population with a concerning convergence of virulence determinants and multidrug resistance. The findings underscore the strengthening of sustained genomic surveillance to monitor AMR and genomic epidemiology of bacterial pathogens. Full article

Efficient and accurate time integration is essential for simulating combustion processes with stiff, multiscale dynamics arising from diffusion, reaction, and advection interactions. Standard implicit–explicit (IMEX) methods employ a two-additive splitting in which these three physical processes are grouped into only two components, often forcing diffusion and reaction to be treated together despite their different numerical characteristics. This coupling may limit computational efficiency and flexibility. Three-additive splitting methods overcome this limitation by treating diffusion, reaction, and advection as separate components, allowing each process to be integrated using a more suitable implicit or explicit method. Although the theoretical properties of three-additive linear multistep methods (LMMs) have previously been established, their performance has been assessed only on a single benchmark problem. This paper presents the first systematic numerical evaluation of implicit–implicit–explicit (IIE) and implicit–explicit–explicit (IEE) linear multistep methods for a stiff one-dimensional combustion model with FKPP, Ignition, and Fisher reaction kinetics. The three-additive splitting methods are compared with standard IMEX-LMMs of up to fourth order using CPU time, RMS error, and a model-based stability analysis. The results show that the IIE and IEE methods consistently improve computational efficiency while maintaining comparable numerical accuracy and stability. In particular, the first-order methods exhibit larger stability regions than their IMEX counterparts, whereas the higher-order methods retain comparable stability while achieving superior work–precision performance. These findings demonstrate that three-additive LMMs provide an efficient and flexible alternative to conventional IMEX methods for stiff diffusion–reaction–advection systems. Full article

Within a circular symmetric domain, we investigate the corresponding dynamic behaviors of a reaction-diffusion predator-prey system with nonlocal competition. First, sufficient conditions for the local stability of the steady state are established and criteria for the occurrence of various bifurcations are derived. Furthermore, based on the center manifold method and normal form theory, the third order normal form formula for the Turing-Hopf bifurcation is refined, which adapts to the systems with nonlocal competing terms of a wide class of kernel functions in principle in a circular domain. Finally, a series of numerical simulations are performed to validate the analytical results and visually illustrate the rich spatiotemporal patterns by nonlocal competition. Full article

In this study, we investigate how lightning-derived electric-field influences nonlinear excitation dynamics in excitable systems. Cloud-to-ground (CG) lightning observations from the National Lightning Detection Network (NLDN), including event time, location, and peak current, were used to reconstruct realistic lightning-derived electric-field inputs. The electric field distribution was estimated from lightning peak current and propagation distance using a physical formulation, and discrete lightning events were converted into continuous time-dependent forcing signals through Gaussian kernel superposition while preserving their spatiotemporal organization. The resulting electric-field signals were then applied to the FitzHugh–Nagumo (FHN) model, where biologically inspired excitation dynamics were simulated and analyzed using normalized external inputs. The simulations demonstrate that temporally accumulated lightning-derived forcing induces nonlinear transitions between excitation regimes. Stronger peak-current inputs more readily exceed excitation thresholds and produce enhanced responses, including repeated excitation events, whereas weaker inputs generate limited or sub-threshold responses. These results show that excitation dynamics depend not only on electric-field amplitude but also on the temporal accumulation and organization of lightning activity. Furthermore, a spatially extended reaction–diffusion FHN model demonstrates that lightning-induced electric-field attenuation coupled with nonlinear dynamics can generate spatially propagating excitation structures. This physics-based framework provides a conceptual approach for linking naturally occurring electric-field environments with nonlinear excitable-system dynamics. Although the present model does not represent direct physiological coupling, it provides a foundation for exploring how structured environmental electric fields may influence threshold-dependent dynamical responses. Full article

We show that halide evaporation during melt growth of ${\mathrm{CsPbBr}}_3$ on polycrystalline FTO under partially open conditions drives discrete phase selection between the line compounds of the CsBr–PbBr₂ system, producing a sharp ${\mathrm{CsPbBr}}_3/{\mathrm{CsPb}}_2{\mathrm{Br}}_5$ bilayer instead of compositional grading. In situ optical imaging shows that solidification begins with nucleation and lateral growth of a planar ${\mathrm{CsPbBr}}_3$ single crystal while the melt layer is still thick enough to average over the FTO relief. As the crystal thickens, the residual melt then becomes inhomogeneous and unstable, producing a buried porous layer of faceted ${\mathrm{CsPb}}_2{\mathrm{Br}}_5$ grains with a characteristic in-plane spacing of 1–10$\mathsf{\mu }\mathrm{m}$). This morphology is consistent with a faceted Mullins–Sekerka-type instability under a non-conservative evaporative boundary condition. Beneath the single-crystal cap, the first-formed faceted islands are large and become progressively smaller as the advancing front approaches the FTO pyramids, while elevated ambient halide partial pressure suppresses the instability, consistent with diffusion–capillarity selection under decreasing residual melt thickness and steepening local gradients, modified by evaporative flux. Oxygen associated with microvoids or the oxide substrate enables a secondary reaction–diffusion pathway forming Pb–Br–O crystallites without altering the primary length scale. These results identify evaporation as an active control parameter coupling phase equilibria and interfacial stability in volatile halide melts. In the buried, porous bilayer morphology observed here, the secondary phases and porosity reduce the active ${\mathrm{CsPbBr}}_3$ volume and are expected to degrade scintillation through increased trapping, nonradiative recombination, and light scattering. Full article

The increasing generation of organic waste and the growing demand for sustainable solid fuels have intensified interest in hydrothermal carbonisation (HTC) as a pathway for biomass valorization within circular bioeconomy systems. HTC uses subcritical water to upgrade moist biomass into hydrochar with improved fuel properties and combustion behavior. This review correlates key HTC parameters, including temperature, residence time, pH, and the nature of feedstock, with the chemical evolution and thermal reactivity of different hydrochars. Data synthesis identifies a typical ‘kinetic optimization’ range between 180 and 220 °C for conventional lignocellulosic feedstocks. Within this thermal interval, activation energy (E_a) decreases from 180–260 kJ/mol for raw biomass to 70–180 kJ/mol for hydrochars, while the high heating value (HHV) reaches up to ~28 MJ/kg. The results further demonstrate that feedstock composition strongly influences combustion reactivity and kinetic behavior under similar HTC conditions. The integration of isoconversional methods with thermodynamic parameters (ΔH, ΔG, ΔS) confirms a transition toward more ordered and thermally stable carbon structures. Additionally, Criado’s master plots indicate a shift from diffusion-controlled to reaction-controlled combustion mechanisms with increasing HTC severity. These findings provide valuable insights into the optimizing of HTC conditions for balance energy densification and combustion reactivity, offering a comprehensive understanding to guide future hydrochar-based energy applications and scale-up studies. Full article

Methane (CH₄) was employed as a carbon source for the reduction and carburization of TiO₂ via a gas-phase infiltration process to synthesize titanium carbide (TiC). The highly reactive and diffusible carbon species derived from CH₄ decomposition enable a significant reduction in both reaction time and temperature compared with conventional carbothermal reduction methods. The phase evolution during the CH₄-driven reduction–carburization of TiO₂ was analyzed, and the effects of CH₄ volume fraction, reaction temperature, and reaction time on the carburization efficiency were systematically investigated, with the phase composition and microstructure of the products also characterized. The optimal conditions in a CH₄-Ar system were found to be 10%CH₄–90%Ar at 1270 °C for 8 h, yielding a carburization efficiency of 79.1% for TiO₂ pellets. Increasing the CH₄ proportion led to more severe carbon deposition, with deposited carbon adhering to the pellet surface and clogging the internal pores. Raising the temperature promoted the reduction–carburization reaction, but excessive acceleration of CH₄ cracking above 1270 °C caused carbon accumulation on the TiO₂ surface, forming a carbon shell that lowered the carburization efficiency. Prolonging the reaction time was beneficial for achieving a higher degree of carburization. Full article