Search Results (161)

Traditional Turkish marbling (Ebru) art is an intangible cultural heritage characterized by highly asymmetric, fluid, and non-reproducible patterns, making its long-term preservation and large-scale dissemination challenging. It is highly sensitive to environmental conditions, making it enormously difficult to mass produce while maintaining its original aesthetic qualities. A data-driven generative model is therefore required to create unlimited, high-fidelity digital surrogates that safeguard this UNESCO heritage against physical loss and enable large-scale cultural applications. This study introduces a deep generative modeling framework for the digital reconstruction of traditional Turkish marbling (Ebru) art using a Deep Convolutional Generative Adversarial Network (DCGAN). A dataset of 20,400 image patches, systematically derived from 17 original marbling works, was used to train the proposed model. The framework aims to mathematically capture the asymmetric, fluid, and stochastic nature of Ebru patterns, enabling the reproduction of their aesthetic structure in a digital medium. The generated images were evaluated using multiple quantitative and perceptual metrics, including Fréchet Inception Distance (FID), Kernel Inception Distance (KID), Learned Perceptual Image Patch Similarity (LPIPS), and PRDC-based indicators (Precision, Recall, Density, Coverage). For experimental validation, the proposed DCGAN framework is additionally compared against a Vanilla GAN baseline trained under identical conditions, highlighting the advantages of convolutional architectures for modeling marbling textures. The results show that the DCGAN model achieved a high level of realism and diversity without mode collapse or overfitting, producing images that were perceptually close to authentic marbling works. In addition to the quantitative evaluation, expert qualitative assessment by a traditional Ebru artist confirmed that the model reproduced the organic textures, color dynamics, and compositional asymmetrical characteristic of real marbling art. The proposed approach demonstrates the potential of deep generative models for the digital preservation, dissemination, and reinterpretation of intangible cultural heritage recognized by UNESCO. Full article

We analyze the formal equivalence between the electromagnetic energy conservation law derived from Maxwell’s equations in an optical microcavity and the conservation of a probability fluid associated with the de Broglie–Bohm theory for an effective massive particle describing a photon in this cavity. This work is part of a critical analysis of recent experiments by Sharoglazova et al. carried out with a view to refuting the de Broglie–Bohm theory. Furthermore, the consequences of our analysis for microphotonics go far beyond these experiments. In particular, extensions that take into account photon spin and stochastic aspects associated with radiative or absorption losses are considered. From the point of view of symmetries and probability current, here the effective photon behaves like a spin-1/2 particle. Full article

Wind-induced response poses a significant challenge to the stability of extra-large-span heavy steel box girders during synchronous lifting operations. This study adopted a method combining numerical simulation with on-site monitoring to investigate the aerodynamic characteristics the beam during the overall hoisting process of the Xiaotun Bridge. A high-fidelity finite element model was established using Midas NFX 2024 R1, and fluid–structure interaction (FSI) analysis was conducted, utilizing the RANS k-ε turbulence model to simulate stochastic wind fields. The results show that during the lifting stage from 3 m to 25 m, the maximum horizontal displacement of the steel box girder rapidly increases at wind angles of 90° and 60°, and the peak displacement is reached at 25 m. Under a strong breeze at a 90° wind angle and 25 m lifting height, the maximum lateral displacement was 42.88 mm based on FSI analysis, which is approximately 50% higher than the 28.58 mm obtained from linear static analysis. Subsequently, during the 25 m to 45 m lifting stage, the displacement gradually decreases and exhibits a linear correlation with lifting height. Concurrently, the maximum stress of the lifting lug of the steel box girder increases rapidly in the 3–25 m lifting stage, reaches the maximum at 25 m, and gradually stabilizes in the 25–45 m lifting stage. The lug stress under the same critical condition reached 190.80 MPa in FSI analysis, compared with 123.83 MPa in static analysis, highlighting a significant dynamic amplification. Furthermore, the detrimental coupling effect between mechanical vibrations from the lifting platform and wind loads was quantified; the anti-overturning stability coefficient was reduced by 10.48% under longitudinal vibration compared with lateral vibration, and a further reduction of up to 39.33% was caused by their synergy with wind excitation. Field monitoring validated the numerical model, with stress discrepancies below 9.7%. Based on these findings, a critical on-site wind speed threshold of 9.38 m/s was proposed, and integrated control methods were implemented to ensure construction safety. During on-site lifting, lifting lug stresses were monitored in real time, and if the predefined threshold was exceeded, contingency measures were immediately activated to ensure a controlled termination. Full article

The development of highly sensitive approaches for detecting tumor cells in biological samples remains a critical challenge in laboratory and clinical oncology. In this study, we investigated the structural and magnetic properties of iron oxide nanoparticles incorporated into cellulose microspheres of two size ranges (~100 and ~700 μm) and evaluated their potential for targeted tumor cell isolation. In the smaller microspheres, magnetite-based magnetic nanoparticles (MNPs) were synthesized in situ via co-precipitation, whereas pre-synthesized MNPs were embedded into the larger microspheres. The geometrical characteristics of the resulting magnetic cellulose microspheres (MSCMNs) were assessed by confocal microscopy. Transmission electron microscopy and X-ray diffraction analyses revealed an average magnetic core size of approximately 17 nm. Magnetic properties of the MNPs within MSCMNs were characterized using a highly sensitive nonlinear magnetic response technique, and their dynamic parameters were derived using a formalism based on the stochastic Hilbert–Landau–Lifshitz equation. To evaluate their applicability in cancer diagnostics and treatment monitoring, the MSCMNs were functionalized with a TKD peptide that selectively binds membrane-associated Hsp70 (mHsp70), yielding TKD@MSCMNs. Magnetic separation enabled the isolation of tumor cells from biological fluids. The specificity of TKD-mediated binding was confirmed using Flamma648-labeled Hsp70 and compared with control alloferone-conjugated microspheres (All@MSCMNs). The ability of TKD@MSCMNs to selectively extract mHsp70-positive tumor cells was validated using C6 glioma cells and mHsp70-negative FetMSCs controls. Following co-incubation, the extraction efficiency for C6 cells was 28 ± 14%, significantly higher than that for FetMSC (7 ± 7%, p < 0.05). These findings highlight the potential of TKD-functionalized magnetic cellulose microspheres as a sensitive platform for tumor cell detection and isolation. Full article

This study probabilistically assesses magma ascent by modeling dike propagation as a fully coupled fluid-flow, thermo-mechanical problem, explicitly accounting for the stochastic heterogeneity of the crustal host rock. We study felsic (rhyolite) lava flow and two distinct basaltic feeding regimes that correspond to the conditions necessary to produce the contrasting pāhoehoe and ʻaʻā surface morphologies. Basaltic dikes demonstrate high propagation efficiency to the surface (pāhoehoe-feeding regime 99.5%; ʻaʻā-feeding regime 97.5%), whereas rhyolite dikes have an 89% failure rate, attributed to significant friction. Both regimes represent distinct constructal approaches aimed at maximizing flow persistence. The pāhoehoe-feeding regime is a thermally regulated, stable design characterized by low-velocity, cooling-dominated dynamics. Its slow, persistent flow allows for significant conductive heating of the surrounding rock wall, creating an efficient, pre-heated thermal conduit. In contrast, the ʻaʻā-feeding regime is a mechanically dominated design governed by high-velocity, stochastic dynamics. This morphology is driven by forceful flow, and its thermal budget is supplemented by intense viscous dissipation (internal friction). Rhyolite magma flow fails upon losing constructal viability, driven by a coupled mechanical–thermal cascade. The sequence begins when a mechanical barrier halts the magma velocity, which triggers a freezing event and leads to permanent arrest. Full article

To address the challenge of head stability in a biomimetic robotic dolphin during self-propulsion, this study systematically investigates the passive stabilization mechanism of a bio-inspired caudal keel. A combined experimental and computational fluid dynamics (CFD) approach was employed to evaluate four keel geometries across a tail oscillation frequency range of 0.5–2 Hz. The experimental results demonstrate that the optimal keel configuration reduced the standard deviation of the head pitch angle by 20.9% at 2 Hz. CFD analysis revealed a dual stabilization mechanism: an effective keel not only attenuates the intensity of the primary disturbance moment at the driving frequency but, more critically, also enhances the spectral purity of the signal by suppressing high-frequency harmonics and broadband stochastic noise through the systematic reorganization of caudal vortices. A systematic investigation of keel geometry identified non-dimensional height (h/c) as the dominant parameter, with its stabilizing effect exhibiting diminishing returns beyond an optimal range. Furthermore, a quantifiable design trade-off was established, showing an approximate 9.1% increase in the Cost of Transport (CoT) for the most stable configuration. These findings provide quantitative design principles and a deeper physical insight into the passive stabilization of biomimetic underwater vehicles, highlighting the importance of both disturbance intensity and spectral quality. Full article

This study presents an analysis of transverse vibration behavior of a fluid-conveying pipe mounted on an elastic foundation, incorporating both classical analytical techniques and modern physics-informed neural network (PINN) methodologies. A partial differential equation (PDE) architecture is developed to approximate the solution by embedding the physics PDE, initial, and boundary conditions directly into the loss function of a deep neural network. A one-dimensional fourth-order PDE is employed to model governing transverse displacement derived from Euler–Bernoulli beam theory, with additional terms representing fluid inertia, flow-induced excitation, and stochastic force modelled as Gaussian white noise. The governing PDE is decomposed via separation of variables into spatial and temporal components, and modal analysis is employed to determine the natural frequencies and mode shapes under free–free boundary conditions. The influence of varying flow velocities and excitation frequencies on critical buckling behavior and mode shape deformation is analyzed. The network is trained using the Resilient Backpropagation (RProp) optimizer. A preliminary validation study is presented in which a baseline PINN is benchmarked against analytical modal solutions for a fluid-conveying pipe on an elastic foundation under deterministic excitation. The results demonstrate the capability of PINNs to accurately capture complex vibrational phenomena, offering a robust framework for data-driven modelling of fluid–structure interactions in engineering applications. Full article

This paper presents a comprehensive study of nonlinear wave and oscillator dynamics under both deterministic and stochastic influences. By comparing soliton-like and dispersive waveforms, we employ spectral solvers, Darboux transformations, and nonlinear diagnostics, including Lyapunov exponents, power spectral analysis, and multidimensional phase-space reconstructions, to examine transitions from quasiperiodic motion to chaotic and stochastic regimes. The results highlight the robustness of soliton solutions in preserving energy and structure, in contrast to the degradation observed in dispersive waves under noise and damping. We also show that spectral broadening, entropy growth, and ergodic phase-space patterns are caused by the critical influence of initial conditions and noise intensity on system behavior. Incorporating control strategies such as OGY chaos control, this work provides a flexible framework for analyzing, modeling, and stabilizing nonlinear systems. Applications span nonlinear optics, fluid flows, and electrical lattices, offering insight into the interplay of nonlinearity and noise with implications for both theoretical understanding and practical system design. Full article

This study presents a novel framework for uncertainty propagation in power-law, Bingham, and Casson fluids through rectangular ducts under stochastic viscosity (Case I) and pressure gradient conditions (Case II). Using the computationally efficient Stochastic Finite Difference Method with Homogeneous Chaos (SFDHC), validated via comparison with quasi-Monte Carlo simulations, we demonstrate significantly lower computational costs across varying Coefficients of Variation (COVs). For viscosity uncertainty (Case I), results show a 0.54–2.8% increase in mean maximum velocity with standard deviations reaching 75.3–82.5% of the COV, where the power-law model exhibits the greatest sensitivity (velocity variations spanning 71.2–177.3% of the mean at COV = 20%). Pressure gradient uncertainty (Case II) preserves mean velocities but produces narrower and symmetric distributions. We systematically evaluate the effects of aspect ratio, yield stress, and flow behavior index on the stochastic velocity response of each fluid. Moreover, our analysis pioneers a performance hierarchy: Herschel–Bulkley fluids show the highest mean and standard deviation of maximum velocity, followed by power-law, Robertson–Stiff, Bingham, and Casson models. A key finding is the extreme fluctuation of the Robertson–Stiff model, which exhibits the most drastic deviations, reaching up to 177% of the average velocity. The significance of fluid-specific stochastic analysis in duct system design is underscored by these results. This is especially critical for non-Newtonian flows, where system performance and reliability are greatly impacted by uncertainties in viscosity and pressure gradient, which reflect actual operational variations. Full article

Spiral concentrators are gravity and centrifugal force-based devices designed for mineral concentration. During processing operations, dynamic variations in the slurry’s liquid film thickness can induce hydrodynamic instability, generating roll waves on the free surface that compromise particle separation efficiency. To ensure operational stability and efficacy, this study establishes a theoretical shallow-water flow model for slurry dynamics in spiral concentrators based on hydraulic principles. Through L₂₇(3¹³) orthogonal experiments and real-time ultrasonic film thickness monitoring, the influence of key parameters on roll wave evolution is quantified. Results indicate that roll waves follow an “instability-development-dissipation” sequence. The pitch-to-diameter ratio (P/D) exerts a highly significant effect on roll wave intensity, while particle properties (density and size) exhibit moderate significance. In contrast, feed flow rate and solid concentration show negligible impacts. Roll waves amplify fluid turbulence, triggering stochastic migration of particles (especially low-density grains), which increases the standard deviation of zonal recovery rates (ZRR) and degrades separation precision. This work provides critical insights into particle behavior under roll wave conditions and offers a theoretical foundation for optimizing spiral concentrator design and process control. Full article

The Backtracking Search Algorithm (BSA) has emerged as a promising stochastic optimization method. This paper introduces a novel hybrid evolutionary algorithm, termed LOBSA, integrating the strengths of BSA and Lemurs Optimizer (LO). The hybrid approach significantly improves global exploration and convergence speed, validated through rigorous tests on 23 benchmark functions from the CEC 2013 suite, encompassing unimodal, multimodal, and fixed dimension multimodal functions. Compared with state-of-the-art algorithms, LOBSA presents a relative improvement, achieving superior results and outperforming traditional BSA by up to 35% of global performance gain in terms of solution accuracy. Moreover, the applicability and robustness of LOBSA were demonstrated in practical constrained optimization and a fluid–structure interaction problem involving the dynamic analysis and optimization of a submerged boat propeller, demonstrating both computational efficiency and real-world applicability. Full article

Osteoarthritis (OA) is a multifactorial chronic musculoskeletal disorder characterised by cartilage degradation, synovial inflammation, and subchondral bone remodelling. Conventional diagnostic modalities, including radiographic imaging and symptom-based assessments, primarily detect disease in its later stages, limiting the potential for timely intervention. Inflammatory biomarkers, particularly Interleukin-6 (IL-6), Tumour Necrosis Factor-alpha (TNF-α), and Myeloperoxidase (MPO), have emerged as biologically relevant indicators of disease activity, with potential applications as companion diagnostics in precision medicine. This review examines the diagnostic and prognostic relevance of IL-6, TNF-α, and MPO in OA, focusing on their mechanistic roles in inflammation and joint degeneration, particularly through the activity of fibroblast-like synoviocytes (FLSs). The influence of sample type (serum, plasma, synovial fluid) and analytical performance, including enzyme-linked immunosorbent assay (ELISA), is discussed in the context of biomarker detectability. Advanced statistical and computational methodologies, including rank-based analysis of covariance (ANCOVA), discriminant function analysis (DFA), and Cox proportional hazards modelling, are explored for their capacity to validate biomarker associations, adjust for demographic variability, and stratify patient risk. Further, the utility of synthetic data generation, hierarchical clustering, and dimensionality reduction techniques (e.g., t-distributed stochastic neighbour embedding) in addressing inter-individual variability and enhancing model generalisability is also examined. Collectively, this synthesis supports the integration of biomarker profiling with advanced analytical modelling to improve early OA detection, enable patient-specific classification, and inform the development of targeted therapeutic strategies. Full article

In celebration of 50 years of the endoreversible Carnot-like heat engine, this work aims to link the thermodynamic success of the irreversible Carnot-like heat engine with the stability dynamics of the engine. This region of success is defined by two extreme configurations in the interaction between heat reservoirs and the working fluid. The first corresponds to a fully reversible limit, and the second one is the fully dissipative limit; in between both limits, the heat exchange between reservoirs and working fluid produces irreversibilities and entropy generation. The distance between these two extremal configurations is minimized, independently of the chosen metric, in the state where the efficiency is half the Carnot efficiency. This boundary encloses the region where irreversibilities dominate or the reversible behavior dominates (region of success). A general stability dynamics is proposed based on the endoreversible nature of the model and the operation parameter in charge of defining the operation regime. For this purpose, the maximum ecological and maximum Omega regimes are considered. The results show that for single perturbations, the dynamics rapidly directs the system towards the success region, and under random perturbations producing stochastic trajectories, the system remains always in this region. The results are contrasted with the case in which no restitution dynamics exist. It is shown that stability allows the system to depart from the original steady state to other states that enhance the system’s performance, which could favor the evolution and specialization of systems in nature and in artificial devices. Full article

Differential equations need boundary conditions (BCs) for their solution. It is widely acknowledged that differential equations and BCs are representative of independent physical processes, and no correlations between them are required. Two recent studies by Hilhorst, Chung et al. argue instead that, in the specific case of diffusion equations (DEs) in bounded systems, BCs are uniquely constrained by the form of transport coefficients. In this paper, we revisit how DEs emerge as fluid limits out of a picture of stochastic transport. We point out their limits of validity and argue that, in most physical systems, BCs and DEs are actually uncorrelated by virtue of the failure of diffusive approximation near the system’s boundaries. When, instead, the diffusive approximation holds everywhere, we show that the correct chain of reasoning goes in the direction opposite to that conjectured by Hilhorst and Chung: it is the choice of the BCs that determines the form of the DE in the surroundings of the boundary. Full article

Wind turbine blades face significant challenges from stochastic wind loads, impacting structural integrity. Traditional analysis often isolates Computational Fluid Dynamics (CFD) from Building Information Modeling (BIM) in the design process. This study bridges this gap by integrating BIM forward design with CFD simulation. A universal BIM modeling framework is developed for rapid blade modeling, which is compatible with ANSYS Workbench 2022 R1 through intermediate format conversion. The influence of wind load on the blades under various wind speed conditions is analyzed, and the results indicate a significant correlation between wind load intensity and blade structural response. The maximum windward pressure reaches 4.96 kPa, while the leeward suction peaks at −6.28 kPa. The displacement at the tip and middle part of the blades significantly increases with the increase in wind speed. The growth rate of displacement between adjacent speeds rises from 1.20 to 1.94, and the overall increase rate within the entire range rises from 1.02 to 4.16. These results demonstrate the feasibility of using BIM forward design in accurate performance analysis, and also extends the value of BIM in wind energy. Furthermore, a bidirectional information flow is established, where BIM provides geometry for CFD, and simulation results will inform BIM design refinement. Full article