Search Results (278)

Background/Objectives: Glioblastoma (GBM) exhibits heterogeneous, nonlinear invasion patterns that challenge conventional modeling and radiomic prediction. Most deep learning approaches describe the morphology but rarely capture the dynamical stability of tumor evolution. We propose an AI framework that approximates a latent attractor landscape of GBM morphodynamics—stable basins in a continuous manifold that are consistent with reproducible morphologic regimes. Methods: Multimodal MRI scans from BraTS 2020 (n = 494) were standardized and embedded with a 3D autoencoder to obtain 128-D latent representations. Unsupervised clustering identified latent basins (“attractors”). A neural ordinary differential equation (neural-ODE) approximated latent dynamics. All dynamics were inferred from cross-sectional population variability rather than longitudinal follow-up, serving as a proof-of-concept approximation of morphologic continuity. Voxel-level perturbation quantified local morphodynamic sensitivity, and proof-of-concept control was explored by adding small inputs to the neural-ODE using both a deterministic controller and a reinforcement learning agent based on soft actor–critic (SAC). Survival analyses (Kaplan–Meier, log-rank, ridge-regularized Cox) assessed associations with outcomes. Results: The learned latent manifold was smooth and clinically organized. Three dominant attractor basins were identified with significant survival stratification (χ² = 31.8, p = 1.3 × 10⁻⁷) in the static model. Dynamic attractor basins derived from neural-ODE endpoints showed modest and non-significant survival differences, confirming that these dynamic labels primarily encode the morphodynamic structure rather than fixed prognostic strata. Dynamic basins inferred from neural-ODE flows were not independently prognostic, indicating that the inferred morphodynamic field captures geometric organization rather than additional clinical risk information. The latent stability index showed a weak but borderline significant negative association with survival (ρ = −0.13 [−0.26, −0.01]; p = 0.0499). In multivariable Cox models, age remained the dominant covariate (HR = 1.30 [1.16–1.45]; p = 5 × 10⁻⁶), with overall C-indices of 0.61–0.64. Voxel-level sensitivity maps highlighted enhancing rims and peri-necrotic interfaces as influential regions. In simulation, deterministic control redirected trajectories toward lower-risk basins (≈57% success; ≈96% terminal distance reduction), while a soft actor–critic (SAC) agent produced smoother trajectories and modest additional reductions in terminal distance, albeit without matching the deterministic controller’s success rate. The learned attractor classes were internally consistent and clinically distinct. Conclusions: Learning a latent attractor landscape links generative AI, dynamical systems theory, and clinical outcomes in GBM. Although limited by the cross-sectional nature of BraTS and modest prognostic gains beyond age, these results provide a mechanistic, controllable framework for tumor morphology in which inferred dynamic attractor-like flows describe latent organization rather than a clinically predictive temporal model, motivating prospective radiogenomic validation and adaptive therapy studies. Full article

In this study, a comprehensive numerical investigation of amplitude-driven flow dynamics in shocked heavy-fluid layers is presented to focus on the evolution of the Richtmyer–Meshkov instability (RMI). A high-order mixed local discontinuous Galerkin scheme is employed to resolve the complex interactions between shock waves and perturbed interfaces within a compressible viscous flow framework. Impacts of the initial interface amplitudes are systematically examined through a series of single-mode configurations with amplitude–wavelength ratios ranging from $a_0/\lambda =0.025$ to $0.4$. The simulations capture the complete transition from early linear growth to nonlinear roll-up and subsequent mixing. This investigation illustrates that increasing the initial perturbation amplitude enhances baroclinic vorticity generation, intensifies interfacial deformation, and accelerates the onset of secondary instabilities. Low-amplitude interfaces maintain nearly symmetric deformation with delayed nonlinear transition, whereas high-amplitude cases exhibit pronounced spike–bubble asymmetry, stronger curvature, and rapid Kelvin–Helmholtz roll-ups. Quantitative diagnostics of the circulation, enstrophy, and kinetic energy demonstrate that both baroclinic torque and mixing intensity scale directly with the initial perturbation amplitude. This study offers new physical insight into amplitude-dependent shock–interface interactions and elucidates the mechanisms governing vorticity amplification and energy redistribution in RMI flows. Full article

The dynamics of gas–liquid two-phase flow at fracture junctions are crucial for optimizing fluid transport in the complex fracture networks of coal seams, particularly for coalbed methane (CBM) extraction and gas hazard management. This study presents a comprehensive numerical investigation of transient air–water flow in a two-dimensional, symmetric, cross-shaped fracture junction. Using the Volume of Fluid (VOF) model coupled with the SST k-ω turbulence model, the simulations accurately capture phase interface evolution, accounting for surface tension and a 50° contact angle. The effects of inlet velocity (0.2 to 5.0 m/s) on flow patterns, pressure distribution, and energy dissipation are systematically analyzed. Three distinct phenomenological flow regimes are identified based on interface morphology and force balance: an inertia-dominated high-speed impinging flow (Re > 4000), a moderate-speed transitional flow characterized by a dynamic balance between inertial and viscous forces (∼1000 < Re < ∼4000), and a viscous-gravity dominated low-speed creeping filling flow (Re < ∼1000). Flow partitioning at the junction—defined as the quantitative split of the total inflow between the main (straight-through) flow path and the deflected (lateral) paths—exhibits a strong dependence on the Reynolds number. The main flow ratio increases dramatically from approximately 30% at Re ∼ 500 to over 95% at Re ∼ 12,000, while the deflected flow ratio correspondingly decreases. Furthermore, the pressure loss (head loss, ΔH) across the junction increases non-linearly, following a quadratic scaling relationship with the inlet velocity (ΔH ∝ V₀^1.95), indicating that energy dissipation is predominantly governed by inertial effects. These findings provide fundamental, quantitative insights into two-phase flow behavior at fracture intersections and offer data-driven guidance for optimizing injection strategies in CBM engineering. Full article

This paper presents an energy-aware Artificial Intelligence of Things framework designed for intelligent remote device control in residential settings. The system architecture is grounded in the Power Administration Device (PAD), a cost-effective and non-intrusive smart plug prototype that measures real-time electricity consumption and actuates appliance power states. The PAD transmits data to a scalable, cross-platform cloud infrastructure, which powers a web-based interface for monitoring, configuration, and multi-device control. Central to this framework is Cross-Feature Time-MoE, a novel neural forecasting model that processes the ingested data to predict consumption patterns. Integrating a Transformer Decoder with a Top-K Mixture-of-Experts (MoE) layer for temporal reasoning and a Bilinear Interaction Layer for capturing complex cross-time and cross-feature dependencies, the model generates accurate multi-horizon energy forecasts. These predictions drive actionable recommendations for device shut-off times, facilitating automated energy efficiency. Simulation results indicate that this system yields substantial reductions in energy consumption, particularly for high-wattage appliances, providing a user-friendly, scalable solution for household cost savings and environmental sustainability. Full article

A plane strain analytical model was developed for the interaction between inclined multilayered rock strata and concrete tunnel lining in deep buried tunnels, with both structures treated as homogeneous isotropic elastic bodies and two contact modes, no-slip and full-slip, considered. A non-iterative complex variable function method was employed, by which analytical challenges in multiply connected domains were overcome and explicit stress and displacement solutions were obtained. Validation was performed through boundary-condition checks and comparative numerical simulations. The results show that under different tangential contact modes, layer inclinations, and lateral pressure coefficients, the stress error on the inner surface of the lining remains in the order of 10⁻² Pa. The stress and displacement components on both sides of each interface satisfy the associated continuity conditions with excellent agreement. The proposed analytical method nearly perfectly satisfies all boundary and continuity conditions. Under non-hydrostatic loading conditions, the numerical and analytical results for different tangential contact modes also show excellent agreement. The von Mises stress errors are generally controlled within 0.03 MPa, and the maximum relative error—located near the inner surface of the lining—remains below 4%, while displacement errors stay below 0.2 mm. Interface stress jumps are accurately captured and oscillations in zones with high stiffness contrast are effectively avoided. The method is presented as a fast and reliable analytical tool for tunnel design under complex multilayered rock conditions. Full article

We propose and demonstrate a novel motion blur-free hybrid image sensing technique. Unlike the previous hybrid image sensors, we developed a homogeneous hybrid image sensing technique including 60 fps CMOS Image Sensor (CIS) and 1440 fps pseudo Dynamic Vision Sensor (DVS) image frames without any performance degradation caused by static bad pixels. To achieve the fast readout, we implemented two one-side ADCs on two photodiodes (PDs) and the pixel output settling time can be reduced significantly by using the column switch control. The high-speed pseudo DVS frame can be obtained by differentiating fast-readout CIS frames, by which, in turn, the world’s smallest pseudo DVS pixel (1.8 μm) can be achieved. In addition, we confirmed that CIS (50 Mp resolution) and DVS (0.78 Mp resolution) data obtained from the hybrid image sensor can be transmitted over the MIPI (4.5 Gb/s four-lane D-PHY) interface without signal loss. The results showed that the motion blur of a 60 fps CIS frame image can be compensated dramatically by using the proposed pseudo DVS frames together with a deblur algorithm. Finally, using the event simulation, we verified that a 1440 fps pseudo DVS frame can compensate the motion blur of the CIS image captured in the situation of jogging at a 3 m distance. Full article

In natural gas gathering and transportation projects, efficient gas-liquid separation equipment is crucial to ensuring the stable operation of subsequent processes. Conventional separation units often have problems such as low efficiency, high energy consumption and poor resistance to load fluctuations when dealing with foam-containing gas-liquid mixtures. For this purpose, numerical simulation and structural optimization of multi-stage foam separation units were carried out in this study. Based on FLUENT software fluid analysis software, a three-dimensional, multi-physics coupled model incorporating cyclonic defoaming components and axial-flow separation tubes was developed. The volume of fluid (VOF) multiphase flow model was used to capture the dynamic characteristics of the gas-liquid interface, and the population balance model was used to simulate the coalescence and fragmentation of the foam. The results show that in the non-working fluid stage, the optimal operating pressure is 5.0–5.5 MPa, and the droplet concentration should be maintained below 50 × 10⁻⁵. The system performance during the working fluid stage is significantly influenced by foam size. The efficiency of millimeter-sized foams is stable above 88% in the 5.0–6.0 MPa range, while the efficiency of micrometer-sized foams is optimal in the 5.3–5.7 MPa range. It is recommended to control the foam proportion below 35% and add a pre-defoaming unit to improve overall performance. Full article

Smoothed particle hydrodynamics (SPHs) is a Lagrangian meshless method with distinct strengths in managing unstable and complex interface behaviors. This study develops an integrated multiphase SPH framework by merging multiple algorithms and techniques to enhance stability and accuracy. The multiphase model is validated by several benchmark examples, including square droplet deformation, single bubble rising, and two bubbles rising. The selection of numerical parameters for multiphase simulations is also discussed. The validated model is then applied to simulate oil–water–gas bubbly flows. Interface behaviors, such as coalescence, fragmentation, deformation, etc., are reproduced, which helps to take into account multiphysics interactions in industrial processes. The rising processes of many oil droplets for oil–water separation are first simulated, showing the advantages and stability of the SPH model in dealing with complex interface behaviors. To fully explore the potential of the model, the model is further extended to the field of wax removal. The melting process of the wax layer due to heat conduction is simulated by coupling the thermodynamic model and the phase change model. Interesting behaviors such as wax layer cracking, droplet detachment, and thermally driven flow instabilities are captured, providing insights into wax deposition mitigation strategies. This study provides an effective numerical model for bubbly flows in petroleum engineering and lays a research foundation for extending the application of the SPH method in other engineering fields, such as multiphase reactor design and environmental fluid dynamics. Full article

This study examines the flow behavior of water-lubricated heavy oil transport utilizing the core annular flow (CAF) technique. The goal is to enhance efficiency and minimize risks in pipeline operations. The flow was numerically simulated in a horizontal pipe using a Large Eddy Simulation (LES) model within a commercial Computational Fluid Dynamics (CFD) framework. The Geo Reconstruct scheme is employed to accurately capture the oil–water interface, and both oil and water initialization methods were assessed against experimental data. Results show that the LES model accurately reproduces the main flow features observed experimentally, particularly for low-viscosity oil–water systems. This suggests that the model can be a reliable tool for predicting flow behaviour in similar fluid systems. Further validation with varying parameters could enhance its applicability across a broader range of conditions. In cases of heavy oil, the velocity profile remains nearly constant within the oil core, indicating rigid body-like motion surrounded by a turbulent water annulus. Turbulence intensity and oil volume fraction distributions were closely related, with higher turbulence in water and lower in oil. Although wall adhesion modelling limited fouling prediction, simulations confirmed that fouling can significantly increase pressure losses. This illustrates the value of considering both fluid dynamics and material interactions in such systems. Future studies could explore the impact of varying temperature and pressure conditions on fouling behaviour to further refine predictive models. Overall, the LES approach proved suitable for analysing turbulent CAF, offering insights for optimizing viscosity ratios, flow rates, and design parameters for safer and more efficient heavy oil transport. Full article

This paper describes a real-time system for recognizing voice commands for resource-constrained embedded devices, specifically a PIC microcontroller. While most existing speech ordering support solutions rely on high-performance processing platforms or cloud computation, the system described here performs fully embedded low-power processing locally on the device. Sound is captured through a low-cost MEMS microphone, segmented into short audio frames, and time domain features are extracted (i.e., Zero-Crossing Rate (ZCR) and Short-Time Energy (STE)). These features were chosen for low power and computational efficiency and the ability to be processed in real time on a microcontroller. For the purposes of this experimental system, a small vocabulary of four command words (i.e., “ON”, “OFF”, “LEFT”, and “RIGHT”) were used to simulate real sound-ordering interfaces. The main contribution is demonstrated in the clever combination of low-complex, lightweight signal-processing techniques with embedded neural network inference, completing a classification cycle in real time (under 50 ms). It was demonstrated that the classification accuracy was over 90% using confusion matrices and timing analysis of the classifier’s performance across vocabularies with varying levels of complexity. This method is very applicable to IoT and portable embedded applications, offering a low-latency classification alternative to more complex and resource intensive classification architectures. Full article

To realistically simulate the entire slip-surface process from crack initiation to run-out, we couple the simplified Bishop method (LEM) with zero-thickness cohesive elements (CZM): LEM first pinpoints the critical slip circle, then CZM tracks interface opening, progressive damage, and sliding along that exact surface. Benchmarked against ACADS EX11, the framework reproduces the classical factor of safety while delivering the post-failure displacements, energy dissipation, and crack paths that LEM or traditional FEM cannot capture, offering a practical tool for landslide-prone slope design. Full article

One of the key indicators of the foundation soil consolidation is the smear effect brought on by the insertion of a Prefabricated vertical drain (PVD), which also smears the extent of disturbance. Prior research primarily examined the impact of the diameter of the Prefabricated vertical drain sleeves, ignoring the impact of pile shoe size on smear effect. The penetration process of pile shoes of varying sizes in layered soils was simulated using transparent soil model experiments, and Particle Image Velocimetry (PIV) technology was used to visualize and assess the soil disturbance caused by the pile shoes. Theoretical and experimental data are used to suggest and analyze the correction coefficients for the geometric characteristics of pile shoes using the Mohr–Coulomb criterion and reaming theory. The study’s findings demonstrate that transparent soil and the PIV method can successfully capture the dynamic evolution of the “inverted cone” in the smeared area, which is consistent with the theory of cylindrical pore expansion’s prediction. The horizontal disturbance range will increase as the equivalent radius of the pile shoes increases, and it is 4.5d for pile shoes with an equivalent radius of 1.5 mm and 5d for pile shoes with an equivalent radius of 2.0 mm. The discontinuity of the soil layer interface will be made worse by pile shoes with a high equivalent radius, making the phenomenon of stress concentration more noticeable. Its quantitative analysis demonstrates the reasonableness of the correction factor λ, which offers a trustworthy tool to quantify the perturbation effect of the pile shoe size. A correction factor λ is proposed so that the error between the corrected theoretical value and the test value is less than 5%. Full article

We present a new finite element framework for modeling compressible, turbulent multiphase flows with heat transfer. For two-fluid systems with a free surface, the Volume of Fluid (VOF) method is implemented without the need for interface reconstruction, while turbulence is resolved using a dynamic Vreman large eddy simulation (LES) model. Unlike most two-phase VOF studies, which neglect heat transfer, the present approach incorporates energy transport equations within the VOF formulation to account for heat exchange, an effect particularly important in turbulent flows. Conjugate heat transfer is often challenging in finite volume methods, which require explicit specification of heat fluxes at the solid–fluid interface, limiting accuracy and predictive capability. By contrast, the finite element formulation does not require heat flux inputs, allowing more accurate and robust simulation of heat transfer between solids and fluids. The method is demonstrated through three representative cases. First, a two-fluid instability with a single-mode perturbation is simulated and validated against analytical growth rates. Second, conjugate heat transfer is examined in a high-temperature flow over a cold metal cylinder, with validation performed both quantitatively—via pressure coefficient comparisons with experimental data—and qualitatively using vector field topology. Finally, compressible spray injection and breakup are modeled, demonstrating the ability of the framework to capture interfacial dynamics and atomization under turbulent, high-speed conditions. In the compressible spray injection and breakup case, the results indicate that the finite element formulation achieved higher predictive accuracy and robustness than the finite-volume method. With the same mesh resolution, the FEM reduced the root mean square error (RMSE) and mean absolute percentage error (MAPE) from 6.96 mm and 26.0% (for the FVM) to 4.85 mm and 12.7%, respectively, demonstrating improved accuracy and robustness in capturing interfacial dynamics and heat transfer. The study also introduced vector field topology to visualize and interpret coherent flow structures and instabilities, offering insights beyond conventional scalar-field analyses. Full article

In this study, experiments and numerical simulations were conducted to investigate the oscillation phenomena in a pressure swirl injector. The flow field was captured using high-speed photography, and the gray values were analyzed using the Matlab image processing program. The oscillation frequency was recorded using FFT transform. Additionally, the flow field of the pressure swirl injector was simulated based on the volume of fluid (VOF) interface-tracking method. Both the experimental and numerical results revealed periodic oscillations in the pressure swirl injector, with a corresponding frequency of several hundred Hertz. The oscillation frequency is closely related to the behavior of the central gas core, which has greater turbulent kinetic energy than the liquid phase. As the mass flow rate increases, the velocity of the gas core is increased. The turbulent kinetic energy of the central gas core increased, which led to an increase in the oscillation frequency. Finally, the relationship between Re and the oscillation frequency was obtained. Full article

Wettability significantly influences fluid distribution and flow behavior in hydrocarbon reservoirs, yet traditional macroscopic measurements fail to capture the micro- and nanoscale interfacial interactions that govern these processes. This study addresses a critical knowledge gap by employing molecular dynamics simulations to systematically investigate how salinity and mineral composition control wettability at the atomic scale—insights that are experimentally inaccessible yet essential for optimizing enhanced oil recovery strategies. We examined five typical reservoir minerals—kaolinite, montmorillonite, chlorite, quartz, and calcite—along with graphene as a model organic surface. Our findings reveal that while all minerals exhibit hydrophilicity (contact angles below 75°), increasing salinity weakens water wettability, with Ca²⁺ ions exerting the strongest effect due to their high charge density, which enhances electrostatic attraction with negatively charged mineral surfaces and promotes specific adsorption at the mineral–water interface, thereby displacing water molecules and reducing surface hydrophilicity. In oil–water–mineral systems, we discovered that graphene displays exceptional oleophilicity, with hydrocarbon interaction energies reaching −7043.61 kcal/mol for C₁₈H₃₈, whereas calcite and quartz maintain strong hydrophilicity. Temperature and pressure conditions modulate interfacial behavior distinctly: elevated pressure enhances molecular aggregation, while higher temperature promotes diffusion. Notably, mixed alkane simulations reveal that heavy hydrocarbons preferentially adsorb on mineral surfaces and form highly ordered structures on graphene, with diffusion rates inversely correlating with molecular size. These atomic-scale insights into wettability mechanisms provide fundamental understanding for designing salinity management and wettability alteration strategies in enhanced oil recovery operations. Full article