Search Results (1,163)

A computational approach for early breast cancer detection using Dynamic Infrared Thermography (DIT) was developed. Thermograms are represented by multivariate time series extracted from thermal hotspots in the breast, capturing five features: maximum and mean temperature, spatial heterogeneity, heat flux, and tumor depth, over 20 thermograms. Features are estimated based on the inverse solution of the Pennes bio-heat equation. Classification is performed using a Time Series Forest (TSF) and a Long Short-Term Memory (LSTM) network. The TSF achieved an accuracy of 86%, while the LSTM reached 94% accuracy. These results indicate that dynamic thermal responses under cold-stress conditions reflect tumor angiogenesis and metabolic activity, demonstrating the potential of combining multivariate thermographic sequences, biophysical modeling, and machine learning for non-invasive breast cancer screening. Full article

Soil organic carbon (SOC) plays a crucial role in climate change mitigation by regulating atmospheric CO₂ and maintaining ecosystem balance; however, its stability is influenced by land use in anthropized areas such as the tropical Andes. This study developed a dynamic compartmental model based on ordinary differential equations to simulate carbon fluxes among litter, humus, and microbial biomass under four land uses in the Las-Piedras River basin (Popayán, Colombia): riparian forest (RF), ecological restoration (ER), natural-regeneration (NR), and livestock (LS). The model includes two decomposition rate constants: k₁, for the transformation of fresh organic matter, and k₂, for the turnover of humified organic matter. It was calibrated using field data on soil physicochemical and biological properties, as well as carbon inputs and outputs. The results showed clear differences in SOC dynamics among land uses: RF had the highest SOC stocks (148.7 Mg ha⁻¹) and microbial biomass, while LS showed the lowest values and the greatest deviation due to compaction and low residue input. The humus fraction remained the most stable pool (k₂ ≈ 10⁻⁴ month⁻¹), confirming its recalcitrant nature. Overall, the model reproduced SOC behavior accurately (MAE = 0.01–0.30 Mg ha⁻¹) and provides a framework for improving soil carbon management in mountain ecosystems. Full article

This paper investigates the flow of a second-grade hybrid nanofluid through a Darcy–Forchheimer porous medium under Cattaneo–Christov heat and mass flux models. The hybrid nanofluid, composed of alumina and copper nanoparticles in water, enhances thermal and mass transport, while the second-grade model captures viscoelastic effects, and the Darcy–Forchheimer medium accounts for both linear and nonlinear drag. Using similarity transformations and the spectral quasilinearisation method, the nonlinear governing equations are solved numerically and validated against benchmark results. The results show that hybrid nanoparticles significantly boost heat and mass transfer, while Cattaneo–Christov fluxes delay thermal and concentration responses, reducing the near-wall temperature and concentration. The distributions of velocity, temperature, concentration, and microorganism density are markedly affected by porosity, the Forchheimer number, the bio-convection Peclet number, and relaxation times. The results illustrate that hybrid nanoparticles significantly increase heat and mass transfer, whereas thermal and concentration relaxation factors delay energy and species diffusion, thickening the associated boundary layers. Viscoelasticity, porous medium resistance, Forchheimer drag, and bio-convection all have an influence on flow velocity and transfer rates, highlighting the subtle link between these mechanisms. These breakthroughs may be beneficial in establishing and enhancing bioreactors, microbial fuel cells, geothermal systems, and other applications that need hybrid nanofluids and non-Fourier/non-Fickian transport. Full article

The fractal extension of the time-dependent Ginzburg–Landau (TDGL) equation, formulated within the framework of Scale Relativity, generalizes superconducting dynamics to non-differentiable space–time. Although analytically well established, its numerical solution remains difficult because of the strong coupling between amplitude and phase curvature. Here we develop two complementary deep learning solvers for the fractal TDGL (FTDGL) system. The Fractal Physics-Informed Neural Network (F-PINN) embeds the Scale-Relativity covariant derivative through automatic differentiation on continuous fields, whereas the Fractal Graph Neural Network (F-GNN) represents the same dynamics on a sparse spatial graph and learns local gauge-covariant interactions via message passing. Both models are trained against finite-difference reference data, and a parametric study over the dimensionless fractality parameter $\mathcal{D}$ quantifies its influence on the coherence length, penetration depth, and peak magnetic field. Across multivortex benchmarks, the F-GNN reduces the relative L₂ error on ${\left|\psi \right|}^2$ from 0.190 to 0.046 and on B_z from approximately 0.62 to 0.36 (averaged over three seeds). This ≈4× improvement in condensate-density accuracy corresponds to a substantial enhancement in vortex-core localization—from tens of pixels of uncertainty to sub-pixel precision—and yields a cleaner reconstruction of the 2π phase winding around each vortex, improving the extraction of experimentally relevant observables such as ξ_eff, λ_eff, and local B_z peaks. The model also preserves flux quantization and remains robust under 2–5% Gaussian noise, demonstrating stable learning under experimentally realistic perturbations. The $\mathcal{D}$—scan reveals broader vortex cores, a non-monotonic variation in the penetration depth, and moderate modulation of the peak magnetic field, while preserving topological structure. These results show that graph-based learning provides a superior inductive bias for modeling non-differentiable, gauge-coupled systems. The proposed F-PINN and F-GNN architectures therefore offer accurate, data-efficient solvers for fractal superconductivity and open pathways toward data-driven inference of fractal parameters from magneto-optical or Hall-probe imaging experiments. Full article

We use deep Physics-Informed Neural Networks (PINNs) to simulate stratified forced convection in plane Couette flow. This process is critical for atmospheric boundary layers (ABLs) and oceanic thermoclines under global warming. The buoyancy-augmented energy equation is solved under two boundary conditions: Isolated-Flux (single-wall heating) and Flux–Flux (symmetric dual-wall heating). Stratification is parameterized by the Richardson number $(R_i\in [-1,1\left]\right),$ representing $\pm 2 °\mathrm{C}$ thermal perturbations. We employ a decoupled model (linear velocity profile) valid for low-Re, shear-dominated flow. Consequently, this approach does not capture the full coupled dynamics where buoyancy modifies the velocity field, limiting the results to the laminar regime. Novel contribution: This is the first deep PINN to robustly converge in stiff, buoyancy-coupled flows ($\mid R_i\mid \le 1$) using residual connections, adaptive collocation, and curriculum learning—overcoming standard PINN divergence (errors $>28\%$). The model is validated against analytical ($R_i=0$) and RK4 numerical ($R_i\ne 0$) solutions, achieving $L_2$ errors $\le 0.009\%$ and $L_{\mathrm{\infty }}$ errors $\le 0.023\%$. Results show that stable stratification $(R_i>0)$ suppresses convective transport, significantly reduces local Nusselt number ($Nu$) by up to $100\%$ (driving $Nu$ towards zero at both boundaries), and induces sign reversals and gradient inversions in thermally developing regions. Conversely, destabilizing buoyancy $(R_i<0)$ enhances vertical mixing, resulting in an asymmetric response: $Nu$ increases markedly (by up to $140\%$) at the lower wall but decreases at the upper wall compared to neutral forced convection. At $5–10\times$ lower computational cost than DNS or RK4, this mesh-free PINN framework offers a scalable and energy-efficient tool for subgrid-scale parameterization in general circulation models (GCMs), supporting SDG 13 (Climate Action). Full article

In this work, a high-order modal discontinuous Galerkin (dG) method is employed to solve the Euler equations using entropy variables. Entropy conservation and stability are ensured at the spatial semi-discrete level through entropy-conserving/stable numerical fluxes and the over-integration technique. For time integration, linearly implicit Rosenbrock-type Runge–Kutta schemes are used. However, since these schemes are not provably entropy-conserving/stable, their use to predict unsteady flows may lead to solutions that lack the desired entropy properties. To address this issue, a relaxation technique is applied to enforce entropy conservation or stability at the fully discrete level. The accuracy, conservation/stability properties and robustness of the fully-discrete scheme equipped with the relaxation technique are assessed through the following numerical experiments: (1) the isentropic vortex, (2) the Kelvin-Helmholtz instability, (3) the Taylor–Green vortex. Full article

The ultimate objective of this research is to concentrate nutrients—nitrogen (N), phosphorus (P), and potassium (K)—and produce process water from a chemically pretreated liquid digestate using an FO-RO hybrid process. However, in this manuscript, we assessed the suitability of (NH₄)₂SO₄ and NaCl as draw solutes in a series of FO experiments employing a commercial CTA membrane and DI water as the feed solution. We also examined the regeneration of (NH₄)₂SO₄ in a series of RO experiments at various feed concentrations and pressures using a commercial polyamide (PA) thin-film composite (TFC) membrane, ACM4. Additionally, the RO experiments enabled the experimental determination of the osmotic pressure of (NH₄)₂SO₄ at various feed concentrations, which is crucial for designing the FO part of the hybrid process. The CTA membrane exhibited a significantly greater selectivity for (NH₄)₂SO₄ than for NaCl at any osmotic pressure. The RO experiments demonstrated the possibility of reconcentrating (NH₄)₂SO₄ to 0.5 mol/L, with a corresponding water flux of 60 L h⁻¹ m⁻² at 40 bars. The experimentally determined osmotic pressures were lower than those predicted by van’t Hoff’s equation but were consistent with those reported in the literature using an indirect hygrometric method. Full article

We investigate the nonrelativistic quantum dynamics of a spinless particle in a screw-type spacetime endowed with two independent twist controls that interpolate between a pure screw dislocation and a homogeneous twist. From the induced spatial metric, we build the covariant Schrödinger operator, separate variables to obtain a single radial eigenproblem, and include a uniform axial magnetic field and an Aharonov–Bohm (AB) flux by minimal coupling. Analytically, we identify a clean separation between a global, AB-like reindexing set by the screw parameter and a local, curvature-driven mixing generated by the distributed twist. We derive the continuity equation and closed expressions for the azimuthal and axial probability currents, establish practical parameter scalings, and recover limiting benchmarks (AB, Landau, and flat space). Numerically, a finite-difference Sturm–Liouville solver (with core excision near the axis and Langer transform) resolves spectra, wave functions, and currents. The results reveal AB periodicity and reindexing with the screw parameter, Landau fan trends, twist-induced level tilts and avoided crossings, and a geometry-induced near-axis backflow of the axial current with negligible weight in cross-section integrals. The framework maps the geometry and fields directly onto measurable spectral shifts, interferometric phases, and persistent-current signals. Full article

The flow field evolution in the first turn of the spiral concentrator is decisive for the separation efficiency of solid particles. A laboratory-scale Φ300 mm spiral concentrator was employed as the study subject. The fluid phase was simulated using the RNG k-ε (Renormalization Group) turbulence model and the VOF (Volume of Fluid) multiphase model, while the particles were calculated with an Eulerian multi-fluid VOF model that incorporates the Bagnold effect. The influence of the cross-sectional curve equation on the evolution of flow field parameters in the first turn and on the separation behavior of hematite and quartz particles was systematically investigated. The results indicated that the evolution characteristics of fluid parameters, such as the depth of flow film, the tangential velocity of surface flow, the velocity of secondary circulation, and radial flux, were similar. All parameters were observed to undergo an initial decrease or increase, eventually stabilizing as the longitudinal travel progressed. A negative correlation was identified between the index of the cross-sectional curve equation and both the depth of flow film and the tangential velocity of surface flow in the inner half of the trough, whereas an inverse relationship was noted in the outer half. With an increase in the index of the cross-sectional curve equation, the outward circulation velocity in the initial stage and its radial flux in the outer zone were enhanced, while the fluctuations in the evolution of local fluid parameters were suppressed, with more active fluid radial migration observed at the indices of the cross-sectional curve equation of 2.5 and 3. As the flow field evolved, axial separation between hematite and quartz particles was progressively achieved by gravity due to their density difference. In the middle and inner-outer zones, the migration directions of hematite and quartz were observed to become opposite in the later stage of evolution, while the difference in their migration magnitudes was also found to be widened. With an increase in the index of the cross-sectional curve equation, the disparity in the axial separation and movement between hematite and quartz was enhanced, albeit with a diminishing rate of increase. The maximum separation efficiency between hematite and quartz particles was significantly improved with increased longitudinal travel, reaching over 60% by the end of the first turn; higher indices were determined to be more favorable for achieving this performance. Based on the previous research, the variation in separation indices in the third turn was investigated under both independent adjustment of the index of the cross-sectional curve equation and its combined adjustment with the downward bevel angle. Relatively high and stable separation performance was achieved with the indices of the cross-sectional curve equation of 2.5 and 3, where a maximum separation efficiency of 82.02% was obtained, thereby validating the high efficiency and suitability of the selected spiral concentrator profile. This research elucidated the decisive role of the flow field evolution through the first turn in particle separation behavior from the perspective of quantitative description of hydrodynamic parameters, providing beneficial references for the cross-sectional structure design of spirals and the prediction of the separation index of specific feed. Full article

Reproducing the fluid dynamics of rivers is a challenging task that involves considering various factors such as water level and flow velocity. Although numerical modeling research has been performed in this field, the intricacy of establishing these models can vary considerably based on the numerical techniques applied. Hence, this study aims to validate the effectiveness of fluid flow reproduction technology based on game engines, highlighting its real-time performance. In particular, a prototype of the digital twin (DT) of the River Experiment Center, a full-scale hydraulic experimental facility, was constructed using the Unreal Engine 5 game engine, emphasizing visibility and real-time reproduction. Fluid Flux, based on shallow-water equations and 2D height fields, was utilized to reproduce flow results, and a comparative validation was conducted using the experimental data obtained from full-scale empirical tests. The findings validated the practicality of replicating real flow patterns, even with a simplified fluid simulation aimed at reproducibility and real-time efficiency. However, specific factors must be considered for public usage such as managing rivers and urban floods. The results indicate that graphics-based flow reproduction technology can more easily integrate with other fields. Moreover, the ability to reproduce events in real time and employ visual effects is essential for efficient disaster response. Full article

Cloud feedback is a major source of uncertainty in climate projections. In particular, Arctic clouds, arguably one of the most poorly understood aspects of the climate system, strongly modulate radiative energy fluxes from the Earth’s surface to the top of the atmosphere. In situ and satellite observations reveal the existence of ubiquitous optically thin ice clouds (TICs) in the Arctic during polar nights, whose influence on atmospheric energy is still poorly understood. This study quantifies the effect of TICs on the atmospheric energy budget during polar winter. A reanalysis-driven simulation based on the Canadian Regional Climate Model version 6 (CRCM6) was used with the Predicted Particle Properties (P3) scheme (2016) to produce an ensemble of 3 km mesh simulations. This set is composed of three simulations: CRCM6 (reference, the original dynamically coupled cloud formation), CRCM6 (nocld) (clear-sky) and CRCM6 (100%cld) (overcast, 100% cloud cover as a forcing perturbation). Using the regional energetic equations (Nikiema and Laprise), we compare the three cases to assess TIC forcing. The results show that TICs cool the atmosphere, with the difference between two simulations (cloud/no clouds) reaching up to −2 K/day, leading to a decrease in temperature on the order of ~−4 KMonth⁻¹. The energetics cycle indicates that the time-mean enthalpy generation term G_M and baroclinic conversion dominate Arctic circulation. The G_M acting on the available enthalpy reservoir (A_M) increased by a maximum value of ~5 W·m⁻² (58% on average) due to the effects of TICs, increasing in energy conversion. TICs also lead to average changes of 9% in time-mean available enthalpy and −5.9% in time-mean kinetic energy. Our work offers valuable insights into the Arctic winter atmosphere and provides the means to characterize clouds for radiative transfer calculations, to design measurement instruments, and to understand their climate feedback. Full article

This paper presents an efficient and high-order WENO-based Upwind Rotated Lattice Boltzmann Flux Solver (WENO-URLBFS) on graphics processing units (GPUs) for simulating three-dimensional (3D) compressible flow problems. The proposed approach extends the baseline Rotated Lattice Boltzmann Flux Solver (RLBFS) by redefining the interface tangential velocity based on the theoretical solution of the Euler equations. This improvement, combined with a weighted decomposition of the numerical fluxes in two mutually perpendicular directions, effectively reduces numerical dissipation and enhances solution stability. To achieve high-order accuracy, the WENO interpolation is applied in the characteristic space to reconstruct physical quantities on both sides of the interface. The density perturbation test is employed to assess the accuracy of the scheme, which demonstrates 5th- and 7th-order convergence as expected. In addition, this test case is also employed to confirm the consistency between the CPU serial and GPU parallel implementations of the WENO-URLBFS scheme and to assess the acceleration performance across different grid resolutions, yielding a maximum speedup factor of 1208.27. The low-dissipation property of the scheme is further assessed through the inviscid Taylor–Green vortex problem. Finally, a series of challenging three-dimensional benchmark cases demonstrate that the present scheme achieves high accuracy, low dissipation, and excellent computational efficiency in simulating strongly compressible flows with complex features such as strong shock waves and discontinuities. Full article

Denitrification and nitrification are two pivotal microbial processes relating to N₂O emissions. However, the difference in N₂O emission fluxes and N₂O-producing bacteria between a karst (KA) and non-karst area (NKA) remains unclear. The objective of this study is to compare the differences in soil N₂O emissions, nitrifying bacteria, and denitrifying bacteria during the growth period of rice in KA and NKA, and to explore the mechanisms by which microorganisms and environmental factors drive N₂O emissions. Here, N₂O emission fluxes of paddy fields were collected using the static dark chamber and measured using gas chromatography at KA and NKA in the Maocun Karst Experimental Site in Guilin, China. The nitrifying bacteria (ammonia-oxidizing bacteria, AOB) and denitrifying bacteria (nirK-denitrifier) were determined using real-time PCR and high-throughput sequencing, respectively. Results showed that during the rice growth period, the N₂O emission fluxes in KA was generally lower than that in NKA, with cumulative N₂O emissions of −0.054 and 0.229 kg·hm⁻² in KA and NKA, respectively. The absolute abundance of AOB in KA (8.91 × 10⁶–2.68 × 10⁷ copies·g⁻¹) was significantly higher than that in NKA (1.57 × 10⁶–6.48 × 10⁶ copies·g⁻¹), while the absolute abundance of nirK-denitrifier had no significant difference between the two areas. The composition and diversity of AOB and nirK-denitrifier differed significantly between KA and NKA. Results from partial least squares structural equation modeling (PLS-SEM) indicated that soil properties, carbon sources, and nitrogen sources had positive effects on AOB and nirK-denitrifier, while nirK-denitrifier had a negative effect on N₂O emissions. Partial least squares regression (PLSR) predictions revealed that NO₃⁻-N, SOC, TN, Mg²⁺, Ca²⁺, and pH were the most important factors influencing N₂O emission fluxes. This study highlights the critical role of the typical characteristics of KA soils in reducing N₂O emissions from paddy fields by driving the evolution of AOB and nirK-denitrifier. Full article

The inconsistent efficacy of biochar in mitigating agricultural greenhouse gas emissions remains a major barrier to its widespread adoption and the realization of its environmental benefits. This study aimed to develop a stable and efficient mitigation strategy by optimizing biochar physicochemical properties through urea-N activation (corn stover: urea mass ratios of 5:1 and 15:1). Five treatments were established: CK (control), GC (fertilization), GB (fertilization + raw biochar), GAB5 (fertilization + low-N activated biochar), and GAB15 (fertilization + high-N activated biochar). Mechanisms were elucidated by monitoring soil profile (0–20 cm) gas concentrations and surface fluxes, combined with a comprehensive analysis of soil physicochemical properties, enzyme activities, and microbial biomass. Results demonstrated that activated biochar, particularly GAB15, significantly reduced cumulative CO₂ (9.4%, p < 0.05) and N₂O (45.2%, p < 0.05) emissions and their concentrations in the 0–10 cm layer. This superior efficacy was linked to profound improvements in key soil properties: GAB15 significantly enhanced soil cation exchange capacity (CEC, increased by 17.3%, p < 0.05), NH₄⁺-N content (increased by 88.2%, p < 0.05), Mean Weight Diameter (MWD, increased by 13.0%), the content of water-stable aggregates > 0.25 mm (R_>0.25mm, increased by 57.3%) (p < 0.05), dissolved organic carbon (DOC), and the MBC (microbial biomass carbon)/MBN (soil microbial biomass nitrogen) ratio. Redundancy analysis (RDA) and structural equation modeling (SEM) revealed core mechanisms: CO₂ mitigation primarily stemmed from the physical protection of organic carbon within macroaggregates and a negative priming effect induced by an elevated MBC/MBN ratio; N₂O mitigation was attributed to weakened nitrogen mineralization due to enhanced aggregate stability and reduced substrate (inorganic N) availability for nitrification/denitrification via strong adsorption at the biochar–soil interface. This study confirms that urea-activated biochar produced at a 15:1 corn stover-to-urea mass ratio (GAB15) effectively overcomes the inconsistent efficacy of conventional biochar by targeted physicochemical optimization, offering a promising and technically feasible approach for mitigating agricultural greenhouse gas emissions. Full article

One-dimensional numerical simulations using the Euler equations and irreversible one-step Arrhenius kinetics are conducted to study the instability mechanism of a one-dimensional gaseous detonation. By increasing the activation energy, this study identifies the characteristics of stable detonation, periodic detonation, pulsating detonation, and detonation quenching. The key difference between this study and previous research is that it is the first quantitative analysis of convective flux, kinetic energy flux, and chemical reaction heat flux. These three fluxes undergo intensive change on the detonation front and the flow field at each time step depends on the algebraic summation of them. The mechanisms of detonation instability, detonation reignition, and the detonation quenching process can be revealed quantitatively by analyzing these fluxes. The detonation instability is the intrinsic property of the reactive Euler system. Full article