Search Results (767)

Solute transport in rock fractures is strongly influenced by hydrodynamic conditions, and clarifying the Péclet-number-controlled transition of transport regimes is important for understanding contaminant migration in fractured aquifers. Based on three-dimensional numerical simulations, this study investigates conservative solute transport in idealized rough fractures with perfectly mated walls and uniform aperture under a wide range of Péclet numbers (Pe). The evolution of concentration fields, breakthrough curves (BTCs), and diffusive and advective fluxes was analyzed to identify the dominant transport regimes. The results show that, as Pe increases, solute transport changes from a diffusion-dominated regime (Pe < 0.1), to a mixed macro-dispersion-dominated regime (0.1 < Pe < 1000), and finally to a high-Pe advection-controlled regime with Taylor-dispersion-like characteristics (Pe > 1000). Correspondingly, the concentration field evolves from rapid diffusion-driven spreading to a sharper advective front, while the BTCs change from early diffusion-breakthrough curves to step-like breakthrough behavior. Fracture aperture promotes solute spreading and broadens the mixing zone, especially under low-to-intermediate Pe conditions. In contrast, under the perfectly mated and uniform-aperture fracture conditions considered here, increasing roughness mainly induces local tortuosity of the concentration front and has limited influence on the overall BTCs. Flux decomposition further confirms that diffusive flux dominates at low Pe, whereas advective flux becomes increasingly dominant as Pe increases. These findings provide a mechanistic basis for interpreting Pe-controlled solute transport in idealized fracture channels and offer fracture-scale insights for classified groundwater contamination risk assessment. The implications should be interpreted within the assumptions of conservative transport without matrix diffusion, adsorption, or reactive processes. Full article

Street-level concentrations of nitrogen dioxide (NO₂) and fine particulate matter (PM_2.5) pose serious public health risks in European cities, yet accurate multi-year prediction at traffic-dominated sites remains challenging. This study applies XGBoost (XGB) and Random Forest (RF) to predict hourly NO₂ and daily PM_2.5 at two street monitoring sites in Copenhagen, Denmark, trained on 17 years of observational data and evaluated on two independent years. Three-dimensional variational assimilation (3D-Var) and the Extended Kalman Filter (EKF) are then applied as post-processing corrections to the ML predictions using co-located observations. XGB achieved RMSE values of 9.5 and 7.4 µg/m³ for HCAB and JGTV NO₂, respectively, in the 2018 test year. Both DA methods improved substantially on the ML baseline, with 3D-Var reducing NO₂ RMSE by up to 57% and spike event RMSE by up to 51%. EKF achieved near-complete elimination of systematic bias across all configurations. The framework is computationally lightweight and can be applied to any deterministic model prediction at a monitoring station, including outputs from physics- and chemistry-based dispersion models. Overall, the findings show a practical way to improve street-level air quality prediction, with direct relevance for operational forecasting and public health protection. Full article

A new family of fourth-order accurate time integration schemes is developed by introducing two complex time sub-steps into the classical Newmark family of second-order algorithms. These sub-steps consist of a pair of complex conjugate numbers, enabling the triangularization of a complex-valued effective stiffness matrix. The proposed formulation can be easily implemented in existing codes with only minor modifications to the standard Newmark algorithm. The solution is composed of both real (physical) and imaginary components. The real component provides fourth-order accuracy even in the presence of external loads and physical damping, while the imaginary component offers additional insight into the distribution of numerical errors, an original feature not previously reported for implicit formulations. Compared to the classical Newmark method with a time-step size four times smaller, the proposed scheme exhibits significantly lower numerical dissipation and dispersion errors. Furthermore, the sub-step procedure extends the critical time step of conditionally stable members of the Newmark family by a factor of $\sqrt{3}$. The numerical analysis performed in the proposed time integration method, along with the results obtained for dynamic structural problems, including a complex three-dimensional (3D) application, clearly demonstrate that the method outperforms both Fung’s fourth-order complex scheme and the classical Newmark approach in terms of accuracy. Full article

This study investigates residual stress development in double-lap T-joints fabricated from medium- and heavy-gauge steel plates. A three-dimensional thermo-mechanically coupled finite element model was developed in Abaqus and validated against blind-hole drilling measurements. Four distinct welding sequence schemes were systematically implemented to quantify their influence on the spatial distribution, peak magnitudes, and evolution trajectories of individual residual stress components (σ_x, σ_γ, σ_z). Results demonstrate that the inherent structural rigidity of medium-to-thick plate assemblies strongly constrains global distortion but does not eliminate sensitivity to sequencing at the local stress level. Although equivalent residual stress peaks remain largely insensitive to welding sequence, the distributions of principal stress components exhibit pronounced sequence-dependent heterogeneity. Specifically, single-side continuous unidirectional welding leverages interpass residual heat accumulation to suppress longitudinal tensile stress, achieving a peak value of 449.9 MPa, the lowest among all configurations. In contrast, double-sided alternating reverse welding promotes thermal dispersion across the joint, thereby reducing both transverse tensile stress magnitude and stress concentration in the distal heat-affected zone. Collectively, these findings establish that optimizing welding sequences for double-lap T-joints in medium-to-heavy plates centers not on minimizing global equivalent stress, but on deliberately tailoring the spatial partitioning and balance of individual stress components, a principle that directly informs robust, performance-driven weld path selection in structural fabrication. Full article

This paper addresses bearing-only three-dimensional target localization using three cooperative UAVs under observation inconsistency and degraded geometry. A weighted point-to-line least-squares localization model is established to fuse multiple line-of-sight (LOS) observations derived from image measurements, camera calibration, and UAV poses. To handle unreliable measurements without ground truth, a reliability assessment mechanism is developed by combining geometric stability indicators with observation consistency metrics, enabling weak geometry and abnormal observations to be identified online. Based on this assessment, an adaptive optimization framework is introduced to perform residual-driven adaptive weighting and configuration optimization, thereby suppressing unreliable LOS measurements and improving the conditioning of cooperative geometry. Simulation results under four representative scenarios show that the proposed method consistently improves localization accuracy and robustness. The mean localization error is reduced from 0.545 m to 0.260 m under abnormal observations, from 0.355 m to 0.081 m under degraded geometry, and from 0.711 m to 0.280 m when both effects occur simultaneously. Statistical evaluations including RMSE, standard deviation, maximum error, confidence intervals, and box-plot analysis further demonstrate that the proposed framework effectively reduces error dispersion and improves robustness. Full article

Oil spills represent a critical environmental challenge. The wastewater treatment with porous sorbents presents the advantage of higher uptake and recyclability. In this work, highly porous and low-density three-dimensional reduced graphene oxide aerogels were obtained by hydrothermal reduction followed by lyophilization. The porosity and reduction degree of the aerogels were controlled by the addition of reducing species, namely ethylenediamine, and hydrothermal conditions. The aerogels were characterized using scanning electron microscopy, Raman spectroscopy, and energy-dispersive X-ray analysis. The sorption measurements were performed with vegetable oils, namely canola and olive oil, at varying operating temperatures. The morphological analysis revealed a well-defined porosity gradient along the aerogel length, along with a functionalization gradient. The sorption performance is highly dependent on their combined action. The maximum gravimetric absorption capacity was about 122 g g⁻¹ at room temperature, increasing to 156 g g⁻¹ at 60 °C, with the absorption rate increasing from about 1 g g⁻¹ s⁻¹ to 15 g g⁻¹ s⁻¹ within 10 s. These results demonstrate that anisotropic gradient aerogels could be obtained by simple tailoring of the synthesis conditions, and such aerogels could benefit the sorption of oils with higher viscosities in terms of rate, pore filling and retention. Full article

Accurate prediction of contaminant transport and self-purification processes in rivers remains challenging because pollutant dispersion, biochemical reactions, and hydrodynamic conditions interact across multiple spatial scales. This study aims to develop and compare mathematical models for soluble contaminant transport and biodegradable organic matter removal in channels and rivers. Unsteady advection–diffusion–reaction equations were formulated for one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) transport scenarios and solved through numerical techniques based on the transformation of partial differential equations into systems of ordinary differential or algebraic equations. In parallel, the classical Streeter–Phelps model and an extended formulation incorporating turbulent diffusion were implemented to evaluate organic load degradation and oxygen deficit dynamics. Simulations were performed using a Matlab R2019a-based computational framework under representative hydraulic and reaction conditions obtained from literature data and empirical correlations. The results showed that, under specific conditions, the 3D model reproduced trends comparable to those predicted by the 2D model, while the latter approached the behavior of the 1D formulation. The Streeter–Phelps model predicted an organic load removal efficiency of 97.74%, a purification index of 1.9564, a critical time of 18.43 h, and a critical distance of 6.93 km. These findings provide a useful framework for river water-quality assessment and support future applications involving complex hydrodynamic and pollutant-loading scenarios. Full article

Carbon nanotube (CNT)-reinforced composites are promising advanced materials due to their exceptional mechanical properties. This paper presents a comprehensive investigation of the mechanical behavior of CNT/epoxy composites through theoretical modeling and experimental validation. An equivalent cylindrical fiber model was developed to transform CNTs into effective reinforcement phases, enabling the application of classical composite mechanics. Three reinforcement configurations were analyzed: two unidirectional short fiber models (aligned and staggered) and a three-dimensional four-directional braided long-fiber model. The effects of geometric parameters, including the diameter-to-thickness ratio (D/t) and fiber aspect ratio, on the effective elastic moduli were systematically evaluated. Static and dynamic compression experiments were conducted using an MTS 810 testing system and a Split Hopkinson Pressure Bar (SHPB) to examine the influence of loading rate, vacuum treatment, and reinforcement type (CNT, SiC, and hybrid SiC/CNT) on composite strength. The results indicated that 3 wt% CNT reinforcement increases the Young’s modulus by 30% under static loading and enhanced the dynamic compressive strength under impact loading. The vacuum degassing process significantly affected composite quality, with insufficient vacuum leading to strength degradation due to void formation. Theoretical predictions using Mori–Tanaka and dilute methods showed good agreement with experimental results at low reinforcement volume fractions. Scanning electron microscopy revealed uniform CNT dispersion and provided insights into failure mechanisms, including CNT pull-out and breakage. This work contributes to the understanding of structure–property relationships in CNT-reinforced polymer composites and provides guidelines for achieving their optimal design. Full article

In this study, a detailed analytical-numerical study of the complex modified Korteweg–De Vries (mKdV) model with truncated M-fractional derivative is carried out to investigate the effects of the fractional order on nonlinear wave propagation. The fractional partial differential equation is solved by an appropriate fractional traveling wave transformation, which transforms it into a nonlinear ordinary differential equation. Two very powerful analytical methods are then used: the modified sub-equation method and the Kumar–Malik method, which give the exact closed-form solutions. The obtained semi-analytical numerical approximations are then obtained from the Differential Transformation Method (DTM). Bright and dark solitons, kink-type waves, periodic and rational solutions, exponential solutions, and Jacobi elliptic functions are found for a variety of parametric regimes. Explicit compatibility conditions and parametric constraints, which control the amplitude, width, and propagation, are derived. The DTM approximations are found to converge to the exact solutions with good accuracy, and the absolute errors are almost negligible, which validates the accuracy of the approximations and reliability of the solution. The three-dimensional visualizations of surface plots, two-dimensional profiles, and contour visualization further illustrate the dispersive dynamics and stability properties. Significance: This study shows that the truncated M-fractional derivative is a good operator to model memory-dependent nonlinear wave propagation. A new precise solution and reliable validation methods have been obtained for high-dimensional fractional nonlinear evolution equations in the hybrid analytical-numerical framework, which can be useful in plasma physics, nonlinear optics, and complex media. The present study contains restrictions for constant coefficients, a specific parametric regime, one fractional derivative definition, and experimental validation is not included. Future directions are limitations on constant coefficients, specific parametric regimes, one fractional derivative definition, and experimental validation is not included. The approach is to be extended in the future to variable coefficients, other fractional operators (Caputo, Riemann–Liouville), and to higher-order nonlinearities, and then to be experimentally tested in optical or plasma systems. Full article

Welding aluminum (Al) to titanium (Ti) is particularly challenging because of the large differences in their melting points and the tendency to form cavities and brittle intermetallic compounds. Such issues can be mitigated in friction stir welding (FSW) by understanding the underlying mechanisms of microstructural evolution and tensile fracture behavior. In the present study, FSW was carried out on commercially pure Al and commercially pure Ti. X-ray micro-computed tomography results show that the distribution of Ti fragments depends on their morphology, with fine particles (volume 10³–10⁴ µm³) being distributed homogeneously, while large flakes (10⁷–10⁹ µm³) are concentrated near the joint interface. A three-dimensional analysis of Ti fragment distribution was performed to clarify material flow and particle dispersion within the weld nugget. EDS (Energy-Dispersive Spectroscopy) and EPMA (Electron Probe Microanalysis) composition mapping confirmed the formation of AlTi and Al₃Ti intermetallic phases, with Al₃Ti as the dominant phase (consistent with its lower Gibbs free energy of formation). Because Al is the primary element in the matrix and undergoes the highest degree of deformation, its microstructural evolution in Al was examined using Electron Backscatter Diffraction (EBSD). Grain refinement in Al was attributed to continuous dynamic recrystallization (CDRX). Mechanical mixing and intermetallic formation increased the hardness of the weld, while the tensile response corresponded to a joint efficiency of approximately 77%, alone with an 11% improvement in elongation over base Al. The study further establishes a correlation among Ti particle distribution, local microstructural evolution, and the tensile response of the joint. Fractographic analysis indicates a bimodal fracture mechanism, and failure occurred away from the joint interface, indicating a strong joint. To interpret this behavior, a spring-based model was proposed to relate the fracture location and tensile deformation to the spatial variation in microstructure across the welded zones. This approach provides a conceptual framework that is extendable to other dissimilar material systems with spatially varying microstructures. Full article

Indoor air quality (IAQ) plays a critical role in the health and cognitive performance of students, making its assessment essential for sustainable building design in educational environments. This study evaluates whether the ventilation flow rates prescribed by the Spanish Regulation for Thermal Installations in Buildings (RTIB), together with the occupancy densities defined by the Technical Building Code (TBC), are sufficient to maintain CO₂ concentrations within regulatory limits in classrooms and library reading rooms. A validated three-dimensional CFD model was developed to simulate airflow patterns and CO₂ distribution under typical operating conditions. The model was experimentally validated using measurements from a dedicated test room in the KUBIK experimental building of Tecnalia, demonstrating high predictive accuracy with average relative errors between 14% and 20%. Results indicate that, under current RTIB and TBC design criteria, (modelled for a 36 m² classroom with 24 occupants and a fresh air supply of 1080 m³/h), CO₂ levels frequently exceed the 910 ppm regulatory thresholds established by the RTIB’s direct method, highlighting potential shortcomings in existing standards for educational spaces. Additionally, two mechanical ventilation configurations were analyzed, revealing that floor-supply ventilation promotes more homogeneous pollutant dispersion and lower concentration peaks compared with ceiling-mounted systems. These findings underline the need to reconsider ventilation design strategies in educational buildings and demonstrate the value of CFD modelling as a tool to support evidence-based decisions toward healthier and more sustainable indoor environments. Full article

Background/Objectives: The precise characterization of the key microstructural and physicochemical attributes in sustained-release microspheres remains a technical bottleneck, hindering the understanding of drug release mechanisms, and limiting insights into the “process–structure–performance” relationship. To address this, we developed novel methods to conduct in-depth research on the microscopic properties of microspheres. Methods: Scanning electron microscopy (SEM) combined with a deep learning-based image segmentation (DLIS) algorithm was established for quantitative analysis of the pore structure. Laser confocal Raman spectroscopy (LCRS) was employed for in situ, non-destructive, three-dimensional (3D) visualization and quantitative mapping of the active pharmaceutical ingredient (API) distribution within microspheres. Results: This study successfully developed and applied SEM-DLIS and LCRS as reliable tools for microstructural and physicochemical characterization. SEM-DLIS analysis revealed significant differences in surface and internal pore structure among microspheres from different manufacturers and between particles of different sizes from the same batch. LCRS imaging further identified distinct API distribution patterns: uniform dispersion, outer-layer enrichment, and heterogeneous distribution. The combined data elucidate that the initial burst release is governed by the synergistic effect of surface porosity and API surface enrichment, whereas the sustained release kinetics are jointly regulated by the internal pore structure, particle size, and API spatial distribution. Conclusions: The findings establish that microstructure dictates release behavior and that all observed structural variations are linked to critical process parameters (CPPs), validating the “process determines structure” hypothesis. The established methodology provides a critical technical framework for the reverse engineering and quality equivalence assessment of generic microspheres, as well as for the quality-by-design-based optimization of innovative drug products, thereby advancing both pharmaceutical development and regulatory science. Full article

Achieving carbon neutrality requires simultaneous advances in both the quantity and quality of low-carbon technology innovation (LCTI). This paper uses a country–industry–year three-dimensional panel dataset covering 25 EU member states and 39 two-digit NACE Rev. 2 industries over the period 2003–2020 to examine the effects of energy market integration (EMI) on LCTI quantity and quality. An EMI index is constructed based on cross-national energy price dispersion, and the analysis employs Poisson pseudo-maximum likelihood estimation with three-way fixed effects, complemented by a Bartik instrumental variable and double/debiased machine learning as supporting robustness evidence. Results show that: (1) EMI exerts significant positive effects on both LCTI quantity and quality; (2) Mechanism tests reveal that EMI operates through two channels: expansion of energy R&D investment and intensification of cross-border knowledge spillovers; (3) Heterogeneity analysis shows that the promoting effects are concentrated in countries with adequate R&D investment and active energy market competition, and in industries with low emission intensity and low energy intensity. These findings suggest that deepening regional energy market integration constitutes a meaningful institutional complement to conventional low-carbon innovation policy. Full article

Background/Objectives: Early and accurate discrimination of neurological conditions, dementia, stroke and healthy aging, remains a critical clinical challenge. Electroencephalography (EEG) is a non-invasive measure of brain dynamics and entropy-based features obtained from multichannel EEG have shown strong discriminative ability. However, existing deep learning approaches do not sufficiently address the combined challenges of small clinical cohorts and high-dimensional entropy feature spaces. In this study, a novel architecture is proposed for multi-class neurological EEG classification under extreme small-sample conditions. Methods: A novel dual-branch Channel-wise Transformer and Attention-Branch Network (EEG-ChTABNet) are pr to classify 19-channel EEG entropy features into three classes (dementia, stroke, healthy control; N = 45; 15 per class). The architecture suggests four new designs. First, the Channel Importance Attention (CIA) block, which adaptively learns to re-weight the importance of electrodes via squeeze-excitation. Second, the dual-branch encoder, which combines the global multi-head self-attention with the local depthwise-separable convolution. Third, the gated sigmoid fusion mechanism. Fourth, the bottleneck residual classification head, to solve overfitting. Eight entropy feature sets: Amplitude-Aware Permutation Entropy (AAPE), Attention Entropy (AttEn), Dispersion Entropy (DisEn), Distribution Entropy (DistrEn), Fluctuation-based Dispersion Entropy (FDispEn), Fuzzy Entropy (FuzEn), Linear Gaussian Estimation of the Conditional Entropy (LinEn), and Symbolic Dynamics (SyDy) were evaluated individually with stratified 5-fold cross-validation on within-fold SMOTE augmentation. Results: EEG-ChTABNet consistently outperformed the baseline Transformer on all 8 feature sets. DisEn and SyDy features yielded peak classification accuracy of 73.3% (AUC: 0.823 and 0.857, respectively) compared to the corresponding baseline of 57.8% and 55.6%. SyDy achieved the best overall AUC of 0.857 and the dementia detection sensitivity was up to 86.7% over multiple feature sets. Conclusions: EEG-ChTABNet shows the effectiveness of channel-adaptive, dual-branch Transformer Designs for EEG-based neurological classification from Small-Sample Entropy Feature Data, and Identifying SyDy and DisEn as the Most Discriminative Feature Representations for Three-Class Neurological EEG Classification. Full article

Direct utilization of biomass-derived silica in neutral surfactant-templated mesoporous synthesis remains underexplored with respect to mesostructure control and functional integration. High-purity silica extracted from acid-treated rice husk ash (~98.4 wt% SiO₂) was employed as the sole precursor in a fluoride-assisted sol–gel route to synthesize MSU-X frameworks without chemical modification. Systematic parametric variation—pH, Si/surfactant ratio, hydrothermal temperature, and aging duration—establishes quantitative structure–processing correlations. Under optimized conditions (pH 2, Si/Tergitol = 8, 60 °C, 96 h), the resulting material exhibits a wormhole-like mesoarchitecture with a BET surface area of 816 m² g⁻¹, mean pore diameter of ~3.6 nm, and three-dimensionally interconnected channels, confirmed by SAXS, TEM, and N₂ sorption. EDXRF analysis confirms effective impurity removal and high silica incorporation efficiency (~95–96%); thermal stability persists to 700 °C, with incipient crystallization near 800 °C. As a functional demonstration, MSU-X served as an anti-agglomeration scaffold for ZIF-8 crystallization during DDT adsorption. Despite attenuated kinetics relative to pristine ZIF-8—where severe agglomeration occludes active imidazole nodes—the Z8/MSU-X composite achieved near-quantitative DDT removal (74.10 mg g⁻¹). This performance stems from the mesoporous matrix driving size-confined, highly dispersed ZIF-8 growth, thereby maximizing active-site exposure. Operating within a reagent-limited regime rather than a capacity-saturated boundary, this efficient depletion confirms that the scaffold successfully suppresses site loss. Ultimately, these findings validate biogenic silica as a directly integrable precursor for tailored mesostructure assembly, positioning agricultural waste as a high-performance feedstock for hierarchical adsorption architectures. Full article