Search Results (362)

Vibration-assisted water flooding (VA-WF) can improve sweep efficiency. However, unclear macro-scale mechanisms limit its wider adoption in heavy oil reservoirs. This study combines previous sandpack experiments with two-dimensional Volume-of-Fluid (VOF) simulations to show how vibrations reshape permeability fields and, in turn, pressure and production behaviour. Heavy oil sandpacks were water-flooded under conditions of no vibration and 2 Hz and 5 Hz axial excitation. Measured injection pressure histories and oil production were used to calibrate a VOF model in which absolute permeability follows a log-normal distribution with directional anisotropy. Only when axial and radial permeabilities were assigned a negative local correlation did the model reproduce key observations: secondary pressure spikes, irregular viscous-fingering morphologies, delayed production drops, and variability in cumulative recovery. Parameter sweeps quantify the sensitivity of VA-WF performance to the variance and correlation of the permeability field, and multiple runs estimate the variability in outcomes introduced by stochastic heterogeneity. This study proposes a transferable workflow—comprising sample testing, parameter inference, and probabilistic simulation—to screen excitation conditions and forecast VA-WF performance prior to field implementation, enabling operators to optimize vibration frequency based on reservoir-specific permeability characteristics and to anticipate production variability under uncertainty. These results highlight the dominant factors affecting swept volume and oil recovery, supporting data-driven decision making in VA-WF projects. Full article

We evaluate the Sample-based Krylov Quantum Diagonalization (SKQD) algorithm on one- and two-dimensional Heisenberg models, including strongly correlated regimes in which the ground state is dense. Using problem-informed initial states and magnetization sector sweeps, we investigate SKQD for problems with non-sparse ground states, where energy accuracy and sampling efficiency are theoretically anticipated to degrade. Our studies reveal that SKQD reproduces ground-state energies and field-dependent magnetization across a range of anisotropies. Benchmarks against DMRG and exact diagonalization show consistent qualitative agreement, with accuracy improving systematically in more anisotropic regimes. We further demonstrate SKQD on quantum hardware by implementing 18- and 30-qubit Heisenberg chains, obtaining magnetization curves that match theoretical expectations. Simulations on the IBM Nighthawk processor for 64-qubit two-dimensional square lattice systems further indicate that the method remains effective beyond one-dimensional geometries. Full article

Multistatic sonar provides enhanced target detection in complex underwater environments. The wide-angle bistatic scattering characteristics of targets, particularly the bistatic reflection coefficients, are important for evaluating system performance and designing acoustic absorbing coatings. However, obtaining full-angle experimental measurements is challenging, and conventional finite-element simulations become computationally prohibitive for large structures, high frequencies, or exhaustive angle sweeps. To overcome these challenges, a fast wide-angle scattering prediction method for acoustically coated plates is proposed. The method constructs a scattering transfer matrix from the surface mesh and retrieves the equivalent source density from a small subset of scattered-pressure samples, enabling reconstruction of the full-angle scattering field and rapid extraction of reflection coefficients. The approach is demonstrated on both rigid and coated plates, with predictions compared against finite-element calculations. The results demonstrate that the proposed method accurately reproduces the bistatic reflection coefficients, including non-linear dispersion effects and interference fringes, across a wide frequency band from 100 Hz to 5 kHz. Compared to traditional FEM sweeps, this method significantly reduces computational time while maintaining high accuracy, providing an efficient tool for the design of acoustic stealth materials and laying a foundation for rapid target strength prediction of complex targets using the Planar Element Method. Full article

Microbial polysaccharides are attracting increasing interest as water-processable polymers for extrusion-based additive manufacturing due to their ability to form physically stabilized networks without covalent cross-linking. In this study, a kefiran–propylene glycol (PG) formulation was developed to investigate whether time-dependent supramolecular reorganization can be exploited to control print fidelity. Extrusion performance was assessed through quantitative filament collapse analysis, while rheological behavior was characterized by oscillatory strain, frequency, and time sweep measurements. Filaments printed 5 min after PG addition showed pronounced sagging (δ/(L/2) ≈ 0.35 at the largest spans), whereas after 15 min the normalized deflection decreased below 0.03, indicating a marked improvement in self-supporting capability. Time sweep experiments revealed a continuous increase in storage modulus from ~100 to ~1200 Pa over 1800 s, consistent with progressive viscoelastic stiffening. Freeze-dried constructs exhibited an interconnected porous architecture with a predominant pore population between 6 and 20 µm and an apparent porosity of 60.9 ± 1.2%. Upon rehydration at 37 °C, samples swelled to ~350% within 5 h and showed gradual mass loss over 56 days while remaining intact. ATR–FTIR confirmed the preservation of the polysaccharide backbone without evidence of new covalent functionalities. Extrusion fidelity is therefore governed by progressive supramolecular consolidation within a physically assembled network, rather than by any form of chemical cross-linking. Full article

Modeling ship maneuvering dynamics presents numerous challenges, including long-term multi-step recursive error accumulation, insufficient generalization under distributed control rates, and high-frequency disturbance amplification effects. Traditional analytical models heavily rely on vessel-specific trials to characterize strongly nonlinear coupling terms and perform parameter identification, making it difficult to balance efficiency and accuracy under complex operating conditions. This paper presents a ship maneuvering-oriented integration of an invertible Koopman representation and a NeuralODE-based continuous-time predictor. The IKN reconstructs strongly coupled state spaces while enhancing representational invertibility, whereas NeuralODE directly fits the control differential equations governing ship maneuvering dynamics and supports continuous-time prediction. Experiments validate multi-rate control performance under ideal and disturbed data conditions, assessing error accumulation and extrapolation stability through long-term multi-step propagation. Evaluations utilize the KVLCC2-type L7 ship model with a 0.25 s sampling interval and a 200 s prediction horizon, validated against a multi-rate control test set. The results indicate that, compared to the baseline neural ODEs model without IKN, the normalized root mean square error (NRMSE) of state quantities decreased by 12.68% on average. In typical operational scenarios such as constant-speed emergency turns and variable-speed sine sweep maneuvers, the average state NRMSE was 7.96% lower than the LSTM model and 53.85% lower than the IKN–Koopman operator network. Noise experiments demonstrated that when introducing simulated sensor noise at 5%, 10%, and 20% into the dataset, the average state NRMSE remained at 5.98%, 8.24%, and 10.06%, respectively. This confirms the method’s stable prediction performance under varying noise intensities. Full article

Ecotourism in protected areas creates a conservation paradox: tourism revenue funds protection, yet tourism infrastructure simultaneously degrades the wildlife it protects. We examined this paradox in white-faced capuchins (Cebus imitator) in Tortuguero National Park, comparing behaviour across a high-tourism accommodation site (2152 monthly guests) and a strictly regulated terrestrial trail. Using focal animal and sweep sampling methods, we recorded 477 behavioural units across 261 min, analysing locomotion, feeding, and agonistic behaviours through generalized linear models. Primates in accommodation areas exhibited significantly reduced high substrate use (p = 0.005), showed a trend toward increased anthropogenic food reliance (p = 0.070), and higher—but not statistically significant—rates of agonistic behaviours (p > 0.05). The negative correlation between natural foraging and active food supply (r = −0.31) is consistent with anthropogenic provisioning that may alter primate ecological functions. These findings demonstrate that effective conservation in tourism contexts requires integrated management addressing three interconnected challenges: (1) habituation to human presence, (2) food provisioning with cascading consequences, and (3) ecosystem-level degradation through altered primate functions. We recommend evidence-based interventions including secured waste management, enforcement of wildlife feeding prohibitions, and environmental education programs with community participation. Ecotourism sustainability requires managing human–wildlife interactions and integrating local stakeholder perspectives to preserve animal welfare and ecosystem functions essential for conservation. Full article

Local path tracking is a critical challenge for autonomous vehicles, requiring precise trajectory following under nonlinear dynamics, strict constraints, and real-time execution. While Nonlinear Model Predictive Control (NMPC) has emerged as a leading approach, many existing methods decouple velocity planning from tracking, lack formal stability guarantees, or do not demonstrate feasibility on embedded platforms. We present a unified NMPC framework that integrates curvature-aware velocity adaptation directly into the cost function. The controller makes use of cubic spline paths, recursive feasibility constraints, and Lyapunov-based terminal costs to ensure stability. The nonlinear optimization problem is implemented in CasADi and solved using IPOPT, with warm-starting and efficient discretization techniques enabling real-time performance. Our approach has been validated in the CARLA simulator across a variety of urban scenarios, including straight roads, intersections, and roundabouts. The controller achieves a mean cross-track error of 0.10 m on straight roads, 0.44 m on roundabouts, and 1.36 m on tight intersections, while maintaining smooth control inputs and bounded actuator effort. A curvature-aware cost term yields a 14.4% reduction in lateral tracking error compared to the curvature-unaware baseline. Benchmarking results indicate that the Raspberry Pi 5 outperforms the NVIDIA Xavier AGX by 1.5–1.6×, achieving mean execution times of 38–45 ms across all scenarios. This demonstrates that advanced NMPC can run in real time on low-cost consumer hardware ($80 vs. $700). Systematic ablation studies reveal the critical role of state weighting (Q) and input regularization (R): removing Q degrades tracking by 10% and destabilizes control effort (+54% acceleration, +477% steering), while omitting R induces oscillatory behavior with +907% acceleration effort. Euler integration provides no computational benefit (+8% solver time) while degrading accuracy by 25%, confirming RK4 as strictly superior. Sensitivity analysis via Latin Hypercube Sampling identifies the prediction horizon (N) and discretization timestep ($\mathrm{\Delta }t$) as dominant parameters. Per-scenario Pareto analysis yields a balanced operating point ($N=15, \mathrm{\Delta }t=0.055$ s) used for all primary evaluations, while a global sweep identifies a robust alternative ($N=12, \mathrm{\Delta }t=0.086$ s) suitable for general deployment tuning. This framework bridges the gap between spline-based local planning and stability-guaranteed NMPC, offering a simulation-validated, real-time solution for embedded autonomous driving research. Future work will focus on hardware-in-the-loop and full-vehicle deployment, integration with high-level decision-making, and learning-enhanced MPC. Full article

Herein, (Nafion-treated) (30 wt%) NiO_x/graphene (GP) were prepared at 250 °C and 450 °C and investigated as materials for dopamine electrochemical detection. Initially, characterization of the samples was performed using high-resolution transmission electron microscopy (HRTEM), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS) techniques. Subsequently, they underwent electrochemical evaluation using cyclic voltammetry, linear sweep voltammetry (LSV), differential pulse voltammetry (DPV), and chronoamperometry (CA) techniques. All electrochemical measurements of the dopamine oxidation reaction (DOR) were performed in a 0.1 M phosphate buffer solution (PBS) at pH of 7.00 and at temperature of 36.6 °C. It was found that Nafion addition to the electrocatalysts surface facilitates access of the cationic dopamine molecule to their active centers being attributed to Nafion cation permeability. Nafion-NiO₂₅₀/GP exhibited higher activity towards the DOR reaction. The limit of detection (LOD) for the lower linear range of 0.5–10 μM was calculated to be 0.8 μM, with a sensitivity of 3.086 μA μM⁻¹cm⁻². Furthermore, the Nafion NiO₂₅₀/GP/GC electrode exhibited high selectivity towards DA, as well as good repeatability and reproducibility with an acceptable level of deviation, and excellent storage stability. The six electrodes produced from the Nafion-NiO₂₅₀/GP showed 8.28% reproducibility (RSD), indicating adequate behavior, while the same electrode after six measurements over a 30-day period showed an RSD of 5.50%, indicating a reliable electrode. Full article

Folding-wing Unmanned Aerial Vehicles (UAVs) have become a key platform in modern aerial applications, owing to their superior portability and rapid deployment capabilities. While the tandem-wing configuration offers a compact solution for strict folding constraints, the resulting high wing loading necessitates a maximized lift coefficient (C_L) to ensure efficient low-speed loitering. This study presents an aerodynamic optimization framework aiming to maximize the C_L of a folding tandem-wing UAV. A combined optimization strategy integrating Optimal Latin Hypercube Sampling (OLHS), orthogonal polynomial surrogate models, and the Multi-Island Genetic Algorithm (MIGA) is established. With aft wing parameters determined, global sensitivity analysis identifies the fore wing span as the dominant factor, contributing 47.40% to lift performance. Crucially, although vertical separation contributes only 6.53% to C_L and sweep angle just −1.22% to drag coefficient, their strong interaction effects with wing span confirm their non-negligible role. Finally, the flow field characteristics at the wing root of the optimized configuration undergo significant changes, resulting in a 4.28% increase in the C_L. This work validates the important role of parameter interaction effects in aerodynamic optimization and provides a theoretical basis for the design of geometrically constrained aerial vehicles requiring high lift coefficients. Full article

A sequenced ultrasonic atomization coupled with a pyrolysis process is developed to synthesize a series of cross-scale (Co/Cu)-NC catalysts. The catalysts demonstrate high metal utilization efficiency with a metal loading of 22.45 ± 0.07 wt%. Electrochemical evaluations for the oxygen reduction reaction (ORR) suggest that the best (Co/Cu)-NC catalysts are prepared with a Co/Cu ratio of 1/1 and a calcination temperature of 800 °C, which achieve a half-wave potential of 0.87 V and an electrochemical impedance spectroscopy semicircle radius as low as 30 ohms. Linear sweep voltammetry measurements indicate that (Co/Cu)-NC catalysts exhibit the highest current density. Under a potential of −0.73 V versus the reversible hydrogen electrode, (Co/Cu)-NC catalysts demonstrate long-term stability with the CO Faradaic efficiency of about 70% for catalyzing carbon dioxide reduction reaction (CO₂RR). Overall, the above metrics identify CoCu-800 as the optimal bifunctional catalyst among the tested samples for ORR and CO₂RR under the investigated conditions. Full article

Ageing significantly affects the long-term durability of asphalt pavements, yet quantitative correlations between laboratory ageing protocols and actual field ageing remain insufficiently defined. This study investigates the ageing behaviour of an 80/100 penetration-grade bitumen at binder, mixture, and field levels to establish equivalence relationships among different ageing pathways. Binder samples were subjected to RTFO, PAV (20–60 h), and coupled thermal–photo-oxidative ageing (RTFO + PAV + UV, 6–18 d). Asphalt mixtures were oven-aged at 85 °C for 5–10 d, followed by binder extraction and recovery, and field-aged binders were obtained from a 12-year-old pavement in Xinjiang, China. Rheological properties were characterised using frequency sweep and multiple stress creep and recovery tests, from which ageing index (AI), low-temperature ageing index (LAI), Glover–Rowe (G–R) parameter, and nonrecoverable compliance (J_nr) were derived. AI increased from 1.00 for virgin binder to 1.12 under coupled ageing, while G–R increased from near zero to 318 kPa after 60 h PAV ageing and exceeded 400 kPa under coupled ageing. UV exposure increased G–R by approximately 20%–65% relative to thermal ageing alone. Nonlinear growth models described property evolution with high reliability (R² = 0.995–0.999). Equivalent ageing analysis showed that RTFO + PAV required over 50 h to reproduce field ageing, whereas coupled ageing and mixture oven ageing achieved comparable states within shorter durations. These results demonstrate that photo-oxidation and mixture-scale interactions significantly influence ageing pathways and should be considered in laboratory simulations of field ageing. Full article

Broadband piezoelectric energy harvesters (PEHs) are attractive for powering self-sustained sensing nodes in industrial monitoring, structural health monitoring, and distributed IoT systems, where ambient vibration spectra are often uncertain, drifting, and broadband. However, tuning multiple resonant peaks in PEH arrays usually relies on time-consuming finite element (FE) parameter sweeps or iterative optimizations, which becomes a practical bottleneck when rapid, site-specific customization is required. This study presents a data-driven inverse-design framework for a five-beam PEH array based on a tandem neural network (TNN). A forward multilayer perceptron (MLP) surrogate is first trained using 10,000 COMSOL-generated samples to predict the array’s characteristic frequencies from the design variables (end masses $M_1$–$M_5$ and tilt angle $\alpha$), achieving >98% prediction accuracy with a prediction time <1 s, thereby enabling efficient replacement of repeated FE evaluations during design. The trained MLP is then coupled with an inverse-design network to form the TNN, which maps target characteristic-frequency sets directly to physically feasible parameters through the learned surrogate. Multiple representative target frequency sets are demonstrated, and the TNN-generated designs are independently verified by COMSOL frequency–response simulations. The resulting arrays achieve broadband operation, with bandwidths exceeding 10 Hz. By shifting most computational cost to offline dataset generation and training, the proposed spectrum-to-parameter pathway enables near-instant parameter design and reduces reliance on exhaustive FE tuning, supporting rapid, application-specific deployment of broadband PEH arrays. Full article

This paper examines precise timestamping of LoRaWAN messages (particularly beacons) to enable wide-area synchronization for end devices without GNSS. The need for accuracy demands hardware-level timestamping architectures, possibly using time-domain cross-correlation (matched filtering) against internally generated chirp references. Focusing on Time-of-Arrival (TOA) estimation from raw IQ samples, the authors analyze effects of non-idealities—additive white Gaussian noise (AWGN), Carrier Frequency Offset (CFO), Sampling Phase and Frequency Offset (SPO and SFO, respectively), and radio parameters such as spreading factor (SF) and sampling rate of the baseband signals. A MATLAB (R2020) simulation mimics preamble detection and Start-of-Frame Delimiter (SFD) timestamping while sweeping SF (7, 9, 12), sampling rates (0.25–10 MSa/s), SNR (−20 to +20 dB), and CFO/SFO offsets (−10–10 ppm frequency deviation). Errors are evaluated in terms of mean and dispersion, the latter represented by the P95–P5 range metric. Results show that oversampling not only improves temporal resolution, but sub-microsecond error dispersion can be achieved with high sampling rates in favorable SNR and SF cases. Indeed, SPO and SNR greatly contribute to error dispersion. On the other hand, higher SF values increase correlation robustness at the cost of longer chirps, making SFO a dominant error source; ±10 ppm SFO can induce roughly ±3 μs SFD bias for SF12. CFO largely cancels after up-/down-chirp averaging. As a concluding remark, matched-filter hardware timestamping can ensure sub-μs errors thanks to oversampling but requires SFO compensation for accurate real-world synchronization in practice. Full article

Wire-directed energy deposition (W-DED) produces steep thermal gradients and rapid heating-cooling cycles due to the moving heat source, where modest variations in process parameters significantly alter heat input per unit length and therefore the full thermal history. This sensitivity makes process tuning by trial-and-error or repeated FE sweeps expensive, motivating inverse analysis. This work proposes an inverse thermal process design framework that couples single-track experiments, a calibrated finite element (FE) thermal model, and a parametric physics-informed neural network (PINN) surrogate. By using experimentally calibrated heat-loss physics to define the training constraints, the PINN learns a parameterized thermal response from physics alone (no temperature data in the PINN loss), enabling inverse design without repeated FE runs. Thermocouple measurements are used to calibrate the convection film coefficient and emissivity in the FE model, and those parameters are used to train a parametric PINN over continuous ranges of arc power (1.5–3.0 kW) and travel speed (0.005–0.015 m/s) without using temperature data in the loss function. The trained PINN model was validated against the calibrated FE model at 3 probe locations with different power and travel speed combinations. Across these benchmark conditions, the mean absolute errors are between 6.5–17.4 °C, with cooling-tail errors ranging from 1.8–12.1 °C. The trained surrogate is then embedded in a sampling-based inverse optimization loop to identify power-speed combinations that achieve prescribed interlayer temperatures at a fixed dwell time. For target interlayer temperatures of 100, 130, and 160 °C with a 10 s dwell time, the optimized solutions remain within 3.3–5.6 °C of the target according to the PINN, while FE verification is within 4.0–6.6 °C. The results demonstrate that a physics-only parametric PINN surrogate enables inverse thermal process design without repeated FE runs while establishing a single-track baseline for extension to multi-track and multi-layer builds. Full article

To address the problems of weak geometric features, low signal response amplitude, and insufficient spatial resolvability of near-surface defects in metal substrates, a high-resolution spatiotemporal-coded eddy-current array probe is proposed. The probe adopts an array topology with time-multiplexed excitation and adjacent differential reception, achieving a balance between high common-mode rejection ratio and high-density spatial sampling. First, a theoretical electromagnetic coupling model between the probe and the metal substrate is established, and finite-element simulations are conducted to investigate the evolution of the skin effect, eddy-current density distribution, and differential impedance response over an excitation frequency range of 1–10 MHz. Subsequently, a 64-channel M-DECA probe and an experimental testing platform are developed, and frequency-sweeping experiments are carried out under different excitation conditions. Experimental results indicate that, under a 50 kHz excitation frequency, the array eddy-current response achieves an optimal trade-off between signal amplitude and spatial geometric consistency. Furthermore, based on the pixel-to-physical coordinate mapping relationship, the lateral equivalent diameters of near-surface defects with different characteristic scales are quantitatively characterized, with relative errors of 6.35%, 4.29%, 3.98%, 3.50%, and 5.80%, respectively. Regression-based quantitative analysis reveals a power-law relationship between defect area and the amplitude of the differential eddy-current array response, with a coefficient of determination ${\mathrm{R}}^2=0.9034$ for the bipolar peak-to-peak feature. The proposed M-DECA probe enables high-resolution imaging and quantitative characterization of near-surface defects in metal substrates, providing an effective solution for electromagnetic detection of near-surface, low-contrast defects. Full article