Search Results (4,279)

This study investigates active geometric modulation in a variable-length pendulum energy harvester combining radial electromagnetic, rotational electromagnetic, and piezoelectric transduction. A reciprocal seven-state electromechanical model is formulated and analysed using phase-aligned continuation, transverse Floquet stability, Lyapunov diagnostics, physical load variation, and paired control-on/control-off energy accounting. A minimal threshold-based shift of the radial spring equilibrium serves to reveal the branch-support mechanism rather than to provide a final control strategy. The results show that an established finite-amplitude branch may persist below the local instability boundary of the inactive response, while piezoelectric loading can modify this boundary through electromechanical back-action. All three transduction channels contribute to gross electrical output, and physical load matching increases that output. Nevertheless, the continuous modulation remains energetically unfavourable after actuator, power-conditioning, and auxiliary demands are included and does not robustly retain the branch under the tested nonstationary excitations. The results therefore define requirements for phase-aware, adaptive, latching, or regenerative implementations with substantially lower actuation work. Full article

In short-duration power-support applications of energy storage stations, state of power (SOP) estimation should reflect the constant-power boundary over the target horizon, while constant-current extrapolation may misrepresent the current rise caused by voltage decline. This study proposes a 30 s constant-power SOP evaluation framework for portable inspection, decoupling parameter inversion from boundary propagation. The method uses a single-particle model with electrolyte dynamics (SPMe) with degradation factors for ohmic resistance, kinetics, and diffusion. The ohmic degradation factor is determined through time-zero voltage-drop hard calibration, while the kinetic and diffusion degradation factors are identified from 30 s constant-current pulse responses using physics-informed neural network (PINN)-based inversion, and the constant-power boundary is solved by Runge–Kutta integration and bisection search. In model-consistent closed-loop verification, which assesses numerical and inversion consistency under matched-model assumptions rather than independent physical accuracy, the method achieved a mean absolute error (MAE) of 0.100%, a 95th-percentile error of 0.503%, and a maximum error of 2.019%, below the constant-current approximation and first-order equivalent circuit model baselines within the matched-model synthetic setting. Its Jetson Nano-equivalent runtime was approximately 0.630 s. An external proxy comparison using 154 discharge pulses from a public HPPC dataset for an LCO-graphite cell showed an MAE of 0.41 W relative to the pulse-power proxy. This result measures agreement with the selected pulse-power proxy rather than accuracy against a strictly defined 30 s constant-power ground truth. The 10 mV-noise case increased the SOP MAE to 3.868%, indicating substantial sensitivity to voltage-measurement disturbance and the need for validated signal conditioning. These results indicate a physically interpretable and computationally feasible candidate framework for rapid battery power screening, while direct constant-power experiments, broader chemistry coverage, and measured-noise validation remain necessary before field deployment. Full article

Controlled bridge simulation and laboratory damage identification require models that can simulate structural responses and infer localized stiffness loss from limited measurements. Existing Fourier Neural Operator (FNO)-based vehicle–bridge interaction (VBI) models provide efficient surrogates for these mappings, but the global Fourier representation can smooth localized damage transitions and introduce boundary-related errors for finite-span bridge responses. This study adapts the Wavelet Neural Operator (WNO) to the VBI setting and develops the Vehicle–Bridge Interaction Wavelet Neural Operator (VIWNO), an application-oriented framework for wavelet-domain operator learning between structural response fields and damage fields. VIWNO is pre-trained on a numerical VBI finite-element dataset (VBI-FE) and fine-tuned using only healthy-state measurements from a scaled VBI experimental dataset (VBI-EXP), before being evaluated on unseen laboratory damage scenarios. Under the controlled VBI-FE setting, where bridge, vehicle, speed, and measured road-profile parameters are fixed and the main variation is the damage field, VIWNO reduces forward response errors by 20–30% and inverse damage-estimation errors by 26–32% relative to the FNO-based Vehicle–Bridge Interaction Neural Operator (VINO) baseline. Additional morphology and operating-condition stress tests show that the error increases under sharper damage fields and perturbed VBI conditions, but VIWNO remains more accurate than VINO and the added convolutional or frequency-domain baselines in the tested cases. On VBI-EXP, projection-only healthy-state fine-tuning reduces intact false-damage levels and yields sharper damage estimates than VINO under both displacement and acceleration inputs. Stability checks over five initializations and repeated vehicle passages show limited variation in the reported inverse metrics. These results support the feasibility of wavelet-domain neural operators for calibrated VBI simulation and scaled laboratory damage identification, while field-scale bridge health monitoring still requires validation under broader traffic, environmental, support, and damage-morphology variability. Full article

Bacterial vaginosis (BV) and aerobic vaginitis (AV) are usually described as distinct forms of vaginal dysbiosis. BV is characterized by depletion of lactobacilli, overgrowth of anaerobic microorganisms, and biofilm formation, whereas AV is associated with inflammatory changes, epithelial disruption, and predominance of aerobic bacteria. Over the past two decades, this distinction has profoundly influenced the understanding, diagnosis, and management of vaginal disorders. Despite their apparent differences, the relationship between BV and AV remains incompletely understood. Growing evidence indicates that many women exhibit microbiological and microscopic patterns that cannot be readily classified within existing diagnostic frameworks. Intermediate Nugent scores, mixed vaginitis, transitional ecological states, post-treatment microbiota reconstitution, physiological hypoestrogenic conditions, and uncommon inflammatory patterns all challenge the traditional view of BV and AV as strictly separate entities. This narrative review examines the historical evolution of BV and AV concepts, compares their microbiological, immunological, and epithelial characteristics, and evaluates the diagnostic paradigms currently used to identify vaginal dysbiosis. The strengths and limitations of clinical and diagnostic approaches are discussed together with the extent to which contemporary international guidelines reflect current biological knowledge. Particular emphasis is placed on intermediate, mixed, and overlapping states that blur the boundaries between established diagnostic categories. The available evidence supports the clinical usefulness of distinguishing BV and AV as separate clinicopathological entities. However, it also indicates that vaginal ecosystem disturbances frequently extend beyond rigid dichotomous classifications. Current diagnostic categories remain valuable tools for clinical practice, yet they may only partially capture the complexity and dynamic nature of vaginal dysbiosis. Understanding how microbial communities, host responses, epithelial integrity, and physiological factors interact remains a major challenge for future research and clinical interpretation. Full article

Belt conveyors are essential to continuous ore and bulk-material transport, yet the diagnostic signatures of slippage, belt breakage, deviation and overheating tend to appear as coupled changes across multiple variables rather than isolated in a single signal. This study presents a multi-model and explainable diagnostic framework using belt speed, motor temperature, motor current, drum temperature and belt tension. A balanced dataset containing 1500 observations from five operating states was analyzed under a unified protocol. Eleven classifiers were compared using identical stratified partitioning and cross-validation, and a perturbation-based validation set was introduced to test whether near-ceiling baseline scores would hold under sensor noise and boundary-state drift. The measurements were preserved without alteration, and the perturbation set was treated explicitly as a sensitivity analysis. The baseline test confirmed strong class separability; the robustness-validation setting, in contrast, gave a more conservative picture of model behavior and revealed differences invisible in the ordinary hold-out split. Logistic regression reached accuracy = 0.800, macro-F1 = 0.798 and weighted AUC = 0.946 under perturbation. SHAP analysis showed that motor current, belt tension, motor temperature and belt speed were the dominant contributors, while drum temperature played a much weaker role. The results suggest that conveyor fault diagnosis should be reported with model comparison, robustness analysis and feature-level explanation together, particularly when ordinary ROC curves approach one and the practical value of a model must be weighed against uncertainty. Full article

Triply periodic minimal surface (TPMS) heat exchangers offer high surface-area-to-volume ratios and interconnected flow pathways, making them attractive for compact thermal management. However, accurately predicting nanofluid heat transfer over a wide range of nanoparticle concentrations and operating conditions in complex TPMS geometries remains computationally challenging because of the coupled effects of porous architecture, flow dynamics, and concentration-dependent thermophysical properties. In this study, a hybrid physics-informed neural network (PINN) framework was developed to reconstruct concentration-dependent ${Al}_2{O}_3–water$ nanofluid temperature fields in TPMS gyroid heat exchangers. The originality of the proposed approach lies in integrating sparse thermocouple measurements, a steady-state convection–diffusion equation, boundary condition residuals, concentration-dependent nanofluid property models, and a physics-based concentration scaling procedure within a unified framework. The proposed framework was applied to aluminum and silver TPMS heat exchangers over a wide range of nanofluid volume fractions and flow conditions. The trained PINN accurately reconstructed the experimentally measured temperature field, demonstrating excellent agreement with the reference experimental data. Predictions at concentrations beyond the experimentally measured reference condition were obtained using the physics-based concentration scaling model. The effective heat transfer coefficient and Nusselt number were subsequently evaluated from the predicted mean TPMS temperature through an energy balance formulation. Increasing nanoparticle concentration reduced the predicted TPMS temperatures by approximately 17.5–18.5%, while the combined increase in concentration and flow rate produced an overall temperature reduction of about 33.5%. Relative to the selected baseline condition, the combined variation in concentration and flow rate was associated with calculated increases of 62.08% in $h_{eff}$, 58.33% in Nu, and 59.32% in Re. These results demonstrate the potential of the proposed hybrid PINN framework as a computationally efficient surrogate for evaluating nanofluid-enhanced TPMS heat exchangers, while acknowledging that predictions away from the training concentration depend on the validity of the concentration scaling model. Full article

Buried interfaces and interphases often govern performance loss in rechargeable batteries, although their functions are frequently inferred from spatially averaged composition, surface-sensitive measurements, or cell-level electrochemical response. In this Perspective, an interface denotes the geometrical boundary between adjacent phases, whereas an interphase denotes a finite-thickness region whose composition or structure differs from those of the adjoining bulk phases. Rather than organizing the discussion by focused ion beam (FIB) modality or battery chemistry alone, we adopt an architecture-first, evidence-bounded framework and compare three classes of buried-interface architecture: engineered particle coatings; electrochemically generated solid electrolyte interphase (SEI) and cathode–electrolyte interphase (CEI) regions together with lithium-metal deposits; and solid–solid contacts in all-solid-state batteries. For each class, the formation route and required function are related to spatial descriptors, including thickness distribution, lateral continuity, pore or gap topology, chemical gradients, contact area, and contact retention. FIB-enabled cross-sectioning, tomography, and correlative spectroscopy can register morphology, chemistry, and contact geometry within a common spatial frame, but they do not directly measure ionic conductivity, electronic leakage, adhesion energy, or local reaction rate. Such functional attribution therefore requires complementary electrochemistry, spectroscopy, modeling, temporal observation, and representative sampling. Across the three classes, durable interfacial function depends on chemically selective transport pathways that remain spatially continuous and mechanically viable during processing, cycling, and storage. Full article

A solid solution treatment (SST) followed by single-stage aging (0–30 h, 140 °C) was performed on 7075 aluminum alloy specimens with longitudinal axes oriented at 0°, 45° and 90° to the rolling direction. The mechanical properties and microstructure were analyzed by tensile testing, optical microscope (OM), electron backscatter diffraction (EBSD), scanning electron microscope (SEM) and transmission electron microscope (TEM). The results show that the average tensile strengths of the as-received 7075 aluminum alloy in the three directions were 304 MPa (0°), 295 MPa (45°) and 297 MPa (90°), respectively, with an anisotropy index (AI) of 0.97, indicating that the as-received samples exhibited negligible anisotropic mechanical properties. After SST, elongated grains with coarse size were formed, which is primarily attributed to the inheritance of the deformed fiber texture introduced by hot rolling. EBSD analysis of the 30 h aged specimens revealed that, within the same analyzed area, the total grain-boundary length in the 45° direction (16.4 cm) was much larger than that in the 0° (10.4 cm) and 90° (13.5 cm) directions. As the grain morphology showed no significant change between the SST and aged conditions, this grain-boundary distribution was representative of the microstructural state established during SST and persisted throughout the artificial aging process, contributing to the anisotropic mechanical properties. During artificial aging, prolonged aging time significantly facilitated the precipitation, with the 30 h aged sample exhibiting a significantly higher density of precipitates compared to the 6 h aged sample, leading to enhanced mechanical properties. The tensile strengths of the 30 h aged samples increased to 165 MPa, 236 MPa and 196 MPa in the three directions, respectively. Meanwhile, due to the fixed crystallographic orientation relationship between the precipitates and the Al matrix, the precipitates tended to form on specific planes, which enhanced the anisotropic mechanical properties. Consequently, the AI value increased from 0.97 (as-received) to 1.43 (30 h aged) with prolonged aging time. Full article

Charge accumulation at polyimide (PI)/sulfur hexafluoride (SF₆) boundaries can reorganize the molecular environment of gas-insulated equipment, yet near-surface population is often treated as a single adsorption response. We used atomistic molecular dynamics to resolve three distinct quantities at static surfaces of four 4,4′-oxydianiline-based PIs: gas loading, reservoir-referenced interfacial partitioning, and right-censoring-aware molecular residence. A matched, sign-swapped bipolar design combined real-fluid 5–20 atm reservoir states with charge-scale, duration, energy-decomposition, model-form, and finite-cell controls. Neutral loading increased the absolute interfacial population but reduced enrichment relative to the distal gas at 20 atm. The amplified perturbation produced PI-model- and loading-dependent surface-excess responses, while residence changes remained small and followed no common population trend. Literature-scaled charge densities were below the trajectory-level resolution, and 300 ps extensions showed that the initially resolved contrasts were transient rather than persistent cross-model rankings. An evidence hierarchy prevents supplied amount, spatial preference, and molecular persistence from being collapsed into one adsorption metric. It provides a reproducible molecular screening framework for comparing SF₆-facing polymer interfaces and a quantitative benchmark for future polarizable, flexible-interface, and coupled-field studies. Full article

We present and analyze a time-fractional, partly diffusive model of two electrically coupled Hindmarsh–Rose neurons in which only the membrane potentials undergo spatial transport, through a nonlinear Neumann m-Laplacian, while the temporal evolution is governed by a Caputo derivative of order $\rho \in (0,1]$. The fractional operator incorporates hereditary relaxation, whereas the m-Laplacian represents gradient-dependent degenerate transport and recovers ordinary diffusion when $m=2$. In a Gelfand triple adapted to the no-flux boundary condition, we derive a fractional energy inequality, a uniform dissipative estimate, and the existence of a global weak solution by a Faedo–Galerkin approximation, fractional compactness, and Minty’s method. Uniqueness and continuous dependence are obtained in the stated bounded solution class. We prove global Mittag–Leffler synchronization above an explicit coupling threshold and establish a practical synchronization bound under parameter mismatch. A fully implicit L1 finite-volume method is then constructed; every time step is solvable, uniqueness follows under an explicit monotonicity condition, and the scheme is unconditionally energy dissipative and convergent. Manufactured-solution tests recover the expected $2-\rho$ temporal and second-order spatial rates. In the neuronal simulations, reducing $\rho$ from 1 to $0.90$ lengthens the mean bursting period from about 379 to 565 time units, an increase of roughly one half, and raises the number of spikes per burst from about $31.7$ to $38.8$. Over $2\le m\le 4$ the temporal rhythm is essentially unchanged, the burst period staying near 362 time units, while the diffusion exponent reshapes the peak amplitude and the spatial gradient profiles of the traveling fronts. The empirical synchronization threshold for the canonical parameter set is approximately $11.0$, far below the global sufficient bound $2.3917\times {10}^4$, which quantifies the conservatism of the analytical certificates. Full article

Global path planning for autonomous orchard robots must balance obstacle clearance, path smoothness, and trajectory trackability. This balance is difficult to achieve in unstructured orchards, where irregular tree rows, scattered trunks and ground obstacles, and narrow inter-row passages can cause conventional planners to generate low-clearance paths with frequent local turns. This study proposes SDF-Theta*, a safety- and smoothness-aware global path planning framework for orchard robots in unstructured environments. The framework constructs a Euclidean signed distance field (ESDF) from a two-dimensional occupancy planning map and defines a safe navigable domain using the robot width and a grid-discretization approximation margin. Within this domain, the bidirectional SDF-Theta* search performs candidate-node screening, applies Safe LOS checks along candidate connection segments, and uses safety–geometry multi-criteria state selection based on minimum clearance, mean clearance, path length, and turning cost. The resulting initial discrete path is processed through path skeleton refinement and local Bézier curve smoothing. Differential-flatness-based time parameterization then converts the smoothed geometric path into a time-indexed motion reference. In the Orchard Field Experiment, SDF-Theta* increased the minimum obstacle clearance by 16.6% compared with Theta* (ESDF) and achieved a safe path ratio of 100.00%. It also reduced the 99th-percentile curvature, maximum curvature, and total curvature variation by 54.3%, 85.5%, and 82.1%, respectively. Trajectory Tracking Validation yielded a path-overlap ratio of 94.65% at a nearest-path distance threshold of 0.03 m, with no physical collision or map-boundary violation. These results show that SDF-Theta* improved path safety and geometric smoothness and demonstrated trajectory trackability under the tested orchard conditions. Full article

An adaptive fault-tolerant boundary controller is developed for large UAV leader–follower formations affected by leader actuator faults. The formation-error dynamics are modeled by a one-dimensional parabolic PDE with reaction and convection terms, with the leader represented as the actuated boundary. The faulty boundary input contains an unknown loss of effectiveness and an unknown additive bias. The controller and estimator use a reliable interior UAV as the sensing node, avoiding leader-side measurements that may be compromised by faults on the leader platform. A filter-based input–output parameterization relates this interior measurement to the actuator efficiency, the bias fault, and an equivalent spatial residual kernel. Normalized projection-based adaptive laws estimate the unknown fault parameters and keep the efficiency estimate strictly positive. The stabilizing command is obtained from a PDE backstepping transformation, and estimation mismatch is treated as a boundary residual. The analysis proves closed-loop boundedness, ideal convergence under asymptotically exact fault compensation and boundary-relevant state reconstruction, and practical stability in the presence of residual modeling error, measurement noise, and input saturation. Simulations on a continuum PDE, finite-dimensional UAV chains, and a moving planar UAV formation confirm the recovery mechanism. Full article

At present, the detection methods for the microstructure and mechanical properties of aluminum alloys are mainly based on SEM, EBSD, TEM, tensile tests, and microhardness tests, which are time-consuming and destructive. In this paper, laser ultrasonic non-destructive detection is employed to obtain ultrasonic signals from Al-Cu-Mg aluminum alloy subjected to various heat treatment processes. The results reveal empirical correlations between the characteristic values of the ultrasonic signals and the material’s state. Specifically, the characteristic values exhibit an inverse correlation with the precipitated phase content. When both the precipitated phase content and the average grain size vary significantly, distinct deviations in the characteristic values are observed, which can serve as indicators of microstructural changes. The extracted ultrasonic eigenvalues also show promising, empirically derived correlations with mechanical properties, with frequency-domain attenuation coefficients demonstrating relatively higher sensitivity based on fitting analyses within the current dataset. These observed variations are tentatively discussed as plausible consequences of grain boundary scattering and changes in matrix solid solution strengthening associated with precipitate dissolution. Overall, the findings suggest the potential of laser ultrasonics as a rapid non-destructive evaluation tool, providing a preliminary scientific basis for further development of methods to assess the microstructure and mechanical properties of Al-Cu-Mg alloys within the tested parameter space. Full article

Siphon vertical axial-flow pump systems are widely used in large-scale low-head pumping stations, irrigation and drainage projects, urban flood control, and water diversion engineering, where safe and stable operation is essential for hydraulic system reliability. Compared with steady operating conditions, the startup process involves rapid variations in impeller speed, flow rate, pressure distribution and hydraulic energy transfer, resulting in highly transient hydraulic behavior and obvious instability in the internal flow field. To clarify the transient hydraulic response of a siphon vertical axial-flow pump system during startup, an unsteady numerical model was established by coupling the impeller speed control equation with a dynamic boundary condition implemented through a user-defined function. The time-dependent evolution of flow rate, head, blade loading, pressure distribution, internal flow pattern and energy conversion characteristics was then investigated during the whole startup process. The results show that the pump system undergoes a distinct transition from an initially unstable flow state to a quasi-steady operating condition. At the early startup stage, the flow passage is not fully established, and the pump operates under a low-flow transient condition, leading to strong flow disorder, uneven blade loading and a sharp fluctuation in head. With the increase in rotational speed and flow rate, the internal flow gradually becomes organized, the pressure distribution on the blade surface tends to be more uniform, and the hydraulic energy transfer process becomes progressively stable. The pump head first exhibits an abnormal transient peak, then decreases rapidly, and finally recovers to a stable value as the flow field is fully established. The study reveals the transient evolution mechanism of hydraulic performance and energy conversion during startup, providing a theoretical basis for improving startup control strategy and operational stability of siphon vertical axial-flow pump systems. Full article

Many natural and engineered systems evolve under constraints that restrict the set of admissible states. Classical frameworks study invariant sets, viability regions, survival probabilities, and exit-time events, while the explicit treatment of threshold-defined admissible subsets induced by sampled finite-horizon persistence is not usually isolated as a primary state-space object. This paper formulates a finite-horizon framework for Persistence Under Declared Constraints (PSUC). For a fixed constraint set, sampling step, persistence horizon, and tolerance level, the associated survival domain is the set of initial conditions whose sampled trajectories remain inside the declared constraint set with probability of at least $1-\alpha$. Under explicit regularity assumptions, survival domains are closed superlevel sets of the persistence field and form a nested filtration as the persistence horizon increases. This filtration admits a canonical intrinsic representation through a maximal admissible sampled-horizon field whose sampled superlevel sets recover it exactly. The same field also induces a canonical admissibility preorder; after quotienting by horizon-indistinguishability, this yields a partial order and its associated Alexandrov topology, in which the sampled survival filtration is represented as an upper-set filtration. The Alexandrov construction itself is classical; the contribution lies in the canonical order induced by the sampled admissibility-depth field and in the resulting canonical order-theoretic representation of the filtration. A secondary scalar ordering is also obtained for any lower-bounded auxiliary scalar function. Under an additional continuity assumption, the framework further yields boundary localization at the threshold level and an inheritance relation for connected components along the filtration. Finally, the paper shows that sampled survival domains need not coincide with continuous-time survival sets, thereby clarifying the intrinsically protocol-dependent nature of the object studied. The contribution is therefore a restricted but explicit analysis of threshold-defined admissible-state filtrations induced by sampled finite-horizon persistence, together with a canonical order-theoretic representation of the same filtration, formulated in a way that remains compatible with existing work on viability, stochastic survival, and exit-time analysis. Full article