Search Results (19,279)

This work presents a comprehensive physics-based simulation framework designed to model and assess the performance of acoustic intensity dual-microphone probes under dynamic conditions. The measured quantity, acoustic intensity, is a vector representing the net flow of sound energy through a unit area, allowing for the estimation not only of the amount of energy transmitted but also its direction. This characteristic is of great interest for locating sound sources and evaluating the acoustic power radiated by a source enclosed within a bounding surface. To analyze the quality of the intensity determination, an acoustic propagation model was developed in a MATLAB R2025a environment that allows for the simulation of the generation, three-dimensional propagation, and measurement of sound signals under free-field conditions. The model incorporates moving sources and sensors, precise calculation of propagation delays, and simulation of triaxial probes, also allowing for sensor translations and rotations. From the simulated pressure signals, intensity estimates are obtained using time and frequency-domain methods, and the quality of the estimates is evaluated using error metrics against known geometric references. The results demonstrate the simulator’s ability to generate coherent and controlled datasets and highlight its usefulness as a tool for comparative analysis of intensity estimation methods and for predicting the performance of real probes in dynamic scenarios. Full article

Smart hydrogels are increasingly explored as adaptive platforms for controlled drug delivery because their polymer networks can respond dynamically to chemical, biological, and physical cues. Among these systems, dynamic polymer networks formed through reversible covalent and noncovalent interactions provide a unique means of coupling molecular-scale bond exchange with time-dependent changes in hydrogel structure, mechanics, degradation, and therapeutic transport. This review presents an integrated framework linking dynamic crosslink chemistry, exchange kinetics, network properties, and drug-release behavior. Dynamic covalent, supramolecular, metal–ligand, and hybrid network strategies are compared with particular emphasis on how crosslink lifetime, mesh accessibility, swelling, viscoelasticity, stress relaxation, and degradation govern therapeutic cargo loading, retention, diffusion, and release. Representative release mechanisms and kinetic models are discussed together with cargo-specific design considerations for small molecules, proteins and peptides, nucleic acids, and nanoparticle-based therapeutics. Biomedical applications are further considered according to therapeutic requirements and administration routes, including injectable local depots, topical and transdermal delivery, and regenerative systems. Finally, key translational challenges involving physiological complexity, biocompatibility, reproducibility, scale-up, sterilization, and storage stability are critically assessed. By connecting molecular interaction dynamics with network-level behavior and therapeutic performance, this review provides design principles for developing more predictable, programmable, and clinically translatable dynamic hydrogel drug-delivery systems. Full article

Dense 3D reconstruction and accurate pose measurement of non-cooperative satellites are essential for on-orbit servicing missions. Multi-view stereo (MVS) is the preferred approach for high-precision dense reconstruction from monocular imagery, yet current methods struggle with the speed–accuracy trade-off and reconstruction degradation under complex on-orbit conditions, while keypoint-based pose measurement is further constrained by limited training data and heavy reliance on manual annotations. To address these challenges, we present a monocular vision-based method coupling dense 3D reconstruction with pose estimation. We develop MambaMVS, an end-to-end MVS network featuring a Mamba Cost Volume (MCV) Module for efficient cost volume regularization via the linear complexity of structured state space models, and an improved focused linear attention module for robust cross-view feature matching. The reconstructed point cloud provides 3D keypoint coordinates for pose measurement, where a few-shot scheme employing 3D Gaussian Splatting-based view augmentation and reprojection-driven self-labeling enables training with minimal manual annotations. During online inference, detected 2D keypoints are combined with offline-reconstructed 3D coordinates via the Perspective-n-Point algorithm for fast solving of the 6-DOF pose. Experiments on the Customized Space Target dataset demonstrate state-of-the-art reconstruction quality with 1.5× faster inference than current leading methods. Physical satellite model experiments achieve rotation ME within 0.2° and translation ME below 0.01 m, while robustness is validated across varying illumination, reflective surfaces, motion blur, and occlusion. The proposed reconstruction-guided measurement paradigm offers a practical pathway toward autonomous on-orbit perception and in situ structural assessment. Full article

UAV-enabled Integrated Sensing and Communication (ISAC) systems simultaneously support target sensing and communication and, combined with their abilities in autonomy and control situations, they have the potential to be deployed for search and rescue tasks that were, previously, challenging or impossible. However, the limited apertures of UAV-mounted physical arrays restrict angular resolution and multi-target sensing capability. This work proposes a method for implementing virtual arrays in UAV-assisted MIMO-OFDM ISAC systems to enhance the multi-target sensing capability. With a range of possibilities including transmit–receive channel pairings and two virtual-array configurations, Virtual-7 and Virtual-10 are developed to form greater effective apertures out of a $4\times 4$ ISAC transceiver. A sensing model based on virtual arrays and the corresponding signal-processing procedure are established for angle estimation and for sensing-performance investigation. Simulation results show that ISAC systems with virtual arrays perform better in terms of multi-target detection. The proposed method for virtual-array establishment can, potentially, be applied in UAV-ISACs for high-resolution sensing with a low-profile configuration requirement. Full article

We examine a nonlinear viscoelastic Kirchhoff model endowed with a logarithmic source. Assuming suitable hypotheses and under the inclusion of nonlinear distributed delay feedback terms, in addition to the acoustic and fractional boundary feedback terms, we obtain global existence, general decay estimates, and finite-time blow-up for negative initial energy, all for a wide range of relaxation kernels. The Kirchhoff term $\mathcal{K}(\parallel \nabla \mathcal{W}{\parallel }_2^2)$ significantly changes the structure of the Nehari manifold and requires a new energy functional based on the primitive $\widehat{\mathcal{K}}$. Our analysis identifies the precise blow-up mechanism and establishes sharp dimension-dependent restrictions on the exponent p. Our outcomes broaden and enhance recent contributions on classical wave equations to the physically meaningful Kirchhoff framework. Full article

High penetration of distributed renewable generation in active distribution networks frequently leads to severe voltage fluctuations and challenges system voltage stability, as the inherent power-flow symmetry is disrupted by stochastic and bidirectional power injections. To address these issues, this paper proposes a symmetry-preserving coordinated control strategy that integrates topology-constrained clustering with state-of-charge (SoC) balancing of battery energy storage systems to actively mitigate voltage fluctuations and enhance operational stability. First, an agglomerative hierarchical clustering algorithm that explicitly enforces physical line connectivity is employed to partition the distribution network into multiple structurally symmetric autonomous control zones. Within each zone, the SoC of distributed storage units is balanced and aggregated into a virtual battery model, thus maintaining energy-level symmetry and reducing the risk of uneven charging/discharging that would otherwise exacerbate voltage deviations. A model predictive control-based rolling optimization framework is then developed to coordinate photovoltaic inverters and energy storage systems across the zones, explicitly targeting the suppression of voltage fluctuations and the maintenance of short-term voltage stability under varying operating conditions. The proposed method is validated on a modified IEEE 34-bus test feeder with high renewable penetration. Simulation results demonstrate that the strategy effectively limits voltage fluctuation magnitudes, keeps nodal voltages within the required bounds, improves the SoC balance among the storage units, and maintains the nodal voltages within the required bounds across all evaluated coordination strategies. Full article

Ground testing of space-based gravitational-wave missions requires causal and low-noise horizontal-velocity estimation for vibration-isolation platforms. Fixed sensor fusion becomes suboptimal when sensor noise power varies, especially when complementary sensors have strongly frequency-dependent colored-noise spectra. This paper proposes a colored-noise power-adaptive Kalman filtering algorithm (CN-PAKF) for fusing laser interferometric displacement sensor (IFO) and broadband seismometer (SEIS) observations. The method constructs a motion-canceling synchronous difference channel and uses band-decomposed power statistics with asymmetric spectral priors to estimate sensor noise-power scales online. Scaling only the corresponding colored-noise drive covariances preserves the normalized spectral shapes of the modeled colored components and avoids unrestricted covariance adaptation; bounded physical-process and residual measurement-covariance safeguards may alter the total modeled error spectrum. In paired open-loop simulations, CN-PAKF reduced the target-band velocity RMSE by 31.3% and 70.5% relative to the IFO and SEIS observations under nominal noise conditions, while remaining within 1% of causal colored-noise baseline filters. When the IFO noise power increased 30-fold, CN-PAKF reduced the stage-wise target-band RMSE by 36.8% compared with a fixed colored-noise Kalman filter. These results indicate that CN-PAKF provides a causal, structure-preserving, and performance-tested fusion algorithm for synchronized sensors with time-varying noise power and approximately fixed colored-noise spectral shapes. Full article

Physics-Informed Machine Learning has emerged as a powerful paradigm for fatigue and fracture analysis, combining the data-driven flexibility of neural networks with the consistency of physical laws. This scoping review maps the extent, range, and nature of research on PIML applied to fatigue life prediction, crack growth and propagation, multiaxial fatigue, damage mechanics, prognostics and health management, and structural health monitoring, covering the literature published from 2018 up to August 2026. A comprehensive search strategy integrated the core terminology of the field with broader synonym terms, complemented by forward and backward citation searching and manual screening of the most productive venues. The combined strategy retrieved 124 records, of which 76 primary research articles satisfied the inclusion criteria and were charted and characterized in this review. The included studies are characterized through the lens of a three-bias taxonomy—observational, learning, and inductive—which organizes how physical knowledge enters the learning pipeline: through data-level constraints, loss-function modifications, and structural architectural changes, respectively. The review maps the architectural landscape of the field, from standard multi-layer perceptron-based PINNs to specialized variants including sequential attention models, physics-informed Kolmogorov–Arnold networks, geometry-aware finite encodings, graph neural networks, and probabilistic Bayesian formulations. The mapped literature indicates that PIML methods are reported to improve generalization from sparse and noisy experimental data, achieve computational gains over conventional simulation, and quantify prediction uncertainty. Persistent challenges nonetheless emerge across the mapped literature, including high training costs, sensitivity to hyperparameter and loss-weighting choices, limited transferability across problem configurations, and the black-box nature of deep models. The review identifies knowledge gaps and concludes with an agenda for future research, emphasizing generalizable and adaptive architectures, multi-scale and multi-physics formulations, and standardized benchmarks. Full article

Coal gangue backfill serves as a primary supporting structure for overlying strata in mined-out areas, and its internal damage evolution is directly associated with the safety and stability of mining operations. Acoustic emission (AE) technology provides an effective approach for investigating damage evolution by capturing transient strain energy release events within materials in real time. However, conventional AE analysis methods predominantly rely on statistical interpretations of individual parameters, making them insufficient for revealing the underlying physical mechanisms governing the influence of fracture characteristics on energy evolution pathways. To address the aforementioned limitations, this paper proposes a fracture mode-informed physics-enhanced machine learning framework for predicting acoustic emission energy evolution in coal gangue backfill. First, based on the AE monitoring data of coal gangue backfill under uniaxial compression (a total of 18,992 valid AE events from three parallel specimens with identical mix proportion), the RA–AF parameters combined with the K-means unsupervised clustering algorithm were employed to automatically identify tensile and shear fracture modes, thereby constructing physically meaningful fracture mode labels. Second, the fracture mode information was integrated with AE statistical features, including rise time, duration, amplitude, counts, peak frequency, and center frequency. After selecting the most informative features using the minimum-redundancy, maximum-relevance (mRMR) algorithm, a Bayesian optimization-based support vector regression (BO-SVR) model was developed for AE energy prediction. The results demonstrated that after incorporating the fracture mode-based physical labels, the proposed model achieved a coefficient of determination (R²) of 0.9027 on the testing dataset, with an RMSE of 0.1562 and an MAE of 0.1204. Ablation experiments further confirmed that the introduction of fracture mode labels improved the R² value by approximately 3.4% compared with the model without physical constraints. Fivefold cross-validation yielded an average R² of 0.9460 with a standard deviation of 0.0023 for the SVR model. Furthermore, per-specimen independent holdout validation yielded an average R² of 0.9044 with a standard deviation of 0.0512 across three independent specimens, confirming the excellent stability and repeatability of the proposed framework. The original single-specimen results (4083 events, R² = 0.9727) are provided as a baseline reference. Mechanistic analysis revealed that the average energy release associated with shear fractures was approximately 739 times that of tensile fractures, demonstrating that fracture mode information provides physically consistent mechanical constraints for the machine learning model. This study provides an effective new method for the stability evaluation and intelligent monitoring of coal gangue filling materials. Full article

Automated inspection of utility-scale photovoltaic plants requires reliable detection and inventory of individual panels from overlapping aerial images. We developed a physics-guided deep learning and geospatial aggregation framework for panel detection and duplicate-free plant-level counting. Scene-targeted augmentation introduces bounded specular-reflection, occlusion, perspective, and boundary-ambiguity perturbations into training images. A You Only Look Once version 11 (YOLO11)-based detector combines adaptive input resolution, reflection-oriented channel attention, and a decoupled feature pyramid network. Frame-level detections are projected into a common geographic coordinate system using unmanned aerial vehicle (UAV) pose and camera geometry, and spatial clustering merges repeated observations without image stitching. On 4200 UAV images containing approximately 110,000 annotated panel instances, the complete detector achieved 74.3% average precision at an intersection-over-union threshold of 0.5 (AP@0.5) and 62.9% AP@0.5:0.95 across five runs. Geospatial aggregation achieved 99.2% plant-level counting accuracy, compared with 75.1% for image stitching. On an independent held-out desert-site dataset containing approximately 45,000 panel instances, the Site-A-trained model was applied without retuning and retained 72.8% AP@0.5 and 98.4% plant-level counting accuracy. Component, attention, regional-counting, layout, cross-site, and runtime experiments further evaluated the framework. Final-pass throughput was 105.6 frames/s; a separate two-pass runtime experiment reported 15.1 ms/image for the complete processing chain, equivalent to 66.2 frames/s. The results support integrated panel detection and inventory under the evaluated imaging and survey conditions. Full article

Congestion at shared intersections can limit the scalability of Automated Guided Vehicle (AGV) fleets in high-density warehouses. This study evaluates a Deep Q-Network (DQN) controller for adaptive intersection management in a SUMO-based warehouse model with five crossings, including the shared central crossing CP1. After one 200-episode training run, the DQN was evaluated in three seeded 6200 s simulations per demand scenario. Against a study-specific cyclic fixed-time controller at 250, 350, and 400 AGVs/h, the DQN achieved unweighted mean reductions of 62.9% in signal queue, 59.1% in average waiting time, and 18.0% in AGVs waiting to load, while loading/unloading occupancy changed by less than 1%. A matched 750 AGVs/h stress test showed that the benefit did not persist after saturation: at CP1, the mean signal queue increased from 13.15 to 30.68 AGVs and the mean number waiting to load from 9.64 to 15.93 AGVs. Sustained CP1 congestion began between 350 and 400 AGVs/h for the tested layout. Retained test outputs quantify test-level dispersion, but the single training run cannot estimate training variability. These results quantify the DQN traffic-control policy under ideal availability of SUMO state information and do not validate a VLC system. VLC is presented separately as a proposed future implementation layer; communication delay, packet loss, occlusion, and other physical-channel effects were not modelled. Full article

Background/Objectives: Computer-assisted posturography is utilized to quantify human postural control, yet existing dedicated systems frequently present physical constraints, such as limited sensing surfaces and rigid hardware barriers. In this study, a methodological framework is presented and a structural arrangement (OpenBalance) is designed to implement balance assessment accommodating a wider anthropometric range through zone-based foot placement using a decentralized load cell array, with preliminary implementation details provided in a repository. Methods: The configuration comprises an array of 16 discrete uniaxial vertical-force load cells embedded within four mechanically decoupled sub-platforms to minimize mechanical cross-talk. Biomechanical moment equations were implemented in a custom Python pipeline to compute a localized Center of Pressure (COP) for each sub-platform independently. A vector-based data fusion algorithm maps these local coordinates into a unified global coordinate system. The system evaluation incorporated baseline signal-to-noise ratio (SNR) analysis, mechanical crosstalk testing, and digital low-pass filtering. Results: A proof-of-concept evaluation using empirical data confirmed that the cascading coordinate model produces a continuous global COP trajectory and quadrant-specific load distributions. The platform dimensions (355 × 460 mm) and sensor topography geometrically accommodate natural external foot rotation (incorporating a 10° toe-out angle projection) and foot lengths corresponding to EU shoe sizes up to 55. Mechanical crosstalk between adjacent sub-platforms remained minimal (<1% of applied load). Conclusions: The OpenBalance framework confirms the technical feasibility of deriving a continuous global COP from a decentralized array of distributed load cells. While baseline component specifications and static verifications are established, comprehensive dynamic cross-validation against reference standards remains a necessary next step for the future development of open hardware that can be produced using 3D printing. Full article

In the intelligent operation and maintenance of core process equipment in semiconductor manufacturing, the thickness and morphological parameters of sub-micron multilayer transparent films on wafer surfaces are critical quality indicators that govern device performance and yield. White Light Interferometry (WLI) enables the high-precision measurement of thin-film thickness and topography parameters, and is widely deployed in high-precision manufacturing fields such as semiconductors. However, when measuring sub-micron multilayer transparent films, WLI faces challenges including low computational efficiency, severe parameter coupling, and non-unique solutions, making it difficult to meet the demands of high-throughput online inspection. To address these issues, this paper proposes a deep learning-based method for decoupling and optimizing WLI signals through inverse modeling. The approach establishes an innovative hybrid intelligent framework: first, a training dataset is generated based on interferometric system modeling and simulation; then, a classification model is employed to intelligently categorize the acquired interference signals, decomposing the complex multimodal inversion problem into several sub-problems with simpler patterns. Next, for each signal category, a specialized closed-loop deep network model is designed and trained. This network integrates an inverse prediction network with a forward reconstruction network in series. During training, both prediction error and reconstruction error are jointly used as the loss function, ensuring solution uniqueness and physical consistency, thereby enhancing inversion reliability and robustness. This research provides an effective solution for high-precision online optical measurement of complex thin-film structures, offering significant theoretical value and broad industrial application prospects. Full article

Transmission line monitoring is undergoing a significant transformation driven by the increasing demand for enhanced grid utilization, Dynamic Line Rating (DLR), and cost-effective monitoring solutions. However, the large-scale deployment of physical monitoring devices remains constrained by installation and maintenance time and costs. This paper presents the development and validation of an artificial neural network (ANN)-based Digital Twin (DT) framework for conductor temperature monitoring through virtual sensing. The investigated approach is part of a multi-layer Digital Twin architecture that integrates data preparation, virtual sensing, and DLR applications into a unified monitoring concept. The research was conducted within the Horizon Europe TwinEU project using datasets collected from three power system operators operating overhead lines at voltage levels between 132 kV and 400 kV. The developed ANN models applied weather observations and SCADA-derived loading data as inputs, while conductor temperature measurements obtained from physical sensors served as training and validation targets. To enhance robustness and generalization, different data-splitting methodologies, normalization techniques, and site-specific hyperparameter optimization strategies were investigated using a Bayesian optimization framework. The results demonstrate that ANN-based virtual sensors can reproduce conductor temperature measurements with high accuracy, achieving RMSE values of 0.791–1.087 °C, when sufficient high-quality training data are available. The study also highlights the importance of local weather measurements and seasonally representative datasets for reliable model performance. The findings confirm that DT technologies can partially substitute physical monitoring assets without significant loss of accuracy, reducing monitoring complexity and operational costs. The proposed approach offers a practical pathway toward scalable transmission line monitoring and provides a foundation for future applications in DLR, ampacity calculation, market-oriented grid operation, and asset management. Full article

Reliable national-scale, multi-depth soil property maps remain scarce across much of Africa, particularly in arid regions. Here, we present the first national-scale digital soil mapping (DSM) for Namibia: sixteen physical and chemical soil properties predicted on a 90 m prediction grid for three depth intervals (0–30, 30–60 and 60–100 cm). The framework draws on 4958 legacy profiles and augerings and 65 covariates, including locally produced datasets that capture soil–environment relationships not fully represented by global covariates. A semi-automated workflow combined Random Forest modelling in Google Earth Engine with R-based depth harmonisation, Boruta-feature selection and hyperparameter tuning. Performance and bootstrap variability were quantified over 20 bootstrap iterations per property–depth combination, with pedological evaluation and independent validation. pH was the most consistently predicted property, and base saturation, sand and silt captured useful broad spatial patterns, whereas bulk density, organic carbon, phosphorus, magnesium and clay were less predictable, and electrical conductivity and sodium showed low predictive skill. Performance generally declined with depth, and bootstrap variability was highest in sparsely sampled regions and deeper layers. Against independent samples from arable land, root mean square error was lower than SoilGrids in all 14 property–depth combinations evaluated and lower than iSDA in 8 of 12. Predicted patterns were consistent with known Namibian soil–landscape relationships and pedogenic processes, except for sodium. National-scale DSM is therefore feasible in data-sparse arid environments when harmonised legacy data are combined with global and locally relevant covariates, using simple but operationally robust methods. The products are intended for national and regional assessment, rather than site-specific decisions, and the modular workflow supports future updates and is potentially adaptable to other data-limited regions. Full article