Search Results (987)

To address the challenges of structural deformation and limited load-bearing capacity in morphing wings, this paper proposes a novel rigid–flexible composite morphing wing based on a foldable membrane–skeleton structure with a high folding ratio. Inspired by the deployment mechanics of biological wings and the cooperative support principle of multi-bar mechanisms, an optimization model was established to resolve hinge interference in the skeletal design. Through geometric reconstruction of the skeleton, the design achieves compact stowage in the folded state and maximizes wing area in the deployed configuration. Furthermore, an integrated design model for the membrane–skeleton interface was established based on rigid–flexible hybrid connection principles, followed by an analysis of the wing’s static structural characteristics via finite element simulation. A prototype was fabricated to experimentally validate its morphing functionality and out-of-plane load-bearing performance. Results demonstrate that the mechanism attains an effective folding ratio of approximately 7.19. Additionally, the influence of membrane prestress on the overall structural load capacity was systematically investigated. Full article

To address the insufficient flexibility in power systems caused by renewable energy output uncertainty during extreme weather events, a coordinated source–network–load–storage (SNLS) dispatch method that combines a flexibility supply–demand balance approach with a partitioned grid framework is proposed to achieve the effective enhancement of operational resilience. Firstly, a convolution method is employed to aggregate net load forecast error distributions, and expected flexibility demand metrics are introduced to construct a probabilistic model of compound weather impacts, thereby improving flexibility requirement quantification. Secondly, uncertainties arising from extreme meteorological conditions are considered, and an integrated economic dispatch model for the partitioned grid is established based on chance-constrained reserves and regulation capability envelopes, in order to co-optimize generation costs, demand response, and expected flexibility insufficiency penalties. Then, inter-zone power exchange and spatiotemporal unit commitment dynamics are introduced to optimally redistribute spatial generation surpluses and load deficits, so that a system-wide flexibility supply–demand balance is enabled. Finally, simulations are conducted on the real-world Guangdong 500 kV transmission network under typhoon, heatwave, and rainstorm scenarios, and the results demonstrate the effectiveness of the proposed method in eliminating flexibility deficits, reducing total dispatch costs, and capturing distinct weather-adaptive operational patterns. Full article

This study presents the development and validation of an intelligent error compensator based on Long Short-Term Memory (LSTM) recurrent neural networks for dynamic weighing systems in copper concentrate belt conveyors. Conventional weighing systems fail to capture nonlinear temporal dynamics, leading to measurement inaccuracies during container filling operations. The methodology comprised data acquisition from load cells, speed sensors, and inclinometers; systematic hyperparameter optimization; and evaluation using Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and coefficient of determination (R²). Hyperparameter optimization identified an optimal configuration with one LSTM layer (20 units, learning rate 0.001, window size 20 steps). Evaluation on an independent test set showed that the compensator reduced MAPE from 8.5% (uncompensated system) to 3.01%, representing a 64.6% improvement, and reduced RMSE from 12.3 to 4.2 tons (65.9% improvement), with an R² of 0.95. Feature importance analysis confirmed physical consistency, with load cell voltage as the dominant predictor (42%). These results demonstrate that LSTM-based compensation significantly enhances weighing accuracy. The study provides a replicable framework for industrial metrology modernization, contributing to sustainable mining operations through material loss reduction and logistics optimization. While the proposed model has been validated offline using historical data, its deployment in the live production environment remains pending. 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

To address the challenges of integrating multi-source heterogeneous data, fragmented fault knowledge, and the limited capability of traditional rule engines in recognizing edge cases for ultra-high voltage (UHV) bushing fault diagnosis, this paper proposes a fault identification and decision-making method based on knowledge graph (KG) rule reasoning and inductive graph convolutional network (Inductive GCN). First, a triple-matching strategy is employed to perform entity extraction and relation mining from fault cases, constructing a fault knowledge graph that transforms unstructured fault case texts into a structured knowledge graph. Second, a rule engine based on a multi-source feature rule set is designed, utilizing the entropy weight method and the RETE algorithm to achieve interpretable symbolic reasoning. On this basis, a double-layer inductive graph convolutional network is introduced to learn implicit fault patterns by aggregating topological information from neighboring nodes, and a confidence-driven dynamic weighted fusion strategy is adopted to achieve complementary advantages between the two models. Finally, a large language model is introduced to generate operation and maintenance decision recommendations. Experimental results demonstrate that the proposed method achieves an identification accuracy of 98.1% on a test set of 159 samples, which is 10.7 percentage points higher than that of a single rule engine and 6.9 percentage points higher than that of a single inductive graph convolution network. The standard deviation of accuracy across different test batches is only 0.0029. These results demonstrate the effectiveness and stability of the proposed method, providing a practical technical solution for UHV bushing fault identification. Full article

In the present work, graphene-modified zinc oxide (ZnO-rGO) and fluorine-doped tin oxide (FTO) thin films were successfully fabricated using a simple, low-cost pulsed spray pyrolysis technique. The structural, morphological, optical, electrical, and surface electronic properties of the deposited films were systematically characterized. X-ray diffraction (XRD) analysis confirmed the formation of polycrystalline ZnO- and SnO₂-based phases with crystallite sizes in the nanometer range. The crystallographic parameters, microstrain, and dislocation density of the deposited films were found to be influenced by the incorporation of reduced graphene oxide (rGO) and fluorine dopants. Scanning electron microscopy (SEM) revealed compact and homogeneous surface morphologies with good film coverage and well-defined nanocrystalline features. Optical characterization demonstrated the wide-bandgap semiconducting behavior of the deposited films, with optical bandgap energies ranging from 3.262 to 3.312 eV for the ZnO-rGO films and from 3.91 to 4.01 eV for the FTO films. Kelvin probe measurements yielded work-function values in the range of approximately 5.0–5.2 eV, indicating favorable surface electronic characteristics suitable for optoelectronic applications. Furthermore, fluorine incorporation enhanced the dielectric response of the SnO₂ films, particularly in the low-frequency region owing to increased interfacial polarization effects. The obtained results demonstrate that pulsed spray pyrolysis provides a simple, cost-effective, and efficient route for fabricating ZnO-rGO and FTO thin films with desirable structural, optical, electrical, and surface electronic properties. These findings highlight the considerable potential of the developed materials for transparent electrodes and a wide range of optoelectronic applications. Full article

The rapid growth of artificial intelligence (AI) data centers introduces highly variable and mission-critical load profiles that challenge conventional power supply strategies. This paper proposes an islanded microgrid gas turbine generator (GTG) and long-duration energy storage (LDES) hybrid architecture to provide both short-term load balancing and extended energy support under prolonged outage conditions. A probabilistic multi-phase workload model is developed to capture the temporal characteristics of training, fine-tuning, and inference processes, incorporating both high-frequency fluctuations and multi-day workload variations. Based on reliability requirements, an LDES sizing methodology is formulated to ensure long-duration autonomy for mission-critical operation in a 12 MW power-block AI data center system, with the storage capacity determined based on a 12-h autonomy criterion. The GTG operating point is then evaluated using four storage performance metrics: charge/discharge transition frequency, charging time ratio, state-of-charge (SoC) deviation, and cumulative energy movement. The results indicate that the optimal GTG operating point ranges from approximately 40–73.3% of the initially selected rating, closely tracking the time-varying average load and significantly reducing LDES utilization and storage stress. While GTG fixed-output operation may induce SoC drift under sustained workload variations, applying the identified optimal operating point maintains SoC within the desired range, demonstrating stable LDES operation without dynamic adjustment. The proposed framework provides quantitative design and operational guidelines for GTG–LDES hybrid systems in next-generation AI data centers. Full article

Symbolic regression and symbolic classification generate explicit mathematical expressions that combine predictive modelling with direct model interpretability. However, symbolic learning based on genetic programming can be computationally expensive because large populations of candidate expressions must be repeatedly evaluated over multiple generations. This paper presents FastSymbolicGP, a lightweight Python library for symbolic regression, binary classification, and multiclass classification through a compact, scikit-learn-compatible interface. The library implements tree-based genetic programming, protected mathematical operators, tournament selection, subtree crossover, subtree, hoist, and point mutation, elitism, validation-aware model selection, adaptive parsimony, expression complexity analysis, and Numba-compiled postfix evaluation. FastSymbolicGP was evaluated through 940 successful benchmark runs covering real-world scientific regression, binary and multiclass classification, physical law recovery, dynamical system identification, parameter sensitivity, ablation, and scalability. Across four real-world scientific regression datasets, FastSymbolicGP achieved the highest mean test $R^2$ on every dataset and obtained significantly better pooled paired results than gplearn and PySR under the evaluated configurations. These results are specific to the selected hyperparameters, primitive sets, stopping criteria, and computational budgets, and should not be interpreted as evidence of universal superiority. In binary classification, it achieved a mean balanced accuracy of 0.8507, compared with 0.7458 for gplearn, while validation-based threshold optimization and class weighting further improved performance under severe class imbalance. FastSymbolicGP also achieved competitive multiclass and dynamical system results while generally producing substantially simpler models than gplearn. In the scalability experiment with 50,000 samples, FastSymbolicGP was approximately 6.20 times faster than PySR and 1.63 times faster than gplearn, while obtaining predictive performance nearly identical to PySR. physical law experiments showed that nondimensionalization increased the dimensional validity rate of recovered FastSymbolicGP expressions from 24% to 96%, while reducing runtime and expression complexity. Analysis of the stored equation pools further showed that near-optimal model selection reduced symbolic complexity by an average of 28.6% when a simpler candidate was available, with negligible predictive degradation. These results indicate that FastSymbolicGP provides a practical balance of predictive performance, computational efficiency, task coverage, and symbolic interpretability for reproducible scientific and machine learning applications. Full article

Rubber particles shed from aging seals in converter transformers can distort local electric fields and degrade insulation. As a critical but understudied impurity in converter transformers, the migration and accumulation mechanisms of rubber particles remain unclear. This study investigates the movement and accumulation behaviors of nitrile butadiene rubber (NBR) particles under a DC electric field. High-speed camera observations were conducted under DC voltages up to 6 kV to capture the migration of 100–200 µm NBR particles under parallel-plate and sphere-plate electrodes. A multiphysics model was developed to simulate field gradients and particle trajectories. Results show that particles form dynamic bridges via reciprocating migration in uniform fields, whereas they exhibit staged movement under non-uniform fields. A dielectrophoretic (DEP) potential well model reveals that high-gradient regions (e.g., electrode edges) form steep wells that trap particles when their kinetic energy falls below the well depth. Quantitative analysis indicates that particles with a radius below the critical value (e.g., 150 µm) are prone to local accumulation under applied voltages above 3.5 kV. Above this critical value, accumulation is suppressed, depending on the voltage and oil velocity conditions. These findings clarify the critical criteria for rubber particle accumulation, supporting insulation design in converter transformers. Full article

Online signature verification (OSV) remains challenging under few-shot enrollment and multi-posture conditions, where limited reference samples and writing variations increase intra-writer variability and degrade verification performance. Existing methods often rely on simple feature concatenation and insufficiently exploit interactions between global statistical and temporal dynamic representations, while attention mechanisms in variable-length sequences may be affected by invalid padded regions. To address these limitations, this study introduces FT-Transformer, a hybrid CNN–Transformer framework for few-shot online signature verification. The model jointly encodes 18-dimensional local dynamic descriptors and 51-dimensional global statistical features. A bi-directional-guided fusion mechanism is introduced to facilitate interaction between heterogeneous representations, while a closed-loop feedback pathway and padding-aware attention masking are incorporated to improve feature robustness. Experiments on the public SVC2004 benchmark and a custom multi-posture dataset (MPIS-Sig) evaluate the performance of this framework. Under the WD-10/10 protocol, FT-Transformer achieves an Equal Error Rate (EER) of 0.16% on SVC2004 Task 2 and maintains an EER of 2.40% under the more challenging WD-5/5 setting. On MPIS-Sig, the model achieves over 98% verification accuracy across different writing postures. These results demonstrate that FT-Transformer effectively improves OSV robustness under data-scarce and multi-posture conditions. Full article

This manuscript presents a patent-based engineering case study of a compact LiBr–H₂O evaporator–absorber unit, tracing its progression from conceptual embodiment to laboratory validation through contextualized technology-readiness assessment and bounded data-driven analysis. The study reconstructs three successive prototypes, applies the international Technology Readiness Level (TRL) framework together with the Mexican TRL guideline, and includes an exploratory artificial neural network (ANN) analysis of the validated Prototype III. The results show a maturation pathway: Prototype I corresponded to conceptual embodiment, Prototype II provided proof-of-concept evidence through observable cooling, and Prototype III was a protected, instrumented, and experimentally validated laboratory prototype, conservatively assigned to TRL 4 as the highest level supported by the evidence. The auxiliary ANN gave only a limited approximation of the thermal response (mean test R² = 0.41, dispersion ±0.32), consistent with a single-run laboratory-scale dataset rather than robust predictive modeling. Overall, the study integrates prototype trajectory, intellectual-property protection, TRL assessment, and bounded analysis into a rigorous evaluation of a protected pre-commercial thermal subsystem. Beyond this device, the framework offers inventors, designers, and research groups a transferable reference, letting them build on a documented pathway rather than restarting from the lowest readiness levels and identifying the evidence needed for each TRL transition. Full article

This paper presents an attitude filter that encodes both orientation and uncertainty in a single four-dimensional vector, requiring no covariance propagation or normalization constraints. The filter state is the natural parameter of the von Mises–Fisher (vMF) distribution on S³, whose exponential family structure reduces measurement updates to vector addition. Prediction is governed by a continuous-time ODE (Ordinary Differential Equation) that couples rotational kinematics with concentration decay. The QUEST-based construction of measurement natural parameters with a Fisher-information-matched concentration, an antipodal switching mechanism for the quaternion double cover, and a global exponential convergence analysis of the attitude error are described. The filter construction is left-invariant: it commutes with rotations of the reference frame, making the error dynamics trajectory-independent. The result is a filter with the computational simplicity of a complementary filter and the statistical grounding of Bayesian vMF fusion, operating entirely in unconstrained ℝ⁴ space. The filter is validated in simulation, on two recorded flights—including an evaluation against an EFIS attitude reference—and its computational cost is measured down to on-target microcontroller cycle counts. Gyroscope bias estimation is not included and is left to future work. Full article

This article proposes an approach for acquiring HSQC (Heteronuclear Single Quantum Coherence) spectra using a high-resolution probehead originally designed for solutions. The method is suitable for studying organic substances with relatively low melting points, such as copolymers, composites, waxes, resins, and similar materials. The procedure involves preparing a melt of the substance directly inside the NMR (Nuclear Magnetic Resonance) sample tube prior to analysis. A comparison of HSQC spectra obtained from both the molten state and a solution of the same substance demonstrates that representative spectra can be acquired, enabling detailed analysis of their fine structure. The method has been successfully tested on a range of materials, including: paraffin, wax, honey, vanillin, polycaprolactone, a copolymer of lactide with phenol and maleic anhydride, composites of polycaprolactone and vanillin. This approach enables the identification of impurities in polymers and biological samples without requiring expensive deuterated solvents. Full article

Achieving sustainable structural health monitoring remains a critical challenge for intelligent railway infrastructures, where distributed sensing networks require continuous power supply and long-term maintenance. Although low-frequency railway bridge vibrations simultaneously contain harvestable mechanical energy and structural state information, existing systems generally exploit these functionalities independently, resulting in increased system complexity and limited energy utilization efficiency. Here, we present an integrated triboelectric vibration energy harvesting and displacement monitoring device (THM) for low-frequency railway bridge vibrations. By incorporating a quasi-zero-stiffness (QZS) mechanism, the energy harvesting unit achieves an enhanced low-frequency response, delivering an open-circuit voltage of 280 V, a short-circuit current of 28 μA, and a peak power of 9 mW. The device charges a 22 μF capacitor to 4 V within 45 s under 1.5 Hz excitation, demonstrating its capability to power low-power electronics. Simultaneously, a freestanding triboelectric sensing unit enables real-time girder–pier displacement monitoring, displacement-direction identification, and structural safety warning, exhibiting excellent linearity (R² = 0.9886) and stable operation over 20,000 cycles. This work provides an integrated strategy for simultaneously harvesting energy and monitoring structural displacement from low-frequency railway bridge vibrations, offering a promising route toward self-sustained intelligent bridge health monitoring systems. Full article

This paper presents a novel method for designing manufacturable porous infill structures using a Voronoi-based topology optimization framework. By integrating discrete Voronoi representations into density-based topology optimization in a differentiable manner, the method enables variable-thickness edge structures, with Euclidean distance fields generated from seed points. The material distribution and structural shape are determined by the seed point locations and the distance tensor, which serve as the design variables in this work. As the seed points are directly associated with the dual graph of the Voronoi diagram (VD), namely the Delaunay triangulation (DT), a constraint is formulated based on the DT to ensure the manufacturability of the infill structure. This is achieved by constraining all edge angles of the DT to satisfy the overhang requirement. Since 3D printers can fabricate overhanging structures up to a certain length, VD edges shorter than this threshold are exempt from the self-supporting constraint. To reduce the number of design variables and simplify the manufacturability constraint, a merging strategy is introduced to combine seed points that are sufficiently close during the optimization process. To ensure manufacturability of the outer surface, a set of seed points is additionally sampled on the outer boundary and kept fixed throughout optimization. The proposed method is validated on both regular and irregular 2D design domains, and the results demonstrate its capability to generate manufacturable porous infill structures with satisfactory mechanical performance. Full article