Search Results (277)

To solve the low efficiency of traditional sheet metal measurement, this paper proposes a digital inspection method for sheet metal parts based on 3D point clouds. The 3D point cloud data of sheet metal parts are collected using a 3D laser scanner, and the topological relationship is established by using a K-dimensional tree (KD tree). The pass-through filtering method is adopted to denoise the point cloud data. To preserve the fine features of the parts, an improved voxel grid method is proposed for the downsampling of the point cloud data. Feature points are extracted via the intrinsic shape signatures (ISS) algorithm and described using the fast point feature histograms (FPFH) algorithm. After rough registration with the sample consensus initial alignment (SAC-IA) algorithm, an initial position is provided for fine registration. The improved iterative closest point (ICP) algorithm, used for fine registration, can enhance the registration accuracy and efficiency. The greedy projection triangulation algorithm optimized by moving least squares (MLS) smoothing ensures surface smoothness and geometric accuracy. The reconstructed 3D model is projected onto a 2D plane, and the actual dimensions of the parts are calculated based on the pixel values of the sheet metal parts and the conversion scale. Experimental results show that the measurement error of this inspection system for three sheet metal workpieces ranges from 0.1416 mm to 0.2684 mm, meeting the accuracy requirement of ±0.3 mm. This method provides a reliable digital inspection solution for sheet metal parts. Full article

Graphene is considered one of the most promising flexible transparent electrode materials as it has high charge carrier mobility, high electrical conductivity, low optical absorption, excellent mechanical strength, and good bendability. However, graphene-based flexible transparent electrodes face a critical challenge in balancing electrical conductivity and optical transmittance. Here, we present a green and scalable direct ink writing (DIW) strategy to fabricate graphene grid patterns by optimizing ink formulation with sodium dodecyl sulfate (SDS) and ethanol. SDS eliminates the coffee ring effect via Marangoni flow, while ethanol enhances graphene flake alignment during hot-pressing, achieving a high conductivity of 5.22 × 10⁵ S m⁻¹. The grid-patterned graphene-based flexible transparent electrodes exhibit a low sheet resistance of 21.3 Ω/sq with 68.5% transmittance as well as a high stability in high-temperature and corrosive environments, surpassing most metal/graphene composites. This method avoids toxic solvents and high-temperature treatments, demonstrating excellent stability in harsh environments. Full article

Hydraulic fracturing is a crucial technology for developing unconventional oil and gas resources. However, conventional geophysical methods struggle to efficiently and accurately image proppant-connected channels created by hydraulic fracturing. The borehole-to-surface electromagnetic imaging method (BSEM) overcomes this limitation by utilizing a controlled cased well source. Placing the source close to the target reservoir and deploying multi-component receivers on the surface enable high-precision lateral monitoring, providing an effective approach for dynamic monitoring of hydraulic fracturing operations. This study focuses on key aspects of forward modeling for BSEM. A three-dimensional finite-volume method based on the Yee grid was used to simulate the borehole-to-surface electromagnetic system incorporating metal casings, validating the method of simulating metal casing using multiple line sources. The simulation of the observation system and the frequency-domain electromagnetic monitoring simulation based on actual well data confirm BSEM’s high sensitivity for monitoring deep subsurface formations. Critically, well casing exerts a substantial influence on surface electromagnetic responses, while the electromagnetic contribution from line sources emulating perforation zones necessitates explicit incorporation within data processing workflows. Full article

Icing disasters can significantly reduce the reliability of overhead transmission lines, while limited budgets of power grid enterprises constrain the scale of investment. To improve investment efficiency, it is essential to balance the reliability and economic performance of anti-icing strategies. Most existing studies on the performance evaluation of anti-icing strategies for transmission lines focus primarily on reliability, neglecting their economic implications. To address this gap, this paper proposes a time-evolution-based performance evaluation model for overhead transmission line anti-icing strategies. First, a lifetime distribution function of transmission lines during the icing period is constructed based on the Nelson–Aalen method and metal deformation theory. Subsequently, a quantitative risk model for iced transmission lines is developed, incorporating the failure rate, value of lost load, and amount of lost load, providing a monetary-based indicator for icing risk. Finally, a performance evaluation method for anti-icing strategies is developed based on the risk quantification model. Implementation cost is treated as risk control expenditure, and strategy performance is assessed by integrating it with residual risk cost to identify the optimal strategy through composite cost analysis. The proposed model enables a comprehensive assessment of anti-icing strategy performance, improving the accuracy of strategy selection and achieving a dynamic balance between implementation cost and transmission line reliability. The case study results demonstrate that the proposed method effectively reduces the risk of failure in overhead transmission lines under ice disasters while lowering anti-icing costs. Compared with two existing strategy selection approaches, the strategy based on this method achieved 46.11% and 32.56% lower composite cost, and 60.26% and 48.41% lower residual risk cost, respectively. Full article

This paper presents a symmetry-driven broadband circularly polarized magnetoelectric dipole antenna with bandpass filtering response, where the principle of symmetry is strategically employed to enhance both radiation and filtering performance. The antenna’s circular polarization is achieved through a symmetrical arrangement of two orthogonally placed metallic ME dipoles combined with a phase delay line, creating balanced current distributions for optimal CP characteristics. The design further incorporates symmetrical parasitic elements—a pair of identical inverted L-shaped metallic structures placed perpendicular to the ground plane at −45° relative to the ME dipoles—which introduce an additional CP resonance through their mirror-symmetric configuration, thereby significantly broadening the axial ratio bandwidth. The filtering functionality is realized through a combination of symmetrical modifications: grid slots etched in the metallic ground plane and an open-circuited stub loaded on the microstrip feed line work in tandem to create two radiation nulls in the upper stopband, while the inherent symmetrical properties of the ME dipoles naturally produce a radiation null in the lower stopband. This comprehensive symmetry-based approach results in a well-balanced bandpass filtering response across a wide operating bandwidth. Experimental validation through prototype measurement confirms the effectiveness of the symmetric design with compact dimensions of 0.96${\lambda }_0$ × 0.55${\lambda }_0$ × 0.17${\lambda }_0$ (${\lambda }_0$ is the wavelength at the lowest operating frequency), demonstrating an impedance bandwidth of 66.4% (2.87–5.05 GHz), an AR bandwidth of 31.9% (3.32–4.58 GHz), an average passband gain of 5.5 dBi, and out-of-band suppression levels of 11.5 dB and 26.8 dB at the lower and upper stopbands, respectively, along with good filtering performance characterized by a gain-suppression index (GSI) of 0.93 and radiation skirt index (RSI) of 0.58. The proposed antenna is suitable for satellite communication terminals requiring wide AR bandwidth and strong interference rejection in L/S-bands. Full article

In this paper, a solution to the problem of electromagnetic field scattering on a periodic, constrained, planar antenna structure placed on the boundary of two dielectric media was formulated. The scattering matrix of such a structure was derived, and its generalization for the case of an antenna with a multilayer dielectric substrate was defined. By applying the Galerkin spectral method, the problem was reduced to a system of algebraic equations for the coefficients of current distribution on metal elements of the antenna grid, considering the distribution of the electromagnetic field on Floquet harmonics. The finite transverse dimension of the antenna was considered by introducing, to the solution of the case of an unconstrained antenna, a window function on the antenna aperture. The presented formalism allows modeling the operation of periodic, dielectric, composite antenna arrays. Full article

Distribution transformers serve as critical nodes in smart grids, and management of their recycling plays a vital role in the full life-cycle management for electrical equipment. However, the traditional manual dismantling methods often exhibit a low metal recovery efficiency and high levels of hazardous substance residue. To facilitate green, cost-effective, and fine-grained recycling of distribution transformers, this study proposes a fine-grained dismantling decision-making system based on a knowledge graph subgraph comparison and multimodal fusion perception. First, a standardized dismantling process is designed to achieve refined transformer decomposition. Second, a comprehensive set of multi-dimensional evaluation metrics is established to assess the effectiveness of various recycling strategies for different transformers. Finally, through the integration of multimodal perception with knowledge graph technology, the system achieves automated sequencing of the dismantling operations. The experimental results demonstrate that the proposed method attains 99% accuracy in identifying recyclable transformers and 97% accuracy in auction-based pricing. The residual oil rate in dismantled transformers is reduced to below 1%, while the metal recovery efficiency increases by 40%. Furthermore, the environmental sustainability and economic value are improved by 23% and 40%, respectively. This approach significantly enhances the recycling value and environmental safety of distribution transformers, providing effective technical support for smart grid development and environmental protection. Full article

Determining the physical parameters of pulsating variable stars such as RR Lyrae is essential for understanding their internal structure, pulsation mechanisms, and evolutionary state. In this study, we present a machine learning framework that uses feedforward artificial neural networks (ANNs) to infer stellar parameters—mass (M), luminosity (log($L/L_{\odot }$)), effective temperature (log($T_{\mathrm{eff}}$)), and metallicity (Z)—directly from Transiting Exoplanet Survey Satellite (TESS) light curves. The network is trained on a synthetic grid of RRab light curves generated from hydrodynamical pulsation models spanning a broad range of physical parameters. We validate the model using synthetic self-inversion tests and demonstrate that the ANN accurately recovers the input parameters with minimal bias. We then apply the trained model to RRab stars observed by the TESS. The observed light curves are phase-folded, corrected for extinction, and passed through the ANN to derive physical parameters. Based on these results, we construct an empirical period–luminosity–metallicity (PLZ) relation: log($L/L_{\odot }$) = (1.458 ± 0.028) log(P/days) + (–0.068 ± 0.007) [Fe/H] + (2.040 ± 0.007). This work shows that ANN-based light-curve inversion offers an alternative method for extracting stellar parameters from single-band photometry. The approach can be extended to other classes of pulsators such as Cepheids and Miras. Full article

The paper presents the control of the ablated gap of required dimensions in an axisymmetric domain made of metal. For this purpose, two parameters of the laser interacting on this layer were identified, which means laser intensity and characteristic time of the laser pulse. A hyperbolic two-temperature model was applied. This is a model in which there are two coupled equations for electrons and phonons. The model was supplemented with appropriate boundary and initial conditions. The direct problem was solved using the finite difference method with a staggered grid. An artificial immune system was used for the identification process. Full article

Two-dimensional transition metal carbides/nitrides (MXenes) have shown potential in biosensors, cancer theranostics, microbiology, electromagnetic interference shielding, photothermal conversion, and thermal energy storage due to their unique electronic structure, ability to absorb a wide range of light, and tunable surface chemistry. In spite of the growing interest in MXenes, there are relatively few studies on their applications in phase-change materials for enhancing thermal conductivity and weak photo-responsiveness between 0 °C and 150 °C. Thus, this study aims to provide a current overview of recent developments, to examine how MXenes are made, and to outline the combined effects of different processes that can convert light into heat. This study illustrates the mechanisms that include enhanced broadband photon harvesting through localized surface plasmon resonance, electron–phonon coupling-mediated nonradiative relaxation, and interlayer phonon transport that optimizes thermal diffusion pathways. This study emphasizes that MXene-engineered 3D thermal networks can greatly improve energy storage and heat conversion, solving important problems with phase-change materials (PCMs), like poor heat conductivity and low responsiveness to light. This study also highlights the real-world issues of making MXene-based materials on a large scale, and suggests future research directions for using them in smart thermal management systems and solar thermal grid technologies. Full article

The present research proposes an Artificial Neural Network (ANN) model to predict the critical buckling load of six different types of metallic aerospace grid-stiffened panels: isogrid type I, isogrid type II, bi-grid, X-grid, anisogrid, and waffle, all subjected to compressive loading. Six thousand samples (one thousand per panel type) were generated using the Latin Hypercube Sampling method to ensure a diverse and comprehensive dataset. The ANN model was systematically fine-tuned by testing various batch sizes, learning rates, optimizers, dense layer configurations, and activation functions. The optimized model featured an eight-layer architecture (200/100/50/25/12/6/3/1 neurons), used a selu–relu–linear activation sequence, and was trained using the Nadam optimizer (learning rate = 0.0025, batch size = 8). Using regression metrics, performance was benchmarked against classical machine learning models such as CatBoost, XGBoost, LightGBM, random forest, decision tree, and k-nearest neighbors. The ANN achieved superior results: MSE = 2.9584, MAE = 0.9875, RMSE = 1.72, and R² = 0.9998, significantly outperforming all other models across all metrics. Finally, a Taylor Diagram was plotted to assess the model’s reliability and check for overfitting, further confirming the consistent performance of the ANN model across both training and testing datasets. These findings highlight the model’s potential as a robust and efficient tool for predicting the buckling strength of metallic aerospace grid-stiffened panels. Full article

Objective: The rapid growth of electric vehicle (EV) adoption has led to an unprecedented increase in lithium-ion battery (LIB) demand and end-of-life waste, underscoring the urgent need for effective recycling strategies. This review evaluates current progress in EV battery recycling and explores future prospects. Design: Review based on PRISMA 2020. Data sources: Scientific publications indexed in major databases such as Scopus, Web of Science, and ScienceDirect were searched for relevant studies published between 2020 and 15 April 2025. Inclusion criteria: Studies were included if they were published in English between 2020 and 15 April 2025, and focused on the recycling of electric vehicle batteries. Eligible studies specifically addressed (i) recycling methods, technologies, and material recovery processes for EV batteries; (ii) the impact of recycled battery systems on power generation processes and grid stability; and (iii) assessments of materials used in battery manufacturing, including efficiency and recyclability. Review articles and meta-analyses were excluded to ensure the inclusion of only original research data. Data extraction: Data were independently screened and extracted by two researchers and analyzed for recovery rates, environmental impact, and system-level energy contributions. One researcher independently screened all articles and extracted relevant data. A second researcher validated the accuracy of extracted data. The data were then organized and analyzed based on reported quantitative and qualitative indicators related to recycling methods, material recovery rates, environmental impact, and system-level energy benefits. Results: A total of 23 studies were included. Significant progress has been made in hydrometallurgical and direct recycling processes, with recovery rates of critical metals (Li, Co, Ni) improving. Second-life battery applications also show promise for grid stabilization and renewable energy storage. Furthermore, recycled batteries show potential in stabilizing power grids through second-life applications in BESS. Conclusion: EV battery recycling is a vital strategy for addressing raw material scarcity, minimizing environmental harm, and supporting energy resilience. However, challenges persist in policy harmonization, technology scaling, and economic viability. Future progress will depend on integrated efforts across sectors and regions to build a circular battery economy. Full article

To evaluate the ecological remediation effect of Salix matsudana × alba on saline coastal soils, we established a five-year field experiment in Rudong County, Jiangsu Province, China. The experiment was designed with three salinity gradients (low, medium, and high) and five plant spacing treatments (2 × 2 m, 2 × 3 m, 3 × 3 m, 3 × 4 m, and 4 × 4 m). Soil samples were collected annually at a depth of 0–20 cm using grid and random sampling methods. Indicators of soil physicochemical properties and heavy metal content were measured, including soil organic matter (SOM), pH, total nitrogen (TN), total phosphorus (TP), total potassium (TK), electrical conductivity (EC), total salinity (TS), and bulk density (BD). Additionally, eight heavy metals were analyzed: zinc (Zn), chromium (Cr), nickel (Ni), copper (Cu), cadmium (Cd), lead (Pb), arsenic (As), and mercury (Hg). Results showed that the hybrid willow significantly improved SOM content by up to 90% and reduced EC and TS by 52% and 60% over five years, especially under low and medium salinity conditions with dense planting (2 × 2 m, 2 × 3 m). The content of most heavy metals exhibited a decreasing trend or remained stable, indicating the plant’s phytostabilization potential (i.e., stabilization of heavy metals via plant-soil interaction). Principal component analysis (PCA) and random forest (RF) modeling identified SOM, EC, TS, and BD as the dominant factors influencing soil quality improvement. A soil quality index (SQI) was constructed based on PCA-derived weights, which further confirmed the positive ecological effect of this hybrid species on coastal saline soils. This study provides scientific evidence supporting the use of Salix matsudana × alba as a promising species for large-scale ecological restoration in coastal saline-alkaline lands. Full article

Flywheel energy storage systems (FESSs) can reach much higher speeds with the development of technology. This is possible with the development of composite materials. In this context, a study is being carried out to increase the performance of the FESS, which is especially used in leading fields, such as electric power grids, the military, aviation, space and automotive. In this study, a flywheel design and analysis with a hybrid (multi-layered) rotor structure are carried out for situations, where the cost and weight are desired to be kept low despite high-speed requirements. The performance values of solid steel, solid titanium, and solid carbon composite flywheels are compared with flywheels made of different thicknesses of carbon composite on steel and different thicknesses of carbon composite materials on titanium. This study reveals that wrapping carbon composite material around metal in varying thicknesses led to an increase of approximately 10–46% in the maximum rotational velocity of the flywheel. Consequently, despite a 33–42% reduction in system mass and constant system volume, the stored energy was enhanced by 10–23%. It was determined that the energy density of the carbon-layered FESS increased by 100% for the steel core and by 65% for the titanium core. Full article

The optical detection of thioacetamide was investigated using a metalated porphyrin, Mn(III)-5,10,15,20-tetrakis-(3,4-dimethoxyphenyl)-21H,23H-porphyrin chloride (Mn-3,4-diMeOPP), a gold colloid solution (AuNPs), and a complex formed between them (Mn-3,4-diMeOPP–AuNPs) in order to select the most sensitive material and to achieve complementarity between methods. Mn-3,4-diMeOPP, AuNPs, and their complex were synthesized and characterized by means of UV–Vis, FT-IR spectrometry, and AFM investigations. It could be concluded that Mn-3,4-diMeOPP could detect/quantify thioacetamide (TAA) in the range 3.13 × 10⁻⁸ M–7.67 × 10⁻⁷ M in a linear fashion, with a 99.85% confidence coefficient. The gold colloidal particles alone could detect TAA in an extremely narrow concentration domain of 2–9.8 × 10⁻⁷ M, slightly complementary with that of Mn-3,4-diMeOPP. The complex between Mn-3,4-diMeOPP and gold colloid proved to be able to quantify TAA in the trace domain with concentrations of 1.99 × 10⁻⁸ M–1.76 × 10⁻⁷ M in a polynomial fashion, with the method being more difficult. A potential mechanism for TAA detection based on Mn-3,4-diMeOPP is discussed based on computational modeling. The distorted porphyrin conformation and its electronic configuration favor the generation of a grid of electrostatic interactions between porphyrin and TAA. Full article