Search Results (2,041)

With the strategic shift toward reducing reliance on critical raw materials, Cobalt-free eutectic high-entropy alloys (EHEAs) have emerged as a pivotal frontier for high-performance structural applications. This review systematically elucidates the synergistic relationship between Co-free alloy design and the non-equilibrium solidification mechanisms of Selective Laser Melting (SLM). The ultra-high cooling rates (10⁵–10⁸ K/s) inherent in SLM are shown to refine eutectic lamellae to the sub-micron scale (typically <300 nm), effectively suppressing the macro-segregation common in conventional casting. We evaluate the design principles of Al-Cr-Fe-Ni and related systems, noting that SLM-processed Co-free EHEAs frequently achieve yield strengths exceeding 1000 MPa and ultimate tensile strengths (UTSs) surpassing 1300 MPa, while maintaining tensile elongations above 10%—a significant improvement over the coarse-grained structures produced by traditional methods. Furthermore, the study identifies critical processing windows, such as laser energy densities (60–120 J/mm³), required to mitigate micro-cracking and achieve near-full density (>99.5%). By synthesizing recent experimental breakthroughs and AI-driven modeling, this review provides a quantitative roadmap for the precision manufacturing of cost-effective, high-performance EHEAs, bridging the gap between theoretical alloy design and industrial additive manufacturing. Full article

Relaxor ferroelectric single crystals, such as Pb(In₁/₂Nb₂/₃)O₃–Pb(Mg₁/₂Nb₂/₃)O₃–PbTiO₃, possess extraordinary electro-optic (EO) coefficients, offering immense potential for next-generation integrated modulators. However, the application of PIN-PMN-PT in fiber-optic gyroscopes (FOGs) is hindered by the challenge of fabricating high-quality optical waveguides with strict mode selectivity, as conventional diffusion typically excites multi-mode propagation. Here, the fabrication of high-quality, mode-selective waveguides is achieved in rhombohedral PIN-PMN-PT via a nickel in-diffusion technique. The resulting graded-index structures exhibit a Gaussian profile with a maximum refractive index change (∆n) of 1.53% while preserving the single crystal structure. Under specific processing conditions, we achieve precise mode selectivity, enabling exclusive transverse electric (TE) mode transmission. This mode selectivity fulfills the requirements for single-mode Y-branch geometries, establishing a robust platform for ultra-compact, low driving voltage modulators and advancing the miniaturization of inertial navigation and integrated photonic systems. Full article

To address challenges such as the sudden onset of urban fires, data synchronization delays in early warning systems, response lags, and insufficient routine monitoring, this paper proposes a Spatio-Temporal Collaborative Optimization for Joint Control and Scheduling (STCO-JCS) algorithm tailored for unmanned aerial vehicles (UAVs) and wireless sensor networks (WSNs). First, spatial autocorrelation analysis based on fire data classifies areas into ultra-high, high, medium, and low risk zones to assist in determining UAV access priorities. Second, we construct optimal inspection trajectories for the UAV by taking into account the inspection sequence and the city’s topography. By modeling the path deviations caused by wind interference and designing precision control algorithms, we improve the accuracy of the UAV’s flight path, ultimately achieving the goal of reducing UAV inspection time. Finally, by coordinating the spatiotemporal operations of drones and wireless sensor networks, we can achieve early detection and rapid response in high-risk fire zones, thereby reducing drone energy consumption while enhancing the efficiency of the UAV-WSN fire monitoring system. Simulation results demonstrate that under a 20-square-kilometer simulation area, STCO-JCS controls inspection paths within 14–17 km. In the multi-UAV scenario, the proposed method achieves approximately 3.17–9.66% improvement in energy efficiency, while in the single-UAV scenario, improvements of 10.83%, 50.54%, and 9.26% are observed in metrics. This provides effective decision support for the dynamic deployment of firefighting and rescue resources. Full article

Event cameras are bio-inspired vision sensors offering low latency, low power consumption, and high dynamic range, capturing motion with microsecond-level precision via a per-event triggering mechanism. Despite these advantages, the inherent sparsity and lack of color in event data hinder direct analysis, necessitating advanced deep learning approaches. To achieve low-latency and high-precision motion segmentation for indoor robotic applications, this paper introduces a dual-branch decoupled CNN framework. Specifically, Principal Component Analysis (PCA) is utilized to project 3D event point clouds into 2D motion trend maps, capturing local motion priors while suppressing ambiguity in structured environments. Concurrently, an Event Leaky Integration (ELI) model, inspired by biological membrane potentials, is designed to enhance the structural representation of sparse events. Within this framework, separate branches respectively perform motion validation and shape extraction and are fused via a Spatial Gated Fusion (SGF) module to suppress static background interference. It is demonstrated experimentally that with an input window of only 10 ms, the proposed method achieves a 77% average mIoU across five indoor test scenarios from the EV-IMO dataset with an inference latency of 10 ms per frame. Compared to state-of-the-art methods like MSRNN and GCN, which required 30–300 ms event slices, our framework achieves a favorable trade-off between computational efficiency and segmentation accuracy, maintaining competitive performance under ultra-short time windows for indoor event-based motion processing. Full article

Non-invasive, real-time biomass estimation is critical for smart aquaculture, yet high computational latency and the cost of specialized optical sensors remain significant bottlenecks. This study proposes an ultra-low-cost, edge-deployable stereo-vision framework utilizing a dual-webcam architecture synchronized with a lightweight YOLOv11n-pose model. To address the spatial uncertainties in non-rigid fish locomotion, we integrated advanced spatial loss functions to achieve precise anatomical keypoint extraction. These coordinates are processed through a three-point Bézier curve interpolation and a mathematically derived Dynamic Shape Factor (K) to correct for optical refraction and morphological variations. As a proof-of-concept, the proposed system was validated on a live multi-species cohort (N = 10), achieving a Mean Absolute Percentage Error (MAPE) of 8.64% and an R² of 0.92 under strict Leave-One-Out Cross-Validation (LOOCV), drastically outperforming traditional naive volumetric baselines (MAPE > 54%). Requiring only 6.7 GFLOPs and 5.5 MB of memory, the model achieves 111.6 FPS. These results demonstrate the feasibility of highly efficient, cost-effective AI solutions for precision aquaculture while clearly defining the validity boundaries and statistical constraints for future large-scale deployment. Full article

To address the problems of insufficient precision reserve, limited rotational speed, and excessive temperature rise in high-speed ultra-precision machine tool spindle bearings, the influence of frictional power loss on the thermo-mechanical behavior of the bearing system was investigated. Firstly, based on the analysis of the heat source of the bearing, the friction power consumption model of the bearing assembly is established, and the analysis of the bearing temperature field is realized by studying the heat energy transfer. Secondly, the test bench is built for experimental verification. Finally, through the study of thermal-mechanical coupling performance, the influence of different rotational speeds on bearing stress and life is analyzed. The results show that the friction power consumption generated by the spin sliding of the bearing rolling element accounts for the largest proportion, accounting for 31% of the total friction power consumption; the increase in bearing speed will increase the bearing temperature. At 55,000 r/min, the highest temperature at the rolling element is close to 75 °C, followed by the inner ring up to 68 °C, and the lowest outer ring temperature is 57 °C. The temperature has a great influence on the bearing performance. Under the same working conditions, the equivalent stress is increased by 21%, the contact pressure is increased by 25%, and the fatigue life of the bearing is reduced by 5.6%. Bearing performance is significantly affected by thermodynamic behavior. Full article

The development of high-performance and sustainable carbon electrodes is increasingly important for next-generation supercapacitors, yet controlling heteroatom doping and hierarchical pore evolution in biomass-derived carbons remains a key challenge. Lignin, as an abundant aromatic biopolymer, offers a structurally rich platform for designing functional carbons, but its rigid cross-linked architecture limits precise pore regulation and efficient nitrogen incorporation. In this work, nitrogen-doped hierarchical porous carbons were engineered from enzymatically treated lignin through a synergistic urea-assisted nitrogen doping and KOH activation strategy. The urea–KOH co-activation drives the coordinated evolution of micropores and mesopores. This approach yields an optimized carbon material possessing a high BET surface area of 2569 m² g⁻¹, an interconnected micro–mesoporous architecture, and a favorable distribution of pyridinic, pyrrolic, and graphitic nitrogen species. The engineered pore hierarchy is correlated with enhanced ion transport kinetics, as evidenced by a high b value of 0.99 and a capacitive contribution of 98.5% at 100 mV s⁻¹; nitrogen functionalities introduce redox-active sites and improve interfacial wettability. As a result, the selected material delivers a high specific capacitance of 221 F g⁻¹ at 0.5 A g⁻¹, strong rate capability with 84.4% retention at 20 A g⁻¹, and excellent cycling durability with 90.7% capacitance retention after 50,000 cycles. This study demonstrates a potentially mechanistically informed, scalable pathway for coupling enzymatic structural regulation with chemical activation, offering a sustainable route for transforming lignin into high-value carbon electrodes suitable for advanced supercapacitor applications. Full article

Planar microwave (MW) sensors offer high-resolution, non-invasive technology for monitoring critical soil properties, serving as a support for modern precision agriculture. While laboratory studies confirm their exceptional sensitivity, the widespread adoption of these sensors is severely impeded by critical translational challenges that constitute a defining “lab-to-field gap”. These barriers include high sensor-to-sensor variability, debilitating thermal cross-sensitivity, soil heterogeneity necessitating unique site-specific calibration, and the enduring tension between high-performance and cost-effective scaling. This review systematically synthesizes the current state of planar permittivity MW technology, moving beyond technical mechanisms to critically assess these operational limitations. We detail advanced architectural strategies designed to bridge this gap, focusing particularly on the transition toward more robust solutions. The key strategies analyzed include the adoption of differential sensor designs using microstrip patch antennas to mitigate common-mode environmental errors, the integration of ultra-compact metamaterial structures such as split-ring resonators (SRRs) and complementary split-ring resonators (CSRRs) for enhanced field robustness and deep soil sensing, and the necessity of multi-parameter sensing capabilities (moisture, pH, and salinity). By establishing a comprehensive roadmap that prioritizes field stability, cost efficiency, and seamless IoT integration, this review demonstrates that planar MW sensors are poised to become reliable and scalable tools. Addressing these critical translational hurdles will ensure optimal resource management, significantly enhance crop productivity, and enable sustainable practices within smart farming ecosystems. Full article

To tackle the drawbacks of small effective area and limited incident angle in conventional Fabry–Pérot (F-P) etalons, this paper presents a high-utilization laser frequency-selective ultra-narrow band F-P filter module, along with systematic investigations into its operating principle, simulation, design, and verification. A simulation model with a central wavelength of 532.20 nm (±0.1 nm) and a free spectral range of 300 pm is developed, and a prototype filter is fabricated accordingly. The prototype exhibits a transmission peak linewidth better than 0.037 nm and a peak transmittance over 68%, matching well with the simulation. Simulations also reveal symmetric high-transmittance peaks at ±2.9° with stable frequency selection performance. On this basis, an integrated module is proposed using field-of-view angle conversion and optical path multiplexing. Under combined incidence at 0° and ±2.9°, the module achieves 1.67 times the energy of single-angle incidence while satisfying wavelength and bandwidth requirements. The proposed structure breaks through the conventional normal-incidence restriction and offers a novel approach for high-efficiency, multi-angle multiplexing applications of F-P etalons in precision optical systems. Full article

Memristor-based neuromorphic computing offers a promising pathway for efficient in-memory processing. However, the scalability and reliability of such systems are severely compromised by parasitic resistances (including line and input resistances) in crossbar arrays, which cause significant IR-drop during vector–matrix multiplication (VMM). Existing research often suffers from high computational latency or relies on the precise extraction of parasitic parameters, which is impractical and computationally expensive for large-scale integration. To overcome these limitations, we propose a Parameter-Agnostic Adaptive Compensation (PAAC) method based on a distributed linear approximation model. By analyzing the circuit characteristics, we conquered the challenge of coupling between parasitic effects and output current, deriving a simplified linear relationship that requires no prior knowledge of specific resistance values. The PAAC method involves only a single-step pre-calibration experiment to determine a global compensation factor, achieving an ultra-low computational complexity during inference. We validated the method using a comprehensive two-stage strategy: board-level hardware experiments confirmed its feasibility by reducing current distortion from 71% to 2%, while extensive large-scale HSPICE simulations verified its scalability, restoring classification accuracy from 89% to 95%. This work provides a robust, low-overhead solution that eliminates the dependency on precise parameter modeling, facilitating the realization of large-scale, high-precision neuromorphic hardware. Full article

In uncrewed aerial vehicle (UAV)-based ground observation and detection missions involving high-speed moving targets or low-light conditions, Time Delay Integration (TDI) cameras enhance image brightness through multi-stage charge accumulation. However, the imaging quality is susceptible to motion blur induced by platform vibrations and velocity mismatch. Based on TDI imaging technology, a TDI image degradation model for a UAV-based imaging platform is formulated. To address spatial blurring caused by platform vibration and velocity mismatch during TDI imaging, we propose a TDI image restoration algorithm based on sharpness-optimized partitioned Point Spread Function (PSF) estimation. The main innovation lies in the first application of partitioned PSF estimation combined with image sharpness optimization in TDI imaging. By formulating an accurate TDI image degradation model, spatial motion blur kernel estimation is transformed into an iterative search problem for partitioned optimal PSF. Solving for optimal sharpness yields the optimal PSF and corresponding local motion parameters, achieving image restoration. Simulation and experimental results demonstrate that the proposed algorithm in this paper effectively removes motion blur in TDI dynamic imaging, while suppressing artifacts and ringing, thus significantly enhancing image quality. Full article

Environmental contamination by persistent industrial dyes such as Amido Black demands highly efficient photocatalysts for advanced water treatment. Structural, chemical, and optical strategies based on TiO₂ nanotube engineering are widely explored for this purpose. In this work, highly ordered TiO₂ nanotube arrays were fabricated by electrochemical anodization and subsequently decorated with Pd nanoparticles via potentiostatic electrodeposition (10–300 s), enabling precise control of Pd nanoparticle size and loading. The resulting materials were systematically characterized by SEM, TEM, XRD, XPS, UV–vis DRS, and PL spectroscopy, and their properties were correlated with the photocatalytic degradation of Amido Black under both UV and visible light irradiation. The study reveals a clear size-dependent duality in the role of Pd. For intermediate Pd nanoparticles (≈9 nm, 20 s), Pd behaves predominantly as an electron sink, forming an efficient Schottky junction with anatase TiO₂ that markedly suppresses charge carrier recombination. This configuration yields ≈ 97% Amido Black removal after 120 min of UV irradiation, with an apparent rate constant about three times higher than that of bare TiO₂ nanotubes. In contrast, for ultra-small Pd nanoparticles (≈6 nm, 10 s), interfacial defect states sensitize TiO₂ to visible light, enabling ≈ 65% degradation after 270 min and a rate constant roughly four times higher than that of undecorated nanotubes under visible illumination. At long deposition times (≥150 s), Pd agglomeration leads to enhanced photoluminescence and markedly reduced photocatalytic activity, indicating increased recombination and less effective utilization of photogenerated charges. This provides a practical design rule to rationally tailor Pd–TiO₂ nanotube photocatalysts for targeted UV or visible light applications in dye removal and broader environmental remediation scenarios Full article

Synthetic aperture radar (SAR) ship detection is severely limited by the artifacts caused by motion. Due to the complex six-degree-of-freedom (6-DOF) motion of ships, the ship imaging exhibits aberration phenomena including spatial blurring, discrete ghosting, and Lorentz linear blurring. Traditional detectors rely on the identification of static spatial features. When the phase coherence is disrupted, they tend to fail. To overcome this problem, we propose a multimodal fusion framework based on physical principles. This framework establishes a theoretical connection between the ship hydrodynamic response and imaging degradation through short, long, and ultra-long coherence processing intervals (CPI). The framework adopts a cascaded architecture: first, a lightweight YOLOv8 performs rapid global screening, followed by a signal backtracking mechanism that extracts high-fidelity time-frequency domain (TFD) and range instantaneous Doppler (RID) features from the original distance compressed data. In the second-level detection, these physical features are adaptively fused with spatial intensity through a YOLOv8 network integrated with the convolutional block attention module (CBAM) to reduce the false detection rate. The validation on high-fidelity simulations and real GF-3 datasets shows that this method consistently achieves an average precision (mAP) of over 95%, outperforming several widely used detectors, and demonstrates strong generalization ability in extreme imaging conditions, suitable for maritime detection scenarios. Full article

A comprehensive ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method was developed for the simultaneous quantification of 50 pesticide biomarkers across nine current-use chemical classes in human urine. These classes include organophosphorus insecticides (which encompass dialkyl phosphates and specific metabolites), pyrethroid insecticides, fungicides, neonicotinoid insecticides, herbicides, insect repellents, organochlorine pesticide metabolites, and plant growth regulators. The method employs solid-phase extraction (SPE) for sample preparation, requiring only 0.2 mL of urine. Chromatographic separation was optimized using a Hypersil Gold AQ column, achieving a total run time of 18 min. Mass spectrometric detection utilized polarity switching in electrospray ionization mode with multiple reaction monitoring. Method validation demonstrated satisfactory linearity (R² > 0.99), high sensitivity with limits of detection ranging from 0.01 to 0.88 ng/mL, and extraction efficiencies between 85% and 113%. Precision and accuracy were within acceptable ranges, with relative standard deviations generally below 15%. The method’s robustness was confirmed through participation in external quality assessment schemes. Application to real samples revealed significant inter-individual variability in pesticide biomarker concentrations, with total measured biomarker levels ranging from 89 to 1242 ng/mL across the 10 individuals analyzed. This method offers comprehensive coverage of current-use pesticide chemical classes, including 30 biomarkers from the U.S. National Health and Nutrition Examination Survey (NHANES) biomonitoring program, and demonstrates improved sensitivity and broader analyte coverage compared to existing methods. The developed assay provides a valuable tool for large-scale biomonitoring studies and environmental health research. Full article