Search Results (231)

Accurate irrigated cropland mapping is critical for agricultural water management and food security. Existing image-based irrigation mapping workflows primarily rely on vegetation indices and synthetic aperture radar (SAR) backscatter features, which have limited capacity to characterize the temporal evolution of irrigation processes and crop growth conditions. The AlphaEarth Foundation (AEF) model developed by Google DeepMind provides compact embeddings with temporal semantic information learned via self-supervision, yet their utility for irrigation mapping has not been systematically assessed. In this study, a comprehensive assessment of AEF embeddings for irrigated cropland mapping was performed in terms of feature separability, classification performance, and spatiotemporal transferability. Experiments were conducted in two representative irrigated regions: the Guanzhong Plain in China and Kansas in the USA. Class separability of the 64 embedding dimensions was quantified using the Jeffries–Matusita (JM) distance. Then, the AEF embeddings were compared with the Sentinel feature set (Sentinel-2 bands, normalized difference vegetation index(NDVI), enhanced vegetation index(EVI), normalized difference water index(NDWI) and Sentinel-1 vertical transmit vertical receive(VV), vertical transmit horizontal receive(VH)) using K-means clustering and supervised classifiers, including Decision Tree (DT), Random Forest (RF), Gradient Boosting Decision Trees (GBDT), Support Vector Machine (SVM), and Multi-layer Perceptron (MLP). Finally, transfer experiments across 2022 and 2024 in the Guanzhong Plain and Kansas were conducted to examine cross-year and cross-region performance. The results showed that AEF embeddings consistently provide stronger class separability in both study areas, with a maximum JM distance of 1.58 (A29). Using AEF embeddings, RF achieved overall accuracies (OA) of 0.95 in the Guanzhong Plain and 0.93 in Kansas, outperforming models based on Sentinel-1/2 bands and indices. Notably, unsupervised K-means clustering on AEF embeddings yielded OA > 0.85, indicating high intrinsic separability between irrigated and rainfed croplands. Transfer experiments further demonstrate stable temporal transfer (cross-year OA > 0.87), whereas cross-region transfer is constrained by differences in irrigation regimes, crop phenology and management practices, resulting in limited spatial generalization (OA~0.3). Overall, this study demonstrates the potential of high-information-density representations from geospatial foundation models for irrigated cropland mapping and provides methodological and technical insights to support transfer learning and operational mapping over large areas. Full article

Composite ZnO/Fe nanostructured thin films are synthesized via physical vapor deposition using radio frequency magnetron sputtering in conventional, as well as in glancing angle deposition (GLAD) geometries. ZnO is employed as a compact nanocolumnar template to direct Fe growth in bilayer and multilayer architectures. Morphological analysis reveals well-defined ZnO/Fe interfaces for normal deposition geometry, with diminished interface clarity and reduced layer thickness in GLAD samples. Crystallographic characterization indicates clear ZnO-{002} and α-Fe-{110} texture. Magnetostatic characterization investigates the effects of morphology on coercivity and domain nucleation. GLAD-deposited Fe films exhibit clear in-plane magnetic anisotropy, with remanence to saturation magnetization (M_REM/M_SAT) equal to 1 for the easy axis and equal to 0.24 for the hard axis, consistent with inclined nanocolumn morphology. Our findings show that deposition geometry, rather the ZnO template, mostly affects the morphology of Fe films. The above, highlight the potential of engineered ZnO/Fe nanocomposites for magnetic, spintronic, and magnetoplasmonic applications, by tuning morphology and interface quality through deposition parameters. Full article

A compact, low-cost star tracker system tailored for small satellite applications was designed and prototyped. The system was designed with a fast f/1.2 aperture, a 20 × 13° field of view, and a theoretical angular resolution of 10 arcs—sufficient for the determination of attitude and orbit of a satellite. The optical design is based on a monolithic Maksutov–Cassegrain architecture, with lens assemblies fabricated from CR39 or PMMA to eliminate collimation requirements and improve vibration resistance. The lens was machined using Single-Point Diamond Turning to a precision better than λ/14. It was coated with a multilayer antireflective and highly reflective coatings applied via magnetron sputtering to reduce stray reflections and improve light throughput. The housing was produced using electron beam powder-bed fusion with Ti-64 alloy, while the use of commercial imaging sensors minimizes overall cost. Prototype testing confirmed to plate-solve star patterns with precision better than 27 arcs at 100 ms imaging time across all analysed images. Full article

In this work, three functionalized hybrid composites, CS/PVA-VAN, CS/PVA-VAN@TiO₂ and CS/GO/PVA-VAN@TiO₂, were synthesized and applied for adsorption evaluation on two common non-steroidal anti-inflammatory drugs, i.e., diclofenac (DCF) and ketoprofen (KTP). The structural and morphological characteristics of new composites were identified via Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), X-ray diffraction (XRD) and BET techniques. BET analysis demonstrated that the CS/GO/PVA-Van@TiO₂ composite has a surface area 64.86 m²/g, which is twice that of CS/PVA-Van. Moreover, adsorption evaluation was achieved at an optimum pH condition (pH 5.0) for both drugs. In addition, the kinetic data fitted better in a pseudo-second-order kinetic model, while the adsorption was heterogeneous and multilayer. The adsorption capacity of CS/GO/PVA-VAN@TiO₂ was found to be 114.53 mg/g and 65.20 mg/g for diclofenac and ketoprofen, respectively. Thermodynamic analysis confirmed that the adsorption process was endothermic and spontaneous for all pollutants. Moreover, the kinetic swelling and stability studies demonstrated that graphene oxide contributed to improving the structural compactness and stability of composite. Finally, the adsorption performance of the optimal composite material was investigated in a binary system of non-steroidal anti-inflammatory drugs in various ratios. Full article

The on-chip detection of circularly polarized light is pivotal for advancing applications in quantum optics, information processing, and spectroscopic sensing. However, conventional chiral metasurfaces often suffer from complex multilayer fabrication, material incompatibility, or modest performance, hindering their integration with photonic circuits. Here, we introduce a monolithic all-silicon metasurface that overcomes these limitations through a singular structural innovation. By strategically truncating four corners of a conventional Z-shaped meta-atom, we induce a hybridization of optical modes that profoundly enhances chiral light–matter interaction. This deliberately engineered perturbation yields a colossal circular dichroism with an extinction ratio exceeding 66 dB, a performance that surpasses existing state-of-the-art designs by approximately three orders of magnitude. Furthermore, the proposed metasurface exhibits remarkable fabrication robustness, owing to its single-layer architecture and CMOS-compatible material. We demonstrate that this exceptional metasurface can be directly integrated with a Mercury Cadmium Telluride (MCT) photodetector to form a highly efficient, compact circular polarization detector. Our work provides a simple yet powerful paradigm for creating high-performance chiral photonic devices, paving the way for their widespread adoption in integrated optoelectronics. Full article

This study proposes a tension-spring-based unidirectional rotational stiffness mechanism (TS-URM) and its implementation in a Unidirectional Series Elastic Actuator (USEA). Unlike conventional bidirectional rotary SEAs, the proposed design is structurally optimized for unidirectional torque transmission, improving deformation utilization efficiency in pulling-type applications. An analytical model was derived to establish the geometric relationship between spring elongation and rotational deformation, enabling explicit formulation of the torque–angle relationship. The influence of the installation angle on stiffness linearity was systematically analyzed, and a multilayer spring configuration was optimized to achieve a target rotational stiffness of approximately 42 Nm/rad. A preload adjustment mechanism was incorporated to eliminate nonlinear behavior in the initial operating region. Experimental results validated the analytical model and demonstrated stable unidirectional force control up to 130 N with steady-state errors within 1 N. The proposed mechanism provides predictable stiffness characteristics and an efficient structural solution for compact USEA systems. Full article

Visual localization plays a critical role for mobile robots to estimate their position and orientation in GPS-denied environments. However, its efficiency, robustness, and generalization are fundamentally undermined by severe viewpoint changes and dramatic appearance variations, which present persistent challenges for image-based feature representation and pose estimation under real-world conditions. Recently, map-free visual relocalization (MFVR) has emerged as a promising paradigm for lightweight deployment and privacy isolation on edge devices, while how to learn compact and invariant image tokens without relying on structural 3D maps still remains a core problem, particularly in highly dynamic or long-term scenarios. In this paper, we propose the Debiased Multiplex Tokenizer as a novel method (termed as DMT-Loc) for efficient and versatile MFVR to address these issues. Specifically, DMT-Loc is built upon a pretrained vision Mamba encoder and integrates three key modules for relative pose regression: First, Multiplex Interactive Tokenization yields robust image tokens with non-local affinities and cross-domain descriptions. Second, Debiased Anchor Registration facilitates anchor token matching through proximity graph retrieval and autoregressive pointer attribution. Third, Geometry-Informed Pose Regression empowers multi-layer perceptrons with a symmetric swap gating mechanism operating inside each decoupled regression head to support accurate and flexible pose prediction in both pair-wise and multi-view modes. Extensive evaluations across seven public datasets demonstrate that DMT-Loc substantially outperforms existing baselines and ablation variants in diverse indoor and outdoor environments. Full article

This study investigates the sound insulation performance of an anechoic chamber, exploring the influence patterns of different multilayer material combinations on wall sound insulation characteristics. Based on sound transmission theory, a predictive model for multilayer material wall sound insulation was established. The finite element method was employed to simulate the sound propagation characteristics of walls and glass doors with various material combinations. After validating the simulation results through a double-room method experiment, the material combination scheme for the anechoic chamber walls and glass doors was optimized. Based on this, a 1000 mm × 1000 mm × 2300 mm soundproof room prototype was designed and constructed. Its sound insulation performance under reverberant conditions was tested using the insertion loss method and compared with simulation data. Simultaneously, a hybrid calculation method combining low-frequency finite element analysis with high-frequency statistical energy analysis enabled precise and efficient prediction of the overall sound insulation performance of the soundproof room. Research revealed that single-pane glass with thicknesses between 5 and 20 mm conformed to the mass law, with sound insulation increasing by an average of 0.8 dB per additional millimeter. The 10 mm single-pane glass emerged as the optimal choice for the soundproof room’s glass door due to its ideal thickness and excellent low-to-mid-frequency sound insulation. The optimized wall structure featured compact thickness, outstanding low-frequency sound insulation, and balanced mid-to-high-frequency performance. Simulation and experimental results for the core frequency range of 63–1000 Hz showed high consistency, which validates the reliability of the theoretical model and simulation methodology within this frequency band. The deviation of simulation results from experimental data in the frequency range above 1000 Hz is mainly caused by acoustic leakage due to experimental sealing defects, and the high-frequency simulation results are only used for trend analysis rather than conclusion support. This study identifies the optimal multi-layer material combination for soundproof rooms, providing practical material strategies for acoustic design. It also reveals the sound insulation mechanisms of multi-layer composite structures. The findings offer significant reference for optimizing soundproofing materials and structures in architectural acoustics and transportation noise control. Full article

The Green Oasis (GRO) Project is a targeted urban greening intervention designed to evaluate the environmental and health impacts of compact, high-density plantings in dense built environments. Initiated in downtown Louisville, the project transformed Founders Square, a 0.64-acre sparsely planted park, into a microforest (“Trager Microforest”), a multilayered planting of 119 trees and more than 200 shrubs. The impact of this intervention is being assessed through a randomized crossover study in which participants walk in the microforest and a nearby impervious parking lot. Physiological outcomes include heart rate, heart rate variability, arterial stiffness, and stress biomarkers measured in saliva, urine, and sweat. Environmental conditions are continuously monitored by fixed and mobile weather stations, air pollution sensors, and biodiversity surveys. Baseline assessments were conducted in 2023 and 2024, with post-planting evaluations now underway (2025–). Power calculations indicate adequate sensitivity (n ≈ 40–50) to detect changes in cardiovascular stress responses in participants. Complementary ecological measurements include soil microbiome composition, greenhouse gas fluxes, and avian diversity. This study addresses critical gaps in understanding how small-scale, high-density greening interventions affect cardiovascular resilience, stress physiology, and microclimatic regulation. By integrating environmental, biological, and human health data, GRO establishes a comprehensive framework for evaluating the efficacy of urban microforests as nature-based solutions. The results are expected to inform urban planning, public health strategies, and climate adaptation policies, demonstrating how compact greening interventions can simultaneously mitigate heat, reduce pollution, enhance biodiversity, and promote human wellbeing in dense urban cores. Full article

Lightweight aggregate concrete (LWAC) is a practical alternative to conventional concrete in civil engineering, offering advantages such as reduced density, enhanced insulation properties, and improved seismic performance. However, segregation during compaction remains a limitation, as it can lead to non-uniform material distribution and reduced compressive strength. This study addresses this issue by combining non-destructive techniques with deep learning methods to predict the compressive strength of LWAC. We propose an explainable approach based on a convolutional recurrent neural network architecture, enhanced by unsupervised clustering and SHapley Additive exPlanations (SHAP), to improve interpretability. To optimize predictive performance, several aggregation strategies are evaluated at the recurrent layer before the dense layers, including full-sequence flattening, max pooling, average pooling, and an attention mechanism over the full sequence. Experimental results show that the proposed model outperforms conventional machine learning methods such as multilayer perceptron (MLP), random forest (RF), and support vector regression (SVR), as well as ensemble methods such as gradient boosting (GBR), XGBoost, and weighted average ensemble (WAE). Furthermore, when combined with unsupervised clustering, the model identifies latent behavioral patterns that are not observable through traditional evaluation techniques. This demonstrates the potential of integrating non-destructive testing with interpretable deep learning as a reliable approach for the structural assessment of LWAC. Full article

Atmospheric visibility nowcasting is vital for safety-critical operations but remains challenging due to complex atmospheric dynamics. We propose a compact stacking ensemble merging a multilayer perceptron (MLP) and gradient-boosted regression trees (GBRT). The model, trained on seven months of minute-scale resolution data with a variability-adaptive filter to suppress sensor noise, employs cross-validation. Results demonstrate that the ensemble achieves its peak performance in the operationally critical low-visibility regime (V < 5 km). This range is particularly significant as it encompasses the Category I and II (CAT I/II) operational thresholds defined by the World Meteorological Organization (WMO) for aviation and surface transportation safety. In this regime, the ensemble yields an R² of 0.82 and an MAE≈385 m, significantly outperforming single learners during rapid weather transitions. Conversely, in the high-visibility regime (V > 20 km), the explanatory power decreases (R² of 0.46) due to inherent forward-scattering sensor uncertainties and low aerosol concentrations. Despite these range-specific physical limitations, the model maintains high robustness with narrowly centered residuals. This efficient approach, utilizing cost-effective in situ sensors, is highly suitable for airport and road-weather applications and offers strong potential for multi-site scalability. Full article

Geogrid reinforcement is an effective subgrade treatment technique that plays a critical role in improving structural stability and controlling deformation in highway widening projects. In this study, the reinforcement mechanisms and performance of geosynthetic-reinforced embankments with varying heights were systematically investigated using finite element simulations conducted in ABAQUS. An improved nonlinear soil-reinforcement interface model was incorporated and implemented through a user-defined FRIC subroutine, allowing for a more accurate representation of nonlinear shear behavior at the soil-geosynthetic interface and providing deeper insight into the reinforcement mechanism within the subgrade structure. The results indicate that bottom-layer reinforcement offers the most significant improvement in overall stability and deformation control. Although multi-layer reinforcement configurations (top-middle-bottom or middle-bottom) further enhance performance, their additional benefits are limited for low embankments. Tensile strain within the reinforcement decreases with increasing distance from the existing slope, with the bottom geosynthetic layer exhibiting the most uniform strain distribution and playing a dominant role in settlement control. Considering both structural performance and reinforcement efficiency, a “sparse-top and dense-bottom” reinforcement configuration is recommended. Specifically, single bottom-layer reinforcement is suitable for embankments ≤ 3 m in height, double-layer reinforcement (bottom-middle) is optimal for embankments 3–7 m high, and triple-layer reinforcement (top-middle-bottom) is recommended for embankments exceeding 7 m, in combination with ground improvement, compaction control, and slope protection measures to ensure overall stability. The reinforcement optimization strategy proposed in this study provides a scientific basis and practical guidance for the structural design and performance enhancement of highway widening projects. Full article

This paper presents the design, fabrication, and experimental characterization of a novel terahertz receiver comprising two high-performance receiving antennas and a combiner. The low efficiency of local oscillator (LO) power utilization, caused by conventional beam splitters, presents a major bottleneck for large-array terahertz receivers. By eliminating the conventional beam splitter, the proposed system allows the terahertz signal and LO power to be directly and independently received by two dedicated antennas, thereby significantly enhancing LO power efficiency. The receiver is successfully fabricated using micromachining technology into a compact 2.5-dimensional multilayered structure measuring 9 mm × 16 mm × 7.2 mm. Key performance metrics, including the waveguide port S-parameters, radiation patterns, and gains of the two horn antennas, were measured. The experimental results show close agreement with simulations, validating the system’s accuracy and reliability. Furthermore, the system’s equivalent noise temperature was measured to be 395 K, indicating excellent thermal stability and sensitivity. This study concludes that the proposed terahertz receiver design is both feasible and efficient for high-resolution applications, showing great potential for use in satellite-based space observation systems or base stations requiring advanced terahertz signal processing. Full article

Low-Temperature Co-Fired Ceramic (LTCC)-based mechanical sensors are inherently limited by the thickness and rigidity of multilayer ceramic stacks, which restrict miniaturization and mechanical compliance. To overcome these constraints, this work presents a hybrid LTCC/Kapton^® platform enabling high-sensitivity mechanical sensing through mechanically tunable RF passive components. The proposed approach integrates a flexible polyimide membrane, bonded onto an LTCC substrate at low temperatures using selectively electroplated indium pillars that simultaneously define the air gap and provide mechanical fixation. Inductance tuning is achieved via metal-shielding proximity effects, whereas capacitance tuning relies on force-controlled air-gap modulation in a metal–insulator–metal configuration. The fabrication process ensures precise gap control, high compliance, and structural robustness without requiring deformable ceramic membranes. Experimental characterization, including three-dimensional surface profiling and impedance measurements, demonstrates a 48% inductance tuning range with a sensitivity of 0.715 nH/mN and a 36% capacitance tuning range with a sensitivity of 47.3 fF/mN at 1 MHz. The proposed hybrid platform provides a compact and scalable solution for high-sensitivity sensors and mechanically reconfigurable RF components suitable for harsh-environment and adaptive electronics applications. Full article

Decentralized graywater treatment using nature-based systems represents a sustainable, low-energy alternative to centralized wastewater technologies, particularly in water-scarce regions. This study evaluates the performance of a rain-garden-based constructed wetland implemented at Zain Park in Jerash, Jordan, for on-site graywater treatment and potential non-potable reuse. The system consists of two filtration beds with multi-layer gravel–sand media planted with ornamental vegetation to promote physical filtration, adsorption, and biologically mediated transformations. Influent and effluent samples were monitored monthly from April 2024 to January 2025 and analyzed for biodegradable and oxidizable organic fractions (BOD₅ and COD), nutrients (TN, PO₄³⁻), suspended solids, turbidity, salinity indicators, and microbial parameters (E. coli and total coliform). Average removal efficiencies reached 98% for BOD and 96% for COD, while turbidity and TSS were reduced by more than 96%, indicating effective organic degradation and particulate retention. Nutrient removal was moderate, with 40% reduction in Total Nitrogen and 74% in nitrate, reflecting partial nitrification–denitrification and plant uptake. Microbial removal was variable, with an average reduction of 0.8 log₁₀ (64.7%) for E. coli and 1.1 log₁₀ (82.6%) for total coliforms, indicating that passive filtration alone may not ensure complete pathogen attenuation. Post-treatment disinfection and substrate enhancements (aeration and plant selection) can strengthen system efficiency and support sustainable graywater reuse in water-stressed regions, contributing directly to SDG 6 (Clean Water and Sanitation), SDG 11 (Sustainable Cities and Communities), and SDG 12 (Responsible Consumption and Production). These findings support the applicability of compact constructed wetland systems as decentralized wastewater treatment solutions in arid and semi-arid urban environments. Full article