Search Results (2,838)

Microwave dielectric characterization is essential for material evaluation, process monitoring, biomedical sensing, and nondestructive testing. This review critically evaluates planar microwave sensors for dielectric characterization, with the core scope restricted to printed microstrip and coplanar-waveguide structures, SRR/CSRR and DGS configurations, substrate-integrated waveguides, interferometric sensors, and microfluidic platforms. Adjacent non-planar or system-level techniques are included only when they provide transferable lessons in calibration, inversion, or deployment. Unlike earlier surveys that primarily catalog devices or extraction methods, the literature is organized here through a design-decision hierarchy linking architecture, operating principle, readout mechanism, sample interface, and application. Representative approaches are compared not only by frequency, sensitivity, Q-factor, and sample volume, but also by calibration burden, fabrication tolerance, environmental robustness, cost, and scalability. Particular attention is given to uncertainty sources in practical measurement chains, FR-4 and PCB manufacturing variability, long-term drift and sensor aging, and the application-specific limitations of machine-learning-assisted inversion. The resulting synthesis provides design-oriented guidance for selecting and translating planar microwave sensors into reliable industrial, biomedical, and microwave-processing measurement systems. Full article

The development of nuclear fusion as a viable energy source will require the manufacturing of large-scale and complex components capable of withstanding extreme operational conditions. The RODAS project will address this challenge by developing and validating advanced manufacturing technologies, particularly additive manufacturing and Hot Isostatic Pressing, to fabricate demonstrator components representative of those needed in ITER (International Thermonuclear Experimental Reactor) and EU-DEMO (European Demonstration Power Plant) reactors. The project will focus on the production and qualification of fusion-relevant materials such as EUROFER97, CuCrZr, and AISI 316L (ITER-grade), using various additive manufacturing techniques adapted to large-scale applications. Through a coordinated effort among Spanish public research institutions and private technology centers and companies, RODAS will establish specifications, develop hybrid manufacturing routes, and implement non-destructive testing methods to ensure the integrity and performance of the produced components. Additionally, it will explore strategies for future in-service repair using remotely operated systems. The project will significantly strengthen the technological capabilities and international competitiveness of the Spanish industrial sector in the field of nuclear fusion. Full article

Seed quality profoundly affects productivity, marketability, and food security, yet conventional evaluation methods are destructive, slow, and unsuited to high-throughput screening. Non-destructive techniques, being rapid, non-invasive, and capable of measuring multiple indicators, have therefore gained substantial momentum. This review critically surveys the principles, applications, and limitations of major non-destructive techniques for seed quality assessment. Near-infrared spectroscopy (NIRS) enables fast, simultaneous multi-component analysis in portable formats, but its shallow penetration and poor sensitivity to subtle chemical shifts restrict single-seed vigor tests. Hyperspectral imaging (HSI) uniquely merges spectral with spatial data to map composition and surface defects, though large data volumes, high cost, and limited portability hinder practical use. Machine vision offers low-cost, high-throughput external sorting but captures only surface traits and is illumination-sensitive. X-ray/CT imaging visualizes internal cracks and insect damage, yet radiation safety and bulky hardware preclude field deployment. Complementary tools (NMR, electronic nose, Raman, dielectric, fluorescence, acoustic) address niche needs but face stability, sensitivity, or dimensionality trade-offs. Future breakthroughs demand multi-sensor data fusion, deep learning optimization, and ruggedized low-cost hardware. Bridging laboratory innovation and industrial reality requires concurrent algorithmic, optical, and engineering advances, ultimately transforming seed testing into a reliable, intelligent, and deployable ecosystem. Full article

Viscosity is a key quality parameter in cosmetic manufacturing, yet conventional rheological measurements require direct contact with the sample and are not suitable for continuous monitoring of the same specimen. This study investigates the feasibility of electromechanical impedance (EMI) sensing as a proof-of-concept approach for non-destructive monitoring of viscosity changes in cosmetic gels. Hydroxyethyl cellulose (HEC)-based model gels with HEC concentrations ranging from 0.0 to 1.0 wt% were prepared, providing viscosities between 1 and 794 cP. An acrylic-coated piezoelectric (PZT) sensor embedded in each gel was used to measure the electrical admittance spectra. The resonance peak conductance decreased progressively with increasing viscosity, whereas the resonance frequency remained nearly constant, indicating that resonance peak conductance is sensitive to viscosity-related changes in the surrounding gel. Continuous monitoring over 24 h under naturally varying temperature conditions further demonstrated that the EMI response changed consistently with the thermal behavior of the gel. These findings demonstrate the feasibility of EMI sensing as a non-destructive technique for continuously monitoring viscosity-related changes in cosmetic gels and provide a foundation for future studies using practical cosmetic formulations. Full article

Metal additive manufacturing (MAM) enables the fabrication of geometrically complex and high-performance components but is accompanied by steep thermal gradients, repeated thermal cycling, phase transformation, residual stress, and deformation. These effects can reduce dimensional accuracy, manufacturing stability, fatigue resistance, and service reliability. This review systematically examines residual-stress and deformation behavior in MAM from the perspectives of formation mechanisms, influencing factors, measurement and prediction methods, mitigation strategies, and service-related consequences. The temperature gradient, mechanical constraint, and phase transition mechanisms are discussed as quantitatively coupled rather than independent processes. Comparative attention is given to process-specific differences, alloy-dependent thermophysical and metallurgical behavior, multi-track and multi-material interactions, and complex geometries. Destructive and non-destructive measurement techniques are compared in terms of penetration depth, spatial resolution, uncertainty, and cross-validation. Thermo-mechanical finite element, inherent strain, analytical, reduced-order, machine-learning, physics-informed, and digital-twin approaches are evaluated according to accuracy, efficiency, transferability, and applicability. Mitigation strategies are further compared considering residual-stress reduction, deformation control, manufacturing cost, and mechanical-property retention. Finally, challenges associated with uncertainty quantification, service environments, post-machining stress redistribution, and closed-loop control are identified. This review provides an integrated framework for selecting measurement, prediction, and mitigation approaches for reliable and high-precision MAM. Full article

Accurate, simple, cost-effective, and non-destructive estimation of heartwood content is essential for the sustainable management and commercial valuation of Pterocarpus santalinus L.f. (red sanders), one of the world’s most valuable tropical timber species. Conventional methods for heartwood assessment, including increment coring and destructive sampling, are invasive, time-consuming, and unsuitable for large-scale field applications. Electrical Resistance Tomography (ERT) offers a promising alternative by exploiting differences in electrical resistivity associated with variations in wood moisture content and anatomical characteristics. The present study standardized the application of ERT for the identification and quantification of heartwood in standing red sanders trees and validated its performance against conventional core sampling. Fifty-eight trees representing two diameter classes (10–20 cm and 20–30 cm) were evaluated using a PiCUS TreeTronic Electrical Resistance Tomograph, followed by increment core extraction at breast height for validation. Distinct resistivity gradients were observed, with higher electrical resistivity in the central heartwood region and lower resistivity in the peripheral sapwood. The resistivity values ranged from 153 to 1031 Ω in trees with diameters of 10–20 cm and from 342 to 1444 Ω in trees with diameters of 20–30 cm. Linear regression analysis showed excellent agreement between ERT-estimated and measured heartwood diameters (R² = 0.98), with an average similarity of 91.5%. The observed resistivity distribution closely reflected variations in moisture content, wood density, and anatomical structure across the stem radius. The findings demonstrate that ERT is a reliable and non-destructive technique for estimating heartwood content in standing red sanders trees. These results demonstrate that ERT can accurately estimate heartwood dimensions in standing Pterocarpus santalinus trees under the conditions of the present study and provide a reliable approach for non-destructive assessment of heartwood in this species. The technique has considerable potential for timber valuation, harvest planning, tree breeding, forest inventory, and conservation programs involving high-value tropical hardwood species. Full article

Conventional methods of plant distance and volume measurements are limited by low efficiency, limited spatial coverage, and high measurement error. LiDAR and RGB-D imaging offer cost-effective, precise, and non-destructive techniques for plant distance and volume measurements. This study aimed to measure cabbage height, volume, and distance using LiDAR and RGB-D imaging. The sensors were mounted on a 1.6 kW electric field scouting platform (EFSP) for data collection. Point cloud (PCD) data were collected using LiDAR, whereas data processing, visualization, and measurements were done using commercial software and open-source programming scripts. A total of 20 cabbage plants were analyzed. LiDAR data processing included data frame screening, outlier removal, denoising, voxelization, and generation of 3D PCD density maps. Depth image processing included importing raw data and metadata shaping using intrinsic camera parameters, visualization, extraction of depth points, and pixel-level measurements of distances and volume. RGB image processing involved image conversion, segmentation, normalization, binary masking, mask cleaning, region extraction of cabbages, separation of ROI and preparation of contours, Delaunay triangulation and convex hull preparation, ROI overlay, bounding box preparation, sharing boundary between two boxes, conversion to pixel distances, and for visualization, plant height, volume measurements, and center to center distance measurement for measuring the plant distance. LiDAR demonstrated higher measurement accuracy for cabbage plant height, circumferential volume (geometric canopy volume), and plant distance, followed by RGB-D imaging, while RGB imagery showed comparatively lower performance under the study field conditions. Overall, LiDAR and RGB-D imaging provided reliable and non-destructive approaches for cabbage geometric characterization under field conditions, although accurately capturing complex plant geometry remains challenging. Positive and negative values of bias represent the over- and under-estimated results, respectively. Future studies should include larger and more diverse plant datasets exhibiting diversified size, shape, and geometric structure to further improve the robustness and general applicability of the proposed sensing approaches. Full article

Complex geometries of additively manufactured (AM) metal parts present challenges for rapid post-build part qualification with most conventional nondestructive measurement techniques. This study explores the potential of nonlinear reverberation spectroscopy (NRS) for detecting defects in parts with complex geometries. It focuses on measurements of two CoCrMo hollow pentagonal star-shaped specimens with and without designed-in mesoscale cavities and trapped unmelted powder. Nonlinearity and loss of the specimen with cavities are found to be greater than those of the specimen without cavities, and these differences are found to increase after six years of aging at room temperature. Different behavior of the two specimens with respect to aging and extended acoustic excitation lead to the conclusion that the dominant sources of nonlinearity and loss in the two specimens are different: contacting unbonded internal interfaces in the specimen with cavities, and dislocations in the other specimen. This conclusion is partly supported by calculations showing that the time scale of an aging-induced decrease in nonlinearity of the specimen without cavities is consistent with expected rates of migration of vacancies and nitrogen interstitials to dislocations. The results support the idea that NRS measurements would be useful for rapid nondestructive qualification of AM parts with complex geometries. Full article

Metals are widely used in various types of structural applications such as the automotive, aerospace and construction industries. However, their service life is limited due to the various loads they experience during operation. Specifically, cyclic loading can lead to the early fatigue failure of these structures. Therefore, early detection of fatigue deformation is essential to prevent catastrophic failures. In this study, an automated electrical resistance data acquisition system was developed using LabVIEW to obtain measurements from a Keithley 6221 current source for fatigue damage detection. The results showed an increase in electrical resistivity after the application of cyclic loading. It was observed that electrical resistivity increased after each set of loading cycles, with an average increase of 7.38%, a stress level of 260 MPa (high-cycle fatigue), and a 6.5% increase after the application of 25,000 cycles at 165 MPa (low-cycle fatigue). Scanning Transmission Electron Microscopy (S/TEM) was used for microstructural investigation as a proof of concept for the high-cycle fatigue sample interrupted after 25,000 cycles to analyse the modification in dislocation structures as well as a qualitative increment in the dislocation density with respect to the initial microstructural state of the as-machined sample. Such a modification in dislocation structures as well as an increment in dislocation density corroborates the findings proposed by electrical resistivity measurement. The results demonstrated that electrical resistivity measurement provides a promising non-destructive approach for the early detection of fatigue damage in metallic materials. Full article

Ground-penetrating radar (GPR) is a rapid and non-destructive technique for road sub-surface distress (RSD) detection. However, reliable interpretation of GPR images remains challenging because subsurface anomalies often present weak boundaries, ambiguous textures, and high similarity to non-distress targets. This study proposes a cross-verification intelligent algorithm that exploits complementary information from different views of 3D GPR data. Three YOLO-based detectors are trained on view-specific GPR images to identify RSD-related targets, including voids, loose structures, and manholes. By sequentially verifying detection results across different views, the proposed method improves recognition reliability under vague subsurface imaging conditions. The models are trained and evaluated on an expert-annotated field 3D GPR dataset containing 2134 location-level multi-view samples. At the selected operational thresholds, the complete cross-verification procedure achieved 95.9% precision and 98.6% recall for RSD detection in the testing subset. In a field evaluation on 15 roads, all 69 RSD locations in the expert-identified reference set were matched by automatic indications. When integrated into an automatic detection system, the method reduced manual inspection workloads by approximately 90% while maintaining high field reliability. These results demonstrate the potential of multi-view cross-verification for post-survey RSD screening and expert-assisted review. Full article

Macadamia (Macadamia spp.) is an economically important nut crop whose severe recalcitrance to genetic transformation substantially hinders progress in functional genomics and molecular breeding. To overcome this critical technical bottleneck, this study established a highly efficient and broadly applicable in planta hairy root genetic transformation system with integrated visual screening. This system utilizes an Agrobacterium rhizogenes-mediated transformation method, employing multiple visual reporter gene systems (DsRed2, eGFP, RUBY, and AtPAP2) to achieve antibiotic-independent and non-destructive screening of transgenic roots. Notably, the system innovatively incorporates the air layering (marcotting) technique to extend in planta genetic transformation to branches of mature trees in the field. By circumventing the stringent sterile conditions required for conventional in vitro tissue culture, this approach achieves a largely genotype-independent transformation across open-pollinated seedlings with diverse genetic backgrounds (A4, GR1, HAES900, and O.C.). The transgenic hairy root induction frequencies ranged from 39.25% to 47.38%, although the GR1 genotype exhibited a notable developmental stage-dependent decline in transformation efficiency. Furthermore, transgenic hairy roots were successfully induced on mature tree branches, with a maximum induction rate of 28.2%. Gene expression analyses confirmed the stable, high-level expression of the target transgenes in all the transgenic hairy root lines. This in planta transformation system provides a reliable in vivo experimental platform for the rapid functional validation of candidate genes and the investigation of root biology in Macadamia. Moreover, it establishes a novel strategy for plant regeneration via root-to-shoot organogenesis, offering a promising avenue for the genetic improvement of recalcitrant woody plants. Full article

Kinetic shock triggers thigmomorphogenetic responses in plants, posing both an unintended handling risk and a deliberate technique to enhance ornamental value. Detecting its immediate, non-visual impacts remains a challenge. This study used non-destructive contact spectroscopy to detect short-term kinetic stress in three species with distinct leaf anatomies (Alocasia sp., Monstera deliciosa, Ficus elastica) under 20 s and 40 s stimuli. While a pooled global classification model failed due to anatomical variations masking the universal stress signal (70% accuracy), optimized species-specific models revealed distinct dynamics. At 0 min post-stress, high variance and low separability occurred across all species. However, a diagnostic change emerged within 30 min for Alocasia sp. (79.37%) and Ficus elastica (77.50%); the same tendency could not be confirmed for Monstera deliciosa, and further investigation is needed to support this pattern. Ficus elastica’s distinct architecture also proved consistently the least sensitive of three species in the pooled control-versus-treated comparison. A significant dosage effect was captured only in Alocasia sp. (87.50% accuracy). Spectral index analysis showed the dominance of the Normalized Difference Water Index (NDWI), suggesting the change reflects micro-structural leaf turgor modifications rather than biochemical changes. Results indicate that leaf spectroscopy has the potential to diagnose transport injury and monitor conditioning. Full article

Terahertz (THz) waves offer unique advantages, including non-contact operation, high penetration capability, and high resolution, making them particularly well-suited for the non-destructive thickness measurement of film-structured materials. In reflective terahertz time-domain spectroscopy (THz-TDS), thickness measurement approaches are generally classified into three categories: optimization-based methods that rely on theoretical models, time-of-flight (ToF), and machine learning. Model-based optimization techniques require precise knowledge of the optical parameters and structural configuration of each layer; however, they often suffer from slow convergence and are prone to becoming trapped in local optima. In contrast, ToF-based methods determine thickness by calculating the time delay between echo pulses reflected from different interfaces, yet their applicability is limited when the film thickness is extremely small. Machine learning, especially deep learning, enables the establishment of a direct, data-driven mapping between THz waveforms (or their extracted features) and the target thickness. Such approaches offer rapid inference, strong robustness to noise, and good adaptability to thin or structurally complex films, although their accuracy remains dependent on the quality of training data and the generalization capability of the model. In this study, high-fidelity THz waveform data generated via finite-difference time-domain (FDTD) simulations are utilized to conduct a comparative investigation into the film thickness prediction performance of several representative machine learning algorithms, including Back Propagation (BP) neural networks, Support Vector Machines (SVM), Random Forests (RF), Extreme Learning Machines (ELM), K-Nearest Neighbors (KNN), and Partial Least Squares (PLS) regression. The results indicate that, in terms of prediction error, the overall ranking of algorithmic performance from best to worst is: PLS > RF > SVM > BP > ELM > KNN. These findings provide valuable guidance for the future application of machine learning-assisted THz-TDS in precise film thickness measurement. Full article

Existing bridges represent a significant challenge for structural engineering due to aging, increased traffic demands, and lack of original design information. This paper presents an investigation of the structural behavior of the Miguel Hidalgo Bridge, located in Culiacán, Sinaloa, México, which has remained in service for over a century. A preliminary visual inspection identified deterioration mechanisms, including cracking, exposed reinforcement, damaged bearings, and localized structural deformations. Given the absence of original design documentation, a structural health monitoring (SHM) strategy was implemented to assess its condition. The SHM methodology integrates field instrumentation using accelerometers for operational modal analysis under ambient vibration and GPS receivers for displacement measurements, complemented by a geometric survey and material characterization through non-destructive techniques. These data were used to develop and calibrate a three-dimensional finite element (FE) model, which was employed to evaluate structural performance under service loads in accordance with AASHTO provisions. Results indicate that the bridge satisfies serviceability criteria; however, certain components exhibit deficiencies in load-carrying capacity. These findings highlight the importance of integrating monitoring data with numerical modeling to support decision-making related to maintenance, rehabilitation, and extension of service life in existing bridge infrastructure. Full article

Nonlinear acoustics has emerged as a sensitive and promising non-destructive approach to damage detection in concrete structures. This study proposes a method based on nonlinear acoustics that uses collinear mixing ultrasonic waves to detect and locate defects (microcracks, macrocracks, spalling) in concrete. The method consists of analyzing the interaction between a low-frequency pump wave and a high-frequency probe wave, both travelling along the width of the block in opposite directions. This interaction produces a measurable time-of-flight shift in the probe wave, which accumulates along the sampling path. A nonlinear parameter (NL), defined as the spatial gradient of the normalized time shift, is introduced to suppress cumulative propagation effects and improve damage localization. The proposed technique is tested to detect and locate a thermally damaged concrete prism (0.25 × 0.08 × 0.15 m³) cast inside a sound concrete block (0.40 × 0.40 × 0.70 m³). The results enabled the detection, localization, and characterization of the damaged concrete prism with a sensitivity beyond that of conventional linear ultrasound. The effectiveness of the method is therefore validated, and it demonstrates its potential for damage monitoring of structures in civil and nuclear engineering applications. Full article