Search Results (2,536)

Eddy-current inspection of anisotropic composites, such as aeronautical CFRP, demands models that ensure mathematical rigor, tensorial consistency, and clear energetic interpretation. This work presents a novel unified variational framework with a multiplicative tensor perturbation for the time-harmonic eddy-current problem in anisotropic media with defective regions. The formulation is posed in the natural spaces $H(\mathrm{curl};\Omega )\times H^1\left({\Omega }_c\right)$, and the well-posedness is established via the Lax–Milgram theorem under physically consistent assumptions on permeability and conductivity. The sesquilinear form admits a Hermitian decomposition that separates dissipative and reactive contributions, revealing the energetic structure of the weak formulation. Defects are modeled through multiplicative modifications of the baseline anisotropic conductivity tensor. This congruence-based approach preserves symmetry and positive definiteness, ensuring non-negative Joule losses and structural stability, allowing a modular representation of subsurface delamination, fiber breakage, conductive inclusions, and distributed porosity within a single tensorial framework. A central result of the present formulation is the reconstruction of the complex power functional from the evaluation of the weak form at the solution, showing that the active dissipated power and the magnetic reactive power arise directly from the same integral terms. Through the complex Poynting theorem, the quadratic form is linked to the internal complex power, establishing a direct connection between the variational formulation and measurable quantities such as probe impedance variations. Simulations of realistic layered CFRP configurations, including single- and multi-defect scenarios, confirm that, compared with additive perturbations, the multiplicative model provides enhanced energetic contrast, particularly in strongly anisotropic and interacting defect conditions. Agreement with experimental measurements, supported by a quantitative comparison of dissipated power variations obtained from controlled EC experiments, corroborates the physical relevance and robustness of the proposed complex power functional. Full article

Computed tomography (CT) enables non-destructive insight into internal log structure, yet fully automated interpretation of CT images remains limited by inconsistent annotations, boundary ambiguity, and insufficient spatial context in 2D slice-based analysis. These challenges restrict the industrial deployment of deep learning for wood quality assessment. This study applies artificial intelligence (AI) and deep learning to the automated analysis of computed tomography (CT) scans of wood logs for detecting internal qualitative features and segmenting bark. Using convolutional neural networks (CNNs), trained models accurately distinguish healthy and damaged regions and segment bark, including discontinuous parts. We introduce a novel pseudo-spatial representation by merging consecutive slices into red–green–blue (RGB) format, which improves prediction accuracy and model robustness across logs. To enhance interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) highlights regions contributing most to defect detection, particularly knots. Comprehensive evaluation using Sørensen–Dice similarity coefficients and confusion matrices confirms the effectiveness of the proposed approach under industrial conditions. These findings demonstrate that AI-driven CT image analysis can address key limitations of current log-grading workflows and enable more reliable, objective, and scalable quality assessment for timber-dependent economies. Full article

Bothriochloa ischaemum is a key forage species with strong grazing tolerance and high nutritional value, making precise quantification of spike and seed traits essential for germplasm evaluation and yield prediction. However, the compact architecture and minute seed size in natural field conditions render manual counting inefficient and labor-intensive. To address this limitation, this study presents a non-destructive and automated quantification framework integrating advanced object detection and regression analysis for accurate in situ estimation of spikes and seed numbers. To further address the challenges of dense spike detection caused by occlusion and small object sizes, this study developed a modified model named YOLOv12-DAN by integrating DySample dynamic upsampling, ASFF feature fusion, and NWD loss, which achieved a mean average precision (mAP) of 91.6%. Meanwhile, for the detection of dense kernels on compact spikes, an improved YOLOv12 architecture incorporating an Explicit Visual Center (EVC) module was proposed to enhance multi-scale feature representation. The optimized model attained a bounding box precision of 96.5%, a recall rate of 86.4%, an mAP50 of 94.3%, and an mAP50-95 of 73.9%. Furthermore, a univariate linear regression model based on 132 spike samples verified the reliable consistency between the predicted and actual seed counts, with a mean absolute error (MAE) of 6.30, a mean absolute percentage error (MAPE) of 9.35, and an R-squared (R²) value of 0.808. Finally, the model was deployed through a lightweight end-to-end web application, enabling real-time field operation and promoting its applicability in breeding programs and agronomic decision-making. This study provides a robust technical pathway for automated phenotyping and precision forage improvement. Full article

Accurate quantification of cotton content is a key prerequisite for efficient textile recycling. However, it remains challenging due to material heterogeneity and technical limitations. Near-infrared spectroscopy (NIR) combined with advanced data analysis offers a rapid, non-destructive approach. However, systematic evaluations across instrument classes and analysis strategies for industrial textile sorting remain limited. In this study, a unique set of cotton/polyester blends from the same starting material with varying cotton content was analyzed using three NIR systems representing laboratory, handheld, and industrial sensor-based applications. Multiple spectral preprocessing strategies were systematically combined with partial least squares regression and advanced machine learning models. Model performance was evaluated using cross-validation and independent test sets. The benchtop NIR system delivered the highest and most consistent performance, achieving RMSEP values below 1.0% with advanced regression models. The handheld and imaging sensor system exhibited higher RMSEP values (1.2–1.6%), reflecting not only differences in preprocessing and model selection, but also intrinsic instrumental limitations. Overall, the results demonstrate that each NIR instrument class exhibits distinct strengths and limitations with respect to accuracy, sensitivity, and robustness. Consequently, instrument-specific preprocessing, models, and hyperparameters are required, and no universally transferable pipeline was identified. Full article

The concept of self-healing polymers has been prevalent over the last few decades. However, their performance and behaviour in structural applications in the form of layered composites have not been studied extensively. In this study, an attempt has been made to evaluate the recovery of the mechanical properties of Carbon Fibre-Reinforced Polymer composites (CFRPs) with an intrinsically healable polymeric resin system. Destructive tests, including static tensile, compression, and flexural tests, are carried out to evaluate their ability to recover mechanical compliance after healing. Nondestructive tests based on the Acousto-Ultrasonic (AU) approach are carried out to establish and distinguish the state of these composites. The results show that the tested self-healing CFRPs can recover their mechanical properties, particularly their flexural and compressive properties, after unstable matrix damage. On the other hand, the AU approach, supported by Machine Learning (ML) models, demonstrates that the damaged states and the heal states of these composites can be distinguished from the virgin state. Full article

Welding is a critical joining process in civil and transportation infrastructure, enabling the fabrication of complex steel structural systems used in bridges, buildings, and other essential infrastructures. Despite strict adherence to established welding codes and standards, such as AWS D1.1 and AASHTO/AWS D1.5, welding flaws and service-induced defects can occur in welded components. Cause of defects and their structural impact, along with detection, sizing, and localization of these anomalies and flaws, are crucial for adequate maintenance, repair, or replacement planning without compromising the functionality of in-service components. Among available NDT techniques, ultrasonic testing (UT) remains one of the most widely adopted methods of weld inspection due to its depth of penetration, sensitivity to internal defects, and suitability for field deployment. Recent advancements in ultrasonic technologies, particularly Phased Array Ultrasonic Testing (PAUT), along with its emerging approaches such as Full Matrix Capture (FMC) and the Total Focusing Method (TFM), have significantly enhanced inspection accuracy, repeatability, and interpretability. These techniques enable flexile beam steering, multi-angle interrogation, and improved imaging of complex geometries. This paper presents a comprehensive review of PAUT for the inspection of welded steel infrastructure adhering to the recommendations and requirements of the relevant codes and standards, synthesizing the current literature on PAUT principles, wave modes, probe configurations, and data acquisition strategies. Emphasis is placed on the practical implementation of PAUT in civil infrastructure inspection, its advantages over conventional NDT methods, and its potential to support informed decisions related to quality acceptance, repair, and long-term maintenance planning. This paper concludes by identifying current challenges and future research directions for advanced ultrasonic inspection of welded steel structures. Full article

Seed varietal purity and physiological viability are critical determinants of crop yield and quality. However, non-destructive assessment faces significant challenges in fine-grained variety discrimination and the perception of internal defects. This study proposes S3-Net, an AI-driven multimodal sensing framework that integrates vision–language alignment with dual-spectral sensor fusion for autonomous seed quality evaluation. We introduce a Knowledge–Vision Alignment (KVA) module that incorporates encyclopedic morphological descriptions to guide feature learning, significantly enhancing few-shot generalization. Complementarily, a Dual-Spectral Fusion (DSF) module combines high-resolution RGB textures with penetrative Short-Wave Infrared (SWIR) sensing to jointly characterize external and internal traits. Experimental results on a custom multimodal dataset of 6000 samples across 12 crop categories demonstrate that S3-Net achieves 96.9% accuracy for species identification and 95.8% for viability detection. Notably, S3-Net outperforms ResNet-50 by 40.3% in extreme 1-shot scenarios. With a stable inference throughput of 95 fps, the system meets the high-throughput demands of industrial-scale applications, providing a robust and efficient solution for intelligent agricultural phenotyping. Full article

Digital phenotyping is increasingly recognized as an essential tool for the quantitative analysis of fungal morphology, particularly in controlled indoor cultivation systems where large numbers of fruiting bodies must be assessed consistently and non-destructively. While YOLOv8-based deep learning approaches have previously been applied in phenotypic analyses of edible mushrooms, the applicability of newer YOLO architectures to fungal phenotyping remains largely unexplored. In this study, we present a controlled-environment digital phenotyping framework for indoor mushroom cultivation and conduct a systematic benchmarking evaluation of YOLOv11 for phenotypic segmentation in comparison with YOLOv8. Using bottle-cultivated Pleurotus ostreatus and Flammulina velutipes as representative edible basidiomycetes, we performed a controlled comparison of YOLOv8-seg and YOLOv11-seg using identical datasets, preprocessing pipelines, and hyperparameter configurations. The results demonstrate that YOLOv11 achieves segmentation performance comparable to that of YOLOv8 across all evaluated metrics (ΔmAP_50–95 < 0.01) while substantially reducing computational complexity, including fewer trainable parameters, lower FLOPs, and decreased gradient load. Validation against caliper-based physical measurements revealed moderate, trait-dependent agreement, whereas inter-model consistency between YOLOv8 and YOLOv11 remained consistently high across diverse morphological and segmentation scenarios. These findings suggest that recent developments in object detection architectures can improve computational efficiency without compromising phenotypic measurement fidelity. More broadly, this study highlights the importance of periodically evaluating emerging detection architectures within biological phenotyping pipelines to ensure scalable, sustainable, and high-throughput fungal phenotyping under controlled-environment cultivation systems. Full article

The cheese maturation process involves complex physicochemical and structural changes that directly influence its final quality and consumer acceptance. The development of non-destructive and rapid analytical techniques is therefore essential for monitoring these changes and optimizing quality control strategies. This study evaluated the potential of dielectric spectroscopy and near-infrared (NIR) spectroscopy as tools to predict properties associated with the quality of Swiss-type cheese during the maturation process. The cheese samples were matured for 60 days, and NIR profiles (900–1700 nm), dielectric profiles (40¹–10⁶ Hz) and physical characteristics (color and texture) were obtained every 15 days. Based on these data, models were developed to predict the maturation time (days) and physical properties using partial least squares regression (PLSR). The performance of the model was evaluated using the determination coefficient ($R^2$) and the root mean square error (RMSE). The results showed that dielectric spectroscopy provided a better fit for all the parameters evaluated ($R_{\mathrm{day}}^2=0.999$, $R_{L^{*}}^2=0.912$, $R_{a^{*}}^2=0.983$, $R_{b^{*}}^2=0.982$, and $R_{\mathrm{firmness}}^2=0.625$), with prediction errors of ${\mathrm{RMSE}}_{\mathrm{day}}=0.219$, ${\mathrm{RMSE}}_{L^{*}}=1.184$, ${\mathrm{RMSE}}_{a^{*}}=0.163$, ${\mathrm{RMSE}}_{b^{*}}=0.308$, and ${\mathrm{RMSE}}_{\mathrm{firmness}}=91.094$. In conclusion, dielectric spectroscopy combined with PLSR showed slightly superior performance to predict maturation time and physical changes in Swiss-type cheese. Full article

Pipe networks assure the transportation of primary commodities such as water, oil, and natural gas. Quantitative and early detection of defects avoids costly consequences. Due to low cost of water, high-profile accidents, and economic downturns, the research and development of nondestructive evaluation (NDE) and structural health monitoring (SHM) technologies for freshwater mains and urban water networks have received less attention with respect to the gas and oil industries. Moreover, the technical challenges associated with the practical deployment of monitoring systems and the fact that most water pipelines are buried underground demand synergistic interaction across several disciplines, which may limit the transition from laboratory to real structures. This paper reviews the most prominent NDE/SHM technologies for freshwater pipes. The challenges that said infrastructures pose, as well as the methodologies that can be translated into SHM approaches, are highlighted. The scope of this review is to provide a holistic view of the physical principles, the success, and the technological challenges associated with the inspection and monitoring of freshwater pipelines. Full article

Acoustic imaging, especially ultrasound, underpins a wide range of applications from non-destructive evaluation to medical and materials analysis, yet its performance is ultimately constrained by lateral resolution. This review systematically summarizes recent advances in overcoming diffraction-limited resolution, encompassing traditional focusing techniques, transducer optimization, physical metamaterial lenses, and methods based on algorithmic optimization and deep learning technologies. It comprehensively covers approaches for enhancing acoustic lateral resolution, compares the differences and respective advantages and disadvantages of various methods, and proposes clear directions and recommendations for future research. This work provides robust guidance for subsequent research trends and development opportunities in higher-resolution acoustic imaging. Full article

The adulteration of wheat flour with borax poses a serious food safety risk, yet conventional rapid non-destructive screening methods remain limited. This study developed a machine learning-based microwave non-destructive semi-quantitative detection method for identifying borax adulteration in wheat flour. Using a proprietary microwave detection system, which acquires broadband frequency-domain amplitude attenuation and phase shift responses in the 2.5–11.5 GHz band, amplitude attenuation spectra and dimensional phase offset spectra were obtained from 155 samples prepared at three adulteration levels (0%, 0.1–0.9%, 1–5%). These samples simulated real-world adulteration scenarios. To address high-dimensionality and class imbalance, a hybrid Random Forest-Whale Optimization Algorithm (RF-WOA) was employed to synergistically optimize feature selection and model hyperparameters. Through hierarchical repeated validation and macro-level metric evaluation, this approach achieved an overall classification accuracy of 94.6% and a macro F1 score of 0.95 while compressing the original 1800-dimensional feature space to approximately 200 effective features. Confusion matrix analysis indicates 100% recall for undiluted samples, with misclassifications primarily occurring between adjacent adulteration levels and no false negatives introduced for adulterated samples. These results demonstrate that microwave sensing combined with the RF-WOA provides a rapid, non-destructive, and robust preliminary screening and grading evaluation strategy for borax adulteration in wheat flour, exhibiting significant potential in food safety monitoring and regulatory inspection. Full article

Reliable nondestructive evaluation of fine weave pierced carbon/carbon (C/C) composites is essential because these materials are increasingly used in critical components, yet ultrasonic inspection is often compromised by dispersion and frequency-selective filtering that distort waveforms and complicate imaging. This study aimed to experimentally characterize the anisotropic acoustic dispersion and frequency-filtering behavior of up-to-date fine weave pierced C/C composites with a pitch-based matrix. Phase velocities along the three principal directions (x, y, z) were measured over a frequency range of 0.5–5.0 MHz. Along the z-direction, phase velocity increases from 7250 m/s to 13,500 m/s with rising frequency, revealing four selective passbands. This indicates pronounced geometric dispersion and a wave-filtering effect due to the larger-scale fibers aligned in this direction. In contrast, the x- and y-directions exhibit only a single low-frequency passband dominated by the strong viscoelasticity of the matrix, with phase velocities of 8100 m/s at 0.5 MHz and 7100 m/s at 0.3 MHz, respectively. Furthermore, temperature-dependent measurements in the z-direction demonstrate a transition from viscoelastic-dominated to geometric-dominated dispersion as temperature increases. These results provide frequency-selection guidance for reliable ultrasonic nondestructive evaluation of advanced C/C composite components. Full article

Cotton above-ground biomass (AGB) is a key indicator of crop growth and yield potential. Traditional monitoring methods are labor-intensive and destructive, limiting their suitability for precision agriculture. This study developed a high-precision, non-destructive model for estimating cotton AGB by integrating spectral and texture features derived from UAV multispectral and RGB images. UAV data were collected at major growth stages in 2024. Eight vegetation indices (VIs) and eight texture features (TFs) were extracted. Four machine learning algorithms—support vector regression (SVR), random forest regression (RFR), partial least squares regression (PLSR), and extreme gradient boosting (XGB)—were evaluated using independent validation data. Models based on fused spectral and texture features outperformed single-feature models. RFR achieved the best performance (R² = 0.811; RMSE = 2.931 t ha⁻¹). Texture features alone also showed strong predictive capability (R² = 0.789), highlighting their value in capturing canopy structural information. These results demonstrate that spectral–texture fusion significantly improves cotton AGB estimation and that RFR provides a robust modeling framework for UAV-based crop monitoring. Full article

Steels remain among the most widely used structural and engineering materials in modern infrastructure, energy systems, and industrial facilities. Their long-term reliability depends critically on the early detection of corrosion damage and microstructural degradation. This review surveys recent advances in two complementary families of non-destructive evaluation (NDE) methods: magnetic techniques, including magnetic Barkhausen noise (MBN), magnetic flux leakage (MFL), eddy current testing (ECT), and magnetic hysteresis analysis; and electrochemical methods including electrochemical impedance spectroscopy (EIS), linear polarization resistance (LPR), scanning vibrating electrode technique (SVET), and electrochemical noise (EN). Recent progress in sensor miniaturization, signal processing algorithms, and multi-technique integration is reviewed. Particular attention is given to the sensitivity of these methods to microstructural changes reported in the literature, including carbide dissolution, phase transformations, temper embrittlement, and sensitization in stainless steels, as well as to the conditions under which such sensitivity has been demonstrated. The potential synergy between magnetic and electrochemical monitoring is discussed as a possible pathway toward more robust, condition-based maintenance frameworks. Challenges related to field deployment, environmental interference, calibration, and data interpretation are identified, and future directions—including machine learning-assisted analysis and multi-physics sensor arrays—are outlined. Full article