Search Results (4,035)

Girth welds are susceptible to defects under high internal pressure and stress. While phased array ultrasonic testing (PAUT) is widely used for non-destructive evaluation, manual inspection remains inefficient and highly dependent on expertise. Furthermore, existing deep learning models often struggle with low accuracy and high complexity. This paper proposes a PAUT defect classification method based on YOLOv8. First, median filtering is employed for denoising, and the results show that noise is effectively reduced while preserving key features, achieving PSNR values of 35.132, 35.938, and 36.138 for slag inclusion, pores, and lack of fusion (LOF), respectively. Subsequently, the spatial enrichment algorithm (SEA) is applied to enhance image details without amplifying noise, yielding a PSNR of 33.71 and an SSIM of 0.96. Finally, the YOLOv8 model is implemented for defect recognition. Experimental results demonstrate that the proposed approach achieves a superior balance between precision and recall with high reliability. This method offers a robust and efficient solution for automated PAUT evaluation in practical engineering applications. Full article

The mechanical properties of stone masonry and its behavior under monotonic and cyclic loading depend significantly on the local properties of the masonry and the wall typology. This paper presents preliminary results from in situ inspection of stone masonry typologies at several locations in the Kvarner Littoral of Croatia, which revealed the use of earth mortar in a building over 200 years old instead of the commonly used lime mortar. This finding prompted the selection of this building as a case study, for which a detailed visual survey was conducted and laboratory testing employed to characterize the masonry components. The visual inspection showed that the walls of the case study building are constructed from non-degraded stones, with wedges between the blocks and larger corner blocks. The earth mortar is degraded on the wall surface, so non-destructive testing was unsuccessful. Laboratory tests on stone specimens confirmed high compressive strength (over 135 MPa), while laboratory tests on earth mortar specimens indicated compressive strength between 2.22 and 2.65 MPa. The stone compressive strength is comparable to that of high-quality Croatian limestones, while the compressive strength of the earth mortar is comparable to that of historic lime mortars. Microscopic analysis and FTIR spectroscopy of the earth mortar revealed that it does not contain sand or gravel, what distinguishes it from commonly used historic earth mortars, where clay minerals serve as a binder for sand and silt particles. This study presents the first comprehensive research on the material properties of an earth mortar in Croatia. Full article

Ensuring the structural integrity and service reliability of railway wheels has become a key challenge in modern manufacturing and maintenance strategies within the railway sector. In this context, Eddy Current (EC)-based Non-Destructive Testing (NDT) provides an automated and efficient approach for detecting surface and near-surface defects, while reducing inspection time and operator dependency compared to conventional manual methods. This study presents the integration of an EC inspection system into a precision lathe, enabling in-machining evaluation during wheel turning. Experimental validation was conducted on wheels with artificial defects, yielding high signal-to-noise ratios and enabling reliable defect characterization. Furthermore, computationally efficient and easily deployable machine learning algorithms were developed to enable automatic defect detection, localization, and size estimation. The results confirm the feasibility of in-machine EC inspection during machining operations, enabling early defect detection and contributing to safer, more efficient, and higher-quality manufacturing processes in the railway sector. Full article

High-strength bolts are critical structural components that are highly susceptible to corrosion in complex environments, posing significant threats to structural safety and reliability. Although acoustic emission (AE) technology has been widely applied in structural health monitoring, existing studies mainly focus on damage mode identification or source localization, while the identification of corrosion evolution stages based on AE signals remains insufficient. This study develops an intelligent corrosion diagnosis framework for high-strength bolts by integrating multimodal feature fusion and optimized machine learning. AE signals are first collected from the near-end and far-end of bolts using a wireless sensor network and then transformed into time–frequency representations via continuous wavelet transform (CWT). The resulting time–frequency images are fed into a modified ResNet-18 network to extract deep features, while statistical features are simultaneously extracted from the raw signals to preserve global information. These heterogeneous features are subsequently fused to form a comprehensive representation of corrosion characteristics. Furthermore, an artificial protozoa optimizer (APO) is introduced to adaptively optimize the hyperparameters of the XGBoost model. The results demonstrate that AE signals generated by hammering bolts with different corrosion levels can be successfully distinguished. The proposed method achieves high accuracy in corrosion stage classification and outperforms conventional approaches. Even when evaluated on an additional M30 bolt dataset, the proposed method maintains robust performance, demonstrating excellent generalization capability across different bolt sizes. These results demonstrate the practical potential of the proposed method for intelligent bolt corrosion diagnosis. Full article

Aphanomyces root rot is a major threat to legume production worldwide, mainly in pea and lentil, crops on which extensive research programs are targeting the management of the disease. However, other legumes such as common vetch, although known to be severely affected by the disease, remain largely unexplored. This study aimed to identify sources of resistance within V. sativa subsp. sativa accessions. A total of 211 genetically diverse accessions were screened under controlled conditions following inoculation with isolate RB84. Disease progression was monitored through periodic foliar assessments and final root symptom evaluation. To assess resistance stability, a subset of 13 accessions representing contrasting response levels was further inoculated with three additional isolates (Aph-1, AE11, and AE12). In this multi-isolate assay, disease severity was quantified, shoot biomass was recorded, and root system architecture traits were determined using WinRHIZO image analysis. A high correlation between foliar and root symptoms at 20 days indicated that foliar symptom assessment provides a reliable, non-destructive indicator of root health. Considerable variation in disease response was detected, with several genotypes maintaining consistently low symptom levels and three exhibiting near-complete resistance across all isolates. Root architectural traits further corroborated visual disease assessments, showing patterns consistent with resistance and susceptibility responses. Overall, this study demonstrates the presence of genetic variability in the response of V. sativa to A. euteiches, with a subset of accessions showing resistance to the four isolates tested. This resistance potential can be directly used in breeding programs focused on improving tolerance to root rot. Full article

As one of the six major traditional tea types in China, white tea’s quality formation is primarily influenced by the withering process. However, traditional methods for monitoring withering fail to achieve precise and stable control of moisture content. To address this issue, a total of 650 samples were collected at 13 withering time points (0–36 h), and the dataset was split into training and test sets at a 7:3 ratio. This study proposes a PRXBoost ensemble model for quantitative detection of withered white tea, which integrates data augmentation and intelligent algorithms. The ensemble model uses a Bagging-based weighted integration technique to combine Partial Least Squares Regression (PLSR), Ridge, and Extreme Gradient Boosting (XGBoost) models, and it conducts an in-depth analysis of the decision-making process within the PRXBoost model. First, the effectiveness of the data augmentation strategy and the superiority of the gradient descent algorithm are verified through pre-modeling based on the PLSR model and hyperparameter pre-search using the XGBoost model, respectively. Additionally, the Bayes algorithm is employed to optimize the weights of the sub-models, further enhancing the overall predictive performance. The results show that the PRXBoost model achieved the best performance among the compared models on the test set, with R² = 0.854 and RMSE = 0.080, exceeding the highest R² of a single model by 6%. These results indicate that PRXBoost provided improved predictive performance for moisture estimation within the current dataset. Finally, the SHapley Additive exPlanations (SHAP) algorithm is used to analyze the influence of each input feature on the prediction results, successfully identifying the 1916 nm and 1453 nm spectral bands as significant influencers of the prediction outcomes. These results suggest that the proposed model can support rapid, non-destructive monitoring of moisture evolution and provide actionable information for withering endpoint decision control. Full article

Acoustic emission (AE) and digital image correlation (DIC) techniques enable real-time capture of damage signals and full-field deformation at anchored rock–concrete interfaces under shear loading, which is critical for quantitatively characterizing freeze–thaw (F-T) degradation and preventing geological disasters in cold regions. This study synchronously monitored full-shear-process AE signals using a broadband AE system (150 kHz resonant frequency, 5 MS/s sampling) and captured high-precision full-field deformation via a 5-megapixel monocular DIC system (25 fps). F-T cycle and direct shear tests were conducted on sandstone–concrete anchored specimens with varying F-T cycles and anchor depths to investigate their effects on shear mechanical properties, AE characteristics and failure modes. Results show that AE peak ring count first decreases by 44.9% then increases by 56.5%, while cumulative ring count exhibits a three-stage evolution. Shear crack proportion first decreases then increases, with tensile failure remaining dominant throughout. DIC reveals that F-T cycles shift failure from crack propagation to surface delamination and interface slip, while different anchor depths induce distinct failure patterns. This study confirms that AE and DIC can accurately characterize F-T degradation, providing a reliable non-destructive monitoring method for cold-region anchorage engineering. Full article

Capacitive micromachined ultrasonic transducers (CMUTs) have been developed over the past 30 years and achieved practical applications in both medical imaging and industrial non-destructive testing. This article presents the fundamental principles of CMUTs and surveys fabrication technologies, offering a comprehensive review of major advances and challenges in medical ultrasound and photoacoustic imaging applications. The article further reviews and analyzes three primary challenges currently confronting CMUTs in medical imaging applications: lower output acoustic pressure, dielectric charging effects, and the need for high bias voltage. It also presents and discusses a potential combined approach to comprehensively address these challenges, with the aim of enhancing CMUT performance and broadening clinical adoption. Full article

Ensuring the durability and safety of modern infrastructure critically depends on the quality and strength of concrete. The Ultrasonic Pulse Velocity (UPV) method is a widely used non-destructive testing technique for evaluating concrete properties; however, factors such as aggregate size distribution, compaction methods, and surface quality can significantly influence UPV results and their correlation with compressive strength. This study investigates the effects of different aggregate sizes and an innovative vibration-assisted compaction method—developed using piezoelectric (PZT) transducers—on the mechanical, ultrasonic, and surface properties of concrete. Four distinct aggregate size distributions were employed to produce sixteen concrete specimens with constant mix proportions. Unlike conventional low-frequency, high-power vibration practices, a high-frequency (40 kHz), low-power (120 W) vibration protocol was applied through PZT elements placed within the molds to enhance compaction and reduce entrapped air. Experimental results indicated that the heaviest specimen (7.13 kg) was the medium-aggregate sample compacted using tamping and rodding methods. The highest UPV value (4143 m/s) was obtained from the coarse-aggregate specimen subjected to three minutes of vibration. In contrast, the best compressive strength performance (22.73 MPa) was observed in the medium-aggregate specimen without any vibration treatment. The findings revealed that both aggregate size and advanced vibration techniques have significant effects on the mechanical properties, ultrasonic response, and surface quality of concrete. In addition, a proof-of-concept portable surface-finishing prototype consisting of a steel plate instrumented with multiple PZT transducers was developed, and preliminary trials qualitatively suggested improved surface leveling when applied in contact with the concrete surface. Surface roughness was quantified via image processing (Light Map 150 and Specular Map 150). The rough-area fraction decreased from ~29.8% in the untreated specimen to ~4.3% after ultrasonic application, indicating a marked improvement in surface leveling and overall surface quality. The results indicate that the applied PZT vibration protocol did not improve compressive strength; in several cases, particularly under prolonged excitation, a reduction in strength was observed. In contrast, a significant improvement in surface quality was achieved, with the rough-area fraction decreasing from approximately 29.8% to 4.3%. However, due to the limited number of specimens, the findings should be interpreted as preliminary. Overall, the method appears more promising as a surface enhancement technique rather than a direct alternative to conventional compaction methods. Full article

S. Tomé and Principe (STP) islands have been studied in recent years for their wide range of medicinal plants which exhibit several biological activities of great medicinal interest for some diseases. Experimental design for optimization of several parameters was carried out by a full-factorial test of two levels of three factors for secondary metabolite extraction from Rauvolfia caffra leaves. The best conditions for highest extraction of phenolic compounds (i.e., 89.90 μmoles gallic acid equivalent/g leaves) were obtained at 25 °C in H₂O and at 5 days of incubation. Several phytochemical assays were performed for characterization of these plant extracts, and the highest levels of TFC, DPPH and reducing power were obtained with aqueous plant extraction at 25 °C and for 5 days of incubation, whereas leaf extraction with water at 40 °C for 5 days of incubation revealed the highest levels of ABTS scavenging activity. The levels of SOD and superoxide radical scavenging activities were highest in plant extraction, with hexane at 25 and 40 °C for 5 days of incubation, respectively. The present report consists of a novel and intrinsic synchronous fluorescence and phosphorescence characterization of secondary metabolites from this plant extract. Intrinsic and non-destructive synchronous fluorescence was carried out in the range of 250 to 750 nm with a Δλ range of 5–30 nm, which exhibited several fluorescence peaks in hexane and aqueous plant extracts. On the other hand, intrinsic and non-destructive synchronous phosphorescence was also performed which also exhibited several peaks in aqueous and hexane extracts. 3D spectra of secondary metabolites confirmed the fluorescence peaks observed in SFS in plant extracts. FTIR spectroscopy was selected to investigate the structural properties of secondary metabolites in these plant extracts. Therefore, the present work describes a novel characterization of secondary metabolites by a non-destructive and intrinsic synchronous fluorescence technique for plant extracts. Full article

Composite materials are critical components in advanced equipment such as aerospace, however, delamination defects that readily arise during manufacturing and in service present serious risks to equipment safety. Terahertz non-destructive testing is highly effective for analyzing the internal structure of composite materials, making it an effective approach for precise identification of delamination defects. Current terahertz detection approaches mainly depend on single domain features, making it difficult to capture complementary information from both the time and frequency domains. To address this, a Time-Frequency Feature-fusion Network (TFFN) is proposed. In this network, a three-branch architecture is employed: local transient patterns and pulse-related structural features are extracted by the local time-frequency branch; damage-sensitive frequency bands are focused on by the frequency-domain branch through a channel-space-frequency band attention mechanism; and deep integration of time-frequency features is achieved by the time-frequency fusion branch using Manifold Mixup. Finally, the features extracted from the three branches are adaptively fused via a cross-branch attention mechanism, and defect identification is accomplished by the classifier. Experimental results show that this method achieves accuracies of 98.40% on the glass fiber reinforced polymer (GFRP) dataset and 98.63% on the quartz fiber reinforced polymer (QFRP) dataset, surpassing the best existing method by 2% and 1.25%, respectively. A substantial improvement in both defect identification accuracy and the model’s generalization ability for layered structures is thereby achieved. Full article

Type 1 diabetes (T1D) is an autoimmune disease characterized by T-cell-mediated destruction of pancreatic β-cells. Antigen-specific peptide immunotherapy represents a promising strategy to restore immune tolerance. Reliable identification of relevant T-cell epitopes requires accurate prediction of peptide binding to disease-associated major histocompatibility complex (MHC) molecules. In this study, we developed and validated artificial intelligence (AI)-driven machine learning (ML) predictive models for peptides binding to the NOD mouse-specific MHC class I molecules H-2D^b and H-2K^d and the class II molecule I-A^g7. Balanced datasets of experimentally validated binders and non-binders were compiled, divided into training and test sets, and used to construct position-specific logo models and supervised ML classifiers based on z-scale physicochemical descriptors. External validation demonstrated moderate predictive performance for the logo models (ROC AUC 0.685–0.738), whereas AI models, including Random Forest, Support Vector Machine, and Gradient Boosting, achieved substantially improved discrimination (ROC AUC 0.888–0.906). The validated models were applied to the major T1D autoantigens glutamic acid decarboxylase 65, insulin-1, insulin-2 and zinc transporter 8 and predicted multiple binders, with some overlapping with previously reported immunodominant regions. Selected binders were prioritized for further synthesis and in vivo immunogenicity testing in NOD mice. Full article

To investigate the aging behavior of high-temperature-vulcanized silicone rubber (HTV-SiR) used in composite insulator sheds under coupled electrical, thermal, humidity, and mechanical stresses, accelerated aging tests were conducted to emulate the service conditions of ±800 kV ultra-high-voltage direct current (UHVDC) systems in Guangzhou, China. The physicochemical, mechanical, and electrical properties of the specimens were systematically characterized. The results show simultaneous degradation of both electrical and mechanical performance. In particular, the tensile strength exhibits a significant monotonic decrease and drops to 49.52% of its initial value under the most severe condition (0.5 kV·mm⁻¹ and 5% tensile strain) after 75 days. In contrast, the DC breakdown strength shows a non-monotonic “rise-then-fall” trend and decreases more markedly with increasing tensile strain. To address the one-shot and destructive nature of tensile testing and the associated statistical uncertainties, a lifetime prediction framework was developed by integrating a generalized Eyring acceleration relation with a stochastic degradation process. Under representative service conditions of 0.09 kV·mm⁻¹ and 0.2% tensile strain, the predicted lifetimes corresponding to failure probabilities of 10%, 75%, and 90% are 1.77, 9.08, and 17.90 years, respectively. The applicability of the model is supported by field-aged specimens. These findings provide a mechanistically grounded and reliability-oriented basis for condition assessment, lifetime-margin evaluation, material screening, and maintenance planning of UHVDC composite insulators operating in hot–humid environments. Full article

Perovskite solar cells (PSCs) have quickly achieved certified energy conversion efficiency reaching a certified record of 27.3% for single-junction cells, while having a low mass, thin-film form factor and high specific power, which are attractive for space energy systems. However, their long-term reliability in extraterrestrial environments is not adequately ensured by terrestrial qualification routes, and standardized space-related test protocols remain insufficiently developed. This review critically summarizes the current understanding of the degradation of PSCs under the influence of key environmental factors in space—ionizing and non-ionizing radiation, thermal vacuum exposure and thermal cycling, and ultraviolet radiation AM0, as well as atmospheric oxygen in low orbits. The central task of the work is to develop and justify the need to create specialized PSCs test protocols for space applications, since existing ground standards do not reflect the multifactorial nature and extreme orbital loads. It has been shown that thermal vacuum accelerates ion migration, interphase reactions, and degassing, while AM0 UV and atomic oxygen introduce additional photochemical and oxidative mechanisms of destruction; at the same time, stressors often act synergistically and are not detected by single-factor tests. Next, the limitations of the current IEC and ISOS are discussed and an approach to their expansion is formulated through the ISOS-T-Space and ISOS-LC-Space protocols, which integrate high vacuum, AM0 lighting, extended temperature ranges and controlled particle irradiation. It is concluded that the development and interlaboratory validation of such space-oriented protocols is a key condition for the correct qualification of PSCs and targeted optimization of materials and interfaces to meet the requirements of space energy. Full article

Historic structures that possess cultural heritage value are important documents that convey the architectural understanding, material technology, and construction techniques of past civilizations to the present day. However, these structures are exposed over time to physical, chemical, and mechanical deterioration due to environmental effects, climatic conditions, the natural aging processes of materials, and human interventions. The conservation and faithful restoration of historic structures necessitate the scientific determination of the properties of original building materials. In this study, we aimed to determine the physical, chemical, mineralogical, thermal, and mechanical properties of the original building materials used in the Old Armenian Church located in the city of Çanakkale. In order to reveal the chemical and mineralogical compositions of the samples, XRD, SEM, Raman, and FTIR analyses were applied. The thermal behaviors of the materials were examined through TGA. To determine the physical properties, tests for unit volume weight, specific gravity, compactness, porosity, and water absorption capacity were carried out. For the determination of mechanical properties, compressive strength tests—as well as non-destructive testing methods such as the Schmidt hammer and UPV measurements—were employed. The analysis results indicate that the materials used in the structure have a carbonate-based mineralogical composition and that calcite-bonded systems are dominant. While the physical and mechanical data reveal that the materials possess a compact internal structure, they also indicate that microcracks and weathering processes may be effective in certain areas. These findings emphasize the importance of using lime-based mortars and stones compatible with the original materials in restoration works. Full article