Search Results (225)

Opisthorchis viverrini (OV) is a liver fluke endemic to the Lower Mekong Basin. Infections often begin in childhood and are causally linked to cholangiocarcinoma, an often-fatal bile duct cancer. Anthelmintic treatment is the primary control strategy, but infection can recur. Therefore, additional strategies are needed. This study assessed the impact of “The Magic Glasses Opisthorchiasis” (MGO), a cartoon-based intervention, on schoolchildren’s OV-related knowledge, attitudes and practices (KAP). A cluster (school)-randomised controlled trial was conducted in Cambodia, Laos and Thailand. Clusters were randomised into either school health education only or with MGO. OV KAP was measured using a standardised questionnaire. FGDs and interviews were also conducted in intervention schools with schoolchildren, parents, and teachers. Cambodia intervention knowledge and attitude scores improved by 19.2 (p < 0.001) and 25.3 (p < 0.001) percentage points, respectively, relative to the control. Laos intervention knowledge and attitude scores improved by 19.0 (p < 0.001) and 14.2 (p < 0.001) percentage points. However, Thailand’s intervention knowledge and attitude scores declined by 23.3 (p < 0.001) and 15.8 percentage points (p < 0.001). There were no improvements in behaviour scores in any country, but parents and schoolchildren in Cambodia and Laos reported improved fish preparation practices, suggesting positive spillover effects from MGO. The findings support MGO as an effective tool for school-based health education. Full article

This study develops a Pore-Prior and Progressive-Sampling Transformer architecture, termed PPFormer, for the laboratory-scale analysis of microscopic remaining-oil images acquired from photolithographic glass-micromodel displacement experiments. The architecture integrates pore-prior embedding, progressive sampling of morphology-sensitive tokens, multi-scale self-attention encoding, relative position encoding, and boundary-enhanced decoding. PPFormer identifies five microscopic remaining-oil morphologies: cluster-like remaining oil, columnar remaining oil, droplet-like remaining oil, film-like remaining oil, and blind-end remaining oil. Under the investigated experimental conditions, the model achieved an overall pixel accuracy of 93.6%. The resulting morphology identification maps were used for pore-space-normalized area characterization and displacement-efficiency analysis under three permeability conditions and four displacement strategies. Relative to conventional waterflooding, the area-reduction ranges of cluster-like remaining oil, columnar remaining oil, and droplet-like remaining oil were from 2.29% to 12.66%, from −0.46% to 21.86%, and from 0.09% to 10.75%, respectively. Film-like remaining oil and blind-end remaining oil exhibited smaller changes, ranging from −0.50% to 8.19% and from −0.59% to 5.39%, respectively. Uncertainty was evaluated across independent replicate runs and by comparing predicted masks with consensus ground-truth masks. Full article

Filter paper-based sampling has been widely used for the collection, transport, and storage of biological samples. This review and meta-analysis aggregated the performance of commercial filter paper matrices for nucleic acid detection across human and veterinary viral pathogens. The review was conducted according to PRISMA guidelines using PubMed^®, Web of Science^®, and Scopus™ databases. Using eligible studies, nucleic acid detection rates were calculated as the number of PCR-positive filter paper samples divided by the total number of expected positive sampling units, based on direct testing or experimental design. Detection rates were analyzed using a multilevel meta-analysis of proportions with nested random effects to account for clustering within studies. A total of 145 studies representing 39 filter paper types were included. Cellulose-based matrices, particularly Whatman^® and FTA™ products, predominated in the literature, although polyester and glass fiber substrates were also represented. Detection rates varied widely by filter paper type (46.1% to 97.0%) and virus target (63.7% to 92.8%). Experimental conditions, including storage temperature, drying time, and humidity, were inconsistently reported across studies, but the findings indicated that filter paper composition and experimental conditions influenced viral nucleic acid recovery and detection. Overall, this review showed that the recovery and detection of viral nucleic acid from filter paper is variable. The review also highlighted the need for experimental designs providing rigorous comparisons of filter paper performance over a range of conditions. Full article

Accurate estimation of curvature radius from interference fringes is critical in optical metrology and precision manufacturing. Conventional interferogram analytical approaches often require manual intervention and are sensitive to fringe variations related to noise and environmental vibrations. To address these limitations, we combine an improved Twyman–Green interferometer with different artificial intelligence (AI) deep learning models and utilize a self-developed MATLAB analysis program to propose a non-destructive and rapid measurement system for optical coating substrates. The proposed AI-assisted Twyman–Green interferometric system differs fundamentally from conventional wavefront sensing techniques in both principle and implementation. This paper utilizes the Twyman–Green interferometer to generate interference fringe datasets on B270 glass and sapphire substrates, and employs convolutional neural network (CNN), ResNet-18, and VGG-16 models for training and evaluation. The proposed method integrates image enhancement, fringe pattern clustering, and analysis and validation based on fast Fourier transform (FFT). Experimental results show that ResNet-18 outperforms other models, with a mean absolute percentage error of 5.44% on sapphire substrates and 3.40% on B270 glass substrates. These findings highlight the effectiveness and robustness of deep learning models, especially residual networks, in automatic ROC prediction for optical measurement applications. Full article

Carbonate reservoirs exhibit complex combinations of pores, fractures, and vugs, and their strong heterogeneity makes pore-scale displacement mechanisms and recovery enhancement difficult to predict. In this study, six microfluidic glass-etched models representative of pore-type, vuggy, and fracture-pore carbonate reservoirs were designed from cast thin sections of the S oilfield. Experiments were conducted to investigate the effects of different factors on microscopic displacement behavior and residual-oil distribution. The results show that microscopic residual oil in carbonate reservoirs mainly occurs as film flow, droplet flow, columnar flow, multi-pore flow, and cluster flow, with cluster flow dominating the late stage of development in all model types. Under waterflooding, pore-type reservoirs exhibit the most uniform sweep and the highest recovery factor (44.26%), whereas vuggy reservoirs readily develop preferential flow channels and show the lowest recovery factor (41.58%). For fracture-pore reservoirs, injection perpendicular to the fracture provides the best performance, and wider or denser fractures improve displacement efficiency. Compared with gas flooding, waterflooding increases recovery by 10.48% in pore-type reservoirs and by 16.44% in fracture-type reservoirs. High-rate waterflooding and mid-stage flow diversion further improve recovery by 9.05–10.87% and 17.12–19.63%, respectively. These results provide pore-scale evidence for optimizing development strategies for carbonate reservoirs. Full article

This study investigates the acoustic emission (AE) response and damage mode characteristics of ±45° glass fiber-reinforced polymer (GFRP) composites used in wind turbine blade shear webs under quasi-static tensile loading. It aims to establish the relationship between AE features and three typical damage mechanisms—matrix cracking, interfacial debonding, and fiber fracture—to support damage assessment and structural health monitoring. Quasi-static uniaxial tensile tests with synchronous AE monitoring are conducted on specimens with three orientations (0°, 45°, and 90°). AE features are selected using correlation analysis and principal component analysis, and the HAC-initialized K-means clustering method is employed for damage mode identification. The optimal number of clusters is determined to be three, according to the Davies–Bouldin index (DBI) and the Silhouette index (SI). The resulting low-, mid-, and high-frequency clusters are associated with matrix cracking, interfacial debonding, and fiber fracture, respectively. These interpretations are further supported by wavelet-based time–frequency analysis and microscopic fracture surface observations. Full article

This study presents a calibrated analytical micromechanical framework for predicting the linear elastic behavior of unidirectional glass fiber/epoxy and carbon fiber/epoxy composites over a wide range of fiber volume fractions. The approach combines the classical rule of mixtures for the longitudinal Young’s modulus with the semi empirical Halpin–Tsai equations to estimate the transverse Young’s modulus and the in-plane shear modulus. The framework is specifically formulated to support durability-oriented composite design through rapid and physically consistent estimation of elastic properties governing load transfer and stress distribution. Material parameters, including fiber and matrix Young’s moduli (Ef, Em), shear moduli (Gf, Gm), Poisson’s ratios (νf, νm), and fiber volume fraction (Vf up to 0.80), are taken from established material property databases and implemented within a literature-informed modeling scheme. To preserve physical realism at high fiber contents, a shear correction factor is introduced for Vf > 0.50 to account for microstructural interaction and fiber clustering effects. The predicted effective elastic constants (E₁, E₂, G₁₂, ν₁₂) exhibit consistent and physically meaningful trends across the full fiber volume fraction range. The model predictions were evaluated against trends widely reported in the composite micromechanics literature, and the results showed overall agreement in the nonlinear reduction in stiffness gains at elevated fiber volume fractions. Comparative results indicate that carbon fiber/epoxy composites achieve up to approximately 30% higher stiffness than glass fiber/epoxy systems at equivalent fiber contents, reflecting the influence of stiffness contrast on composite response. The analysis further indicates that stiffness saturation begins approximately in the Vf = 0.60–0.70 range, where the incremental gains in E2 and G12 become noticeably smaller for both composite systems. This behavior provides design-relevant guidance by showing that, beyond this range, further increases in fiber content may offer limited stiffness improvement relative to the associated manufacturing complexity. Overall, the calibrated Halpin–Tsai methodology offers a practical and computationally efficient tool for preliminary evaluation and design-stage optimization of the elastic performance of high-performance composite structures. Full article

Unidirectional glass fiber-reinforced thermoplastic (UGFT) composite tapes are promising recyclable structural materials for applications such as composite pressure pipes. However, their durability under hydrothermal environments remains a critical concern. This study emphasizes metrology-driven evaluation of aging behavior in polypropylene-based UGFT tapes. Specimens were conditioned at 95 °C in a deionized-water environment for up to 4 weeks, and multiple complementary measurement techniques were applied to quantify degradation. Mass-change metrology was performed to characterize water uptake kinetics and establish diffusion-driven aging progression. Tensile testing enabled quantitative assessment of mechanical strength retention, defining a >25% reduction in strength as a threshold for significant deterioration. Acoustic emission (AE) acted as the central non-destructive monitoring method, capturing high-fidelity waveforms generated during loading. AE waveform descriptors, such as amplitude, rise time, and frequency content, served as measurable indicators of internal damage mechanisms including matrix cracking, interfacial debonding and fiber breakage. To process large AE datasets, principal component analysis was used for dimensionality reduction, followed by k-means clustering to group signals by damage type. Optical microscopy provided microstructural verification of these classifications. The integrated metrological framework demonstrates a reliable pathway to monitor, identify, and quantify damage evolution in hydrothermally aged UGFT structures. Full article

The objective of this study was to evaluate the impact of the material (plastic or glass) and color (green or colorless) of extra virgin olive oil (EVOO) and virgin olive oil (VOO) bottles on the acquisition of SORS spectra using portable equipment. Sixteen bottles of EVOO and three bottles of VOO were analyzed, including different volumes. A range of similarity indices was calculated between vial-reference (offline measurements) and bottles (online measurements), including R², COS θ, NEAR, and a new index called WSI (Weighted Similarity Index). WSI is calculated from the pondered linear combination of the previous three, and a threshold of >0.95 is established as high similarity. The results showed that plastic bottles, regardless of color and volume, and colorless glass bottles had WSI values > 0.95. In contrast, green glass bottles demonstrated a lower degree of similarity (WSI < 0.95), which impacted the reliability of their spectral fingerprints. A hierarchical cluster analysis (HCA) was performed by locating EVOO bottles according to their material in two clusters. A study of storage under optimal, non-optimal, and commercial conditions showed that both EVOO and VOO maintain highly similar spectral profiles for 10–18 days (WSI > 0.965), even in bottles purchased in supermarkets. These results demonstrate that the SORS technique is suitable for the direct analysis of olive oils in plastic and colorless glass containers, without the need to open the bottles. The SORS technique is a fast, reliable, non-invasive, and non-destructive tool for quality control of olive oil. Full article

Artificial Intelligence (AI) is increasingly shaping materials research, particularly in the development and optimization of Glass Fibre Reinforced Polymer (GFRP) bars used as innovative alternatives to steel reinforcement. Despite this growing intersection, no prior bibliometric study has systematically mapped how AI contributes to the advancement of GFRP technologies. This paper fills this gap through a comprehensive bibliometric analysis based on 102 Scopus-indexed publications from 2015 to 2025. Following PRISMA guidelines, the study combines performance analysis and science mapping using VOSviewer to identify publication dynamics, leading journals, key contributors, and thematic clusters. The results reveal a tenfold growth in annual output (compound annual growth rate, CAGR = 10.1%) and five dominant research directions: (1) machine learning in structural analysis, (2) AI-driven composite materials modeling, (3) smart damage detection, (4) mechanical characterization, and (5) advanced deep learning frameworks. China, India, and the United States collectively account for more than half of global publications, highlighting strong international collaboration. The findings demonstrate that AI has evolved from an exploratory tool to a transformative driver of innovation in GFRP research. This study provides the first quantitative overview of this emerging field, identifies critical gaps such as sustainability integration and standardization, and proposes future directions to foster cross-disciplinary collaboration toward intelligent and sustainable composite structures. Full article

This work presents results on laser-induced fabrication of metal and oxide structures on glass substrates. The Laser-Induced Reverse Transfer (LIRT) technique is applied using Zn and Sn, sintered ZnO and SnO₂, and oxide composite targets. The processing is performed by nanosecond pulses of a Nd:YAG laser system operated at wavelength of 1064 nm. Detailed analyses of the deposited material morphology, composition and structure are presented, as the role of the processing conditions is revealed. It is found that at the applied conditions of using up to five laser pulses, the deposited material is composed of a nanostructured film covered in microsized nanoparticle clusters or droplets. The use of metal targets leads to formation of structures composed of metal and oxide phases. The adhesion test shows that part of the deposited material is stably adhered to the substrate surface. It is demonstrated that the deposited materials can be used as resistive gas sensors with sensitivity to NH₃, CO, ethanol, acetone and N₂O, at concentrations of 30 ppm. The ability of the method to deposit composite structures that consist of a mixture of both investigated oxides is also demonstrated. Full article

Quality control of herbal medicinal products (HMPs) is challenging due to their multicomponent composition. For most HMPs, chemical reference standards (CRSs) required for traditional chromatographic and spectral analyses are unavailable. According to USP and Ph. Eur., an exception is valerian tincture, for which highly specific CRSs have been developed. The aim of this study was to use principal component analysis (PCA) and the novel two-dimensional diffuse laser scattering (2D-DLS) method to identify HMPs and their aqueous-ethanolic extracts according to their botanical genera without relying on specific marker compounds. Spectral data were compiled into an extensive library covering a wide wavelength range—from 0.02 nm to 15,000 nm. PCA of the spectral data (UV spectrophotometry, fluorimetry, FTIR spectroscopy, and X-ray diffraction) enabled clustering of samples by individual botanical genera. The most significant information for sample differentiation was provided by wavenumbers of 1400, 1180, and 931 cm⁻¹ in the IR spectra and wavelengths of 450 nm and 672 nm in the UV and fluorescence spectra, respectively. During model cross-validation, all “blind samples” were correctly classified by botanical genus, achieving a non-error rate (NER) of 100%. Furthermore, the unique 2D-DLS method was used to rapidly identify tinctures without opening the glass bottles. Full article

Bulk metallic glasses (BMGs), classified as metastable materials, necessitate melt quenching at critical cooling rates higher than 10² K/s to kinetically bypass crystalline phase formation during solidification. Owing to this rapid quenching, the microstructure of BMGs can be regarded as melt quenched. This study examines how their melt state governs the thermal stability, structural characteristics, and plasticity behavior of Zr₅₀Cu₄₀Al₁₀ BMG. Rod samples were prepared via injection casting at controlled melt temperatures and suction casting. Experimental observations demonstrated a positive correlation between elevated melt temperatures and enhanced glass forming ability (GFA) along with improved thermal stability (T-A) in BMGs during processing. Structural analyses confirmed the glassy nature of the prepared BMGs with different melt states and revealed their temperature-dependent atomic-scale heterogeneity: the samples quenched at low melt temperatures exhibited significant Cu-rich clustering as determined via energy-dispersive X-ray spectroscopy (EDS) mapping, and those at high melt temperatures formed homogeneous structures. This structure heterogeneity was directly correlated with good plastic deformation behavior, i.e., the rod sample prepared at the lowest melt temperature achieved 9.7% plastic strain. The transition is attributed to liquid-liquid phase transition (LLPT): below the LLPT threshold, metastable Cu-rich clusters persist in the melt and are retained upon quenching, creating structural defects that facilitate shear band multiplication. These findings highlight melt temperature as a crucial factor in tailoring the structure characteristic and mechanical behavior of Zr₅₀Cu₄₀Al₁₀ BMGs. Full article

The subject of this study is the effects of various concentrations of niobium pentoxide nanoparticles (Nb₂O₅ NPs) on the physical, optical, and thermal properties of thin films of poly(N-vinylpyrrolidone) (PVP). The obtained results indicate that the addition of nanoparticles significantly affects the physical properties of the investigated materials, limiting their optical UV transmittance in the range of 300–500 nm by approximately 20–40% and increasing the material’s resistance to moisture that is present in the surrounding environment. Based on the thermal measurements performed using differential scanning calorimetry (DSC) and variable temperature spectroscopic ellipsometry (VASE), two distinct glass transition temperatures T_g for pure PVP and its Nb₂O₅ composites were revealed, with an additional intermediate T_g appearing in the composites, varying in the range of 135–168 °C (ellipsometric temperature cycle). This intermediate transition indicates the formation of an interfacial region with modified polymer chain mobility due to the interactions occurring between Nb₂O₅ nanoparticles and the PVP matrix. The results obtained from the scanning electron microscopy (SEM), Energy Dispersive Spectroscopy (EDS), and detailed Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy (ATR-FTIR) analyses also confirmed the presence of this interfacial area and indicated that it arises from nanoparticle agglomeration and surface cluster formation. The contact angle measurements revealed that the composites containing 15% and 25% Nb₂O₅ exhibited greater hydrophobicity. These results suggest that the investigated composite coatings could be employed as surface coverings to protect against external, environmental influences, such as moisture and UV radiation. Full article

The segmentation and classification of color are crucial stages in image processing, computer vision, and pattern recognition, as they significantly impact the results. The diverse, hand-labeled datasets in the literature are applied for monochromatic or color segmentation in specific domains. On the other hand, synthetic datasets are generated using statistics, artificial intelligence algorithms, or generative artificial intelligence (AI). This last one includes Large Language Models (LLMs), Generative Adversarial Neural Networks (GANs), and Variational Autoencoders (VAEs), among others. In this work, we propose VitralColor-12, a synthetic dataset for color classification and segmentation, comprising twelve colors: black, blue, brown, cyan, gray, green, orange, pink, purple, red, white, and yellow. VitralColor-12 addresses the limitations of color segmentation and classification datasets by leveraging the capabilities of LLMs, including adaptability, variability, copyright-free content, and lower-cost data—properties that are desirable in image datasets. VitralColor-12 includes pixel-level classification and segmentation maps. This makes the dataset broadly applicable and highly variable for a range of computer vision applications. VitralColor-12 utilizes GPT-5 and DALL·E 3 for generating stained-glass images. These images simplify the annotation process, since stained-glass images have isolated colors with distinct boundaries within the steel structure, which provide easy regions to label with a single color per region. Once we obtain the images, we use at least one hand-labeled centroid per color to automatically cluster all pixels based on Euclidean distance and morphological operations, including erosion and dilation. This process enables us to automatically label a classification dataset and generate segmentation maps. Our dataset comprises 910 images, organized into 70 generated images and 12 pixel segmentation maps—one for each color—which include 9,509,524 labeled pixels, 1,794,758 of which are unique. These annotated pixels are represented by RGB, HSL, CIELAB, and YCbCr values, enabling a detailed color analysis. Moreover, VitralColor-12 offers features that address gaps in public resources such as violin diagrams with the frequency of colors across images, histograms of channels per color, 3D color maps, descriptive statistics, and standardized metrics, such as ΔE76, ΔE94, and CIELAB Chromacity, which prove the distribution, applicability, and realistic perceptual structures, including warm, neutral, and cold colors, as well as the high contrast between black and white colors, offering meaningful perceptual clusters, reinforcing its utility for color segmentation and classification. Full article