Search Results (2,182)

In this study, a biodegradable Mg-Zn-Nd-Gd alloy was processed via multi-directional forging (MDF) to evaluate its microstructural evolution, mechanical performance, and corrosion behavior. Electron backscattered diffraction (EBSD) analysis was conducted to evaluate the influence of grain size and texture on mechanical strength and corrosion resistance. The average grain size decreased significantly from 118 ± 5 μm in the homogenized state to 30 ± 10 μm after six MDF passes, primarily driven by discontinuous dynamic recrystallization (DDRX). Remarkably, this magnesium (Mg) alloy exhibited a rare synergistic enhancement in both strength and ductility, with ultimate tensile strength (UTS) increasing by ~59%, yield strength (YS) by ~90%, while elongation improved by ~44% unlike conventional severe plastic deformation (SPD) techniques that often sacrifice ductility for strength. This improvement is attributed to grain refinement, dispersion strengthening from finely distributed Mg₁₂Nd and Mg₇Zn₃ precipitates, and texture weakening, which facilitated the activation of non-basal slip systems. Despite the mechanical improvements, electrochemical corrosion testing in Hank’s balanced salt solution (HBSS) at 37 °C revealed an increased corrosion rate from 0.1165 mm/yr in homogenized condition to 0.2499 mm/yr (after six passes of MDF. This was due to the higher fraction of low-angle grain boundaries (LAGBs), weak basal texture, and the presence of electrochemically active fine Mg₇Zn₃ particles. However, the corrosion rate remained within the acceptable range for bioresorbable implant applications, indicating a favorable trade-off between mechanical performance and degradation behavior. These findings demonstrate that MDF processing effectively enhances the strength–ductility synergy of Mg-rare earth alloys while maintaining a clinically acceptable degradation rate, thereby presenting a promising route for next-generation biomedical implants. Full article

Underwater imagery exhibits markedly more severe visual degradation than their terrestrial counterparts, manifesting as pronounced color aberration, diminished contrast and luminosity, and spatially non-uniform haze. To surmount these challenges, we propose the graph-aware framework for underwater image enhancement (GA-UIE), integrating specialized modules for color correction and texture restoration, a unified framework that explicitly utilizes the intrinsic graph information of underwater images to achieve high-fidelity color restoration and texture enhancement. The proposed algorithm is architected in three synergistic stages: (1) graph feature generation, which distills color and texture graph feature priors from the underwater image; (2) graph-aware enhancement, performing joint color restoration and texture sharpening under explicit graph priors; and (3) graph-aware fusion, harmoniously aggregating the graph-aware color and texture joint representations to yield the final visually coherent output. Comprehensive quantitative evaluations reveal that the output from our novel framework achieves the significant scores across a broad spectrum of metrics, including PSNR, SSIM, LPIPS, UCIQE, and UIQM on the UIEB and U45 datasets. These results decisively exceed those of all existing benchmark techniques, thereby validating the method’s exceptional efficacy in the enhancement of underwater imagery. Full article

This study employs a multi-technique approach to elucidate how graphene incorporation affects phase formation, microstructure, and thermal behavior in PVA-assisted sol–gel synthesized Ca_2.95Eu_0.05Co₄O_x nanomaterials. XRD confirms the preservation of the primary phases (hexagonal CaCO₃ and cubic CoO) alongside a distinct graphene (002) reflection; a systematic low-angle shift of the calcite (104) peak evidences partial relaxation of residual lattice strain with increasing graphene content, while Scherrer analysis indicates tunable crystallite size. Raman spectroscopy corroborates graphene incorporation through pronounced D (~1300 cm⁻¹) and G (~1580 cm⁻¹) bands and supports the XRD-identified phase coexistence via cobalt-oxide and calcite vibrations in the 200–700 cm⁻¹ region, also indicating increased defect/disorder with graphene loading. SEM shows grain refinement, denser/bridged lamellar textures, and reduced porosity at low–moderate graphene contents (1–3 wt.%), contrasted by agglomeration-driven heterogeneity at higher loadings (5–7 wt.%). EDX reveals increasing carbon with Ca/Co redistribution at accessible surfaces, and TG–DSC corroborates the removal of oxygen-containing groups and oxidative combustion of graphene at mid temperatures. Collectively, Raman–XRD-consistent evidence demonstrates that graphene provides a tunable handle over lattice strain, crystallite size, and grain-boundary architecture, establishing a processing–composition basis for optimizing functional (e.g., electrical/thermoelectric) performance. Full article

Alkali metal-modified M-ZSM-5 zeolites (M: Li⁺, Na⁺, K⁺) were synthesized by cationic exchange and characterized using ICP-MS, XRD, N₂ adsorption–desorption, Py-IR and NH₃-TPD techniques to evaluate their elemental composition, structure, textural and acidic properties. In addition, XPS and DFT calculations were employed to study the effects of metal ion doping on the electronic structure and catalytic behavior. The latter catalytic performance was assessed in the methanol-to-olefin (MTO) reaction. The results showed that alkali metal doping facilitated the enhancement of the zeolite structural stability, adjustment of acid density, and increase in the adsorption energy of light olefins onto the active sites. During the reaction, olefin products shifted from Brønsted acid sites to alkali metal sites, effectively minimizing hydrogen transfer reactions. This change in the active site nature promoted the olefin cycle, resulting in higher yields in propylene and butylenes, reduced coke deposition, and prolonged catalyst lifetime. Among all zeolites, Li-exchanged ZSM-5 exhibited the best and extending the catalyst lifetime by 5 h. Full article

The tilapia sector is advancing due to breakthroughs in aquaculture techniques and genetic enhancements. Comprehending sensory qualities is crucial for producers striving to meet market demands efficiently. As consumer preferences play a significant role in shaping the market, enhancing the sensory attributes of both farmed and wild red tilapia will be key to ensuring their success in the competitive aquaculture industry. One of Malaysia’s most prominent aquaculture projects is the Como River Aquaculture Project located in Kenyir Lake, where tilapia fish farming, trademarked as Red Kenyir™, is conducted. Thus, this study aimed to evaluate the nutrient analysis of raw and five sensory attributes (appearance, texture, smell, taste, overall quality) of filets from Red Kenyir™ and wild red tilapia (Oreochromis sp.). Red Kenyir™ were fed three different commercial diets (A, B, and C) from fingerling to adulthood, while wild tilapia (W) was sourced from the market. Proximate and nutritional analyses were conducted based on the standard food analysis protocol by AOAC/AOCS. To the best of our knowledge, this is the first study to comprehensively document the nutrient analysis of raw and consumer sensory perception of cooked Red Kenyir™ aquaculture tilapia in direct comparison with wild red tilapia. The sensory evaluation was conducted using a consumer preference test. Statistical analysis was performed using SPSS. Nutrient analysis showed that Red Kenyir™ tilapia had lower fat (0.25–1.37 g/100 g vs. 4.30 g/100 g) and lower energy (77.38–113.46 kcal/100 g vs. 132.79 kcal/100 g) levels. Protein levels varied across groups (19–26.54 g/100 g vs. 22.95 g/100 g). The tryptophan content of the Red Kenyir™ tilapia samples ranged between 0.13 and 0.23 g/100 g, while the wild tilapia contained 0.19 mg/100 g. Sensory evaluation with 36 panelists revealed no significant differences in appearance, texture, or smell (p > 0.05). However, wild tilapia scored slightly higher in taste (4.14) than Red Kenyir™ (3.54–3.71) for steamed preparation (p < 0.05). In conclusion, these findings suggest that variations in the nutritional composition of Red Kenyir™ do not affect the sensory experience for consumer acceptance, making it a sustainable alternative for customers. Full article

This study aimed to develop an industrially scalable coating route for enhancing the oxidation resistance of carbon fiber fabrics, a critical requirement for next-generation aerospace and high-temperature composite structures. To achieve this goal, synthesis of hexagonal boron nitride (h-BN) layers was achieved via a single wet step in which the fabric was impregnated with an ammonia–borane/THF solution and subsequently nitrided for 2 h at 1000–1500 °C in flowing nitrogen. Thermogravimetric analysis coupled with X-ray diffraction revealed that amorphous BN formed below ≈1200 °C and crystallized completely into (002)-textured h-BN (with lattice parameters a ≈ 2.50 Å and c ≈ 6.7 Å) once the dwell temperature reached ≥1300 °C. Complementary XPS, FTIR and Raman spectroscopy confirmed a near-stoichiometric B:N ≈ 1:1 composition and the elimination of O–H/N–H residues as crystallinity improved. Low-magnification SEM (100×) confirmed the uniform and large-area coverage of the BN layer on the carbon fiber tows, while high-magnification SEM revealed a progressive densification of the coating from discrete nanospheres to a continuous nanosheet barrier on the fibers. Oxidation tests in flowing air shifted the onset of mass loss from 685 °C for uncoated fibers to 828 °C for the coating produced at 1400 °C; concurrently, the peak oxidation rate moved ≈200 °C higher and declined by ~40%. Treatment at 1500 °C conferred no additional benefit, indicating that 1400 °C provides the optimal balance between full crystallinity and limited grain coarsening. The resulting dense h-BN film, aided by an in situ self-healing B₂O₃ glaze above ~800 °C, delayed carbon fiber oxidation by ≈140 °C. Overall, the process offers a cost-effective, large-area alternative to vapor-phase deposition techniques, positioning BN-coated carbon fiber fabrics for robust service in extreme oxidative environments. Full article

Milk products are a diverse group of foods and important sources of essential nutrients, including high-quality proteins, fatty acids, vitamins, and minerals. Among their key quality attributes, texture is particularly critical, as it strongly influences consumer perception and overall product quality. Numerous devices and techniques have been developed to evaluate the texture of milk products, most of which rely on mechanical tests such as puncture, compression, shearing, creep, and relaxation. Instrumental evaluations are essential for correlating physical measurements with sensory perceptions, yet several challenges limit their reliability. Inconsistencies in testing protocols—such as reporting force versus penetration depth versus force versus time; variations in testing temperature, sample shape and dimensions; probe geometry; compression depth; and container size for semisolid samples contribute to discrepancies across studies. Additionally, many studies omit these critical methodological details, reducing reproducibility and comparability. This review systematically examines the current methods used to assess dairy product texture, identifies gaps and challenges in standardization, and provides guidance to support future research aimed at obtaining accurate, reproducible, and meaningful texture measurements. Full article

Purpose: Luggage X-rays suffer from low contrast, material overlap, and noise; dual-energy imaging reduces ambiguity but creates colour biases that impair segmentation. This study aimed to (1) employ connotative fusion by embedding realistic detonator patches into real X-rays to simulate threats and enhance unattended detection without requiring ground-truth labels; (2) thoroughly evaluate fusion techniques in terms of balancing image quality, information content, contrast, and the preservation of meaningful features. Methods: A total of 1000 X-ray luggage images and 150 detonator images were used for fusion experiments based on deep learning, transform-based, and feature-driven methods. The proposed approach does not need ground truth supervision. Deep learning fusion techniques, including VGG, FusionNet, and AttentionFuse, enable the dynamic selection and combination of features from multiple input images. The transform-based fusion methods convert input images into different domains using mathematical transforms to enhance fine structures. The Nonsubsampled Contourlet Transform (NSCT), Curvelet Transform, and Laplacian Pyramid (LP) are employed. Feature-driven image fusion methods combine meaningful representations for easier interpretation. Singular Value Decomposition (SVD), Principal Component Analysis (PCA), Random Forest (RF), and Local Binary Pattern (LBP) are used to capture and compare texture details across source images. Entropy (EN), Standard Deviation (SD), and Average Gradient (AG) assess factors such as spatial resolution, contrast preservation, and information retention and are used to evaluate the performance of the analysed methods. Results: The results highlight the strengths and limitations of the evaluated techniques, demonstrating their effectiveness in producing sharpened fused X-ray images with clearly emphasized targets and enhanced structural details. Conclusions: The Laplacian Pyramid fusion method emerges as the most versatile choice for applications demanding a balanced trade-off. This is evidenced by its overall multi-criteria balance, supported by a composite (geometric mean) score on normalised metrics. It consistently achieves high performance across all evaluated metrics, making it reliable for detecting concealed threats under diverse imaging conditions. Full article

Breast cancer is the most commonly diagnosed cancer in women globally and represents the leading cause of mortality related to malignant tumors. Currently, healthcare professionals are focused on developing and implementing innovative techniques to improve the early detection of this disease. Thermography, studied as a complementary method to traditional approaches, captures infrared radiation emitted by tissues and converts it into data about skin surface temperature. During tumor development, angiogenesis occurs, increasing blood flow to support tumor growth, which raises the surface temperature in the affected area. Automatic classification techniques have been explored to analyze thermographic images and develop an optimal classification tool to identify thermal anomalies. This study aims to design a concise description using statistical and texture features to accurately classify thermograms as control or highly probable to be cancer (with thermal anomalies). The importance of employing a short description lies in facilitating interpretation by medical professionals. In contrast, a characterization based on a large number of variables could make it more challenging to identify which values differentiate the thermograms between groups, thereby complicating the explanation of results to patients. A maximum accuracy of 91.97% was achieved by applying only seven features and using a Coarse Decision Tree (DT) classifier and robust Machine Learning (ML) model, which demonstrated competitive performance compared with previously reported studies. Full article

Defect detection in textile manufacturing is critically hampered by the inefficiency of manual inspection and the dual constraints of deep learning (DL) approaches. Specifically, DL models suffer from poor generalization, as the rapid iteration of fabric types makes acquiring sufficient training data impractical. Furthermore, their high computational costs impede real-time industrial deployment. To address these challenges, this paper proposes a texture-adaptive fabric defect detection method. Our approach begins with a Dynamic Subspace Feature Extraction (DSFE) technique to extract spatial luminance features of the fabric. Subsequently, a Light Field Offset-Aware Reconstruction Model (LFOA) is introduced to reconstruct the luminance distribution, effectively compensating for environmental lighting variations. Finally, we develop a texture-adaptive defect detection system to identify potential defective regions, alongside a probabilistic ‘OutlierIndex’ to quantify their likelihood of being true defects. This system is engineered to rapidly adapt to new fabric types with a small number of labeled samples, demonstrating strong generalization and suitability for dynamic industrial conditions. Experimental validation confirms that our method achieves 70.74% accuracy, decisively outperforming existing models by over 30%. Full article

Micro-expressions are facial movements with extremely short duration and small amplitude, which can reveal an individual’s potential true emotions and have important application value in public safety, medical diagnosis, psychotherapy and business negotiations. Since micro-expressions change rapidly and are difficult to detect, manual recognition is a significant challenge, so the development of automatic recognition systems has become a research hotspot. This paper reviews the development history and research status of micro-expression recognition and systematically analyzes the two main branches of micro-expression analysis: micro-expression detection and micro-expression recognition. In terms of detection, the methods are divided into three categories based on time features, feature changes and deep features according to different feature extraction methods; in terms of recognition, traditional methods based on texture and optical flow features, as well as deep learning-based methods that have emerged in recent years, including motion unit, keyframe and transfer learning strategies, are summarized. This paper also summarizes commonly used micro-expression datasets and facial image preprocessing techniques and evaluates and compares mainstream methods through multiple experimental indicators. Although significant progress has been made in this field in recent years, it still faces challenges such as data scarcity, class imbalance and unstable recognition accuracy. Future research can further combine multimodal emotional information, enhance data generalization capabilities, and optimize deep network structures to promote the widespread application of micro-expression recognition in practical scenarios. Full article

TiO₂ is the most used material for the photocatalytic removal of organic pollutants in aqueous media. TiO₂, specifically its anatase phase, is well-known for its great performance under UV irradiation, high chemical stability, low cost and non-toxicity. Nevertheless, TiO₂ presents two main drawbacks: its limited absorption of the visible spectrum; and its relatively low specific surface area and pore volume. Regarding the latter, several works in the literature have addressed the issue by developing new synthesis approaches in which anatase is dispersed and supported on the surface of porous materials. In the present work, two series of materials have been prepared where anatase has been supported on mesoporous silica (MSTiR%) in situ through a hydrothermal synthesis approach, where, in addition to using tetraethoxysilane (TEOS) as a silicon precursor, three organotriethoxysilanes [RTEOS, where R = methyl (M), propyl (P) or phenyl (Ph)] were used at a RTEOS:TEOS molar percentage of 10 and 30%. The materials were thoroughly characterized by several techniques to determine their morphological, textural, chemical, and UV-vis light absorption properties and then the most promising materials were used as photocatalysts in the photodegradation of the emerging contaminant and antiepileptic carbamazepine (CBZ) under UV irradiation. The materials synthesized using 10% molar percentage of RTEOS (MSTiR10) were able to almost completely degrade (~95%), 1 mg L⁻¹ of CBZ after 1 h of irradiation using a 275 nm LED and 0.5 g L⁻¹ of catalyst dose. Therefore, this new synthesis approach has proven useful to develop photoactive TiO₂ composites with enhanced textural properties. Full article

Zibai Jade is a recently identified hydrogrossular-dominant jade originating from Shaanxi Province, China. It constitutes a polymineralic aggregate composed predominantly of hydrogrossular, with minor proportions of vesuvianite, diopside, chlorite, uvarovite, and calcite. A multi-method analytical approach was employed to characterize this jade, incorporating conventional gemological testing, polarizing microscopy, SEM, XRD, BSE, XRF, and EPMA, as well as UV-Vis and infrared (IR). These techniques enabled a detailed examination of its mineralogy, surface features, and color origin. The stone displays a heterogeneous color distribution, featuring a base hue of light green to yellowish-green, accompanied by distinct occurrences of emerald-green spots, dark green spots, mossy green inclusions, white patches, white veinlets, and a black dot with a green ring. Microanalytical results indicate that the emerald-green spots are principally composed of uvarovite; the dark green spots are dominated by hydrogrossular, diopside, and chlorite; fibrous green inclusions consist mainly of chlorite and Cr-bearing grossular; white patches and veinlets are primarily composed of calcite; and the black dot with a green ring predominantly comprises chromite and uvarovite. Coloration is attributed to the combined influence of Fe and Cr³⁺. The formation of Zibai Jade involved three mineralization stages: deposition of a carbonate protolith, high-temperature metasomatism, and retrograde alteration. The metasomatism was driven by hydrothermal fluids derived from granodioritic and ultramafic rocks, which provided Si, Al, and the essential Cr, respectively. The interplay of these processes resulted in the development of Zibai Jade, which exhibits a dense texture and attractive coloration. Full article

Protection of coastal cultural heritage is among the most urgent global priorities, as these sites face increasing threats from climate change, sea level rise, and human activity. This study emphasises the value of innovative geospatial tools and data ecosystems for timely risk assessment. The role of land administration systems, geospatial documentation of coastal cultural heritage sites, and the adoption of innovative techniques that combine various methodologies is crucial for timely action. The coastal management infrastructure in Greece is presented, outlining the key public authorities and national legislation, as well as the land administration and geospatial ecosystems and the various available geospatial ecosystems. We profile the Hellenic Cadastre and the Hellenic Archaeological Cadastre along with open geospatial resources, and introduce TRIQUETRA Decision Support System (DSS), produced through the EU’s Horizon project, and a Digital Twin methodology for hazard identification, quantification, and mitigation. Particular emphasis is given to the role of Digital Twin technology, which acts as a continuously updated virtual replica of coastal cultural heritage sites, integrating heterogeneous geospatial datasets such as cadastral information, photogrammetric 3D models, climate projections, and hazard simulations, allowing for stakeholders to test future scenarios of sea level rise, flooding, and erosion, offering an advanced tool for resilience planning. The approach is validated at the coastal archaeological site of Aegina Kolona, where a UAV-based SfM-MVS survey produced using high-resolution photogrammetric outputs, including a dense point cloud exceeding 60 million points, a 5 cm resolution Digital Surface Model, high-resolution orthomosaics with a ground sampling distance of 1 cm and 2.5 cm, and a textured 3D model using more than 6000 nadir and oblique images. These products provided a geospatial infrastructure for flood risk assessment under extreme rainfall events, following a multi-scale hydrologic–hydraulic modelling framework. Island-scale simulations using a 5 m Digital Elevation Model (DEM) were coupled with site-scale modelling based on the high-resolution UAV-derived DEM, allowing for the nested evaluation of water flow, inundation extents, and velocity patterns. This approach revealed spatially variable flood impacts on individual structures, highlighted the sensitivity of the results to watershed delineation and model resolution, and identified critical intervention windows for temporary protection measures. We conclude that integrating land administration systems, open geospatial data, and Digital Twin technology provides a practical pathway to proactive and efficient management, increasing resilience for coastal heritage against climate change threats. Full article

Topological signal processing is emerging alongside Graph Signal Processing (GSP) in various applications, incorporating higher-order connectivity structures—such as faces—in addition to nodes and edges, for enriched connectivity modeling. Rich point clouds acquired by multi-camera systems in Visual Simultaneous Localization and Mapping (V-SLAM) are typically processed using graph-based methods. In this work, we introduce a topological signal processing (TSP) framework that integrates texture information extracted from V-SLAM; we refer to this framework as TSP-SLAM. We show how TSP-SLAM enables the extension of graph-based point cloud processing to more advanced topological signal processing techniques. We demonstrate, on real stereo data, that TSP-SLAM enables a richer point cloud representation by associating signals not only with vertices but also with edges and faces of the mesh computed from the point cloud. Numerical results show that TSP-SLAM supports the design of topological filtering algorithms by exploiting the mapping between the 3D mesh faces, edges and vertices and their 2D image projections. These findings confirm the potential of TSP-SLAM for topological signal processing of point cloud data acquired in challenging V-SLAM environments. Full article